The container-based cloud is used in various service infrastructures as it is lighter and more portable than a virtual machine (VM)-based infrastructure and is configurable in both bare-metal and VM environments. The Internet-of-Things (IoT) cloud-computing infrastructure is also evolving from a VM-based to a container-based infrastructure. In IoT clouds, the service availability of the cloud infrastructure is more important for mission-critical IoT services, such as real-time health monitoring, vehicle-to-vehicle (V2V) communication, and industrial IoT, than for general computing services. However, in the container environment that runs on a VM, the current fault detection method only considers the container’s infra, thus limiting the level of availability necessary for the performance of mission-critical IoT cloud services. Therefore, in a container environment running on a VM, fault detection and recovery methods that consider both the VM and container levels are necessary. In this study, we analyze the fault-detection architecture in a container environment and designed and implemented a Fast Fault Detection Manager (FFDM) architecture using OpenStack and Kubernetes for realizing fast fault detection. Through performance measurements, we verified that the FFDM can improve the fault detection time by more than three times over the existing method. Container-based clouds are used in various service infrastructures because they are lighter and more portable than a virtual machine (VM)-based infrastructure and are configurable in both bare-metal and VM environments . The intThe container-based cloud infrastructure can be deployed on the VM or bare metal server directly, and it is usually managed by Kubernetes, which is a container orchestrator. Hence, it is necessary to appropriately set the Kubernetes parameters related to fault detection and recovery to meet the requirements of mission-critical IoT services . To studIn Reference , based oAbdollahi et al.  proposedThese studies considered only the performance measurement in the existing environment and the recovery of faults at the service level. Therefore, in order to reduce the fault detection time of a node, a method for quickly detecting a node fault is required. Especially, container environments running on VMs require a method of the fault detection and recovery that considers both VM and container levels. Through this, it is possible to optimize fault detection time to ensure the level of availability required by a mission-critical IoT cloud. However, these studies do not describe how faults can be detected quickly enough for mission-critical IoT services running on the VM.(a)We analyze the fault-detection architecture in a container environment and highlight its limitations(b)We design and implement Fast Fault Detection Manager (FFDM) using OpenStack and Kubernetes; an integrated architecture which provides an automated monitoring function for quick fault detection and recovery.(c)Design and implementation of an architecture for fault information delivery according to the monitoring results.(d)We evaluate the performance of our proposed architecture against the current state of the art approaches and show that it can improve both the fault-detection speed and fault-recovery time.In this study, we analyze the fault-detection architecture in a container environment and design and implement an integrated fault-management architecture for realizing fast fault detection. We consider the addition of a fault detection system at the VM level and its integration with the container fault detection system, as necessary, to plug the gap in swift detection of faults occurring, due to failures in the container environment infrastructure running on the VM. We design and implement Fast Fault Detection Manager (FFDM) using OpenStack and Kubernetes. FFDM is a component for interworking the functions of the VM orchestrator and Container orchestrator, and it provides an automated monitoring function for quick fault detection and recovery and the requisite function to deliver VM fault information to the container orchestrator directly. Further, we show that the proposed architecture can improve both the fault-detection speed and fault-recovery time using measurements from the implementation. In summary, the contributions of this study are as follows:The rest of this study is organized as follows. In We present common fault-management methods for container environments  in TableKubernetes supports three probe methods for checking the status of applications, namely, TCP, HTTP, and command. The probes can be divided into two categories, depending on whether they check for liveness or readiness. Liveness probes provide the ability to periodically check the current application status. When a problem is confirmed, the application is restarted by the Kubelet. Readiness probes are used to check the status of the pod after migrating, restarting, or reconfiguring the pod. If the pod is not ready to perform normal service, Kubernetes removes the IP address of the pod from all the services to prevent faults in the service. In addition to these probes, Kubernetes uses external monitoring tools, such as Prometheus, to compensate for faults that may be missed .Containers run in pods that are deployed on nodes . The node status is periodically updated by the Kubelet running on the worker nodes to the Kubernetes master node. There are four parameters related to this process, as listed in As shown in Adjusting the values of these parameters can reduce the fault detection and recovery time. Several studies have been conducted on the fault detection and repair processes in Kubernetes.The authors of Reference  tested tIn Reference , a methoIn this study, we first analyzed the Kubernetes fault detection times when node failures occurred using the parameters defined in The best case for fault detection is when the node fails just before the Kubelet reports the node status. In this case, the timer of duration From Equations (1) and (2), the range of the fault-detection time isAs shown in In this section, we describe the proposed fault-detection architecture for containers deployed on VMs. Instead of detecting VM faults at the container level, we use VM-level fault detection and notify the container level of the detected faults directly. To this end, we designed a fast fault detection manager (FFDM) that automatically registers information for VM failure monitoring and forwards the failure information delivered to the VM orchestrator to Kubernetes ,22.As depicted in For evaluation, a test environment was built using an OpenStack control node and two OpenStack compute nodes, as shown in The proposed architecture was constructed, as shown in To verify the optimized Kubernetes parameters, VM fault-detection tests were conducted with various parameter sets, and the results are summarized in In In Scenario 1, the default parameters defined in Kubernetes were used. It presents the worst results because the VM faults were only detected at a container level. In Scenario 2 and Scenario 3, We compared these results with the results obtained using the proposed architecture. Fault detection time is the time taken from the time the fault occurs to the time when the master node receives the fault information. In the proposed architecture, it took an average of 0.84 seconds to detect the failure node. The fault detection time is composed of parameters of Equation 4. As shown in We also checked the fault-detection time when the number of worker nodes was increased. To investigate the effect of the background network traffic between the master and worker nodes, we used the Iperf3 tool to generate background traffic . The meaAll the above experiments confirm that the proposed architecture can detect faults faster than the existing method even when the size or overall resource usage in the Kubernetes cluster is increased. The proposed architecture can thus be used for IoT services requiring high infrastructure availability.Discussion: In this paper, we proposed and implemented architecture to improve the fault detection performance of the infrastructure to improve the availability of container-based IoT services running on VMs. To do that, we analyzed the existing fault detection function based on Kubernetes, a representative container orchestrator. By analyzing the fault detection function, we confirmed the parameter setting for the optimized fault detection in the existing method. Although the optimized Kubernetes detected faults faster than the default settings, we found that the optimized settings increased the frequency of status updates and resulted in increased network and CPU resource usage. Further, the node faults were occasionally misjudged. After that, we designed an architecture for the integrated and automated fault management of VM and container manager.Through experimentation, in the VM environment, fault information is checked from the hypervisor every 300 ms, and when a failure occurs, it is delivered to the function responsible for managing the fault immediately, so that failure can be detected faster than in the existing container environment. On the other hand, it is confirmed that fault detection in a container environment needs additional time to check and update fault information. Even in the proposed architecture, format change and transmission process are required when transmitting fault information, but this is a process required only for the node in which the failure occurred, and It was confirmed that the time of process did not significantly affect the overall performance.In addition, we confirmed that the proposed architecture can detect faults faster than the optimized Kubernetes without significant changes in the performance and resource usage even when the number of nodes was increased. Through the experimental results and structural features (event-driven message delivery), it was confirmed that the proposed architecture has higher scalability compared with the existing architecture. However, similarities exist in limitations between our architecture and the existing one regarding the use of one controller, and in order to use multiple controllers, necessitates discussion of a new architecture.Further experiments to investigate the relationship between the resource usage of background IoT applications and fault-detection performance revealed that the performance of Kubernetes fault detection methods declined as the resource usage increased. Although the overall performance of the proposed architecture also decreased, faults could still be detected up to three times faster than in the case of the optimized Kubernetes parameter settings. In the same way, performance measurements were performed while increasing the RAM resource usage of the background application, but the increase in RAM usage affected the CPU usage, and it was confirmed that the results were similar.Through the experimental results, it was confirmed that the performance of the proposed architecture was not significantly affected by resource usage. The most influential factor of fault tolerance in the proposed system is resource usage, so it was confirmed that the proposed architecture offers better fault tolerance.Our first contribution point is automation: When configuring a node, it provides a function to automatically register a fault alarm, and if a problem occurs during operation, the fault information is sent directly to the manager. Through this, each manager can get the notification immediately when the node fails. The second contribution is VM and container manager integration. In the existing method, the fault is judged by receiving data from the agent of each node. However, as shown in However, we observed that for the proposed architecture, the performance gradually decreased as the number of nodes increased or resource usage changed. In other words, it is expected that further research is needed to mitigate the performance degradation. Likewise, for a multi-cluster environment, it is necessary to additionally consider a “collecting the node status” method and a communication method between the multi-container managers. The existing architecture is where multiple sites are managed by a single controller, but if multiple controllers are to be used, it is expected that additional research on the distribution of management functions by site and appropriate data collection methods will be required.In this study, we analyzed the fault-detection function of the container infrastructure used as the infrastructure of an IoT cloud and proposed an integrated infrastructure management architecture to realize fast fault detection. Based on the proposed architecture, we implemented a fast-fault-detection function that can provide high availability for mission-critical services, such as V2V communication and real-time services. The proposed architecture can detect faults faster than the state of the art even when the IoT cloud size or background application resource usage increases. Moreover, it can improve the management function for container clusters currently running on various cloud provider’s infrastructures. By applying the proposed architecture, it is possible to detect fault using fault information of the infrastructure of VMs in which various IoT services operate, which will be useful in terms of service management.Future research will study the optimization of recovery methods for IoT applications in a multi-cloud environment. Accordingly, it is necessary to study the distributed cloud management architecture and how to apply the monitoring and information delivery functions proposed in this paper. In addition, additional monitoring methods should be considered for precise monitoring in a distributed cloud environment. It is expected that this will enable efficient management of IoT services deployed in distributed clouds, such as Edge Cloud.


Individual spacecraft manual navigation by human operators from ground station is expected to be an emerging problem as the number of spacecraft for space exploration increases. Hence, as an attempt to reduce the burden to control multiple spacecraft, future missions will employ smart spacecraft able to navigate and operate autonomously. Recently, image-based optical navigation systems have proved to be promising solutions for inexpensive autonomous navigation. In this paper, we propose a robust image processing pipeline for estimating the center and radius of planets and moons in an image taken by an on-board camera. Our custom image pre-processing pipeline is tailored for resource-constrained applications, as it features a computationally simple processing flow with a limited memory footprint. The core of the proposed pipeline is a best-fitting model based on the RANSAC algorithm that is able to handle images corrupted with Gaussian noise, image distortions, and frame drops. We report processing time, pixel-level error of estimated body center and radius and the effect of noise on estimated body parameters for a dataset of synthetic images. Future deep-space exploration is expected to rely more and more on autonomous spacecraft ,2,3,4. IIn this paper, we address the problem of estimating the centroid and radius of a celestial body from a single raw grayscale image taken by an onboard camera. These estimates can be used to derive the relative distance between the celestial body and the approaching spacecraft. Different sources of noise in an Autonomous Visual Based Navigation pose serious problems as they can potentially introduce large errors in the navigation system. Various works present methods that are designed to cope with numerous families of noise sources. In this work, we provide a novel solution based on a pre-processing pipeline and a final model estimation designed to be robust to three very dissimilar sources of noise—Gaussian noise, image distortion, and frame drop. We developed a multi-step procedure based on Random Sample Consensus (RANSAC)  designedThe problem of Autonomous Navigation Systems for spacecraft navigation has been long studied and various approaches have been proposed ,13. An eThe problem of estimating the radius of a celestial body is usually divided into three phases. First, the target is isolated from the deep-space background (segmentation), then the points that lie on the target limb are filtered from the points that are inside the target and those that lie near the terminator (the line separating the illuminated face from the backlit face) . The thiIn  image diIn , the autIn ,9,11, thtarget.We propose a multi-step method to estimate the radius of a circular-shaped celestial body from a single channel grayscale image. In this section, we will refer to the celestial body as the In the first step, we threshold the image with a tunable threshold value lgorithm ,20. A grIn the second step, we apply an edge detection filter to identify the sharp transition from the dark background to the illuminated target. We approximate the image gradient with a discrete filter, such as Sobel  or PrewiThe third step applies dilation (⊕) and erosion (⊖) operations to the binary image in order to produce a thick ring-shaped mask around the target borders. The edge mask is computed as:We use this edge mask to select only the pixels from the edge image that lie at blob boundaries. This approach removes regions with high frequency components located inside the target boundary, such as regions with irregular texture (craters) or inconsistent lighting conditions. The magnitude values of the masked edges are further compared with a threshold value K bins and define an active bin as a bin with more than N points in it. We discard images that have less than S out of K active bins and preserve images that have at least S out of K active bins In the fourth step, we transform the We used this technique to filter images with points scattered along a circumference, from images that contain points clustered tightly along a short arc, since the first images produce better radius estimations than the latter. For each bin, we select the first points with the largest radius values in order to cope with images that present thin limbs (we refer to it as “sickle effect”). In this kind of image, the gradient has high values both on the true target border and on the terminator, thus points form a sickle-shaped curve that RANSAC can arbitrarily fit with a model for the outer circle or a model for the inner circle. By selecting points with a higher p is the probability of selecting a valid model in the presence of outliers and w is the probability of inliers in the dataset. In our study, we selected the number of iterations to be 100 and tolerance value to 1 and obtained less than 10 pixels error for our dataset even in the presence of noise, image distortion, and frame corruption.In the fifth and last step, we apply a RANSAC procedure for radius estimation. The algorithm returns the best fitting circle model (as center and radius) and the set of inliers points. Compared to a Least Squares estimator, RANSAC is robust to outliers, thus we can tolerate erroneous points originated from previous steps. The RANSAC algorithm is initialized with two parameters: the maximum number of iterations and the minimum tolerance value used to assign a point to the inliers group. The maximum number of iterations can be obtained using the theoretical result from the original RANSAC paper .(2)k=loFurthermore, to improve the radius estimation we can perform Least Squares on the set of inliers returned by RANSAC. According to the results we obtained in our experiment, we believe that performing RANSAC is sufficient to achieve estimations with error < 10 pixels. The proposed algorithm is described in the block diagram in In To validate our method, we performed a set of experiments on a dataset of 50 synthetic 1024 × 1024 16-bit grayscale images of celestial bodies in different lighting conditions and positions, including partial frame occlusions see . The datWe evaluated the accuracy of our method on the original dataset by performing a repeated radius estimation for each image. We reported the mean value and standard deviation for the target center and radius. For brevity, we present only a subset of relevant samples in We compared the radius and center estimation obtained by our method and Least Squares Estimation for circle identification. The effect of scarce illumination is particularly relevant for the Least Squares Estimator (LSE), whereas our RANSAC-based approach is able to provide better results for the same lighting conditions. For each image, we repeated the estimation 1000 times and reported mean value and standard deviation for the radius estimate and center coordinates. We performed the same experiment using LSE. It can be seen that LSE has a lower standard deviation than RANSAC for noise-free images; however, when we introduce noise LSE performance rapidly degrades. We performed a qualitative analysis to estimate the sensitivity of different sources of noise in the input image. We considered additive Gaussian noise with an increasing level of intensity, to simulate channel disturbances.We also reproduced frame errors and data packet drop by altering and removing random image scanlines, see We altered the original image with an additive Gaussian noise as shown in Equation , where Nance see . We set In We performed a similar analysis introducing local frame distortions see . The disx coordinate is higher for RANSAC. The higher error in the x coordinate is related to the horizontal scanline translation.We reported the effect of frame distortion errors in We simulated transmission errors by randomly dropping scanlines see . In FiguOur RANSAC-based method can support various levels of partial occlusion, due to other celestial bodies or projected shadows. In S, and total number of sectors K for the sector binning step, RANSAC number of iterations, and RANSAC tolerance value. The background threshold value K parameter for sector binning allows the user to describe the circle as a composition of K sectors, thus K is related to the resolution of the sector binning. Since the previous steps of the pipeline produce a point cloud that spans over a small arc of the total circle, the S parameter is used to represent the confidence level for the set of points to be included in the final RANSAC estimation. Both parameters K and S have been selected performing grid-search leave-one-out cross-validation. Finally, the RANSAC parameter number of iterations can be computed analytically from Equation  implemented on Virtex6 Field Programmable Gate Array (FPGA) (Development board ML605 REV E) is 542.22 ± 4.90 ms. The RANSAC step execution time only is approximately 244 ms. As reported in , CPU-onlIn this paper, we presented a robust image processing pipeline for estimating the center and radius of planets and moons that can be integrated into an Autonomous Navigation System for modern spacecraft. We evaluated the resilience of our method to various sources of noise and reported appreciable results handling images with significant amount of noise, frame distortions, artifacts, and partial occlusions. We reported the pixel-level accuracy and the effect of noise on the estimated target parameters. We aimed at a computationally low-demanding pipeline able to be run on an onboard computer with limited resources.The presented method allows performing radius and center estimation of planets on board space-qualified processors, such as LEON3, with acceptable latency. This fact is fundamental to perform Visual-Based Navigation for missions having strict requirements in terms of radiation tolerance, such as Medium-Earth Orbit, Geostationary Earth Orbit, or long-term Low Earth Orbit missions ,25,26.However, despite the high immunity to outliers and noise, the applicability of the methods is limited to planets, whose projection on the image plane is a circle. A complex task for future research involves the pose estimation of non-collaborative objects having an arbitrary shape. The complexity of this task is enhanced by the lack of information on the attitude and positions, and on the noise, outliers, occlusions, motions, and others. In addition, the complexity of the algorithms used in these applications is often prohibitive given the strict power consumption and memory footprint requirements of space-qualified platforms . For insIn future works, we expected to extend our pipeline to support for multi-target detection and parameter estimation for non-ellipsoidal objects such as comets and asteroids. Furthermore, we planned to implement FPGA-based hardware accelerators for the different blocks of the proposed pipeline to further reduce the processing latency.


The primordial eye field of the vertebrate embryo is a single entity of retinal progenitor cells spanning the anterior neural plate before bifurcating to form bilateral optic vesicles. Here we review fate mapping data from zebrafish suggesting that prior to evagination of the optic vesicles the eye field may undergo a Maypole-plait migration of progenitor cells through the midline influenced by the anteriorly subducting diencephalon. Such an enigmatic translocation of scaffolding progenitors could have evolutionary significance if pointing, by way of homology, to an ancient mechanism for transition of the single eye field in chordates to contralateral eye fields in vertebrates. The vertebrate forebrain , with its rostral telencephalon and caudal diencephalon, exhibits a cluster of four neuroanatomic anomalies in all species. The evolutionary origins of hemisphericity, contralaterality, optic chiasm, and retinal inversion, continue to defy plausible explanation. If the eye as a neural budding of the light-sensitive diencephalon is central to the development of all, exposure of the enigmatic processes that transitioned the single eye spot of chordates to the paired eyes of vertebrates might explain these anomalies. If cogent, this explanation would clarify embryological homologies, enhancing potential developmental and medical implications.Early in vertebrate gastrulation the neural plate is elongated by convergent extension  where laContraction of the eye field (EF) follows an initial period of expansion characterised by proliferation of RPCs as gastrulation proceeds, with RPCs and their progeny migrating individually . This coalobar holoprosencephaly (cyclopia) is common across all vertebrate species [Historically, the conception that the EF of vertebrate embryos might actually be single before resolving to bilateral optic vesicles was intimately related to the discovery of midline crossings of RPCs , 80. Thi species , 45 and  species , 51, 76. species , 76, 80  species , 27, 66.In wild-type zebrafish however, some RPCs and their progeny translocated across the midline to the contralateral optic vesicle, an observation that was used as primary evidence to argue that the EF was therefore singular prior to bifurcation , 76, 80,Danio rerio, while noting substantial translocation, did not explore any further significance of this translocation because of the more fundamental question at that time relating to whether cyclopia was a consequence of a single EF failing to divide or of two eye fields merging to become one. The incidental finding in this study that RPCs crossed the midline helped establish the general case for a prior single EF [In 1995 a landmark single-cell fate mapping study of the EF in the zebrafish ingle EF  but otheFour years later the most specific fate mapping study ever performed for single cell-lineage in the zebrafish EF was published just prior to the abandonment of this very specific technique in favour of mass-cell microscopy . This stCloser scrutiny of the data as presented further shows that 70% of the RPCs labelled at 80% epiboly later crossed the midline to the contralateral optic vesicle and that some of the remaining 30% may already have crossed due to their proximity to the midline at 80% epiboly. Two outlying cells were omitted from our calculations as they were labelled as ending up on both sides. Another cell was excluded as it was located in the optic stalk. This left our total count at 53 remaining cells injected, with some minor uncertainty where red/blue labelling of a few cells in the original figure failed to clearly distinguish whether two single cells overlapped each other or a single cell contributed to progeny on both sides at tailbud. Our decision to include or exclude these few cells was based on a ten-fold magnification of the figure.This proximity to the midline of the 30% suggests that if the cells had been injected earlier, say at 75% epiboly (8 h post fertilization - hpf) rather than at 80% epiboly, it is possible that more RPCs might have been found to originate on the side contralateral to their destinations. This would be consistent with a primordium that begins translocation across the midline prior to 80% epiboly, translocating up to 30% of its constituent cells by 80% epiboly, to be fully translocated at tailbud. (For stages of embryonic development see ).The more recent studies on migration of RPCs , 64, ratDespite these limitations these studies made major advances, showing as never before the complex shape-changing over time of the developing EF. In particular they showed that prior to neurulation the EF begins posterior contraction toward a focus at the anterior tip of the hypothalamic anlagen , the anterior limit of the neural keel Fig. . As the The uncertainties discussed earlier as to whether these studies were capable of detecting RPCs crossing the midline appeared to be circumvented in a later study that employed Kaeda, a photoconvertible fluorescent protein introduced to the cytoplasm of the RPCs in zebrafish . This waThis finding, however, is confounded by further uncertainties related to the timing of the observation period designed only to capture late cell developments rather than early migrations. The movie showing the photoconversion  depicts In summary, while the early ‘gold standard’ single-cell fate mapping studies point convincingly to significant midline translocation of RPCs, later mass-cell microscopy studies, while failing to confirm this significance, point to an intense transient mixing of RPCs at the midline of the posterior EF in a whorl which advances in tandem with the subducting hypothalamus. Behind its anterior advancement the EF is bisected, the whorl behaving like an advancing conduit, possibly for contralateral translocation of RPCs.To reformulate: Based on presently established data from the most specific fate mapping study performed to date, at least 70% of RPCs that specify the EF domain and its future architecture will contribute to the contralateral optic vesicle. For this to happen, the RPCs that cross the midline must do so during peak contraction of the EF where they all appear to progressively mingle and dive ventrally in a whorl of congested midline hypermobility that advances from the posterior EF to the anterior EF, the latter being the site of the presumptive optic stalks. It is likely therefore, that the subsequent lateral movement of ventral RPCs previously thought to be of those returning to the ipsilateral EF from the midline between 8 and 12 hpf , 64 is iThe mechanisms that drive the distinctive early development of the ANP, particularly the EF and optic chiasm, remain poorly analysed . This isDuring evagination the EF becomes patterned to give rise to a proximal future stalk domain and also a combined distal retinal pigment epithelium (RPE)-neural retinal (NR) domain , 29. RPCAfter positioning in the neural retina RPCs begin differentiating either to retinal ganglion cells (RGCs), horizontal cells, amacrine cells, cone cells, Müller cells, rod photoreceptors, or bipolar cells. While there are different stochastic models for the ordering of this process  the primA general characteristic of vertebrates is the possession of bilateral eyes and an optic chiasm that connects the retinas to the forebrain. At the optic chiasm the left and right optic nerves cross, often completely, but in some groups of vertebrates particularly mammals, a fraction of the nerves remain uncrossed to form a semi-decussation. In the groups that mostly cross completely such as teleost fish, the decussation is mostly uncomplicated, left over right or right over left , 35. TheProximal prevertebrate chordates however, such as cephalochordates and urochordates, possess neither bilateral chambered eyes nor an optic chiasm. Cephalochordates though, possess a single medial eyespot which is now confidently regarded as the homologue of the vertebrate lateral eyes , 77. Unfamphioxus (Branchiostoma) is homologous to the lateral eyes of vertebrates, and Nilsson and Pelger [Inversation – the contralateralising of the non-hemispherical retino-forebrain of chordates by increasing selection of contralateral visual inputs due to maturation of the cyclopean eye. This theoretical framework, developed and published previously , is build Pelger  who show‘cyclops’  following the genome duplication, acting homologously to today where in early embryogenesis ‘cyclops’ induces formation of bilateral optic vesicles from a single optic primordium [cyclops’ gene and increasing the size and complexity of the brain, might have been a key step during early evolution of vertebrates” [Chiasmation – the later duplication of the single eye by a disruptive 3-vector developmental process; translocation, bifurcation, and invagination of the cyclopean eye field in craniates. This theoretical framework is constructed de novo as the most parsimonious way to duplicate a cyclopean eye while conserving the established contralateral retinofugal infrastructure of the primitive vertebrate forebrain Figs. , 5 and 6imordium , 66, 76.ebrates” .While contralateral translocation of RPCs in the primordial eye field of vertebrates might seem counter-intuitive, as well as difficult to detect with time-lapse digital algorithms, there are historical precedents according to the Inversion Hypothesis –49 that the EF should be single.it should translocate prior to bifurcation such that most RPCs (depending on the species) should cross the midline to mature in the contralateral optic vesicle.by this process the incipient optic chiasm should form and the retina invert.bifurcation of the EF should require the action of cyclopia-nulling genes which, if deactivated, will result in cyclopia.Such a critical ancient chiasmation event, contributing as it would to the predatory power of the first vertebrates, should be identifiable in the very early morphological development of all vertebrate embryos today whereby:the single EF of vertebrate embryos .the contralaterality of the forebrain (Inversation by cyclopean eye maturation).the historical ‘point-of-origin’ of cyclopia-nulling genes (the Chiasmation event).the ‘mechanism-of-origin’ of the optic chiasm by Maypoling translocation of the EF (Chiasmation stage 1).the origin of eye duplication and forebrain hemisphericity (Chiasmation stage 2).the mechanism for inversion of the vertebrate retina (Chiasmation stage 3).Much of the above has been demonstrated experimentally in varying degrees in vertebrates such as zebrafish including now the apparent translocation of the EF leading to formation of the optic chiasm, the critical element of the proposed chiasmation process. As techniques for the observation and measurement of the origin and migration of embryonic cells (cell lineage) continue to advance we should expect techniques such as light sheet microscopy to eventually confirm and expand the findings already catalogued by past ‘gold standard’ techniques. If so, the finding that at least 70% of RPCs cross the midline to the contralateral optic vesicle should support the above hypothesis offering insights for:The suggestion that RPCs translocate en masse across the midline and the inference here that this might relate to the formation of the optic chiasm will be discussed below by way of question and response followed by a brief comment on the more peripheral topic of developmental retinal inversion.The novel disruptive reorganisation of the retino-forebrain by the proposed chiasmation event is predicated on the introduction of new genetic, chemotactic, and mechanistic triggers, that while not yet fully elucidated are hypothesised to lead to the formation of the optic chiasm. However, the two distinct processes of translocation and decussation would appear to be coupled, not exactly in time, but almost in place, the former determining the latter.While the sequential processes in the embryological development of the vertebrate optic vesicle, optic cup and retina are now quite well understood, the formative chiasmatic processes remain to be adequately described. This might, in part, be due to our incomplete understanding of the cellular milieu at the midline as the neural keel subducts anteriorly beneath the bifurcating EF to form bilateral incipient optic stalks and optic vesicles.hedgehog-secreting cells forward beneath the medial ANP. A consequent midline anteroventral ‘shearing’ process by the ventral diencephalon on the posterior EF begins separating the EF by initiating convergent migration of RPCs anteroventrally along, and possibly across, the midline of the EF. Our proposed major translocation of RPCs across the midline would appear during resolution of bifurcation to contribute to an early scaffold for the incipient chiasm by deposition of ‘remnant’ midline cells that might later cue RGC axons to decussate back across the midline. This idea of an early template for later growth of retinal axons was mooted by Easter et al. [EF bifurcation follows anterior migration of the prechordal plate , 61 to mr et al.  and Sretr et al. . The latr et al. , precedir et al.  who obseGrowth of RGCs begins in the retina and progresses centrally toward the presumptive optic chiasm after commitment of some of the multipotent progenitors to RGC fate. This happens during, or shortly after, the terminal cell division , 62, 78.As the RGC axons enter the midline region they encounter incipient optic chiasm neurons along its posterior boundary organized into an inverted V-shaped array , 55. TheMap-like representation of sensory information is an evolutionarily conserved principle of brain organisation and function . It follThe classic revision by Sarnat and Netsky  of PolyaWith expert opinion now shifting away from the traditional view that vertebrate eyes developed from an early intermediate bilateral stage exemplified by the rudimentary eyes in the extant hagfish agnathan  and towa


Osteoporosis (OP) is a systemic disease of the skeleton characterized by increased risk of fracture. There is a general consensus on the efficacy of physical activity in the prevention of bone loss, falls and fractures, but there is no agreement on the best setting to exercise. The aim of the study is to evaluate the efficacy of a 12-months exercise protocol for women with post-menopausal OP when administered as individual home training (IHT) versus gym group training (GGT). The study is a randomized trial with two parallel groups. Sedentary patients with primary post-menopausal osteoporosis are recruited at the Istituto Ortopedico Rizzoli of Bologna. In the first group, the 12-month ACTLIFE program is performed as IHT, while in the second as GGT. The program is aimed at improving joint mobility, muscle force, balance, motor coordination and endurance. The study is single blinded. Patients are assessed at baseline and after 6 and 12 months. The primary outcome is the modification of quality of life measured with the Short Osteoporosis Quality of Life Questionnaire (ECOS-16). The findings of this study will highlight advantages and disadvantages of exercising in the two different settings and provide evidence on how to increase physical activity in osteoporotic women. Osteoporosis (OP) is a systemic disease of the skeleton characterized by reduced mass and deterioration of micro-architecture of the bone that is accompanied by an increased risk of fracture with consequent pain, decreased physical and social functional capacity and quality of life (QoL) ,2,3,4. TRegular participation in a physical activity program is vitally important for the geriatric population to prevent decline in mobility function . HoweverThis paper aimed at presenting the protocol of a randomized trial for evaluating the efficacy of a physical activity program (12-month duration) designed to improve the quality of life in women with post-menopausal OP when administered IHT or GGT. We relied on the most recent scientific evidence in this field, to develop the exercise program designed to improve the quality of life in this population ,16,17. WClinicalTrial.Gov (NCT04179903).This study is carried out within the project “Physical ACTivity: the tool to improve the quality of LIFE in osteoporosis people” (ACTLIFE) funded by European Commission within the Erasmus+ Sport program (Grant Agreement N2017-2128/001-001). The study was approved from the Local Ethics Committee  of the Emilia-Romagna Region (reference number AVEC: EM601-2019_696/2018/Sper/IOR_EM2). The trial was registered in The study is a randomized trial with two parallel groups: in the first group the 12-month ACTLIFE exercise program is performed as IHT, while in the second as GGT. It is single blinded since professionals who evaluate patients are not aware to which exercise group patients are assigned. Patients are assessed at baseline and after 6 and 12 months.Sedentary patients with primary post-menopausal OP are recruited by the Centro Osteoporosi e Malattie Metaboliche dello Scheletro (COMMS) of the Istituto Ortopedico Rizzoli of Bologna, Italy. The participation to the study was proposed to all women attending COMMS for a medical visit who met the inclusion criteria. In addition, to facilitate patients’ recruitment, the study was advertised on local media requesting the women who might be interested to contact the COMMS for a preliminary evaluation.A letter was sent to a participant’s home in an anonymous envelope, asking the patient to contact the person in charge of the study (or his staff). The letter described the scientific purposes, not specifying the type of study.During the study, all pharmaceutical treatments, and their modifications were recorded. Patients were recommended to adhere pharmacological treatment for OP, as prescribed.The inclusion/exclusion criteria  were idehttp://wwwservizi.regione.emilia-romagna.it/generatore/). For each of the two groups (IHT and GGT), 26 numbers from 1 to 52 were generated.The professional who enrolled the patients assigned them to the GGT or IHT group after contacting a dedicated person responsible of the maintenance of the randomization list. The randomization list was defined using the random numbers generator available on the web site of the Emilia-Romagna Region  measured with the Short Osteoporosis Quality of Life Questionnaire (ECOS-16) , specifiSecondary outcomes were measured, which were recognized to influence QoL.Body Mass Index (BMI) was calculated as the ratio of body weight squared (kg/m2) and the impedance measurements were performed with bioimpedance analysis  ,26,27 toThe WHO Disability Assessment Schedule (WHODAS)  is compoFalls Efficacy Scale International (FES-I)  is a 16 Physical Activity Scale for Elderly (PASE) ,34, whicIndividuals’ functional capacity is estimated by measuring several domains. Gait performance was evaluated by the 6-Minute Walk Test, which has been proven to be a valid and reliable instrument ,37. It iTo better understand individuals’ functional capacity modifications, we also included measures of standing balance, muscle force and joint mobility, which are considered prerequisites of motor functioning. Standing balance is estimated by the validated Stability Index  for bothFinally, joint mobility of shoulder, hip and knee, which are fundamental prerequisites of healthy and safe motor behavior, were assessed by routinely used clinical measures ,49. ShouPatients’ adherence to the exercise program was recorded by home or gyms weekly logs and measured as the percentage of exercise sessions actually performed/total number of scheduled exercise sessions.The reasons of interruption and abandons were carefully evaluated during the study period. All patients were also instructed to contact the project staff to communicate adverse clinical events or other reasons of non-participation, whenever this was deemed necessary. Finally, patients’ satisfaction is assessed at the end of the study by a specifically designed questionnaire based on seven-point Likert scale ,51 invesThe exercise program was administered to both GGT and IHT groups. It was aimed at improving joint mobility, muscle force, static and dynamic balance, motor coordination and endurance. For each subject of both groups, the program was structured in 2-days/week 1-hour sessions and lasts 12 months.Moreover, subjects were requested to choose an additional third day of the week to carry out at least one of the following activities: brisk walking, cycling or swimming to reach the weekly amount of exercise of 150 minutes recommended by World Health Organization .Each session was structured in the following sections: warm-up, strength, balance, flexibility and cool down. The exercise program was redundant to allow the trainer to adapt the exercise program to the participants’ needs and preferences. The protocol defines the strategies to instruct patients and to check the correct and safe execution of the motor tasks and the criteria for varying workload and number of repetitions to adapt to each individual functional capacity. For both groups, the exercise program was administered by a graduate trainer in Science and Techniques of Preventive and Adapted Physical Activity.GGT was performed in adequately equipped gyms, which had stipulated formal agreements with the University of Bologna under the direct supervision of a graduate trainer. Every 6-8 weeks, the trainer upgraded the exercise program on the basis of the improvement obtained by the gym group.For the IHT group, the trainer explained to the participant the physical activity (PA) program to be performed at home in one or more individual sessions. At the end of these initial instructional session(s), the participant was also given educational material on the purpose of the exercises and how to perform them correctly. Participants were requested to strictly adhere to the given instructions. Subsequently, the trainer contacted the IHT participants at pre-established time intervals: once a week for the initial two weeks and twice a month for the following 11 months to encourage participants to exercise regularly and to get information on the health status. Every 6-8 weeks, an appointment was scheduled to review the exercise program. All contacts of trainers with IHT participants were recorded.Monthly logbooks were used to record the adherence to the exercise program by noting down the execution of this session both at home and in the gym. For the former, a recording was made by the participant, for the latter by the trainer. The logbooks were subsequently collected by research staff or returned by mail with pre-paid envelopes. Finally, a telephone number and a week time schedule in which a trainer was available for further explanations and suggestions were given to them.The qualitative variables are summarized in terms of frequency, the quantitative ones in terms of mean and standard deviation for both groups and for the three times of assessment. For the analysis of the results, the principle of intention to treat is used, adjusting for adherence to the exercise program.To compare the general characteristics between the two groups, the Student’s t-test is used for parametric quantitative variables, the Mann Whitney test for non-parametric variables and Chi-square test for qualitative dichotomous ones.To compare the changes between the two settings among baseline and follow-up assessments the analyses of variance for repeated measures followed by of Sidak post-hoc comparisons tests for paired samples were used for the quantitative variables and the Friedman test, followed by the Wilcoxon test for paired samples with Bonferroni correction for non-parametric ones.Several lines of evidence have consistently proven the importance of regular participation in specific exercise programs to prevent/minimize the osteoporotic bone deterioration and its consequences in post-menopausal women ,6,7. HowSome limitations of the present protocol must be considered because they may influence the interpretations of the findings of the study. The study did not consider a control group performing sham or no activity since we focused on comparing GGT versus IHT. Therefore, we cannot empirically prove the efficacy of the exercise program administered either as GGT or IHT. However, the vast and substantial published evidence ,4,5,6,7 Although guidelines for OP prevention recommend to exercise regularly, there are no specific indications on the best setting to exercise. The results of this study could be relevant for future indication of the best setting and strategy to ensure the adherence to the physical activity and add to the current evidence base for clinicians, exercise trainers and policy makers.


High-pressure injection injuries have been chronicled for decades.We discuss the case of a young man presenting to the emergency department after high-pressure injection injury to the face. He eventually underwent extensive debridement of the face. We discuss differences in caring for an injection wound to an extremity versus the face, including time sensitivity of treatment, initial stabilizing measures, and critical steps.This case demonstrates a rare presentation of a high-pressure paint injection injury. This injury presented a unique surgical challenge where, despite compartment syndrome being less common, cosmetic outcome and infectious complication prevention remained critical priorities.While similarities exist in management of an injection injury to a limb, due to the rarity and deceptive appearance of this particular injury to the face, high suspicion along with urgent imaging and surgical consultation is warranted. High-pressure injection injuries present unique pathology. They cause devastating deep tissue damage despite small, innocuous, and usually painless skin lesions most commonly occurring on the hands, fingers, or feet. The skin lesions associated with these injuries often dwarf the severity of the damage occurring below the surface, as foreign chemical irritants are introduced into a closed compartment.3We report the case of a high-pressure injection injury to a unique anatomical location: the face. This case serves as a platform to explore the importance of the anatomical location of these injuries and how the location may guide treatment. Regardless of the misleading appearance of the wound and the low likelihood of compartment syndrome, high-pressure injection injuries to the face require a high level of suspicion and a low threshold for imaging and surgical consultation.A 19-year-old male with no significant past medical history presented as a transfer to our Level I trauma center with a chief complaint of right facial injury. Eight hours prior to arrival to our emergency department (ED), the patient accidentally discharged a pressurized, spray-paint gun while attempting to troubleshoot his equipment at work. Due to a language barrier, an interpreter was used throughout the patient encounter. The history was obtained from the patient interview and a review of medical records provided by an outside hospital. The patient endorsed right facial pain and dysphagia. He denied any eye pain, dysarthria, decreased vision, or trouble breathing. At the outside hospital, the patient was administered a dose of ampicillin/sulbactam for broad-spectrum antibiotic coverage and underwent computed tomography of the face and sinuses with contrast . The imaReview of systems demonstrated facial swelling, sinus pain, sinus pressure, and trouble swallowing. The rest of review of systems was negative.On arrival to our ED, the patient was hemodynamically stable with normal vital signs and was in no acute distress. His physical exam revealed swelling of the right face extending from his cheek just below the eye to the right upper lip . The affThe patient underwent excision and debridement of the face. He received his nutrition via nasogastric tube. Antibiotics were given and all cultures were pan-negative for bacterial and fungal pathogens. Pain was managed with oxycodone and acetaminophen. He was discharged one week after admission without issue.What do we already know about this clinical entity?High-pressure injection injuries, while appearing innocuous, are dangerous. Usually affecting distal limbs, they can cause compartment syndrome and necrosis.What makes this presentation of disease reportable?A pressure injection injury to the face is rare, with no current guidelines for management. This case may provide guidance in presentation and care.What is the major learning point?There are distinct differences in caring for an injection wound to extremity vs face, such as time sensitivity of treatment and initial stabilizing measures.How might this improve emergency medicine practice?Due to the rarity and deceptive appearance of this injury, high suspicion along with urgent imaging and surgical consultation is warranted.This case demonstrates a rare presentation of a high-pressure paint injection injury. The anatomical location of this injury presented a unique challenge to the management of this patient because the region of the injury was distinct from the more common presentations of paint injection injuries. Most often located on the hand or digits, paint injection injuries are surgical emergencies warranting debridement in a sterile setting and separation of fascial layers with the hope of preventing infections such as necrotizing fasciitis.Initially the decision to attempt bedside incision and drainage (I&D) was made with the hope that irrigation would help flush out the region of the paint and alleviate the need for an invasive surgical debridement. It was quickly discovered that with such an elevated pressure of injection into the region, the paint was affixed very firmly to the surrounding tissue. Bedside irrigation was not thorough enough to address the extent of this injury. The patient was admitted to the hospital to the otorhinolaryngology service in stable condition. While official guidelines do not exist as to whether I&D is indicated for this injury, several case reports suggest that I&D is not successful in fully addressing the extent of paint injection injuries and surgery is indicated regardless of the injury location.5The management of this patient was unique given the rarity, acuity, and severity of his presentation and the lack of concrete guidelines for a high-pressure paint injection injury to the face. Based on our literature review, there have only been two prior cases reporting high-pressure injection injury to the face, neither of which underwent acute surgical management from the ED. One case was observed and discharged as the high-pressure injury was only to the upper lip and the second case detailed granulomatous changes years later due to retained paint in the tissue causing a foreign body reaction.7High-pressure injection injuries to the face have a lower risk of compartment syndrome than high-pressure injection injuries to extremities; however, due to the rarity of reported cases, it is impossible to determine the risk of infectious complications, long-term neurovascular sequelae, or anatomical deformity. This case highlights the high level of suspicion emergency physicians must have despite the deceptive appearance of craniofacial high-pressure injuries and emphasizes the need for a low threshold for imaging and immediate surgical consultation.


The proliferation of smart devices in the Internet of Things (IoT) networks creates significant security challenges for the communications between such devices. Blockchain is a decentralized and distributed technology that can potentially tackle the security problems within the 5G-enabled IoT networks. This paper proposes a Multi layer Blockchain Security model to protect IoT networks while simplifying the implementation. The concept of clustering is utilized in order to facilitate the multi-layer architecture. The K-unknown clusters are defined within the IoT network by applying techniques that utillize a hybrid Evolutionary Computation Algorithm while using Simulated Annealing and Genetic Algorithms. The chosen cluster heads are responsible for local authentication and authorization. Local private blockchain implementation facilitates communications between the cluster heads and relevant base stations. Such a blockchain enhances credibility assurance and security while also providing a network authentication mechanism. The open-source Hyperledger Fabric Blockchain platform is deployed for the proposed model development. Base stations adopt a global blockchain approach to communicate with each other securely. The simulation results demonstrate that the proposed clustering algorithm performs well when compared to the earlier reported approaches. The proposed lightweight blockchain model is also shown to be better suited to balance network latency and throughput as compared to a traditional global blockchain. Ubiquitous interconnected objects can be deployed through the Internet of Things (IoT) infrastructure using cloud platforms in a centralized network . A wide In a centralized network structure, the server controls and enhances the performance of the devices . HoweverMost IoT devices are only able to communicate in short-range transmissions, due to their low-power wireless transmitters and receivers. IoT networks can benefit from utilizing the Multihop Cellular Network (MCN) concept  that facThe increased number of connected devices (in the order of million devices per sq km), heterogeneity of devices and vendors, interoperability, a vast amount of collected data and network traffic, requirements of large bandwidth capacity, communication latency, and trust are the major challenges within the new era of the 5G-enabled IoT. . The newThis paper discusses a multilayer architecture that is based on a new clustering model suited for blockchain implementation to tackle the issues associated with implementation complexity and elaborate on the mechanism for securing IoT communication. The new network model that is based on multi-layer distributed blockchain can be regarded as an organic combination of the blockchain technology and clustering techniques that effectively utilize network clustering performance and capabilities, and significantly improve the overall security and reliability of the IoT. An adapted clustering algorithm has been developed to suit the IoT systems’ blockchain implementation by considering the network performance metrics in defined cost functions. The IoT network clustering aims to reduce the network load, enhance coverage, and minimize the energy (as reflected in the distance) while leveraging the essence of MCNs. The multi-layer structure facilitates the detection of compromised entities within the entire network in each layer. Each transaction in the system needs to be verified by other participants by implementing a consensus algorithm. Blockchain is continuously monitored by the entire network participants, maintaining a copy of the blockchain ledger. Therefore, compromised nodes have no means of inserting fraudulent blocks into the public ledger without immediately being noticed by others. Thus, the multi-layer blockchain removes compromised entities from being a part of the system. This makes it impossible to compromise the integrity of records in the blockchain. Another crucial point is that the new multi-layer architecture allows for upgrading for the existing central cloud server. This makes large-scale deployments possible. Besides, a lightweight authorization and authentication process running in each cluster guarantees secure access to the network resources through implementing smart contracts.The rest of the paper is organized, as follows. In Fast-growing numbers of networked devices characterize modern IoT systems. Consequently, the amount of generated data by the connected devices is also escalating. This inevitably leads to security and privacy concerns. Security  is mainly due to the limited memory capacity and processing power of the devices .Devices require authentication and authorization to enter the IoT system. These measures are considered as a critical juncture of network security . IntercoResearch  developsResearchers have been developing blockchain technology to address the privacy and security challenges in the IoT as an alternative solution. The implementation of several privacy preservation strategies in blockchain-based IoT systems is discussed in . These sLightweight Scalable Blockchain (LSB) is presented in  in orderData operations are performed in the blockchain system through smart contract implementation, including data gathering, invoking, transfer, storage. A new context-aware mechanism is proposed in  for blocHyperledger Fabric (HLF) , which iA platform for facilitating secure communications for smart cities is proposed in . The preSome of the previous works discuss the multi-layer based blockchain approach for the integration of IoT and blockchain technology. The multi-layer based blockchain network model is introduced in  in orderIn , the autUnfortunately, the majority of the solutions proposed in the literature do not address the problems that are associated with the implementation of the blockchain technology in IoT systems, such as device authentication, low scalability, transaction delays, high computational resources for mining, and device heterogeneity. In our previous work , some ofThe heterogeneous IoT network lifetime improvement is achievable by implementing a clustering model along with a multi-layer structure. The clustering concept is the key to achieving the multi-layer architecture, where the cluster heads form the multi-layer structure. Clustering techniques for wireless networks and device-to-device (D2D) communications systems have been widely reported in the literature. They offer reduced energy consumption and higher throughput . A self-A novel, lightweight, private multi-layer model is proposed for reducing the complexity of blockchain technology implementation while improving the network scalability. The proposed model is tailored to meet the requirements of IoT devices by adopting the blockchain technology to suit different layers of the IoT system. The simulation study shows that the proposed Hyperledger Fabric-based method outperforms a traditional blockchain solution, like the Ethereum, in terms of latency and throughput.Clustering is one of the key steps of implementing the multi-layer architecture. Therefore, a new network clustering method is presented. It is based on the evolutionary computation that deploys multi-objective fitness functions that are relevant to heterogeneous IoT networks. The decentralized, fast, and self-clustering method divides the IoT network into clusters while considering the node mobility. The simulation results show that the proposed clustering algorithm outperforms existing solutions.A novel method of authentication and authorization of IoT nodes is implemented in order to provide security for IoT devices and protect device communications through a multi-layer structure.In the multi-layer architecture, devices in each layer have different computational capabilities and energy storage capacity. Consequently, different security strategies are proposed for individual layers. Each design is based on the blockchain. Even so, the blockchain implementation is modified to suit the devices of each particular layer. The key contribution of this research is three-fold:The aim of the proposed network model is to provide a reliable trustful security mechanism for IoT networks while using the performance and capabilities of the cellular system. The intelligent clustering and machine learning approach based on Swarm Intelligence (SI) and Evolutionary Computation (EC) algorithms  is deploThe proposed multi-layer network model divides the entire cellular-enabled IoT network into multiple tiers. Layer-1 consists of various clusters and IoT nodes. Layer-2 includes sink nodes and controlling devices, such as cluster heads. Layer-3 contains the base stations of a cellular network. All of the CHs, as cellular devices, have cellular connectivity with the 5G BSs, and, thus, via the BSs/D2D capability, also with other CHs. The BSs have the processing power (with appropriate servers and CPUs) to implement the decentralized blockchain mechanism at Layer-3. Blockchain implementation can potentially lead to additional overhead and scalability issues . The mulThis level includes IoT objects, nodes, and devices, as well as network elements for communications, network procedures, and protocols. Unsupervised hybrid clustering algorithms, as described in The intra-cluster security and privacy are facilitated by local CH nodes acting as edge processing nodes, as shown in A lightweight session key is assigned to devices when they authenticate to associated CH nodes and establish communications. The session key period validation is carried out by the cluster heads in order to perform the authorization and authentication. The registration services and authentication management, as well as authorization, are also locally performed by the CH nodes to improve scalability and address device heterogeneity. CH nodes maintain the addition of new devices to the network through a local registration process. The cryptographic key distribution or session keys allow the node authentications. Less power-hungry cryptography is provided by edge computing. Alternatively, CH nodes for IoT devices with limited resources could provide long-term cached session keys (cryptographic keys).a new node registration to the network as a new entity;session key (cryptographic key) distribution and assignment;communications management and initiation; and,secure communications management and establishment.Lightweight session keys are assigned by CH nodes in order to maintain the authorization of registered nodes as an authorization entity and authenticate them to the network. Symmetric keys and lightweight cryptography are proposed to tackle the scalability challenges and the limitation of IoT devices with constrained resources. CH nodes perform the following four tasks:Symmetric key-wrapping encrypts the lightweight session keys, called the distribution keys. Every single communication is protected with a session key. The session key is a symmetric key that has a unique ID and a period of validity. The use of cryptographic keys  for encryption, message authentication, and decryption is managed by secure communication. Consequently, selected CH nodes are responsible for managing cryptographic keys. The second layer connects all of the selected CH nodes under the serving Base Station (BS) units. Cluster heads collect and forward data to the higher layer. All of the nodes in the second layer run a private LBC in a distributed manner to achieve a consensus that is based on the defined consensus algorithm . BS and The proposed network model needs to consider secured CH communications while addressing the resource-constrained and decentralized node distribution of the IoT network. The blockchain mining process is computationally intensive and time-consuming. Hence, it is not ideally suited for implementation in an IoT system. Therefore, the proposal suggests a lightweight, private, decentralized blockchain-based method for data communications built upon distributed consensus. It is important to consider the limitation (resource-constraint) of devices in the IoT system. Thus, a lightweight cryptographic mechanism is to be implemented.In the proposed model, the interaction between CH nodes and other networked elements are based on local permissioned HLF blockchain  platformThe main elements of the proposed model are organizations (base stations), IoT nodes and objects, ordering clusters, and peers , and channels ), as shoThe peers maintain distributed ledger and execute transactions. A peer node can be an endorser or committer or both. The orderers are responsible for ordering all of the transactions in the network. In addition, orderers propose new blocks and seek consensus. Ordering service is a collection of orderers. All peers are committers by default. Ordering service sends the ordered state updates in the form of a block of transactions, and committers maintaining the ledger. The peer validates transactions of a new block, commits the changes locally as a copy of ledger, and updates the blockchain by appending it on the block. Peers also can be an endorser for endorsing transactions. An endorser executes the smart contract (ChainCode in HLF) and appends the results with its cryptographic signature  before sending it back to the client. In the proposed model, CH nodes can take the endorsers or committer roles, based on predefined roles.MSP carries out the authentication services in the Hyperledger network. MSP has to verify the network nodes identity. The organizations are the logical representation of the Hyperledger framework. They are responsible for the management of the network members with the help of MSP. Channels facilitate the various connections within the network between its different elements while using private or dedicated channels. The committers perform the validation and update of the shared ledger.The Hyperledger Blockchain is implemented through various transactions for data collection and data transmission. Transactions are defined using smart contracts . Base stAn IoT node becomes an endorser through submitting an endorsement request. This request is sent to the endorser node for approval and consistency monitoring. The process of consistency check proceeds with the smart contract execution. The endorser sends back the response to the associated IoT node requests and grants a specific read and writes access.The transaction block creation is performed by ordering clusters through the OS. The transaction blocks are distributed to all CH nodes. The blockchain system in this level updates the ledger, and transactions are added to the ledger along with IoT node specifications. A copy of the Blockchain ledger is shared with all of the peers in the network after validation.This layer consists of a distributed networked collection of BS nodes acting as an organization owner. Base stations manage devices, generate data, and process requests in a cloud server manner. The trusted nodes in this layer have powerful computing resources with less power and processor limitations. Consequently, more robust asymmetric cryptography mechanisms are proposed for this level with the aid of the global blockchain.The high-level layer consists of BS nodes that can perform independent mining tasks without reliance on the central authentication servers. The nodes in this layer are computationally powerful while forming a distributed network topology. Therefore, deploying a suitable global chain, such as the global Ethereum blockchain framework, along with more sophisticated security techniques is feasible. The deployment of asymmetric cryptography, such as Elliptic Curve Cryptography (ECC) , is an aThe peer-to-peer nature of the blockchain provides a suitable solution for a globally distributed security framework between different network entities, such as BS nodes. The communication among CH nodes and computing edge nodes is done through implementing the blockchain-based communication with the use of certificates. Smart contracts maintain the distribution of the certificates to perform a trustful communication within the blockchain system among different nodes in this layer. CH nodes are required to sign the certificates. The proposed blockchain-based model is enhancing the distributed trust between two CH nodes and related BS nodes when they collaborate for authorization of their entities. It also enhances the trust, while an entity or IoT node establishes communication with other nodes in separate clusters under each BS.The blockchain-based system maintains smart contract execution in order to avoid the requirement of using domain names and fixed addresses while the nodes establish the communications. The fixed addresses and domain names are not needed for the cluster heads in the proposed model for their communications with the edge devices as well as to execute smart contracts.GA is a population-based algorithm that is a subset of metaheuristic methods. It shows a good global-based exploration performance for the search of a problem space . TherefoThe clustering approach also reduces the latency and overhead in the IoT systems via the overall minimization of communication distances among IoT objects and selected cluster heads. With clustering, a lower number of nodes require long-distance transmissions to BS nodes. Therefore, the total energy consumption for the entire system is reduced, while the network coverage is enhanced ,60,61. TThe idea is to achieve the clustering through the deployment of the evolutionary computation algorithms within the network. In the proposed model, critical network attributes, such as the distance, network coverage, energy, and load, are the parameters considered for the clustering of the nodes.A hybrid algorithm  is employed for the selection of a cluster head as well as for cluster optimization, as shown in n bits of data from the transmitter to the receiver node at the distance l can be calculated, as follows [n bits of data is formulated as:The most critical factor in the IoT network design is satisfying the energy constraint. Longer network operation can be achieved through the shortening of communications and transmission links as well as by reducing the power consumption. Shorter communication links are achievable by grouping nodes into independent clusters. Such an approach facilitates the aggregation and forwarding of data, because each cluster member needs to exchange its information with the associated CH. The calculation of energy consumption uses the first order radio communication model . The rad follows :(1)Etx.The cost function for the optimization problem considers the total transmission distance as a key metric that is to be minimized. Furthermore, the fitness function also takes the number of cluster heads into account while optimizing the network load. The following multi-objective cost function evaluates each individual node in the GAs algorithm: Minimize:D is the network scaling dimension, m is the total number of nodes, Load is the max network load, and The goal is to attain a fewer number of CH nodes and enhance the energy. The weights  factors . GA miniThe GA approach helps to achieve distance optimization through its self-organized feature. The next GA starts with the final global solution of the first step GA as the initial solution, as outlined in The second GA phase enhances the GA solution of the first phase by a local search strategy. Optimizing the distance between CH to a node, and the CH to the sink or edge computing node results in the minimization of the total network energy dissipation. The distance-based equation is deployed in order to cluster the nodes in multi groups in the previous step and define the number of clusters through implementing the GA algorithm. The initial population for the current stage is generated from the best solution of the last phase. A multi-objective cost function is used in the GA optimization step. The distance is optimized while maximizing the coverage: Minimize:Simulated Annealing is a meta-heuristic algorithm that is chosen to perform the network changes, including node addition to, moving in, and removing from the IoT network. Generally, a random primary solution is required to start SA. However, in the proposed hybrid GA-GA-SA approach , the iniIn order to study the performance of the proposed clustering algorithms, a network environment for the IoT devices was simulated, as shown in The GAs started from a specific number of individuals, termed population. Each individual in proposed GA algorithms was elevated while using combined cost functions presented in Equations (4) and (5). The network configuration changes were detected by the SA section and then optimized the network accordingly. In the simulation, the energy loss per bit for transmitter or receiver  is based on evolutionary algorithms adopted for Wireless Sensor Network (WSN) applications. It uses GA and SA for CH nodes selection. Energy-aware Routing Algorithm (ERA) is for cluster-based WSNs. The residual energy of the CH nodes and the intra-cluster distances is considered in ERA for cluster formation. Fuzzy Shuffled Frog Leaping Algorithm (FSFLA) employs the memetic Shuffled Frog Leaping Algorithm (SFLA) in order to optimize the Mamdani fuzzy rule-base table based on the application specifications. This protocol deals with node energy and intra-cluster distances as well as with network lifetime. Genetic Algorithm and Particle Swarm Optimization (GAPSO)  is propoThe obtained simulation results indicate the effectiveness of the proposed clustering model as well as the efficiency of the algorithm to minimize the distances and the total network energy. We deployed simulation models in two different environments associated with each level of the multi-layer network in order to demonstrate the feasibility and practicability of the proposed blockchain framework. The first model implements the HLF blockchain in Layer-2 encompassing IoT devices, CH nodes (peers), APIs, and an organization. The global blockchain deployment simulator at Layer-3 is conducted to compare both Etherume and HLF metrics implementations. The simulation model at Layer-3 uses a workstation as the BS server running the blockchain applications.The Layer-2 implementation environment was created in order tto study the efficiency of the proposed blockchain framework of the multi-layer model, as illustrated in A smart contract was installed and instantiated on peers nodes, and data storage was allocated in order to write a block of transactions to the blockchain ledger. The composer-playground is a web interface for designing and implementing smart contracts and managing transactions and assets. The composer Command Line Interface (CLI) provides an environment to deploy, implement, and execute smart contracts and related definitions by the developers. The peers were set up to use the CouchDB for managing the state data that can handle the complex queries against the transaction logs. The Chain Code (CC) was modelled as JavaScript Object Notation (JSON). The client application can invoke a CC to access the state database and perform various queries, such as put, get, and delete, through APIs. Different blockchain functions were defined by deploying a REST server to directly provide RESTful APIs that can be invoked while using a web client or a virtual device. The user can invoke relevant APIs using GET or POST to submit various transactions through HTTP requests. The REST server hosts the Fabric client application to communicate with the HLF network through google Remote Procedures Calls (gRPC) system deployment. All of the peers in the network have a copy replica of the ledger. The ledger has two parts transaction log and all the recorded state changes. The state data also consists of the key-value pairs that are version. All the state database changes are recorded in time order in the ledger, and the blocks are cryptographically linked together. The orderer node ensures ledger consistency by implementing the PBFT algorithm. The HLF framework supports the Execute-Order-Validate and Commit transaction model.The Layer -3 simulation model was created while using a workstation as the BS server running the blockchain applications. This environment facilitated measuring the throughput and latency parameters of the Ethereum and Hyperledger private networks. The networks were set up in similar conditions and provided with a virtually generated workload. A distributed environment that includes the two blockchain networks was considered for the experimental set up. The simulation models used a workstation with Intel Core i7-3770 @ 3.4 GHz processor and 16 GB memory as the BS. For simplicity, the Etherume network was deployed with just one mining node. The experimental results are presented in The Hyperledger Composer  hosted tTransaction Throughput, i.e., the total number of committed transactions by the blockchain System Under Test (SUT) in a given time period in seconds.Transaction Latency, i.e., the amount of time that is taken for a transaction to be stored on the blockchain ledger.The primary objective of a blockchain application is to maintain a number of submitted transactions by the participants. The submitted transactions then proceed to the verification and ordering process, which results in a block generation and storing the transaction outcome on the blockchain ledger. The following metrics are presented by Hyperledger Performance and Scale Working Group  to measuThe system was tested to evaluate the performance of the proposed model in terms of both the latency and throughput. The results were benchmarked against the parameters reported in the literature with the aim of demonstrating the efficiency of the designed framework. The evaluation was conducted while using the Hyperledger Caliper  to facilIn the proposed model, the latency represents the time that is required by CHs to verify new blocks. The block size is an essential factor that affects both node and network latency. The latency is measured by the time that the system requires to reach consensus after the node starts to detect the new block validations. The analysis of the system was conducted with a set of transactions, such as Open, Transfer, and Query. The results were provided for Hyperledger Fabric (proposed blockchain for Layer 2) and Ethereum . Despite security and privacy, latency and throughput are essential performance metrics when selecting an appropriate blockchain platform for IoT applications. The resource allocation for the blockchain network must be done in order to meet the latency requirements (for a given input load). A further experiment was conducted to analyze the SUT behavior consisted of multiple rounds of benchmarks with different transaction sending rates. The sending rates varied from 20 to 500 Transactions per Second (TPS), and 1000 transactions were generated for each benchmark to measure the maximum, average, and minimum transaction latency and throughput. This experiment only considered an individual client in the blockchain network to generate all the transactions. As expected, the performance of the blockchain network highly depends on the underlying hardware. The HLF provides a three-stage revolutionary architecture known as execute-order-validate, in which every stage depends on previously executed transactions.Our experiments revealed that the proposed HLF-based blockchain model for IoT application could process up to 110 transactions per second while maintaining a 100% transaction throughput and an average latency of 500 milliseconds, with a maximum of 110 TPS, with throughout that is very close to 100%. A send rate of 100 TPS is sustainable, as the actual throughput is around 100%. However, increasing the send rate to 100 and 200 TPS only yields to a marginal throughput decrease. This lead to the conclusion that our setup can sustain a send rate of about 110 TPS. Therefore, our proposed architecture could support real-time provisioning of multiple 5G-enabled IoT applications without imposing any considerable latency to the process.The maximum latency grows to nearly 15 s as the number of input transactions increases. This is due to resource restrictions of the containers that are allocated to the peer nodes. The minimum latency remains almost constant, as there are no high loads imposed on the peer nodes at the beginning. Additionally, the blockchain configuration  influences the latency. It can be observed that, in all cases, all of the transactions are successfully completed, i.e., no loss of transactions occurs.The proposed secured IoT multi-layer model that is based on Hyperledger Blockchain technology offers an overall superiority over the previous works reported in the literature, as illustrated by the metrics comparison that is given in Contracts between different entities are recorded in the blockchain system. Therefore, privacy disclosure assessment is required. The identity of an object is encrypted, and the IoT address is recorded in the blockchain as the pseudonym of the entity. The domain name and fixed address for communication are not required, and the blockchain maintains tasks through running smart contracts, as discussed above. In the IoT network, IP address of an object is encrypted and recorded in the blockchain thus leading to the anonymity of the object. The contract context privacy is guaranteed by the Hash Code of the real context within the blockchain network while minimizing the risk of a privacy leak.The proposed framework accommodates various configurations for system security in different scenarios. These include the IoT objects with limited resources, the security of sensitive information, high-risk, and broadcasting. The security configuration options can vary, due to the strength of cryptography techniques and characteristics of key lifetimes , key distribution mechanisms, the selection of different session keys, such as encryption and authentication, cached session keys, including of one, multiple, and unlimited keys, different owners of keys, and the stability of the fundamental protocols (TCP and UDP). Besides, a certain degree of flexibility is achievable by granting an option to a node or entity to connect or leave the system freely. Changes in the network are recorded in the blockchain through the distributed consensus process.The process of authentication is implemented in two parts: (1) local authentication and authorization process in the infrastructure layer and (2) rights to objects by smart contracts. The node requirements and respective rights are recorded in the blockchain that was implemented in different segments. The block summary consists of a contract summary. It is accessible at any time. The non-repudiation nature of this summary guarantees the interests of the object.The multi-layer approach through the network clustering divides the entire IoT network into different tiers, as presented in The framework tackles the following scalability challenges: (1) high data traffic and (2) a massive number of IoT objects and devices. The multi-layer structure facilitates multiple cluster implementation and fulfills the scalability issues. Two different CH nodes can establish different secure communications on a client-server basis. CH establishes secure communication with the entities within the same cluster in order to avoid the overhead incrementation. When networked CH nodes start communicating within a framework that is based on the blockchain, the exchange of cryptographic keys is necessary before beginning the client-server communication by which further overhead is reduced.This paper proposes a multi-layer security model for IoT devices functioning under multi-hop cellular networks based on distributed technology of the blockchain. The developed model provides a feasible solution to establish the decentralized application of the blockchain technology for the security of the cellular-enabled IoT network. The hybrid self-clustering EC algorithm, utilizing GA and SA, is developed to fragment the IoT network into clusters in order to provide the multi-layer structure and enhance the network lifetime. Detailed system implementation is discussed, and the way the blockchain-based model can help to improve the IoT system authentication and authorization is elaborated. The model proposes the open-source HLF blockchain for deployment and verification. The multi-layer model enhances network security, lowers the processing load, and reduces network load and latency. The proposed implementation enhances the efficiency of the communications via the peer-to-peer nature of the blockchain communication and maps it to the device-to-device communication in cellular systems with improved integrity and security. The proposed solution tackles the IoT security challenges, including framework privacy, authentication, heterogenicity, and flexibility, as well as network scalability. The proposed hybrid clustering algorithm has been compared with four existing protocols. The simulations study demonstrates that the proposed algorithm outperforms the competitors in terms of various performance metrics, including network load, network coverage, and distances. The performance of the proposed multi-layer blockchain-based framework was evaluated. It was found that the lightweight blockchain was more effective than the global blockchain Ethereum.The focus of our future work will be on the deployment of a practical scalable test-bed configured as MBS framework of IoT devices to study, analyze, and compare the performance in the real world environment.