This study uses a novel approach to separate and classify different ionospheric troughs from CHAMP satellite data in the winter midnight ionosphere of the southern hemisphere at high solar activity (2000–2002). The main ionospheric trough (MIT) was separated from the high latitude trough (HLT). The separation was performed through an analysis of troughs in the frame of a model of the diffuse auroral particle precipitation. Two types of HLT were distinguished. In the mid-latitude ionosphere, the MIT was separated from the ring ionospheric trough (RIT), which is formed by the decay processes of the magnetospheric ring current. The separation was performed on the basis of an analysis of the prehistory of all geomagnetic disturbances for the period under study. In addition, a decrease in the electron density, which is superimposed on the MIT and masks its minimum position, is quite often observed at American and Atlantic longitudes near the Polar Circle. Since then, many studies have explored its characteristics, which have been described in reviews5. The greatest attention has been paid to the position of the MIT minimum13. These studies reported a large data scatter, which is due to the fact that the MIT can be confused with other troughs, including high latitude troughs (HLT) and low latitude troughs15. Thus, the problem of the separation and classification of ionospheric troughs arises. Significant progress in the separation of the MIT and HLT has been made in a previous study based on CHAMP data15. The MIT is a subauroral structure because it is located equatorward of the auroral oval4. The HLT is observed inside the auroral oval16. With the simultaneous data on the precipitation of auroral particles, the separation of the MIT and HLT would be a routine task. However, particle precipitation on board the CHAMP has not been measured simultaneously; therefore, we were forced to use a statistical model of diffuse auroral precipitation. Thus, the current positions of the troughs have been compared with some average positions of the equatorward boundary of the auroral oval15. However, the current positions of the auroral oval and the troughs are often quite different from the average position. For example, the standard deviation for the MIT position is typically 2°–3°, and the maximal data scatter is as high as ± 10°12. As a result, the highest latitude MIT case can be located inside the statistical auroral oval, while the lowest latitude HLT case can be outside it.The ionization trough was discovered from the Alouette I satellite data and was described by Muldrew18. This model describes the position of zone I diffuse precipitation at the equatorward edge of the auroral oval and zone II at its poleward edge. As is known, the precipitation in zone I forms the poleward wall of the MIT; meanwhile, the effects of zone II have never been considered. Moreover, the positions of both zones change with longitude19. Therefore, for superior efficiency, the analysis of the structures of the high latitude ionosphere was conducted herein in the framework of the longitudinal effect.In the present study, an advanced method is used. The key point is an application of a model of auroral precipitation obtained from the DMSP satellites data15. The RIT is formed during the storm (substorm) recovery phase as a result of the decay of the magnetospheric ring current21. However, equatorward of the MIT, in addition to the mid-latitude RIT, other electron density minima do not necessarily stand out as ionization troughs, but they significantly complicate the identification of the MIT. Therefore, this study also considered in detail the issue of the separation of the MIT and LLT.The problem of low-latitude trough separating from the subauroral MIT in the previous study was solved by removing from the CHAMP data set only the obvious cases of the so-called ring ionospheric trough (RIT)Finally, to complete the pattern, this study highlights the cases of a clearly expressed polar hole. In this way, the title of this article can be interpreted broadly as the classification of electron density depletion structures in the high and mid-latitude ionosphere. Within the framework of the advanced method, all the ionospheric troughs from the CHAMP data in the midnight winter ionosphere were thoroughly analyzed. As the main goal was to derive an accurate statement of the problem for the trough separation, the analysis in the present study was limited to the southern hemisphere and high solar activity.Ne22. Variations in Ne are presented below in terms of plasma frequency fp (Ne[cm–3] = 1.24 × 104fp2[MHz]). The CHAMP altitude has changed from ~ 450 to ~ 300 km, which is close to the height of the F2 layer maximum. It revolved on nearly polar orbit with the inclination of 87°. The CHAMP data time resolution of 15 s is less than 1° of latitude, which allows determining the minimum trough position accurately. The CHAMP data are available on the website http://op.gfz-potsdam.de/champ.The CHAMP satellite carried out in situ measurements of electron concentration The CHAMP data for June, July, and August  in the southern hemisphere were used. The data only for high solar activity with F10.7 ~ 180 sfu for the period of 2000–2002 and the near-midnight conditions (23–01 LT) were considered. About 700 CHAMP passes in the winter high- and mid-latitude ionosphere for relatively quiet geomagnetic conditions with Kp ≤ 4 were examined.The MIT is usually defined by a fairly deep decrease in electron density of at least ~ 30%. We have not determined the level of electron density decrease in the MIT minimum. If the trough was poorly expressed on some satellite path or masked by ionospheric plasma irregularities, then the position of its minimum was determined through coordination with neighboring paths. Stricter criteria were imposed on the selection of the HLT. The HLT is observed in the auroral oval, where the electron density is highly irregular and a number of density minima can be observed. Therefore, the HLT was recorded only in the obvious cases wherein it was clearly structured and when its poleward wall did not extend beyond the poleward diffuse precipitation zone. Similarly, the polar hole was defined only as a broad minimum of the electron density at latitudes above the poleward precipitation zone. Finally, only pronounced troughs were recorded equatorward of the MIT.18. The model is uploaded on the website of the Polar Geophysical Institute (http://apm.pgia.ru). In Fig. An analysis of the structures of the high latitude ionosphere was conducted using a model of auroral particle precipitation constructed from the DMSP satellites data in both hemispheres19, as well as the position of the MIT23. In the southern hemisphere these boundaries were revealed from the TIMED data obtained in 2002–200719. They are presented in Fig. a(Kp(τ) − 2), where Λc is the current position of the structure and the a factor is 2.0° for the MIT8, 1.5° for RIT21, and ~ 1.5° for HLT16. The Kp(τ) index was used as it considers the prehistory of geomagnetic activity development11. In Fig. 15. The upper curve in Fig. The boundaries of the precipitation zones in the midnight ionosphere change with longitudeNi variations recorded on board OGO-6 at heights of 400–1100 km16 and from EISCAT radar data24. In particular, the statistical position of HLT relative to the auroral oval was determined16. The authors observed the HLT exclusively within the auroral oval and attributed its formation ultimately to the action of electric fields in the zone of the convection of the high latitude ionospheric plasma. These fields cause the frictional heating and upward vertical drift of the plasma. The first process leads to an increase in recombination, the second one to the escape of plasma upward along the magnetic field lines. Since this effect is observed in a limited region, the HLT of this type is usually narrow (3°–5° in latitude). Such a trough is observed in Fig. The black dots in Fig. 19. As one might expect, the variations in the magnitude of the PW completely coincide with variations in the precipitation. However, the high degree of coincidence is also surprising. Electron precipitation is much stronger in the western hemisphere than in the eastern hemisphere. Therefore, in the western hemisphere, the precipitation forms a pronounced PW of the MIT, which is always clearly determined. This illustrates the latitudinal fp cross-section in Fig. fp cross-section obtained on August 7, 2000, at longitude of 100° E at 0.5 LT and Kp = 1+. The latitudinal profile 2 shows weak electron density peak at the same latitudes as profile 1, i.e. at latitudes of zone I of precipitation. Hence, we can talk about the formation of weakly expressed MIT. The latitudinal profile 3 was also recorded on August 9, 2000, but at longitude of 92° E. Here is neither a peak nor a minimum of electron density at the latitudes of the MIT, therefore the MIT is not identified in this case. The minimum of the electron density is observed much poleward at latitude of − 68°, and it certainly belongs to HLT1 because its PW is formed by the precipitation in zone II. Note that this trough can be easily confused with the MIT in a cursory analysis. Finally, if both zones have no precipitation, then a monotonous decrease is recorded in the electron density to the pole without peaks and troughs. Such cases correspond to the fp values close to 0 on the top panel in Fig. In the eastern hemisphere, at longitudes of 30°–90° E, the MIT is located at the highest latitudes so that the region of its existence overlaps with the precipitation in zone I and the region of HLT existence. In the region of the intersection of the two sets of troughs, the problem of separation becomes particularly acute. Therefore, all cases of trough observations in this region were analyzed thoroughly. The top panel in Fig. 21. When the MIT and RIT are simultaneously observed, their identification is not difficult; the MIT position corresponds to the model8 and precipitation in zone I, at that time, the equatorward trough is the RIT  of the trough, shallow electron density minimum slightly equatorward (at − 65.5°), and deep and wide minimum at − 55°. How is the position of the MIT determined in this case? The latitude of − 65.5° for Kp = 1− corresponds rather to the PW of the MIT, and the latitude of − 55° completely goes beyond the existence region of “normal” MIT. Similarly, the position of Ne minimum at latitude of − 60.5° for Kp = 1− in Fig. 25. The MIT minimum is located within 5° equatorward of this boundary4, and the minimum distance is about 2°26; therefore, the MIT minimum is usually 3°–4° equatorward of the PW. The minimum of the trough determined in this way in Fig. Figures 27.Finally, Fig. The trough identification is obviously a challenge. In some cases, data analysis turns into a complicated investigation. Figure The vertical lines in Fig. 14, and it was continued in 201915. Fourth, the idea of separating MIT, HLT1, and HLT2 arose from a simple and illustrative model of diffuse auroral precipitation17. It describes precipitation in zone I on the equatorward edge of the auroral oval and in zone II on its poleward edge. It turned out that precipitation in zone II forms the PW of HLT1, similar to the way the precipitation in zone I forms the PW of the MIT. This point is key in the separation of MIT and HLT1.Undoubtedly, considerable progress has been made in the separation and classification of the various structures of the nighttime high latitude and mid-latitude ionosphere. The success is based on several factors. First, the CHAMP large data set allows the consideration of a phenomenon from different angles. Second, all complex cases were analyzed carefully, and automatic data processing was found to be questionable. Third, the problem could only be solved after many years of experience. Note that the solution to the problem could be traced back to 1997‒199819, similar to the longitudinal variations in the MIT position, the analysis is most effective when performed in the framework of the longitudinal effect. The problem of the separation of MIT and HLT1 was found to be radically different in the western and eastern hemispheres. In the western hemisphere, the MIT is located at lower latitudes than in the eastern hemisphere and is equatorward of the auroral oval. In the western hemisphere, the intensive precipitation forms a very steep and high PW of the MIT. These conditions facilitate the separation of MIT and HLT. In the eastern hemisphere, MIT shifts to high latitudes so that the region of its existence at longitudes 30°‒90° E overlaps zone I of the precipitation and region of HLT existence. In addition, the weak precipitation at longitudes of 0°‒90° E produces much less expressed and irregular electron density structures. Therefore, at these longitudes, each case was considered especially carefully, and the separation of MIT and HLT1 was carried out according to the correspondence of the PW to the precipitation in zone I or II. The pattern is complicated by the presence of a second high latitude trough (HLT2) described in24. Fortunately, HLT2 differs in that it is relatively narrow in latitude (3°‒4°).As the boundaries of both zones change with longitude by 2.5°21. It is no less difficult to separate the MIT from the RIT than the MIT from the HLT, but the methodology for such a separation has been carefully developed earlier21. It is based mainly on the prehistory of the disturbance development. Therefore, even the weak geomagnetic disturbances during the period under consideration were carefully analyzed. Note that RITs are more often formed at longitudes with a weak geomagnetic field, i.e., in the western hemisphere.The mid-latitude troughs (RITs) and sub-troughs (Ne minimums) located equatorward of the MIT were also clearly separated from the MIT for the first time. The main one among them is the RIT. It is formed even after a weak enhancement of geomagnetic activity, and it can be observed for a long time (sometimes for two days) at latitudes near L ~ 327.The quasi-trough is understood as an additional minimum of the electron density equatorward of the MIT, and it is often observed at longitudes of America and Atlantic. It is assumed to be related to the decay of the electron density beyond the Polar Circle during the polar night. This minimum deepens the MIT and therefore prevents the determination of the exact position of the MIT. Finally, several troughs too far from the mean MIT position (< 50°) were recorded, and they, apparently, belong to LLTsThe result of this analysis is a more accurate determination of the MIT position: the standard deviation of 1.85° is less than that in other statistical studies, and the scatter has decreased to ± 4°. This allows to significantly refine the model of the MIT position.The study considers the structure of the ionosphere for limited conditions: high solar activity, winter, southern hemisphere, and near-midnight conditions. Preliminary analysis shows that the structure of evening and morning ionosphere is quite different from the considered structure. The same is particularly true for the daytime ionosphere. Consequently, this work should be considered as a statement of the problem, which implies the need for further research.Supplementary Information.


Total body irradiation (TBI) in extended source surface distance (SSD) is a common treatment technique before hematopoietic stem cell transplant. The lungs are organs at risk, which often are treated with a lower dose than the whole body.This can be achieved by the application of blocks. Three‐dimensional (3D) printers are a modern tool to be used in the production process of these blocks.We demonstrate the applicability of a specific printer and printing material, describe the process, and evaluate the accuracy of the product.The blocks and apertures were found to be applicable in clinical routine. This type of collimator reduced the need for photon blocks compared to the standard rectangle jaw collimator. The blocks served to reduce the dose to the normal tissue and to the organs at risk. The increase in application of intensity modulated techniques has superseded the production of photon blocks. The dose to the organs at risk is reduced in the planning process by application of appropriate dose volume objectives. However, for special applications photon blocks are still useful. Lung shields to avoid pneumonitis after total body irradiation (TBI) are rather common.Although for TBI treatments intensity modulating techniques have been developedWe searched for a contemporary solution for the production of photon blocks and electron apertures concerning the age of our former block cutting device and the related software. The software had been released for Windows 7 only and the support for Windows 7 ended in January 2020.Dedicated foam cutting devices have not only been much more expensive than a low‐cost 3D printer. Additionally, as Michiels et al. pointed out,However, a systematic review about the application of 3D printers in radiation oncology by Rooney et al. demonstrated no work about the production of photon blocks.We decided to develop a procedure for the production of photon blocks for TBI using a commercial 3D printer. This printer was also used for the production of electron apertures to boost the partially shielded regions of the thoracic wall. The TBI technique has been described in the following section in short.2We apply a TBI technique, which has been described by Härtl et al.The thoracic wall in the shielded region is boosted by electron fields as recommended by van Dyk et al.We use a Canon Aquilion TSX‐201A scanner  to generate a whole body computed tomography (CT) scan of the patient in supine position. This is used for contouring and dose calculations as described by Härtl et al.The configuration of the blocks is performed using the treatment planning system (TPS) Oncentra . The lung blocks are configured in the beam's eye view of a fixed beam. The gantry angle is 350° so that the central axis is at about the middle of the longitudinal lung extension Figure . The lunThe radiation therapy (RT) plan DICOM file is exported to a program in Matlab® code . This program is an in‐house developed product, which generates a 3D model of the divergent blocks under consideration of the geometric parameters like source to skin distance , block tray distance, block height, and others. For this purpose, the contours of the blocks are projected to several positions between the bottom and top layer of the blocks and a voxelized binary 3D model of the molds is created. The block height is calculated as described by Härtl et al.We used a 3D printer of type Prusa i3 MK3S . This printer had the lowest cost of purchase in a comparison of six 3D printers with comparable potential for clinical utility according to Chen et al.The surface file from the Matlab program is imported in the PrusaSlicer software  which creates a gcode file for the control of the printer. The printer runs with most commercial printing materials. We chose a generic polyethylene terephthalate modified with glycol (PETG) with a diameter of 1.75 mm . The printing temperature of this material is 230 – 270°C and corresponds to the window between melting and boiling range.3. Its main ingredient is lead. The thermostat of the melting pot is set to about 120°C. In the present investigation, we included patients getting a total dose of 12 Gy only. Then, the specified dose to the central lung of 7 Gy is achieved by a block height of 20 ‐ 32 mm according to our experience.Figure Figure The printer is equipped with a print nozzle of 0.6 mm in diameter. The layer's height was set to 0.35 mm with two external contours. As infill of the object, a gyroid pattern with a density of 15% was used with three layers of floor and four layers of roof. Support structures for overhanging parts were generated.The molds, blocks, and apertures for three consecutive patients have been included in this investigation. They consisted of two photon lung blocks and one electron aperture for supine and prone positions, each.3The geometric shape of the molds for photon blocks corresponds very well to the requirements Table . ArrangeThe deformation of the sidewalls has been controlled visually. Even higher samples showed only air gaps of submillimeter width between the sidewall and a ruler attached in top–bottom direction.The average of linear measures in x and y direction deviated only some tenths of a millimeter from the values of the TPS. The blocks were slightly larger, but in most cases deviated less than 1 mm from the planning measure. The planned height of photon blocks should be achieved by printing the mold in the specified height (Table 4The material of the actual medical product – the blocks and apertures – is unchanged compared with the former production process with PS foam cutting devices. Therefore, there is no need for control other than the geometric measures of the products. The blocks can be handled in the accustomed manner.The accuracy and reproducibility of 3D printers was investigated by George et al.PETG demonstrated sufficient heat resistance when filled with molten MCP96 without visible deformation. The upper surface of the blocks and apertures shows some irregularities and less smoothness than the sidewalls in submillimeter range, which results from slight temperature gradients during the cooling process. This is similar to the former production process with molds of PS foam. If a higher precision for the height of the blocks is needed than was achieved by the founding process, a milling machine or manual or machine‐assisted sanding can be applied.The x‐ and y‐dimensions of our blocks were slightly larger than the measured dimensions of the molds Table . The molWe have explained why we did not consider the divergence of the photon blocks in detail. However, our regular quality assurance shows no deviation in the modelling of the divergent sidewalls. Therefore, this production process should also be applicable for TBI treatments with fixed beam, where the correct divergence is more important. Similarly, it is possible for other large field treatments such as total nodal irradiation,The 3D printing process takes more time than cutting a mold from PS foam. This must be considered in the treatment preparation. The printing time could be reduced by using two printers simultaneously. Moreover, the second printer can serve as a backup if the first printer fails. At our hospital the time to prepare for TBI treatments is 3 weeks or more, having about 20 patients per year. From March to December we had produced molds for eight individual patients with our 3D printer. Thus, the printing time is not a critical factor. Until now, no breakdown of a printer during the printing process has been observed. Therefore, most prints can run overnight.3D printers are a promising tool in the production of absorbing blocks and apertures. They offer additional useful applications such as printing of bolus material, quality assurance phantoms, or brachytherapy applicators.Manuel Maerz participated in the development of the project, developed the program code, set up the production process, and contributed details to the manuscript; Marius Treutwein initiated the project and participated in its development, evaluated the results, and drafted the manuscript; Jan Nabo performed first investigations in material and methods and created test molds and blocks; Barbara Dobler participated in the development of the project, supervised it, and helped to draft the manuscript. All authors read the final version and agreed to it.The authors declare that there is no conflict of interest.


When mining the multiple coal seams in close proximity, the coal pillar left in the goaf causes stress concentration in the floor. The layout of mining roadway in lower short distance coal seam is affected by the propagation of concentrated stress caused by the upper coal pillar. To determine the reasonable distance of the roadway in the lower coal seam outside the coal pillar, the attenuation law of concentrated stress outside the coal pillar boundary has been studied through simulation model, theoretical analysis, and example analysis. The results show that the concentrated stress of coal pillar decreases with the distance from the coal pillar. At the coal pillar boundary position, the stress change rate reaches the maximum in the floor with different depths, and the stress decreases rapidly in the floor strata outside the coal pillar. Under the same stress condition, the roadway layout in strata at different depths is different. The joint formula of stress and stress change rate was deduced to determine the reasonable horizontal distance of roadway outside coal pillar. The results obtained by the numerical simulation fitting formula and the theoretical calculation formula are close to each other when calculating an engineering example. The roadway pressure appearance is not obvious in the experiment and physical simulation, which indicates that the theoretical formula can satisfy the requirement of engineering calculation. The method provides a reference for roadway location selection under similar conditions. Due to the small distance between the coal seams, the interaction is obvious in the process of close coal seam mining. In the mining process of close coal seams, the remaining coal pillars left over in the upper coal seam have a greater impact on the lower nearby roadway’s layout and its surrounding rock control mode. If the lower coal seam is within the influence range of the concentrated stress of the upper remaining coal pillar, the coal mining roadway located near the coal pillar can be roughly divided into three kinds of layout forms7, which are called outward-misaligned roadway layout, inward-misaligned roadway layout, and vertical roadway layout  to describe the stress field in the field. Their results argue that the roadway should be preferentially arranged in the area with conditions of "0 ≤ ζ ≤ 1, 0.8 ≤ λ ≤ 1.4, 0 ≤ δ ≤ 1"24.Significant research achievement on the distribution range of the stress reduction zone near the coal pillar boundary have been made in previous research [formula ] in the 20:The equation for the vertical stress distribution in the yield zone20:The equation for the vertical stress in the elastic zoneAt present, determination of the value of the stress change rate and the boundary position of the stress reduction zone in the coal pillar floor are mostly based on numerical simulation and stress fitting function to determine the value of the stress change rate and the boundary position of the stress reduction zone, and more detailed theoretical studies are absent. Based on the concentrated stress propagation law in the floor under the coal pillar, the present paper further analyzes the law of stress attenuation affected by the coal pillar, the reasonable position of the roadway with different depths under the coal pillar is determined, so as to provide a theoretical basis for similar projects.The simulation software UDEC was used for the numerical simulation. A Chinese Mine serves as the engineering background for the mining relationship between #4 and #5 coal seams and coal pillars. Numerical simulation was carried out to study the transfer law of concentrated stress generated by the residual coal pillar in #4 coal seam floor. The width of the remaining coal pillar of the #4 coal seam is 45 m, and the interval between the #5 coal layer under the coal pillar is 5~6.5 m. Brief information of coal and rock strata are shown in Table According to the distance from the surface to the simulated rock layer at the top of the simulation model, a uniform distributed load of − 8.39 MPa, corresponding to a depth of 335 m, was used to represent the overburden weight in the actual stratum. Deformation at the bottom and sides of the model is fixed. In the model, the rock properties of different strata are shown in Table According to the numerical simulation software calculation, the contour map of vertical stress in the roof and floor of the coal pillar of #4 coal seam is shown in Fig. As can be seen from Fig. Three horizontal survey lines of pline1, pline2, and pline3 were set at the depth of 1 m, 5 m, and 10 m below the coal pillar, respectively, to record the stress distribution. Stress distribution along each line can be divided into high stress area, decreasing stress area, and low stress area.As shown in Fig. The arrangement of the roadway under the coal pillar of the close coal seam should avoid the high stress area as much as possible, and the roadway should be arranged in the lower stress area so that the pressure of the surrounding rock of the roadway is small and easy to control. It should also be considered to avoid the vertical stress decline area to prevent the roof of the roadway from being subjected to greater shear stress. Therefore, the position where the vertical stress and its rate of change are both lower is a better layout position for the roadway.For specific conditions, numerical methods can be used to analyze the stress distribution law, so as to determine the location where the stress and its change rate are low. According to the numerical results of the stress distribution in the coal strata under the coal pillar, the horizontal change curves of the vertical stress value in the floor at different depths under the coal pillar were drawn Fig. . Six strAccording to the change law of the vertical stress in the horizontal direction, the horizontal distance between the critical position in the floor and the coal pillar boundary is different at varying depths. Therefore, the relationship between the critical position and the depth can be obtained by measuring the critical position data of the survey line at different depths.Through data analysis, it can be known that the depth of the floor at the critical position and the horizontal distance from the coal pillar boundary have a function relation of one variable. According to the numerical simulation results, the values were obtained and fitted to the unary quartic function formula Fig. . The devAccording to the principle of selecting the optimal layout position of the roadway, the critical position obtained by fitting function can be used as the optimal position of coal pillar floor roadway. The fitting function and the coal seam interval show that the range of the stress decline zone and the optimal layout of the roadway under certain conditions can be obtained at the range of simulation measurement depth, but the universal law of stress change requires further theoretical analysis.28, there are small crushing zones and corresponding stress fluctuations at the coal pillar boundary. Under the condition where the crushing zone widths of coal pillar are much smaller compared with the width of coal pillar, the stress fluctuation of coal pillar boundary has little effect on the floor. For the convenience of calculation, it is assumed that the concentrated force of coal pillar floor is uniform load and the floor strata are uniform elastic body.According to the distribution pattern of floor stress curve Fig.  in numer30 provides a case of semi-infinite plane body with uniformly distributed load as shown in Fig. The elastic mechanics theoryq of the coal pillar is − 10 MPa. The position of the coal pillar center is  and the range of concentrated stress caused by the coal pillar is 45 m. The vertical stress curve and its change rate curve at different depths  in the floor were drawn at the width direction (y direction)of the coal pillar, as shown in Fig. In order to further analyze the law of vertical stress change rate under the conditions of the theoretical calculation formula, it is supposed that the concentrated stress The upper part of Fig. The lower part of Fig. The stress value of each curve near the coal pillar boundary decreases rapidly. There is a region of intense stress change rate and the stress descending speed decreases with the increase of the depth. Also the range of the stress decline zone keeps expanding with the increase of the depth. For example, in the floor with a depth of 2 m from the coal pillar, the stress value decreases to 1.0 MPa, which is signed at position 1 . However, in the floor at a depth of 12 m from the coal pillar, the stress value decreases to 1.0 MPa,which is signed at position 2 .As per the analysis in the previous section, it can be seen that the layout of roadway under coal pillars in close coal seams is not only affected by the magnitude of the concentrated stress, but also by the stress change rate. The roadways should be arranged in areas where the stress changes are small. The vertical stress change rate Eq.  in the hx = 0) that the stress change rate is zero, which has no engineering meaning for the selection of the roadway location outside the coal pillars in the close coal seam.Through the analysis of Eq. , the verAccording to the influence law of the concentrated stress caused by the coal pillar on the stress distribution of the strata in the floor, the vertical stress and the vertical stress change rate generated by the concentrated stress in the floor outside the coal pillar cannot be zero, but they can be rapidly reduced to a extremely small value. Therefore, it is possible to select the roadway position with lower stress and lower stress change rate near the coal pillar boundary. According to the requirements of the roadway construction in the stress environment, Eq.  is used k1 is the stress concentration coefficient and k2 is the stress change rate coefficient. After simplification, another expression of Eq. (In Eq. , k1 is tn of Eq.  can be ok1 and stress change rate coefficient k2. However, the concentrated stress intensity q generated by the coal pillar determines the specific size of the stress value and the stress change rate value at the location. Therefore, the specific coefficients k1 and k2 should be selected according to the roadway support strength and the self-supporting capacity of the roadway surrounding rock.Through the analysis of formula , the reaH of the #4 coal seam pillar is 403 m, and the width l of coal pillar is 45 m. The length D of the goaf on both sides of the coal pillar is 216 m. The average bulk density γ of coal pillar overburden is 25KN/m. The breakdown angle of the roof on both sides of coal pillar is 45º. According to the method of Ultimate Strength Approach31(Fig. q of coal pillar is 52 MPa. According to the Three-Index Method23, when the stress concentration coefficient and stress gradient are both less than 1, the roadway surrounding rock are easier to control. In the engineering application, the critical position where the stress value is less than the original rock stress and the change rate of stress in the horizontal direction is less than 1 MPa/m is the roadway critical position. The roadway is arranged in #5 coal seam under the coal pillar in #4 coal seam. The vertical distance between the roadway and the coal pillar is 5.2 m. According to Eq. (According to the actual situation of the roadway project in the test Mine, the overburden depth ch31Fig.  and thosg to Eq.  and the According to the numerical fitting equation in Fig. Mine pressure observation of the roadway was performed on site, and turned out the roadway mining pressure was not obvious. The overall deformation of the roadway was very small, and there was no layer separation in the roadway roof. Engineering application results that, the roadway has avoided the influence of coal pillar concentrated stress, and a good control of the surrounding rock of the roadway was attained Fig. .Figure 1To study reflect the propagation law of coal pillar concentrated stress in the floor, a physical similarity model was established as per the engineering practice, and the loading test was performed Fig. .Figure 132.The occurrence relation and physical and mechanical properties of the simulation strata are consistent with the numerical simulation model. The geometric size ratio of the model is 1:50, and the ratio of similar materials weight refer to the ratio table of similarity testsThroughout the test, the hydraulic cylinder was used to load the top of the model until the coal stratum under the coal pillar was destroyed. With the loading going on continuously, the coal strata under the coal pillar gradually broke down, and finally the plastic failure occurred in the high stress zone and the stress decreasing zone under the coal pillar. The failure range extended to a horizontal distance of 6.5~7 m from the coal pillar boundary Fig. , which wBased on the numerical simulation results, the influence of the concentrated stress caused by the coal pillar was gradually weakened, and the stress gradually was transferred to both sides of the coal pillar. The range of stress concentration zone under coal pillar decreases with increasing depth, while the range of stress decreased zone increases with increasing depth. The critical position of the stress decline zone can be obtained by fitting the stress function according to the numerical simulation results, and this position is taken as the horizontal critical position of the roadway layout under the coal pillar. The results provide a solution for selecting roadway layout with similar specific geomechanical conditions.According to theoretical analysis of the stress propagation, the stress curve of different depth positions was calculated and the stress propagation characteristics were evaluated. The joint formula of stress and stress change rate were established to determine the appropriate roadway position meeting the engineering requirements, which provides a mathematical method for selecting the horizontal position of roadway under the coal pillar.Engineering application was performed in a typical coal mine based on previous analysis. The horizontal distance between coal pillar boundary and the roadway in the close coal seam under the coal pillar was calculated by using the fitting function of numerical simulation and the elastic-based calculation formula. The reasonable roadway location significantly improved the roadway surrounding support effect. Physical similarity simulation test verifies that the stress transfer range and plastic failure range are outside the roadway layout position. Therefore, the rationality of the roadway location calculation result is further verified.


Multi- and hyperspectral endoscopy are possibilities to improve the endoscopic detection of neoplastic lesions in the colon and rectum during colonoscopy. However, most studies in this context are performed on histological samples/biopsies or ex vivo. This leads to the question if previous results can be transferred to an in vivo setting. Therefore, the current study evaluated the usefulness of multispectral endoscopy in identifying neoplastic lesions in the colon. The data set consists of 25 mice with colonic neoplastic lesions and the data analysis is performed by machine learning. Another question addressed was whether adding additional spatial features based on Gauss–Laguerre polynomials leads to an improved detection rate. As a result, detection of neoplastic lesions was achieved with an MCC of 0.47. Therefore, the classification accuracy of multispectral colonoscopy is comparable with hyperspectral colonoscopy in the same spectral range when additional spatial features are used. Moreover, this paper strongly supports the current path towards the application of multi/hyperspectral endoscopy in clinical settings and shows that the challenges from transferring results from ex vivo to in vivo endoscopy can be solved. Even in 2020, cancer accounts for 23.5% of the deaths despite the pandemic situation2. From these, more than 25% were caused by cancer in the gastro-intestinal tract (GI)2. An important reason for this high death toll of carcinomas in the GI tract lies in the difficulty of their detection. The main issue is that patients normally have symptoms only in later stages of the tumour development. Thus, the best chance to detect early stage carcinomas is regular screening by endoscopy. Currently, the state of the art is using thorough inspection with white light endoscopy with targeted biopsies, or random biopsies in inflammatory conditions. However, intraepithelial neoplasias, adenomas, and carcinomas are difficult to detect during screening colonoscopy, especially if they are flat and/or small in their macroscopic appearance5 or surrounded by inflamed tissue.In Germany in 2014, more than 25% of the deaths were caused by cancer7. MSI and HSI combine machine vision with spectroscopy9. They enable the acquisition of two-dimensional images with the spectral information for each pixel. Considerable progress has been made in many application fields10. So far, HSI has already been proven to be a reliable method for many medical applications6, including carcinoma detection in the oesophagus12. However, most of the studies for endoscopic application have been performed ex vivo14. Moreover, there is currently no in vivo study employing MSI/HSI during colonoscopy known to the authors.To overcome this problem, an approach centred around multi- and hyperspectral imaging  is proposed. MSI is often defined as having ten or less wavelength bands, while HSI is defined as having more than ten wavelength bands. Both MSI and HSI are increasingly used in medical applications15 about gastrointestinal endoscopy, there is a need for further evaluation of spectroscopic quantitative measurements of tissue to allow a direct optical diagnosis of neoplastic lesions during endoscopy. It was already outlined before that such spectroscopic quantitative measurement can be provided by MSI and HSI. For MSI, the benefit could already be demonstrated by our group for the diagnosis of carcinomas of the stomach with an accuracy of 64%16 or an AUC of 0.7217 in vivo in patients. In this current in vivo study, MSI is applied in murine models of spontaneous and inflammation-driven carcinogenesis.As discussed in a review by Swager and colleagues18. The set-up is shown in Fig. The set-up is a modified version from a Coloview high-resolution mouse endoscopy system . The mouse endoscopy system is a rigid endoscope which uses multiple lenses for imaging. Hence, there are no artefacts which occur as it happens from fibre bundles. This system was used with a spectral filter as a fluorescence endoscope system for the investigation of the topical application of Chlorin e6-PVP for improved endoscopic detection of neoplastic lesionsThe overall set-ups consists of the endoscope head  connected to a multispectral light source  and a custom optical system. Both systems are controlled by a personal computer (PC) via a graphical user interface (GUI). The GUI is written in Matlab  and controls the data input, the data output and the external light source. The light source allows seven wavelength bands shown in Table 18: “The custom optics unit incorporates two achromatic lenses with an apochromatic objective in between. The signal initially travels from the endoscopy telescope unit via an adaptor to the first lens with a focal length of 35 mm. This lens then conveys the real image to a 10The modified endoscope was already described in our previous publicationEthical guidelines Animal studies are approved by the Institutional Animal Care and Use Committee of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)  and the government of Lower Franconia. All methods were performed in accordance with the relevant guidelines and regulations. The study was carried out in compliance with the ARRIVE guidelines.Mouse models In general, two models were used: inflammation-based carcinogenesis and spontaneous tumour models. The AOM/DSS model was employed to induce inflammatory carcinogenesis in 11 mice of C57BL/6 background as described previously19. The spontaneous model was based on the genetic APCmin or TP53 tumour model. The use of both models allowed to look for differences in the detection rate in neoplastic lesions of different disease mechanism.Endoscopy procedure The endoscopy procedure is performed as described previously21. In brief, isoflurane is used to establish anesthesia. Then, the bowel is carefully flushed with tap water to remove stool. Afterwards, the bowel is distended by means of careful air insufflation via the working channel. The colon is investigated with white light imaging and lesions that are deemed “definitive” neoplasms based on the assessment of their macroscopic appearance by the experienced endoscopists, were additionally evaluated with MSI. A biopsy for neoplasm assessment could not be done, as the mice were required for other purposes later on.In total, 25 mice were investigated by this procedure. Each mouse had at least one neoplastic lesion. Lesions for spectral analysis were identified by two medical experts by classification as a “definitively neoplastic lesion” based on macroscopic appearance. To increase accuracy, assessment was performed by two medical experts. Only one of them drew the lesion margin.For the MSI in this study, nine images were taken in the following order: one image for mentclass2pt{minimPotential safety risks for human studies The current imaging procedure imposes no major risks to potential patients compared to white light endoscopy as the required light intensities are similar. There are two potential minor risks: First, light with a wavelength of 397 nm is used. Long term exposure of blue light is known to increase the risk of skin carcinomas. However, the exposure time in this study is orders of magnitude lower than it is required for increased risks of carcinomas. Second, the usage of the MSI might increase the time of the endoscopic procedure in comparison to the white light endoscopic procedure.17 with very detailed descriptions in the latter one17. In general, the data analysis consists out of the following steps shown in Fig. The analysis is done with a pixel-per-pixel classification process. All data analysis is done with a leave-one-out strategy (LOOS) in which 24 mice are used for training and one for testing. No mouse was excluded from the analysis. In general, the data analysis is a modified version of that presented in two previous studiesThe classification process is described in detail later in this section. In the pre-processing step, noise is minimized and specular reflections are removed. Then, spatial and spectral-spatial features are generated. The number of features is reduced significantly by the principal component analysis (PCA) to use 99% of the variance in the next step. With the reduced feature set, the classification is done for support vector machine (SVM) and boosting classifiers. In the end, the results are evaluated.Pre-processing The pre-processing consists out of four steps: First, specular reflection and too dark areas are removed. If the intensity of a pixel in the red or yellow region is less than 5% of the maximal intensity, the pixel is excluded. Moreover, the surrounding with a distance of five pixels is also excluded. In total, about 1% of the data is excluded this way. Afterwards, Gaussian filtering is done with a filter size of seven by seven pixels with a standard deviation of one pixel to ensure that the minimum noise fraction (MNF) does not transmit some noise-caused artefacts from one wavelength band to others. As a last step, at the boundary between healthy tissue and neoplastic lesion a margin of five pixels is excluded due to the fact that there is a definite error in finding the tumour margin. In the next step, the data is normalized for every image separately. 22 is used for de-noising and enhancement of spatial features. Spectral bands with lower maximal intensity show a weaker influence while calculation of the MNF. Therefore, they were normalized before. In this study, the modified method from Regeling et al.23 is used. They altered it according to Gao et al.24. Furthermore, the MNF is not calculated by multi linear regression23. It is changed to a regressive support vector machine (rSVM) as this significantly reduced artefacts from the MNF. The sub-image size for the MNF is 20 times 20 pixels and the last three MNF components are removed for noise filtering.In this step, MNFAfterwards, the data is de-normalized again as shown in Eq. mentclass2pt{minimAs final step for pre-processing, a second Gaussian filter is used for further noise reduction with a size of 11 times 11 pixels and a standard deviation of two pixels.Feature generation In general, a high number of features increases the classification accuracy. However in many cases, the amount of memory or the computational power limits the number of features. The limits are especially high for HSI, as there are already a high amount of spectral features. As MSI has often considerably fewer features, spatial features can be added without restriction due to limited memory or computational power. Therefore, four kinds of features are used: First, the intensity of the local multispectral image is used as a feature. Second, the derivative is used as a feature by the convolution with the Sobel operators. Third, the spectral-spatial variation is derived as shown before17. Forth, the spatial features based on Gauss–Laguerre polynomials (LGs) with l and p smaller 3 are used in the same way as described in a previous publication17.q and s are constants, r is a parameter, describing image rotation with , AdaBoost (AB), SVM with linear and Gaussian kernel as well as random forest (RF) from the implemented Matlab functions are used for classification. The following commands are used: fitensemble for AB, RB; fitcsvm for SVM; treebagger for RF. Predictions were conducted using predict in all cases.For RB, 100 stump trees are used with an error goal of 0.2. Replacing of the variables is not allowed, but resampling. Hence, it is the classical boosting approach with re-weighting the single trees. For AB, the learn rate is set to 0.1 and 400 stump trees are trained. Again, the classical boosting approach is chosen. For RF, 400 trees are trained and the minimal leaf size is set to 3. For both SVMs, standardization is used, the kernel scale is set to auto and outlier rate is set to 0.05. All other parameters are kept at standard settings.To speed up the classification process, a random selection of 1% of the training data is used for training. It should be noted that this data reduction is done after the feature generation and reduction. Especially, the results would significantly degrade if the PCA would only be done on 1% of the data. The data points are selected randomly. For the test data, the whole data set is used. Furthermore, repetition of the classification process would not lead to significantly different classification results due to the random selection of the data points.Evaluation of the classifiers For the evaluation of the classifiers, the four measures accuracy (ACC), modified accuracy (ACC2), area under the curve (AUC) and Matthews correlation coefficient (MCC) are used. They are used due to the fact that they are either well established in the medical field  or that they are balanced measures for the case that the amount of data from one class is much higher . The ACC, ACC2 and MCC are defined as follows:Optimization of the parameters There was only a slight optimization process done for this specific data set. Thereby, the following parameters were varied: sub-image size for the MNF (20 and 120) and amount of spatial features (P = L = 2 versus P = 3 and L = 4). The sub-image size for the MNF had nearly no effect while the increase of spatial features lead to a small improvement of the accuracy measures. The rest of parameters were chosen due to the fact that they showed good results of the classification of carcinoma tissue with MSI17. It is also not expected that wide parameter optimization would lead to much better results as the correct margin of the tumour is likely the reason which limits the quality of the results25.The result section consists out of two parts. In the first section, a typical multispectral image is shown. In the second section, the results of the machine learning are shown and discussed.Figure Table 17. The best classifier is RB for all measures. Thus, it is likely that the training data has misclassified data. Therefore, it is likely that the margin of the neoplastic lesion was not correctly identified by the medical expert.The results are strongly dependent on the chosen classifier as well as on the chosen accuracy metric. Nevertheless, there is an overall increase by the usage of spatial features especially for weaker classifiers such as the SVM with linear kernel. Nevertheless, even the results for the stronger classifiers such as RB, AB and RF improve a little bit. In general, the classification between healthy and neoplastic lesion in mice provides better results than the in vivo clinical study on oesophagus in humans in our previous studies26 in the visible range despite their study is an ex vivo study. However, similar results can only be reached for the MSI data set with spatial features. This pinpoints that the usage of spatial features is a very important step in future for multispectral endoscopy. Furthermore, the MCC in this study is nearly identical to the ex vivo results from Collins et al.27 in which they used a 3d convolutional neural network (3DCNN). Finally, the multispectral data set with spatial features will likely have a similar data dimensionality as hyperspectral data sets. Hence, it is a valid alternative.Table 26.An ANOVA shows significant differences (Table Table 28 must be considered. This leads to the problem that - in combination with the sparsity of the data - it is difficult to find the correlations between spectral bands. Due to these points, HSI will be very difficult to set up in future. A further minor issue is that the margin of carcinomas is not known for in vivo endoscopy29.MSI with support of spatial features allowed classification of neoplastic lesions in the colon. Normally there is an expected advantage of real HSI over MSI, HSI has two major drawbacks. The first one is the curse of dimensionality. The problem is that an increase in dimensionality leads to a fast increase of the volume of the feature-space so that the available data become sparse, making classification problems much harder to solve. Second, the high correlation between continuous spectral bands31. Thus, it might make sense to switch back to MSI after the optimal spectral bands have been found by HSI; potentially, it might not even be required to use the HSI at all as almost all absorbers in tissue have a broad spectral effect. Finally, MSI set-ups are also easier to build.Despite the expected advantage of real HSI over MSI, the above points emphasize why it still seems to be an attractive option to revert to MSI. Additionally, this is an issue well known in remote sensing: Not all HSI spectral bands are required as some of them already include all significant information26 in the visible range; however, their results are superior in the NIR range from 1000 to 1600 nm and their combination of NIR and VIS leads to further huge improvements. This allows the conclusion that multispectral endoscopy with a few additional wavelengths in the NIR region is likely to show the same promising results as full HSI without the downside of the latter.This line of arguments is supported by the fact that the classification results of the present study are similar quality to the results from the ex vivo HSI study from Baltusen et al.The main limitation of the present study is the fact that there was a trade-off between the number of available mice and the possibility to perform biopsies as gold standard for the evaluation the neoplastic lesions. This trade-off was solved at the expense of omitting the biopsies, allowing to investigate 25 mice—a quite substantial number. Therefore, only neoplastic lesions without further differentiation could be used as target for classification.


In the last few years, the Internet of things (IoT) has recently gained attention in developing various smart city applications such as smart healthcare, smart supply chain, smart home, smart grid, etc. The existing literature focuses on the smart healthcare system as a public emergency service (PES) to provide timely treatment to the patient. However, little attention is given to a distributed smart fire brigade system as a PES to protect human life and properties from severe fire damage. The traditional PES are developed on a centralised system, which requires high computation and does not ensure timely service fulfilment. Furthermore, these traditional PESs suffer from a lack of trust, transparency, data integrity, and a single point of failure issue. In this context, this paper proposes a Blockchain-Enabled Secure and Trusted (BEST) framework for PES in the smart city environment. The BEST framework focuses on providing a fire brigade service as a PES to the smart home based on IoT device information to protect it from serious fire damage. Further, we used two edge computing servers, an IoT controller and a service controller. The IoT and service controller are used for local storage and to enhance the data processing speed of PES requests and PES fulfilments, respectively. The IoT controller manages an access control list to keep track of registered IoT gateways and their IoT devices, avoiding misguiding the PES department. The service controller utilised the queue model to handle the PES requests based on the minimum service queue length. Further, various smart contracts are designed on the Hyperledger Fabric platform to automatically call a PES either in the presence or absence of the smart-home owner under uncertain environmental conditions. The performance evaluation of the proposed BEST framework indicates the benefits of utilising the distributed environment and the smart contract logic. The various simulation results are evaluated in terms of service queue length, utilisation, actual arrival time, expected arrival time, number of PES departments, number of PES providers, and end-to-end delay. These simulation results show the effectiveness and feasibility of the BEST framework. The smart city covers urban areas equipped with Internet of Things (IoT) devices . These IAnother important consideration in the smart city is providing Public Emergency Services (PESs) to the smart-city people to protect them from serious hazards. The existing PESs only focus on the smart healthcare system to timely provide a treatment based on a healthcare service request to the patient based on the centralised system . SimilarA three-layered BEST framework architecture is presented, consisting of smart homes, IoT controllers, service controllers, and blockchain nodes.The IoT controller and service controller behave as edge computing servers. The IoT controller manages a set of smart homes that belong to its area and sends a PES request  under uncertain environmental conditions using a smart contract. Furthermore, it manages an access control list to keep the information of registered IoT devices that belong to smart homes to protect the PES department from receiving the wrong information. The service controller controls numerous PES departments  and uses the queue model to balance PES requests fairly among PES departments.The various smart contracts are designed to register the smart home, IoT controller, and service controller. The other smart contract, such as request PES and allocate PES, automatically invokes and handles the PES request on the blockchain network. Further, the service controller generates a final reputation value for a PES department after fulfilling the PES request using smart contract logic on the blockchain network.The proposed BEST framework is designed on the Hyperledger Fabric platform to bring trust and transparency to the overall framework architecture. The simulation results are evaluated in terms of waiting time, utilisation, actual reaching time, expected reaching time, final reputation value, and end-to-end delay to show the effectiveness of the proposed framework.To address the above-raised issues, this paper proposes a Blockchain-Enabled Secure and Trusted (BEST) framework to handle the PES in a smart city environment. The proposed BEST framework identifies the presence of fire in a smart home using IoT devices and provides a fire brigade service as a PES to cure severe fire damage. The main contribution of the paper is described as follows: The rest of the paper is organised as follows. In In , the autA blockchain-enabled emergency service architecture is suggested for smart homes, which ensures security and privacy authentication mechanisms . In 23]23], the The term blockchain was popularised from Bitcoin in 2008  by an unNick Szabo proposed the concept of a smart contract in 1994, in which he explained that a smart contract is a self-executable code used to write a business logic or agreement between two or more parties . The defThe block is an essential component in the blockchain network, which consists of a block header and a block body, as shown in (a)Membership service provider: The MSP provides a digital certificate to the Certificate Authority (CA) in the private blockchain network. The MSP keeps the information of generated digital certificates in a certificate list to authenticate a CA when required. The MSP also gives authority to the CA to distribute digital certificates within its organisation.(b)Certificate authority: The CA resides within an organisation and provides digital certificates during the creation of peers and a client.(c)Peer: The peer is categorised into two types: endorsing peer and committing peer. The endorsing peer performs a transaction endorsement to achieve the consensus in the private blockchain network. In contrast, the committing peer validates and manages a set of transactions through a block in the private blockchain network.(d)Client: The client interacts with the private blockchain network using smart contracts to generate transactions.(e)Orderer: The orderer bundles the endorsed transactions and arranges them in a timestamped order to create a valid block in the private blockchain network and broadcast it to the committing peer.(f)Channel: The channel is a medium to connect multiple organisations to receive the same set of transaction information in the private blockchain network to manage consistency.The Hyperledger Fabric is a famous private blockchain platform hosted by the Hyperledger Foundation in 2015 . To undeThis section is divided into four parts. The proposed BEST framework’s system architecture is presented in the first part. In the second and third parts, a queue model is designed to select a PES department for a smart home and a reputation model for a PES department after fulfilling a PES request. The fourth part presents a network setup for the private blockchain network and the implementation of various smart contracts. The working of each part is explained as follows and the abbreviations used in designing the queue and reputation model, as shown in The system architecture of the BEST framework comprises three layers, as shown in The service controller uses the queue model to select an appropriate PES department with a minimum service queue length for a smart home that handles a PES request, as shown in The service queue lengthThe waiting timeThe working of the queue model to select a PES department for a smart home in the BEST framework is given in Algorithm 1 and described as follows: Step 1: The Step 2: The service controller receives the PES request of theCase 1: For instance, the sub-area of the Case 2: For instance, the sub-area of the Step 3: The service controller calls a smart contract on the private blockchain network to confirm the arrival of the PES provider of the selected Algorithm 1: Queue model for PES departmentsInput: Threshold values:Output: Select the PES department;BeginFor p = 1 to       For i = 1 to             If sets up the private blockchain network on the Hyperledger Fabric platform and deploys the smart contracts to perform various blockchain-related operations in the BEST framework, as shown in Thus far, the private blockchain network is established. Now, the trusted authority installs smart contracts on all Fabric organisation peers and channels through the software development kit, which can be expressed as given by Equation (15).As explained below, the IoT and service controllers can interact with the deployed smart contracts.Algorithm 3: Smart Contracts BeginFor p = 1 to      Call      Enter necessary details;       Receive End ForFor i = 1 to     For p = 1 to 1 to          If.Step 3: The committing peer informs the client of successful registration and generates a pair of public/private keys Step 1: The service controller invokes the register service controllerStep 2: The endorsing peer collects the registration request and signs the registration request using its digital signatureThe client receives the signed transaction and sends it to the orderer. The orderer checks received transactions and broadcasts a new block to the committing peer to update their distributed ledger with updated information, which can be expressed as given by Equation (21).Step 3: The committing peer updates the client and obtains a pair of public/private keys , smart home sub-areaStep 1: The registration of the smart home is performed indirectly through the IoT controller. TheStep 2: The The endorsing peer verifies the The client forwards this signed transaction to the orderer. The orderer validates the transaction information and generates a new block. This block is broadcast to the committing peer and can be expressed as given by Equation (25).Step 3: The committing peer informs the client and returns a pair of public/private keys for the , PES department locationStep1: The registration of a PES department is indirectly performed using a service controller. TheStep2: The service controller receives the The endorsing peer checks the received information and signs the transaction using its digital signature The client forwarded this signed transaction to the orderer. The orderer collects the number of signed transactions, generates a new block, and passes it to the committing peer to update their distributed ledger information, which can be expressed as given by Equation (29).Step3: The committing peer notifies the client about the successful registration of .The other Fabric organisation receives this information and uses it later to generate reputation value for Step 1: Thement see , which cStep 2: This information is forwarded on the private blockchain through the Fabric organisation of the Step 3: The orderer receives the reputation information of the Step 1: At the end of the day, the service controller evaluates the final reputation value for the (37) see .(37)finStep 2: The Fabric organisation of the service receives this information and forwards the signed transaction in the private blockchain network, which can be expressed as given by Equation (38).Step 3: The orderer processes this information to create a new block and broadcast it to other Fabric organisations, which can be expressed as given by Equation (39).To set up the private blockchain network for the BEST framework, we used Hyperledger Fabric 2.x. The private blockchain network is deployed on a cloud platform with 16 CPU cores, 12 GB RAM, and 125 GB storage. Further, we used Python to call upon blockchain API for IoT gateways and PES departments. We utilised a random number generator to generate an eight-digit identity for the IoT devices and IoT gateway connected with a smart home to distinguish them from other smart home IoT devices and gateways. The IoT controller stores this information in its access control list to identify the number of IoT devices linked with smart homes with their identity. With the help of an access control list, the IoT controller easily detects the placement of any malicious IoT device in a smart home. The BEST framework consists of nine Fabric organisations running on docker; among them, one is an administrator, seven are IoT controllers, and the last one is a service controller. The administrator’s task is to first deploy a private blockchain network on the cloud platform. The configtxgen tool is utilised to generate the genesis block, which contains the private blockchain network configuration and a channel. Additionally, the cryptogenic tool is used to generate the digital certificate  and public/private key for the endorsing peer, committing peer, and client residing in the Fabric organisation and for the orderer. Once the network is set up, and all credentials are generated using cryptogenic and configtxgen tools, the administrator installs all the smart contracts in the private blockchain network. Few assumptions are considered while designing the smart contracts, queue model, and reputation model in the BEST framework, as shown in The centralised PES system only contains the information of its sub-area PES department and its PES providers. These centralised PES systems only fulfil the PES requests of its sub-area smart homes. In an emergency, these sub-areas are unable to handle the other sub-areas PES requests because they do not have a global view of all the sub-areas smart home locations. Furthermore, a PES department within a sub-area is unable to accept a PES request of its sub-area smart home if all its PES providers are engaged, which could lead to serious fire damage in a smart city. We considered the centralised PES system for comparison with the proposed BEST framework for the state-of-the-art work.A relationship between the expected reaching timeThe final reputation valueA relationship between the service queue lengthA comparison between the centralised PES system and the BEST framework concerning the service queue length is shown in Similarly, in the same sub-area, a PES request is generated by theA comparison between the centralised PES system and the BEST framework with respect to the number of PES departments and the number of PES providers is shown in A relationship between the utilisation ofIn comparison, the utilisation of the PES department available in sub-areas 3 and 6 are low and nearly the same. This happens when this PES provider of the selectedA comparison between the End-to-End (E2E) delay for the proposed BEST framework and the centralised PES system is shown in The blockchain holds the promises of transparency, trust, and privacy for an IoT-based smart city. Therefore, applying blockchain directly to IoT networks is not a good option because of numerous challenges, including resource consumption, processing time, storage, and scalability. In this paper, we proposed a three-layered architecture of BEST that help in providing reliable PES. In the proposed system architecture, the IoT controller uses a queue model to provide fast access to PES providers. The benefit of an IoT controller is off-chain storage, proper management of IoT devices through an access control list, and scalability. In contrast, the queue model helps in selecting an appropriate PES department. The private blockchain network of the BEST framework is designed using a Hyperledger Fabric platform, which maintains records of IoT controllers, service controllers, smart homes and PES departments in the distributed ledger. The transfer of PES requests and the arrival of PES providers are ensured by using smart contract implementation. We also considered the reputation model for the PES department. The smart home rates the PES department according to their service fulfilment and generates either a positive or negative reputation value accordingly. The results indicate that our system model is sufficient to handle PES requests in real-time and ensure minimum waiting to fulfill a PES request. As a limitation of this study, it is noted that a simulation-based experimental study needs further validation in hardware-based prototype testing. We will develop Raspberry-pi-enabled blockchain nodes for prototype validation of the BEST framework. In the future, we will extend our work by utilising neuro-fuzzy logic to identify the presence of fire in a smart home. A hybrid blockchain platform will also be a quest for the implementation of several smart contracts as well as using the incentive mechanism for PES departments to incentivise them based on their reputation value after fulfilling a PES request.


Wireless sensor networks (WSNs) have recently been viewed as the basic architecture that prepared the way for the Internet of Things (IoT) to arise. Nevertheless, when WSNs are linked with the IoT, a difficult issue arises due to excessive energy utilization in their nodes and short network longevity. As a result, energy constraints in sensor nodes, sensor data sharing and routing protocols are the fundamental topics in WSN. This research presents an enhanced smart-energy-efficient routing protocol (ESEERP) technique that extends the lifetime of the network and improves its connection to meet the aforementioned deficiencies. It selects the Cluster Head (CH) depending on an efficient optimization method derived from several purposes. It aids in the reduction of sleepy sensor nodes and decreases energy utilization. A Sail Fish Optimizer (SFO) is used to find an appropriate route to the sink node for data transfer following CH selection. Regarding energy utilization, bandwidth, packet delivery ratio and network longevity, the proposed methodology is mathematically studied, and the results have been compared to identical current approaches such as a Genetic algorithm (GA), Ant Lion optimization (ALO) and Particle Swarm Optimization (PSO). The simulation shows that in the proposed approach for the longevity of the network, there are 3500 rounds; energy utilization achieves a maximum of 0.5 Joules; bandwidth transmits the data at the rate of 0.52 MBPS; the packet delivery ratio (PDR) is at the rate of 96% for 500 nodes, respectively. A WSN is made up of several sensor nodes that are connected in an ad hoc and transitory fashion to detect data and transmit it to a centralized location known as a base station (BS) or sink node . Sensor A WSN is a network of several nodes which senses the environment, manipulates the acquired data and thereafter transmits and receives the sensed information in a particular network . The facThe above discussion shows that it is not plausible to replenish the motes for some applications such as structural health monitoring, environment monitoring, transportation systems and precision agriculture  because Wireless Communication Optimization: Radio characteristics decide the energy consumption of nodes. If a node is communicating with the other node, then many factors such as its control on transmission power and different modulation schemes incorporated play a significant role. Cooperative communication and cognitive radio also balance the energy consumption by focusing on the quality of data transmission. These factors are explained below.Transmission Power Control: Wireless communication optimization considers transmission power control (TPC). The maintenance of the level of transmission power of a node at a significant specific level with optimized energy consumption is TPC [n is TPC . The enen is TPC . When thn is TPC .Research Gap: Due to the application-specific nature of a WSN, the development of TPC schemes becomes a daunting task. There are two prominent factors, distance and the quality of the link, that decide the required level of transmission power to reach the receiver. Link quality has some dependence on physical barriers such as environmental conditions and physical obstacles in the path of transmission among nodes. TPC concerning the link quality among nodes is an energy- efficient approach as it selects the minimum power needed to maintain a qualitative link; most TPC schemes are proposed for single-hop communication. TPC is handled with two processes, i.e., discovery of neighboring nodes and setting up a feedback or acknowledgment system for the nodes that have received messages. There are several thresholds defined for deciding the link quality and handling the neighboring tables that store the information about the neighboring nodes.Cooperative Communication: In the recent years of technological advancements, MIMO (multiple input multiple outputs) antenna technologies have outperformed the traditional wireless technologies [nologies . While inologies . Consequnologies . When muResearch Gap: The current research demonstrates the cooperative communication framework implemented in the network. It emphasizes the way the cluster head nodes build up communication among the cooperative nodes in the network to efficiently utilize energy resources in the network.Energy Efficient Cognitive Radio: A cognitive radio (CR) is described as a radio that adapts its transmitter parameters to the environment in which it operates [operates . A CR isoperates . The undoperates . Howeveroperates . ConstruSensors can be put either deterministically or randomly in a region of concern . DeploymControlled node deployment: Controlled deployment is usually pursued in indoor applications of WSNs .Random sensor deployment: Randomized positioning of sensors is often the only choice. For instance, in WSN apps in battle recognition tasks, disaster recovery and forest fire detection, sensor deterministic deployment is highly dangerous and/or unfeasible. It is commonly anticipated that helicopters, grenade launchers or clustered bombs will drop sensors. Such deployment implies random sensor spread although to some extent, node density can be monitored. While substantial progress has been made in investigating node positioning optimization in WSNs, many challenges remain .Sleep Scheduling Mechanism: Sleep scheduling implies the proportion of the wake-up moment in a predefined period to the complete duration of the period [e period . For insSynchronous Schemes: In synchronous systems such as S-MAC and T-MAC, sleeping nodes wake up at the same moment to interact with each other regularly, which implies that the network must maintain global synchronization.Semi-Synchronous Schemes: Sensor nodes are usually grouped into clusters concerning semi-synchronous systems. Sensor nodes wake up or go to sleep simultaneously in the same cluster, but clusters behave asynchronously with others.Asynchronous Schemes: Each sensor node has its wake-up and sleep timetable in terms of asynchronous systems.The purpose of a WSN sleep schedule is to save the WSN’s energy consumption. Here, we list some possible instructions .Mobility for relay and mobility WSNs: The nodes consume energy which further leads to an energy hole problem. The mitigation for this problem is possible with the help of a mobile relay and sink. Similar to a mobile robot, the mobile relay or mobile sink can move around to collect data, providing a nice trade-off between energy consumption, latency and delay in shipment.Clustering process for WSNs: For semi-synchronous sleep scheduling schemes, the network is generally divided into several clusters, and cluster heads are responsible for interacting with other clusters. The cluster heads may need to consume more energy compared to normal sensor nodes .Numerous routing techniques that tend to enhance the energy efficiency of the network have been reported. Clustering proves to be promising in preserving the energy of the nodes. It is defined as the grouping of sensor nodes to form a cluster as shown in The first protocol to define heterogeneity in routing was the SEP (Stable Election Protocol) . It usedThe energy consumption scenarios discussed in the previous section demonstrate that the communication of a node drains almost all of the energy. Unlike the sensing and computational task which consumes very little energy, the transmission and reception of data use tremendous amounts of energy. The motivation behind highlighting the energy efficiency of a WSN is the inevitable energy drainage of a resource-limited sensor node ,27. The The main gap in the existing literature is that the number of nodes are static for every round that does not ensure the number of acquired cluster heads will maximize the lifetime of network. In our proposed protocol, the number of nodes varies for every round which increases the efficiency and lifetime of the network.Data aggregation, multi-objective-based cluster head selection and optimization-based path selection for transmitting data are the three primary processes in the framework network for smart-energy-routing algorithms that are developed.The model for a multi-path network is deployed where the distance D between the transmitter and receiver is more than the threshold value, which is the distance square for the dissipation of radio energy for the multipath model.The cluster head selection method is chosen for every round to achieve network longetivity and energy efficiency.The performance is evaluated based on the metrics and compared with baseline algorithms  with the proposed methodology.The key contributions of this paper are summarized as:Data aggregation, multi-objective-based cluster-head selection and optimization-based path selection for transmitting data are the three primary processes in the framework network for smart energy routing algorithms. It concentrates on the issue of traffic congestion around the sink. Configuration, energy modeling, node aggregation and path selection are the four stages of the process. The sensor network development and installation are explained in the configuration step. Transferring data via the nodes and an analysis of energy use are part of the energy modeling. The node aggregation and cluster-head selection describe how to classify the nodes in the network and create clusters. The SFO method will be used to determine the best path for transferring data during the path selection stage. The proposed flowchart is shown in Assume a network as a graph G = V, E, where V denotes the sink node or base station, and E denotes the link required for transmission among sensor nodes. We make several assumptions for the network. For randomized sensor placement, a two-dimensional Euclidean approach is used. A non-rechargeable power supply is included with each sensor node; being deployed, sensor nodes are unable to change their placement. The dissipation of energy for radio is utilized. When the recipient of radio electronics uses energy, simultaneously, the transmitter amplifies and transmits the energy. The model for the multi-path is deployed where the distance D between the transmitter and the receiver is more than the threshold value, which is the distance square for the dissipation of radio energy for the multipath model. However, the m-bit transmission of packets for energy utilization Equation ,WhenAlgorithm 1: Algorithm for proposed route1:procedureBegin:2:For each and every round of CH   3:   Choose the optimal number of CH’s4:   Organize the clusters5:For each and every CH   6:   Choose the cluster coaggregation based on the remaining energy7:For each and every coaggregation   8:   Merge and forward the data to cluster head9:endfor   10:    CH forwards to the base station11:endfor    12:endfor    Algorithm 1 of the routing protocol is explained to show the mechanism of how the cluster head is transmitted to the base station.As shown in The utilization of energy for packets received of m bits is represented by Equation :(3)Wrec=m”, the number of messages is shown as “q”, and the energy utilized for one-bit aggregation is shown as For the data aggregation, the energy used is represented by Equation :(4)WagreA cluster head with more than one node eligible to act as nodes are named “cluster congregation (CGA)”. The CGAs are in charge of acquiring and consolidating data from the members of the clusters before sending them on to the cluster heads using a hybrid MAC protocol. To transfer sensed data to its CGA (low-level MAC), the members of cluster nodes use a CSMA/CA protocol to acquire the channel. Every CGA, on the other hand, sends the gathered information to its CH during its given TDMA session (high-level MAC).Each member of the cluster turns on its radio communication modules in the steady-state period to transmit the perceived data to the appropriate CGA. As a result, the nodes would only be activated throughout their operating hours, and they will otherwise hibernate. The data are aggregated, compressed and forwarded to the cluster heads by the CGAs. As a result, the intra-cluster transmission mechanism is multi-hopping . MoreoveI where On either side, the overall energy utilized by the members of the clusters and congregations of each cluster node in every round Equation . WMR denThe fitness function for all the clusters for every round in the SFO is described with the objective function: CH fitness value = F1(CH). There are different objectives in preventing failure of the sink node among which is evaluating the bandwidth of the CH which includes the maximal number of data sent to the base station from CH.Network Coverage: Coverage is a metric for WSN service quality that focuses on the preliminary node deployments coverage rate and if these nodes can obtain signals from the ROI entirely and properly.Network Connectivity: Because sensor networks are typically big in scale, connection ensures that data collected by sensors can be transferred to sink nodes [nk nodes .Network Longevity: The timeframe from the inception of the network to when the percentage of dead nodes reaches a certain threshold is referred to as the lifetime of the network.The integrity of the routing protocol has a direct impact on network longevity and efficiency, whereas a robust topology strategy depends on a comprehensive evaluation methodology ,29. DepeAlgorithm 2: Algorithm for selection of cluster headInput: Collection of sensor nodes C =      Output: Choosing the best sensor node as CH     1:procedureBegin:2:for     3:while choosing the CH    4:For every sensor node     5:    Compute proximity 6:if (    7:    choose the sensor node 8:else    9:    Reject the sensor node The Algorithm: 2 for the selection of cluster heads is:The network is being configured with a base station that can obtain the coordinates of all sensor nodes in the sensing area as well as their remaining energy.The monitoring region is separated into several clusters using a Voronoi diagram, and a probabilistic model for perception is developed. The attenuation probabilistic algorithm is used to choose duplicate network nodes, and these nodes are considered the first type of hibernating cluster-head node .When one of the cluster-head nodes dies, another duplicate node takes over as cluster head. If the death node is a current common node, another duplicate node awakens from sleep to become a normal node.If the first type of cluster head nodes expired, the survival time evaluation method could be used to determine the remaining network aggregate energy . CalculaThe enhanced cluster-head selection procedure consists of the following specific steps:The process of sending data from each cluster head to the base station has been consolidated . The SFOIn the initializing phase, based on the coordinates of vectors, the SFO finds the solution in a hyper-dimension. By recognizing the best adjacent cluster head, the route is chosen based on its fitness value. Later, the recognized best adjacent cluster head is replaced with another cluster head and recognizes the best route . The proThe main advantage of the SFO algorithm is that it provides the customization of parameter configurations which enhances the performance compared to the default setting of the parameters.The evaluation of performance is implemented in MATLAB, and the comparison is made with existing algorithms such as GA , ALO 1111 and PSNetwork Longevity: It indicates how long or for how many rounds the network can operate. It refers to the number of rounds after which the nodes in the field will expire whilst completing their jobs. In Energy Efficiency: This is the amount of energy utilized by the sensor nodes in the network. As shown in Bandwidth: It represents the amount of data transmitted to the base station with several nodes. It is denoted as the number of packets transmitted *size of the packet which is divided by the time taken by the number of nodes. As shown in Packet Delivery Ratio: It expresses the ratio of data packets effectively transmitted to the base station with various numbers of nodes. In In this study, a novel solution for IoTs in WSNs, an ESEERP protocol, is designed and developed. It solves the issue of sensor nodes near the base station having a short lifespan. The reduced life issue comes as a result of heavy traffic from various cluster heads to the sink. The ESEERP employs an energy-efficient cluster head selection algorithm that considers a variety of factors such as distance, cost, remaining energy and scope. The SFO is used to choose the best route from the cluster head to the sink node. The ESEERP efficiency is conducted to evaluate the performance of existing optimization-based routing methods. The results have shown that with 500 nodes, the ESEERP endured for roughly 3500 rounds, which is better than the existing ones. The limitations of the proposed approach are that there is unequal consumption of nodes that hugely affect lifetime of the network, and overhead maximizes as the threshold-based functions are maximized. In future work, we will examine the cluster optimization is to be done to improve the efficiency and achieve balanced energy consumption.