The growing interest in soft robotics has resulted in an increased demand for accurate and reliable material modelling. As soft robots experience high deformations, highly nonlinear behavior is possible. Several analytical models that are able to capture this nonlinear behavior have been proposed, however, accurately calibrating them for specific materials and applications can be challenging. Multiple experimental testbeds may be required for material characterization which can be expensive and cumbersome. In this work, we propose an alternative framework for parameter fitting established hyperelastic material models, with the aim of improving their utility in the modelling of soft continuum robots. We define a minimization problem to reduce fitting errors between a soft continuum robot deformed experimentally and its equivalent finite element simulation. The soft material is characterized using four commonly employed hyperelastic material models . To meet the complexity of the defined problem, we use an evolutionary algorithm to navigate the search space and determine optimal parameters for a selected material model and a specific actuation method, naming this approach as Evolutionary Inverse Material Identification (EIMI). We test the proposed approach with a magnetically actuated soft robot by characterizing two polymers often employed in the field: Dragon Skin™ 10 MEDIUM and Ecoflex™ 00-50. To determine the goodness of the FEM simulation for a specific set of model parameters, we define a function that measures the distance between the mesh of the FEM simulation and the experimental data. Our characterization framework showed an improvement greater than 6% compared to conventional model fitting approaches at different strain ranges based on the benchmark defined. Furthermore, the low variability across the different models obtained using our approach demonstrates reduced dependence on model and strain-range selection, making it well suited to application-specific soft robot modelling. Over the last few decades, there has been growing interest in the field of soft robotics . These rActuation of SRs is possible using numerous methods, including, for example, pneumatic, hydraulic, mechanical, chemical and magnetic approaches . HoweverAs with SRs actuated using alternative means, MASRs are manufactured using soft elastomeric polymers with elastic moduli values close to those of biological tissues. While these highly compliant materials support safer tissue interactions in surgical applications, they are much harder to model and thus predict their behavior. Under actuation, the hyperelastic material can produce highly nonlinear deformations that cannot be resolved using techniques conventionally used in robotics. As noted in the research conducted by Several material models of varying complexity have been proposed , includia priori the expected strain can be challenging. Increasing the order of the material model represents another method to reduce fitting errors, however, this can be potentially harmful to the robustness and the stability of the simulation , a material characterization approach aimed at identifying the parameters for a material based on the target application, in our case: MASRs. We aim to characterize materials by using a simulated representation actuated using the same method as used during experiments. Having defined a simulation model, we can then create, deform and observe an equivalent specimen of soft material in a real, controlled environment. Finally, we define an optimization problem where the parameters of the material model comprise the search space while the error between the experimental data and the simulation is minimized. The problem defined is often non-linear and gradient methods may fail to find an optimal solution. We address this issue by using, for the first time, an optimizer based on an evolutionary strategy. An evolutionary algorithm (EA) allows a good balance between exploitation and exploration of the search space . This teExperimental Evaluation section the performances of the models are characterized using the framework presented, and the resulting model parameters are compared with the model parameters obtained using the conventional fitting approach.In the next section, our framework for material characterization is presented. First, we define the FEA model and the fabrication steps to create the equivalent experimental sample. Next, using the proposed comparison metric, we discuss the steps to minimize the fitting errors using an EA. To demonstrate our characterization approach, we focus on a MASR, made using two soft polymers commonly found in soft robotics applications: Ecoflex™ 00-50 and Dragon Skin™ 10 Medium. Finally, in the To demonstrate our characterization approach, we first developed a FEA model of a simple MASR. The design takes the form of a rectangular body with a small permanent magnet embedded at the distal end, as shown in via magnetic fields, the material may experience only a fraction of these deformations. To improve fidelity between testing for characterization and application, we propose simulating the application-specific actuation method  within the FEA simulation and solving an optimization problem using an EA to characterize the material. Due to the nature of the EA, the simulation model may be invoked many times, with the potential to significantly increase computational time. Therefore, we developed our FEA model with a balance between accuracy and computational cost to simulate the magneto-mechanical response of our soft robotic designs. This combines analytical mechanical models used to predict nonlinear elastic behavior of polymers with magnetic interaction forces and torques based on a dipole approximation , body forces due to gravity were integrated in the simulation model, to reconcile with the experimental setup.The soft material of the FE simulation is then deformed through the interaction with a rigid body, in our case a permanent magnet. The forces quations :f=∇ and solved using Multifrontal Massively Parallel Sparse direct Solver. The number of quad mesh elements used for each geometry are summarized in The geometry and constraints of the MASR were selected to allow reduction of the model to a 2D plane , and thuTo provide experimental data for the optimization, samples were prepared and tested under a varying magnetic field. Four versions of the design were prepared with the geometrical parameters summarized in Molds were created for each geometry using a 3D Printer (Ultimaker S5), as shown in As shown in rocessed . The resTo extract image features intrinsically rich in information that could be used to compare the experimental deformation with the simulation model, we developed a post-processing procedure to measure the outer edges of the sample. Bespoke image processing code was developed using MATLAB , to allow segmentation and edge extraction where the deflection/deformation is more prominent along the longitudinal length of the sample. The following steps were followed: 1) high contrast images were obtained; allowing the sample edges to be easily extracted; 2) the boundary of the object was extracted, and the corners of the sample were determined from the edges; and 3) after splitting the edge of the sample into different segments, the relevant ones were saved to be used as a target in the characterization process see .Experimental Evaluation section):After the simulated and experimental results were obtained, a metric was needed to compare the two and allow for optimization. We propose the following approach to solve the problem where for each value of the magnetic flux density  . This alWe characterized two elastomers: Dragon Skin™ 10 Medium and Ecoflex™ 00-50 . Four samples for each material were created using the dimensions listed in Benchmark Functions section) to solve the CMA-ES. The number of evaluations and generations required by the CMA-ES to converge to an optimal solution and obtain a set of model parameters are shown in The data from the samples chosen for the material characterization are used to define the optimization problem  using the same dataset failed to get close to the same results, with the EIMI showing at least a 30% improvement. This underlines the difficulty of choosing the most suitable analytical model and the correct stress-strain selection over the tensile data.For the Dragon Skin™ 10 Medium , the YeoInterestingly, for Ecoflex™ 00-50 , the besOverall, it is possible to see that the models fit using a conventional approach over different ranges of stress-strain data failed to converge to similar results. The box plots in TM 10 (see TM 00-50 (The performance of the different models can be clearly seen in M 10 see , it is pTM 00-50  all 3 moTo understand how performance varies with actuation, we analyzed EIMI and the best models found using the conventional approach for each of the magnetic field values tested. More specifically we analyzed the SE see  at each The performance of the Dragon Skin™ 10 Medium is shown in For Ecoflex™ 00-50 see , we can Overall, our proposed approach is able to maintain a steady error  with incIn this work, we presented an alternative method of material characterization to dynamically find the best material model based on the target application. We present a framework for characterizing hyperelastic materials using an FEA simulation, application-specific experimental evaluation and an EA to solve the optimization problem. The results show that the proposed EIMI approach can fit the parameters for a material model with a high degree of accuracy. Furthermore, thanks to the use of the simulation model in the characterization process, we can intrinsically obtain robust and stable models to use with numerical methods.Our results also highlight the inherent challenges with the conventional model-fitting approach. In this study, we rely on the database of Despite this success, some limitations to our approach exist. When it is possible equiaxial and volumetric testing are generally preferred . Effectsnt model .While EIMI allows the user the freedom to choose any actuation method, we focused on magnetic actuation in this study which is often approximated using the dipole model, and this may not always be accurate. An oversimplification of the physical model for the simulation will negatively impact the characterization and drift the model parameters from their true values. However, if the experimental setup is similar to the final application the error caused by the discretization should be contained.While in this study we focus only on magnetic actuation, the EIMI approach may be expanded and used to validate other actuation schemes such as pneumatic or piezoelectric, with suitable testbed and simulation development. In addition, the decision space may be further expanded by adding more parameters to the characterization such as the magnitude of the actuation forces. For example, if the magnetic moment is unknown, we can include this variable in the decision space with the soft material parameters. In this case, the EA would need to solve an extra degree of freedom to match the experimental data. Finally, while the analytical model is usually the result of empirical observation, we can try to expand the optimization problem by also including the strain energy function. With enough computational power, it may be possible to use a technique such as genetic programming  to deter


For both groups of centers, the lowest level of compliance was recorded for postoperative items (42% and 66%). Sex, age, presence of comorbidities and American Society of Anesthesiologists (ASA) score were not associated with a different probability of compliance to the ERAS protocol. Conclusions: Several items of the ERAS protocol were poorly adopted in colorectal surgery units in the Piemonte region in the baseline period of the ERAS Colon-Rectum Piemonte study and in the ERAS group. No relevant obstacles to the ERAS protocol implementation were identified at patient level.Background: In 2019, the Enhanced Recovery After Surgery (ERAS) protocol for colorectal cancer surgery was adopted by a minority of hospitals in Piemonte . The present analysis aims to compare the level of application of the ERAS protocol between hospitals already adopting it  with the rest of the regional hospitals  and to identify possible obstacles to its application. Methods: All patients surgically treated for a newly diagnosed colorectal cancer during September–November 2019, representing the baseline period of a randomized controlled trial with a cluster stepped-wedge design, were included. Indicators of compliance to the ERAS items were calculated overall and for groups of items  and analyzed with a multilevel linear model adjusting for patients’ characteristics, considering centers as random effects. Results: Overall, the average level of compliance to the ERAS protocol was 56% among non-ERAS centers (N = 364 patients) and 80% among ERAS ones (N = 79), with a difference of 24% (95% CI: −41.4; −7.3,  The Enhanced Recovery After Surgery (ERAS) program is an evidence-based multidisciplinary care pathway which aims to reduce surgical stress and lead to a faster rehabilitation of patients. The ERAS Society has promoted the development of this pathway firstly through the drafting of guidelines in colorectal surgery and then adapting them to different surgical disciplines and procedures ,2. CarefIn the hospital network of the Piemonte region  the average length of stay (LOS) for scheduled interventions was 10 days in 2018, while the corresponding figure in the only ERAS certified hospital was 6 days. Moreover, the average proportion of procedures performed with mini-invasive techniques (laparoscopy or robot assisted) was 66%, with high heterogeneity between centers, in comparison to 82% in the ERAS certified center. On the basis of the regional data and available literature, the systematic adoption of the ERAS protocol was identified as a useful approach to reduce the length of stay and standardize patterns of care in the perioperative period. To support the diffusion of ERAS principles in the hospital network and to estimate its impact, a cluster randomized trial, supported by an audit and feedback strategy, was launched in late 2019.The first quarter of the ERAS Colon-Rectum Piemonte study evaluated the “standard of care” across all centers previous to the audit and feedback strategy adopted to implement the protocol. The present analysis aims to describe the level of application of the ERAS items for colorectal cancer surgery in a real-world setting—a regional network of hospitals—before the implementation of the standardized protocol. The second aim is to identify obstacles to the ERAS application from the experience of the regional centers before the beginning of the study. Healthcare outcomes are not included at this stage, but they will be analyzed at the end of the trial including all enrolled participants.The ERAS Colon-Rectum Piemonte study included all hospitals in the Piemonte region expected to perform more than 30 colorectal cancer surgical procedures per year. A pragmatic, cluster, stepped-wedge, randomized trial, with a sample size of more than 2200 patients, was designed with the aim of evaluating the impact in terms of process and outcomes of a regional application of the ERAS protocol in colorectal cancer surgery. Written informed consent was obtained from patients to take part in the study. The study was approved by the ethics committees of the promoting and participating centers. Details on study protocol have been previously published [Data describing perioperative ERAS items, based on the guidelines of the ERAS Society in colorectal surgery, were collected prospectively for each patient.The present analysis included all patients surgically treated for colorectal cancer during the 3 months first period of the study (September–November 2019), the baseline quarter with standard care.At baseline, few centers were classified as “ERAS centers” if they had previously received specific training in the ERAS pathway, had a multidisciplinary ERAS team and a protocol approved and applied in their unit. These centers were included in the study as a reference group and not randomized. All other centers were classified as “non-ERAS centers”. During the baseline period all the centers were required to continue with their usual perioperative care and to complete a case report form (CRF) for all the enrolled patients.Patients’ characteristics between groups of hospitals were compared through the t-test for continuous variables and the chi-square test for categorical variables.Compliance of each center to ERAS guidelines was measured as the mean percentage of compliance of their patients with a list of indicators  and by means of radar charts.To estimate the difference in compliance levels between ERAS and non-ERAS groups, a multilevel linear model was estimated, adjusting for patients’ characteristics and considering the enrolment centers as random effects. The patients’ covariates included in the models were: sex, age classes , Charlson Comorbidity Index (0 or ≥1), cancer site (colon or rectum) and ASA score (1–2 or 3–4).A multilevel regression model with the same set of covariates and the centers as random effects was also applied to estimate the effect of patient demographic and clinical characteristics on the level of compliance to the ERAS items. The model was also stratified by the type of centers group (non-ERAS and ERAS), in order to highlight the possible change in effect due to the presence of an adopted ERAS institutional protocol.During the baseline period of the study, 443 enrolled patients in 28 hospitals received colorectal cancer surgery, 364 of whom (82%) were in 25 centers not applying ERAS before the beginning of the study.Patients’ characteristics are described in p = 0.0053) (p = 0.0014), where the ERAS group had the highest compliance (91%). Intraoperative items appeared to also be frequently applied among non-ERAS centers (64%) and the difference with ERAS ones was of 16.5 points .Overall, the level of compliance to the ERAS protocol was 56% among non-ERAS centers and 80% among ERAS ones, with an absolute difference of 24 points between the two groups  . Postopep = 0.0022), with a compliance of 41.3% in the non-ERAS centers and 78% in the ERAS centers.In the core set of key items, the estimated difference between the two groups was −37 points , as well as the avoidance of abdominal drainage for colon surgery (44% in non-ERAS and 62% in ERAS). Fluid normovolemia also had low compliance in the non-ERAS group (45%).In the postoperative phase of care, non-ERAS centers showed low levels of compliance for all the postoperative items but early nasogastric tube removal (65%). Among ERAS centers, the levels of compliance were high overall, except for early removal of intravenous infusion (IV) (47%), early mobilization at 1 day postoperative (56%) and early follow up (60%).p = 0.0001) compared to colon cancer cases. These results were confirmed for both groups of centers by a stratified analysis as shown in This study reports the baseline evaluation of a more complex trial aiming to implement the ERAS pathway throughout all surgical units treating colorectal cancer in a north-western Italian region (Piemonte). As most of the literature is dedicated to describing the implementation of the ERAS protocol in referral centers with a high commitment on its application, we were interested in describing ERAS knowledge and dissemination in a real-world contest before the implementation of a controlled protocol supported by an audit and feedback approach to understand how different the clinical practice from the ERAS standards is.Despite evidence supporting the ERAS program in colorectal surgery , the prePrevious surveys ,7,8 inveOur analysis confirms that some ERAS strategies, especially in the pre and intraoperative periods, were already adopted outside of a formal ERAS protocol. This evidence, together with the low compliance to some ERAS items observed in the ERAS group, confirms the need for a structured implementation program to achieve a more complete and homogeneous adoption of the ERAS protocol among the regional centers.One of the main obstacles to the diffusion of the ERAS pathway was due to the complexity of its global structure and the need for multi-professional and multidisciplinary teams. To overcome these issues some centers have developed “ad hoc”, simplified pathways, including only a few ERAS items considered of major importance. Although a simplified pathway may seem easier to apply and has been suggested as a possible initial focus , the appAs expected, our study documented a significant difference in the total overall compliance with the ERAS items between ERAS (80%) and non-ERAS (56%) centers, indicating a wide margin for improvement.Multivariate models showed that age, comorbidity, tumor site and ASA score did not appear to be limiting factors to ERAS compliance, while the main obstacles to the diffusion of the protocol itself were identified in the organization of the activity and clinicians’ preconceptions.The wide differences observed at baseline between the few ERAS centers and the rest of the regional hospitals may be accountable, at least in part, to pre-existing selection factors and not only to the early adoption of the ERAS protocol. However, the true differences at baseline could have been reduced by changes implemented during the discussion of the study protocol and due to the data collection with CRFs that included all the ERAS items, thus mitigating the real pre-existing differences among groups.In any case, it is expected that this experience will contribute to reducing the regional variability in the surgical management of colorectal cancer with a general improvement of the standard of care. In addition, thanks to the large sample size and the availability of data on all the ERAS components, we will analyze the contribution of each item and its interaction on patient outcomes, with the aim of proposing a more simplified and flexible protocol without jeopardizing its effectiveness.The final results of this pragmatic trial centers will contribute to reducing the gap of evidence regarding the real applicability and effectiveness of the ERAS when offered to an entire hospital network.Despite the established efficacy of the ERAS approach and the general acceptance in the colorectal surgical community, compliance with the ERAS in a regional network of hospitals in the north-west of Italy was found to be low in 2019. Compliance with some items has been shown to be suboptimal even in the centers identified as formally implementing the ERAS protocol. Such a limited level of implementation was not associated with patients’ characteristics, such as presence of comorbidities, high ASA score or older age. Potential obstacles to a full ERAS adoption are likely to be due to organisational and cultural factors. To promote the routine implementation of the ERAS protocol, a structured implementation program supported by an audit and feedback approach could be effective and needs to be assessed.


Wireless sensor network (WSN) is made up of tiny sensor nodes. The application of WSN in diverse fields has seen a tremendous escalation in recent years. WSN applications are constrained by the limited set of computing resources possessed by the sensor nodes and the security aspects of data communication in the WSN. Many algorithms based on nature-inspired optimization (NIO) have been proposed in the past to optimize the issue of energy efficiency and security in WSN. In the proposed work, two opportunistic routing algorithms, i.e., intelligent opportunistic routing protocol (IOP) and trust-based secure intelligent opportunistic routing protocol (TBSIOP), are compared against two NIO algorithms developed for achieving energy efficiency and security in WSN for performance analysis. The performance is evaluated by simulating the algorithms on MATLAB and comparing the obtained results with existing ACO-based and PSO-based routing algorithms. It is observed that the TBSIOP outperforms the NIO-based algorithms in terms of energy efficiency, network lifetime, packet delivery ratio, end-to-end delay, and average risk level. All the parameters under consideration are recorded in the presence of a maximum of 50% malicious nodes for 25, 50, and 100 nodes' test cases. The increasing size of the network has a significant effect on the performance of TBSIOP, as the packet delivery ratio is close to 100%. Also, TBSIOP can easily avoid malicious nodes during the routing process as reflected from the results. This will improve the network lifetime of TBSIOP compared to other protocols. As far as the application of the work is concerned, it would be beneficial for smart healthcare services. It can also help in better communication during the sharing of data by providing energy-efficient services and keeping the network alive for a longer period. With the proliferation of microelectromechanical systems (MEMS) in the last decade, the mobile ad hoc network (MANET) has gained tremendous importance. These networks over a while have found their applications in almost every aspect of life. Wireless sensor networks (WSN) being a special type of network under ad hoc networks are composed of nodes that are not fixed for one location. WSN is made up of microsized sensor nodes which are capable of self-configuration. The WSN nodes are deployed randomly for observing and recording the physical phenomenon in locations that are mostly unreachable by humans. The function of these sensor nodes is dependent on constrained computing resources such as battery, storage, and processing capabilities . A typicRouting in WSN is a hot topic of study, with researchers looking for ways to improve WSN. Until now, energy-effective routing has been a significant hurdle to overcome. A protocol for data communication is necessary among nodes (routers) of a WSN to determine a path for data transfer in the network. Many energy-effective data communication protocols are configured to spread the energy load through all nodes, lowering power utilization in a WSN. Similarly, the security of the data transmitted over the sensor network is a significant challenge. Owing to the unrestricted wireless networking aspect of WSN and the restricted capabilities of nodes, maintaining security and confidentiality is a significant challenge . EncryptThe real-world problems are divided into two types, i.e., deterministic and nondeterministic problems. Routing is one of the most difficult problems in WSN for which there is no deterministic solution. As a result, nature-inspired optimization (NIO) algorithms are used to present low-cost routes across a variety of options. Various routing algorithms have been created using swarm intelligence- (SI-)  related This paper describes some of the most popular nature-inspired optimization algorithms for addressing the challenge of routing and security in WSN to achieve energy-efficient and secure sensor networks. A comparative analysis of two protocols, i.e., IOP  and TBSIThe rest of the paper is structured as follows. In With the ever-evolving magnitude of WSN-based applications, the challenges in providing real-time optimal solutions are also increasing. The biggest issue with WSN which is discovered after a detailed study of literature is the ideal consumption of computational means available with the wireless sensor nodes. As these sensor nodes are always resource-constrained, the best utilization of their resources is of paramount significance for the survival and longevity of the sensor network. The sensor nodes are proficient in acquiring and transmitting the data with their respective modules. Moreover, it is the routing protocol that is to be applied for the transmission of data between sensor nodes and between the source node and the destination node, i.e., base station (BS). The challenge with routing in WSN is that it consumes a maximum of the node's resources during the communication process. Due to this significant amount of resource consumption during the transmission of data in the network, the longevity of the sensor node is affected and the overall functionality of the network is compromised. To address this challenge, NIO algorithms are presented in the literature. Their use in the literature has shown significant improvements in respect of energy utilization and network lifetime in the case of WSN. As a consequence, nature-inspired path optimization strategies play a critical role in a WSN, as efficient routing reduces energy usage and thereby increases network lifespan . In a WS2FA-PSO [The authors  have pre2FA-PSO  uses netFrom the literature, it is discovered that not much research work is conducted using NIO techniques in the opportunistic routing protocols along with the security aspect of the WSN. Moreover, it is learned from the literature that SI-based methods are the most popular methods which are used by researchers in the recent past. The next section of the paper describes the challenges which are directly associated with the routing in WSN.Routing is the method of sending data from the source node to the destination node in the sensor network. It is also considered one of the most energy-consuming tasks in the overall operation of the wireless sensor network. If the route chosen during the routing process is not optimal, then this leads to the consumption of sensor node energy. There are many more routing-related issues in the WSN which are discussed in the subsections below.In a WSN, energy use  is a bigAnother issue that arises from the haphazard distribution of nodes in wireless sensor networks is related to the way the sensor nodes are arranged  in the aIn certain instances, identical data from different nodes may be collated to remove duplicate and correlated data, which is an essential phase in WSN . Since iAnother problem in a WSN is load balancing , which mThe term “latency” relates to the time it takes for data packets to travel from point A to point B. This is a new problem in WSN, as multihop and data aggregation will escalate data transmission latency in certain situations .Since there are too many sensor nodes in a WSN , the posSecurity refers to the measure of ensuring that no malicious node participates in the routing process for transmission of data from the source node to the destination node . It is oAll these above-mentioned issues related to data communication in the sensor network have their impact on the energy consumption of the sensor nodes, and therefore, special caution should be applied in designing routing protocols for WSN. A few of the problems listed above are NP-hard (stochastic), which means they cannot be solved using a deterministic solution. We need some sort of nature-inspired optimization techniques or algorithms to deal with these types of problems. Any optimization techniques come with an objective function to find the right and most desirable solution which would be used to address these stochastic types of problems in the routing. The next section of the paper presents a brief introduction related to two recently developed energy efficient and secure opportunistic routing protocols.The challenges associated with the process of routing in WSN are highlighted in the previous section, and the authors of this paper have developed two opportunistic routing algorithms for addressing the issue of energy efficiency and security in WSN. The execution of these above-mentioned algorithms resulted in achieving energy efficiency and security of data in the WSN. The next section of the paper provides an overview of swarm intelligence- (SI-) based techniques for optimization of routing in WSN.There are some problems in the real world that cannot be solved in an absolute manner. These problems are sometimes referred to as NP-hard or stochastic problems. The solution for such types of problems is not available in the traditional deterministic approaches. These problems would be solved with optimization techniques. Routing being one of the NP-hard issues in WSN requires optimization methods for its remedy. The objective function in the optimization techniques is of immense importance. This objective function could offer a single or multiobjective function depending upon the type and requirement of the stochastic problem. 2FA). This protocol is PSO and finite automata-based pathfinding approach. This protocol also employed a route inspection mechanism for secure routing. MATLAB simulations showed good performance in terms of security and network lifetime.The particle swarm optimization (PSO), which Eberhart and Kennedy  developeAccording to the literature, two critical issues of WSN, i.e., clustering and routing, have been addressed by using PSO. The PSO-based routing protocol is developed by authors in  to enhanThe ant colony optimization (ACO) algorithm is a well-known algorithm that is frequently used in metaheuristics in the literature. The ACO is a computer simulation of ants moving randomly in the pursuit of food. If the ants locate a food supply, they collect particles of the food, and on returning to their nest, typically through a path with less number of ants, spread some amount of pheromones to alert other ants to their finding. As nearby ants pick up on the smell of the pheromone, they would almost certainly follow the successful ant in obtaining the food supply. This methodology is implemented to determine the close to optimal alternative with a minimum number of hops for data transmission in a wireless sensor network (WSN).An efficient transmission technique which is based on distance (ODTS) is used with ACO in . This teThe artificial bee colony (ABC) algorithm was developed in 2009, and the algorithm was influenced by the action of natural honey bee swarms . The ABCThe authors in  developeThe FA is easy to set up to work well in multimodal quest settings. It is comparable to the PSO except that the search velocities are not considered in its search for solutions . ConversThe bacterial foraging optimization (BFO) is a branch of swarm optimization motivated by the foraging community of bacteria. Passino  was the The routing challenge along with the clustering problem in WSN is addressed by the authors in . This alTherefore, in this section of the paper, nature-inspired optimization algorithms for routing in WSN have been described. All of these swarm-based optimization algorithms are used to address the routing-related challenges in WSN. The next section of the paper deals with the simulation results of the two NIO algorithms and their comparisons with the already developed opportunistic routing protocols, i.e., IOP  and TBSI2FA [th generation with a processor speed of 2.50 GHz along with RAM capacity of 8 GB and 64-bit Windows 10 has been used. All the compared protocols are simulated on the same platform to maintain uniform results collection.Performance analysis of IOP  and TBSI2FA , discuss2. 2FA, the settings for PSO-specific parameters remain the same. The number of particles has remained as 60, string length was at most 10, a number of loops were 50, and other parameters also remained the same.For this experiment, 25, 50, and 100 sensor nodes are arbitrarily arranged in an observation region of 500×500 mFor simulation analysis, all the protocols are simulated on the same platform and similar network settings. The other protocol-specific requirements may differ, and the results were recorded accordingly. Base station positioned at the center of the area of deployment and all the nodes will send their packets towards this fixed location only. Protocols under consideration start working instantly after the distribution of nodes. The performance factors considered here are the security of the protocols, energy efficiency, packet delivery ratio, end-to-end delay, and network lifetime. The values for these performance factors are recorded, and the graphs are plotted accordingly.2FA applying secure route inspection for removing various malicious nodes showed performance equivalent to TBSIOP. IOP and AOSTEB do not employ any security measures and ultimately showed poor performance as shown in 2FA (The packet delivery ratio for each protocol under consideration is calculated, i.e., the number of packets generated during the simulation and the number of packets successfully received at the base station. The delivery ratio for TBSIOP and LD2FA  is almos2FA, the end-to-end delay is comparable; however, LD2FA involves extra calculations for PSO and needs more time to calculate the secure path as we can see in End-to-end delay is another performance parameter that is used to calculate the QoS of the sensor network. This parameter inside the simulation is computed for successfully received packets at the destination. In presence of malicious nodes, end-to-end delay is high for IOP and AOSTEB. This is because these two protocols need to recalculate the path for certain packet transmissions and introduce overhead on the source node. For TBSIOP and LDThe importance of calculating energy efficiency is very high in wireless sensor networks as all sensor nodes are energy-constrained. As stated previously, the routing process consumes most of the node's energy, and hence, there is a requirement for energy-efficient routing protocols. TBSIOP considered energy as a factor inside trust factor calculations; the energy consumption for the routing process is distributed among each node of the network except malicious nodes. So, from Similarly, the network lifetime of TBSIOP as shown in 2FA-PSO. Analysis of all the protocols was done on a uniform platform, considering the same parameters. Further, during simulation of all protocols, malicious nodes have been introduced for generating grey-hole or black-hole attacks. To launch these attacks, malicious nodes disrupt the packet transmission by dropping packets. Due to this scenario, the performance of network reduces automatically. TBSIOP, a protocol of intelligent lookup for malicious nodes introducing trust factor calculations, has been proved to be the most secure protocol.In this paper, a comparative analysis has been presented between IOP, TBSIOP, and nature-inspired algorithms AOSTEB and LDOther compared protocols failed to detect attack-generating nodes, and risk factors will be high. The average network lifetime using TBSIOP is highest along with the least energy consumption as compared to other protocols. The average risk level is also lowest in the case of TBSIOP as compared to other compared protocols. Security and energy efficiency are important factors to be optimized by any of the routing protocols to cope with existing challenges with WSN. Nature-inspired algorithms may be used to find the best routes in WSN, but modification is required for improving the network lifetime and security as depicted from the results in this paper. To make routing decisions faster for improving data transmission, the researchers can propose hybrid approaches including trust-based and nature-inspired algorithms. In the future, other swarms of intelligence-based nature-inspired algorithms may be simulated for performance analysis and addressing the possibility of the different types of attacks in the layered network architecture of WSN.


This work studies the feasibility of using backscatter-modulated tags to introduce false information into a signal received by a frequency-modulated continuous-wave (FMCW) radar. A proof-of-concept spoofing device was designed in the 24 GHz ISM band. The spoofing device was based on an amplifier connected between two antennas, and modulation was carried out by switching the amplifier bias. The use of an amplifier allowed us to increase the level of spoofing signal compared with other modulated backscattering methods. The simulated and experimental results show that our method has the ability to generate a pair of false targets at different ranges and velocities depending on the modulation frequency of the chosen tag, since sidebands appear due to this modulation. Countermeasures to detect the spoofing attack based on changes in the slope of the frequency sweep between frames are also proposed. Frequency-modulated continuous-wave (FMCW) radars are widely used in short-range applications as an alternative to pulsed radars. Compared with continuous-wave (CW) radars, these radars allow a user to also determine the distance. The range of applications for FMCW radars has increased significantly in recent years to different fields. Some of these applications include altimeters for unmanned aerial vehicles (UAV)  and aircAutomotive radars include technologies that are part of modern advanced driver assistance systems (ADAS), camera-based computer vision systems, or, recently, LiDARS. Radars have some advantages, such as the signal not being affected by bad weather conditions  or dust, especially in computer vision systems or LiDARS . TherefoThese cyberattacks intentionally target the malfunctioning of radars. They can be classified into three main types . The firThe second type of attack is interferences, which are the introduction of unwanted signals into the radar band from other systems. The difference from radio jamming is that interferences are unintentional, and, in this case, the interference comes from communication signals of other users, e.g., from other systems sharing the same frequency band. Some differences in the automotive field can be seen when compared with other radar systems (such as military or aeronautic radars) due to the specificity of vehicular networks . The intFinally, the third type of attack is known as spoofing, which is the retransmission of radar signals to provide false information to the radar, misleading it in target detection, and increasing the risk of collision . Figure In this work, jamming attacks are not considered since modern radars are designed to minimize their impact. In addition, jamming is relatively easy to detect. Thus, this work is focused on attacks caused by spoofing.Studies on cyberattacks on FMCW radars, especially in the automotive field, have been recently reported. In ,31,32, tSpoofing attacks can also be performed on other radar applications ,39,40. IBackscattering communication is a data-transmission method based on the modulation of an incident RF signal from a reader. Backscattering is extensively used in radio frequency identification systems (RFID) and in low-power ambient backscatter, also known as RF backscatter. Backscatter communication has proven to be indispensable for the deployment of Internet of Things (IoT). As communications technology in the millimeter-wave frequency band has matured, the interest in mm wave ID (MMID) and in communications applications using backscatters at these bands has grown in recent years ,46,47. TThe objective of this paper is to study a hypothetical spoofing attack on an FMCW radar using low-cost devices. The main contribution of this work is based on a novel and simple concept, consisting of using a semi-passive modulated transponder or tag, for spoofing applications, instead of using SDR or other radars to generate spoofing signals, as presented in other works found in the literature. Simulated and experimental results show that by changing the modulation frequency of the transponder, it is possible to generate ghost targets at different distances and velocities in order to confuse the radar. Transponder modulation can be easily implemented with a low-cost microcontroller without the need to implement complex modulations or complicated algorithms. Compared to passive backscatters on reflection, the use of a gain-modulated transponder allows the level of the spoofing signal to be increased near to the echo of a small vehicle. Therefore, the spoofing signal can exceed the threshold level set in the radars to eliminate clutter.A proof-of-concept device has been designed using commercial off-the-shelf (COST) components and 3D-printed directive lens antennas integrated into a case. In addition, the device is portable and has low power consumption due the use of low-power SiGe monolithic microwave integrated circuit (MMIC) amplifiers (<12 mA at 3 V). Experimental results of false targets generation and detection are demonstrated using a 24 GHz FMCW radar. The proposed spoofing device does not require a local oscillator; therefore, it can be scaled to other radar bands such as those licensed for automotive radar at 77 GHz. Therefore, the proposed device can be useful in experimentally testing countermeasure algorithms to protect these radars from spoofing attacks.The paper is organized as follows. c being the speed of light in a vacuum. Therefore, the range resolution of the highest automotive bands (77–81 GHz) is better due to the higher available bandwidth. Another important parameter is the sweep slope defined as T being the sweep time. The transmitted wave propagates to the target and is backscattered and received by the radar receiver. The FMCW radar front-end consists of a down-converter phase-quadrature mixer, the IF amplifiers, and the antialiasing low-pass filters. The phase and quadrature signals are sampled at a rate FMCW radars, such as those used in the automotive field, are characterized by the ability to measure range and velocity. By adding MIMO structures based on virtual arrays , modern detected ,26.In ,35,38, sThis work investigates a possible attack using backscatter devices. The backscatter returns the radar signal but introduces a modulation that generates a false response. The retransmission of the same signal is independent of the type of radar and modulation used by it, which makes applying countermeasures difficult. In addition, the devices are simpler than those proposed in previous work and are based on low-cost components.e, Refs. ,49 descrFrom the antenna scattering theory, the backscattered field can be expressed as the sum of two main contributions: the structural mode  load ZL ,54:(1)E¯ollowing :(2)ΓL=ZLThe antenna mode is composed of multiple sidebands located at the harmonics of the modulation frequency (switching rate of the two impedances). The amplitude of these sidebands depends on the Fourier coefficients requency :(3)ΓL=ΔΓThe backscattered field v is the relative velocity between both. The effect of the movement adds a frequency shift Therefore, the received signal n, in practice, only the first The effect of the modulation causes the appearance of the sidebands. The response for the term Other backscatter topologies can be used to increase the signal level by placing an amplifier between the tag’s receiving and transmitting antenna see . In thism is a modulation factor that depends on the Fourier coefficient, and The amplitude of this response is proportional to the differential radar cross section given by the following:waveform . This eqDue to the propagation between the input and output antenna in the semi-passive transmission backscatter used in this work, the modulated signal includes an additional delay compared to the case of a passive reflection backscatter. This delay must be added to the propagation delay between the tag and the radar (L chirps with N samples per chirp sampled at a rate of s so that the columns correspond to the samples for each chirp transmitted.FMCW radar uses a range–Doppler (RD) map to simultaneously detect the range and velocity of the targets  obtainedFor a single backscatter case at distance itten as :(8)s see b. The senΔv) see c. The thFor a certain spacing in the range pression:fm=[ at the desired frequency using a low-cost and low-power microcontroller. Different frequencies profiles can be synthesized by varying the PWM signal as a function of time.The spoofing device was based on an updated version of the backscatter tag presented in ,60. The The input and output antennas of the prototype are integrated lens antennas (ILA). The antenna consists of a microstrip patch with a lens to increase the gain ,62,63. TH is the length of the extension from the substrate, and H is obtained from W is assumed to be equal to half of the wavelength when considering an effective permittivity approximately equal to the average between the lens material (PLA) and the substrate HL=RϵrL+1h is the thickness of the substrate. The patch length was designed to be resonant at Taking this width into account, a better estimate of the effective permittivity possible :(15)ϵrefnsidered :(16)L=c2mated as :(17)ΔL=0The design of the patch antenna, including the effect of the lens material and taper, was carried out with the Momentum software included in the Keysight ADS and with Ansys HFSS. A prototype antenna was manufactured, the sketch of which is presented in To verify the previous theory, a set of simulations were carried out, in which the presence of cluttering interference from the scene was not considered. The synthetic signals received were simulated from each target using –6). The. The6). and from , the 2D-For the spoofing validation, a FMCW radar kit (EVAL-DEMORAD) from Analog Devices Inc.  was used see . The radFMCW radars equipped with multiple receive and transmit antennas can estimate the angle of arrival (AoA), showing the targets on range–angle maps. The ghost targets generated by the backscatter tag used as a spoofing device can be observed on range–angle maps. The FFT-based algorithm for AoA detection is one of the most widely used algorithms because of its low complexity and ease of implementation . Angle eSince spoofing attacks on automotive radars can seriously affect safety and may cause accidents that endanger human lives, providing countermeasures to detect and neutralize such attacks is necessary.Strategies to combat the attacks are derived from Equation . From 1, the gholy, from , the ghoA countermeasure that the radar can implement, which also prevents interference from other radars, is to vary the slope of the frequency sweep, for example, by changing the duration of the chirp signal between frames. The algorithm compares the RD obtained with different frequency slope chirps. This allows the ghost points to appear at different distances or velocities between two consecutive range–Doppler maps. Consequently, identifying them is easy: by comparing the positions of the detected targets between sweeps.The case in which the spoofing device introduces false targets at the same position  from Analog Devices. In both cases, RD maps have been generated. The results obtained agree with those expected from theory, both for the simulations and for the measurements after applying the CA-CFAR detection algorithm.Countermeasures mitigating the effects of potential spoofing using these devices based on random variation in the radar sweep parameters, such as the sweep slope or the duration of the chirps, were proposed.In addition, the low-cost device presented allows for the generation of artificial targets that can be used to calibrate radars in assembly lines, in technical vehicle-inspection centers, and indoors or to avoid clutter interference as an alternative to expensive commercial instrumentation-based systems.


LBP  is a very popular texture descriptor largely used in computer vision. In most applications, LBP histograms are exploited as texture features leading to a high dimensional feature space, especially for color texture classification problems. In the past few years, different solutions were proposed to reduce the dimension of the feature space based on the LBP histogram. Most of these approaches apply feature selection methods in order to find the most discriminative bins. Recently another strategy proposed selecting the most discriminant LBP histograms in their entirety. This paper tends to improve on these previous approaches, and presents a combination of LBP bin and histogram selections, where a histogram ranking method is applied before processing a bin selection procedure. The proposed approach is evaluated on five benchmark image databases and the obtained results show the effectiveness of the combination of LBP bin and histogram selections which outperforms the simple LBP bin and LBP histogram selection approaches when they are applied independently. Texture analysis is one of the major topics in the field of computer vision and has many important applications including face recognition, object detection, image filtering, segmentation, and content-based access to image databases . TextureTo improve the classification performance, many approaches were proposed to reduce the dimension of the feature space based on the LBP histogram(s) . These a(1)a priori chosen among all the available ones, is an example of predefinied compact LBP [To obtain more discriminative, robust and compact LBP-based features, the first strategy consists of identifying the most informative pattern groups based on some rules or on the predefinition of patterns of interest. The uniform LBP operator, where a reduced number of discriminant bins are pact LBP .(2)The second strategy is based on feature extraction approaches, which project features into a new feature space, with a lower dimensionality, where the new constructed features are usually combinations of the original features. Chan et al. use a linear discriminant analysis to project high-dimensional color LBP bins into a discriminant space . Banerji(3)The third strategy consists of applying feature selection methods in order to find the most discriminative patterns . Smith a(4)A fourth strategy to reduce the dimension of the feature space based on LBP histograms was proposed by Porebski et al. in 2013 . In thisL-score) , which iL-score) .For the first minor contribution, the LBP histogram selection approach proposed in , using tWe then propose comparing this Sparse-MCSHS approach with a multi color space bin selection approach, also based on a sparse representation. For this purpose, the sparsity score proposed by Liu  is used These two approaches, Sparse-MCSHS and Sparse-MCSBS, are then combined to form the Sparse-MCSHBS approach (Sparse Multi Color Space Histogram and Bin Selection). This combination of bin and histogram selections represents the main contribution of the paper since such a combination for selecting relevant LBP bins was never previously proposed.In this paper, we propose improving this last selection strategy that selects discriminant LBP histograms in their entirety. Indeed, it is clear that not all bins of the selected histograms are meaningful for modelling the characteristics of textures. As it selects the most discriminating histograms and filter out the rest, we think that it might have some redundant bins in the selected histograms and a loss of some meaningful bins of the discarded histograms. That is why we propose introducing the combination of LBP bin and histogram selections, where a histogram ranking method is applied before processing a bin selection procedure. Two minor and one major contributions are thus proposed in this paper:In P neighbor levels of each pixel and coding the result as a binary number (where P is the number of neighboring pixels). Usually, the histogram of this LBP image is then computed for texture analysis and many authors take an interest in reduction of the The LBP operator is widely applied to define texture features for classification of gray level images due to its inherent simplicity and robustness . It tranformance .The original LBP computation, based on gray level images, was then extended to multiple variants  and also(1)In the first strategy, the original LBP operator is computed from the luminance image and combined with color features. For example, Mäenpää or Ning proposed to represent the color texture by concatenating the 3D color histogram of the color image and the LBP histogram of the corresponding luminance image ,32. Cusa(2)The second strategy is a marginal approach that consists of applying the original LBP operator independently on each of the three components of the color image, without considering the spatial interactions between the levels of two different color components. The texture descriptor is obtained by concatenating the three resulting LBP histograms. This within component strategy was applied by several authors ,36,37,38(3)a priori chosen six histograms are not always the most relevant according to the different considered data sets [The third strategy consists of taking into account the spatial interactions within and between color components. To describe color texture, Opponent Color LBP (OCLBP) was defined . For thiata sets  and it iata sets . It conse images ,31,39,40e images . In the (4)The fourth strategy consists of analyzing the spatial interactions between the colors of the neighboring pixels based on the consideration of an order relation between colors following a vectorial approach. Instead of comparing the color components of pixels, Porebski et al. represent the color of pixels by a vector and compare the color vectors of the neighboring pixels with the color vector of the central one . They usa priori choice of the most discriminant LBP histograms is done. To reduce the dimension of this Among the different extension strategies of LBP to color, MCOLBP and QLBP have the advantage of providing a texture descriptor whose dimension is equal to gray level LBP histogram, and thus a low computation time. However, the classification accuracies obtained with these descriptors on two benchmark texture databases are not as high as those obtained with OCLBP ,48. Thata priori determine the best color space suited to all applications of color texture classification [Color space fusion, which involves fusing the results from several classifiers, each one operating in a different color space ,54,55,56Color space selection, which consists of selecting the most well suited color spaces which are based on some specific quality criteria ,58,59,60Color texture feature selection that evaluates the texture features over different color spaces and selects the set of features that provide the best discrimination between the different textures classed by using a supervised feature selection approach ,62,63,64Many authors have lead studies about the choice of color space for different applications: machine vision, face recognition, texture analysis, etc. ,50,51. Ification . For thiThere exists no study that compares the performance of these color space combination strategies and it could be a great prospect to compare them. In this paper, we propose using the color texture feature selection strategy to compare and combine the approaches of LBP histogram selection and LBP bin selection in a multi color space framework.HSV, HSI, HLS and I-HLS, which belong to the perceptual space family.As explained previously, there exist numerous color spaces. These color spaces take into account different physical, physiologic and psycho-visual properties, and can be classified into four families . NS=9 coThese nine color spaces were chosen since they do not require to know illumination and image acquisition conditions, contrary to CIE curacies ,65.Q is equal to 256 for each histogram  approach analyzes LBP histograms computed from texture images coded into several color spaces . Indeed,MCSHS is an embedded histogram selection approach whose flow chart is represented by i et al. , which ii et al. , which ei et al. , which iL-score) . Only thThe SpASL-score is an extension of the ASL-score proposed by Kalakech et al. . The ASLOnce the score has been computed for each of the ated see . The evaDuring the classification stage, textures of testing images are represented in the so-selected relevant histogram subspace in order to determine their class labels.Like MCSHS, the Multi Color Space Bin Selection (MCSBS) approach analyzes LBP histograms computed from texture images coded into several color spaces. Instead of selecting the most discriminating histograms, MCSBS selects the most discriminating bins of these histograms. A first approach of MCSBS was proposed by Porebski et al. in 2018 : it is aDuring the learning stage, candidate histograms are generated from training images and concatenated to form a vector with  feature . The ranN training images, to compute the sparsity score of each bin  strategy is to filter out the irrelevant bins of the relevant histograms and oppositely, to find the relevant bins out of the irrelevant histograms. The flow chart of this approach is illustrated in Q is the total number of bins of each histogram and where the order of the bins in each It is also an embedded selection method, where the bin ranking strategy is applied after the histogram ranking. During the learning stage, candidate histograms are generated from training images and a histogram ranking is applied by using a histogram selection score. A bin ranking strategy is then applied within each ranked histogram independently (instead of within the concatenated histogram in the MCSBS approach). Here, the SpASL-score is computed to rank the LBP histograms and the sparsity score proposed by Liu is considered to rank LBP bins. We assume that the first bin of each histogram is more relevant than the other bins. Therefore, we propose ranking at first, the group of all the first bins of each histogram in the order of the ranked histograms, then the group of all the second bins are ranked and continuously until the last bin of each histogram. The final bin ranking is a . DuringIn this section, we propose comparing the strategies of sparse LBP histogram selection, sparse LBP bin selection, and the combination of both in the multi color space framework see . These shttp://www.outex.oulu.fi/index.php?page=classification).Outex-TC-00013 is composed of 68 color texture images acquired under controlled conditions by a 3-CCD digital color camera and whose size is 8 pixels . Each ofhttp://www-lisic.univ-littoral.fr/~porebski/USPtex.zip).USPTex set is a more recent database . It conthttp://www-lisic.univ-littoral.fr/~porebski/Stex.zip).The Salzburg texture image database (STex) is a large collection of 476 color texture images, whose acquisition conditions are not defined. Each of the 476 original images is split up into 16 non-overlapping Although Outex-TC-00013, USPTex and STex sets are widely used, these image sets present a major drawback: the partitioning applied to build these three sets consists of extracting training and testing sub-images from a same original texture image. However, such a partitioning, when it is combined with a classifier such as the nearest neighbor classifier, leads to biased classification results . Indeed,http://www-lisic.univ-littoral.fr/~porebski/Parquet.zip). With the experimental protocol proposed by the authors, the classification accuracy reached on this challenging database does not exceed 75.1%.The Parquet database is composed of fourteen varieties of wood for flooring . Each tyhttps://www-lisic.univ-littoral.fr/~porebski/NewBarkTex.zip). The Barktex database includes six tree bark classes, with 68 images per class . Even ifting one : 816 imaSince the considered texture benchmark databases were built in order to apply an holdout evaluation, the classification performance is assessed by following this evaluation scheme. For this purpose, classification results reached by the proposed methods are evaluated by measuring the accuracy as the rate of well-classified testing images during the classification stage.During this stage, the relevant histograms previously selected by one of the proposed approaches presented in As previously mentioned, the texture benchmark databases are composed of only two image subsets (training and testing images), whereas the proposed approaches need three subsets by adding validation images required by embedded selection methods. To evaluate and compare our experimental results with the same conditions of other works, which do not divide the training subset into two parts, we propose using one subset as the training subset and the second both as the validation and testing subset. During the classification stage, the supervised classifier thus uses exactly the same training subset than the one used by the compared works for determining the class labels of the testing images.In the following, the proposed LBP-based feature selection strategies are evaluated, analyzed and compared with the results of the state-of-the-art under the same experimental protocol. Number of classes, size of images, number of images for each class, total number of images and accuracy evaluation method are the same. No changes appear about texture rotation and illumination. Therefore, comparisons exclude some other existing works that apply other protocols to the experimented databases.For each color space, the accuracy a priori choice of the well suited color space is not easy and so, the interest of the multi color space strategy. This table also shows that considering several color spaces significantly improves the accuracy too since an improvement of 7% is obtained compared to a single color space strategy.We can also notice that even if the considered color texture database is fixed, the color space that enables reaching the best accuracy is not always the same and depends on the considered feature selection strategy: We can also notice that even if the dimension of the feature space selected by the bin selection strategy is lower, the Sparse-MCSHS approach (87.3%) significantly outperforms the proposed Sparse-MCSBS approach (83.6%). This confirms the result recently obtained by Porebski et al. with the ICS-based MCSHS approach versus the MCSBS approach using Guo’s selection strategy . FinallyThe rows labelled as gray correspond to experiments that are carried out in this work whereas the other rows correspond to results published by other authors. The first column refers to the related papers and indicates the descriptors which were computed to discriminate the different color texture classes. The color spaces considered to classify the images are presented in the second column of each tables. Finally, the last column shows the obtained rate of well-classified testing images (Accuracy).For the USPTex and STex databases, the best rate is 98.1%. These rates are obtained with our proposed approach of bin and histogram combination and are very encouraging. For Outex-TC-00013, Parquet and NewBarkTex, the results obtained with our approach get into a very satisfying position with the works of the state of the art, since they reach the second position with 95.7%, 83.3%, and 88.4%, respectively. For the Outex database, they are just behind the results obtained by the Multi Color Space Feature Selection proposed in  where maP and R), by adding other color spaces or by considering an ensemble of several descriptors [As the aim of this paper is to reveal the real contribution of the proposed selection strategy as independent as possible from the impact of the classifier and LBP parameters, straightforward methods of texture representation (basic LBP configuration) and classification (1-NN) were preferred as well as texture databases without change of observation conditions. Obviously, the representation of color textures could be improved by using other configurations of LBP parameters , the online classification processing time reached by MCSHBS is close since the dimension of the selected feature sub-space is nearly the same: for instance, with the NewBarkTex dataset, the dimension of the selected feature sub-space is SHBS see . The sliCompared to a bin selection approach (MCSBS), the online classification processing time required by MCSHBS is higher because the dimensionality of the feature sub-space selected with MCSBS is lower: SHBS see . HoweverThese results show the interest of operating a selection thanks to the proposed Sparse-MCSHBS approach.First we extended the multi color space domain with the LBP histogram selection approach using the SpASL-score to define the Sparse Multi Color Space Histogram Selection (Sparse-MCSHS).We then compared this approach with a new multi color space bin selection approach, also based on a sparse representation: the Sparse Multi Color Space Bin Selection (Sparse-MCSBS) approach.A combination of bin and histogram selections (the Sparse-MCSHBS approach) was finally developed and evaluated on several benchmark texture databases.In this paper, different strategies of LBP-based feature selection were developed:The obtained results has shown that the multi-color space strategy contributes to improve the classification performance, and that the proposed Sparse-MCSHBS strategy outperforms the simple MCSBS and MCSHS approaches, while getting into a satisfying position with the works of the state of the art, including CNN-based approaches. Obviously, the classification results are expected to be improved by using more recent and sophisticated methods associated with our strategy, such as the SVM classifier for example.To operate feature selection on high-dimensional and small sample data, we currently work on an approach based on preliminary feature clustering to reduce the number of features candidate for selection. Associated with a filter method, this approach aims both to reduce the learning time and to improve the classification accuracy as well as the selection stability. Our future work will also consist in analyzing and comparing the different strategies that are proposed in the literature to simultaneously exploit the properties of several color spaces.


Vehicle Routing Problems (VRP) comprise many variants obtained by adding to the original problem constraints representing diverse system characteristics. Different variants are widely studied in the literature; however, the impact that these constraints have on the structure of the search space associated with the problem is unknown, and so is their influence on the performance of search algorithms used to solve it. This article explores how assignation constraints (such as a limited vehicle capacity) impact VRP by disturbing the network structure defined by the solution space and the local operators in use. This research focuses on Fitness Landscape Analysis for the multiple Traveling Salesman Problem (m-TSP) and Capacitated VRP (CVRP). We propose a new Fitness Landscape Analysis measure that provides valuable information to characterize the fitness landscape’s structure under specific scenarios and obtain several relationships between the fitness landscape’s structure and the algorithmic performance. The Capacitated Vehicle Routing Problem (CVRP)  solution methods have constantly evolved due to computational advances, ranging from the analysis of exact algorithms to sophisticated metaheuristics. Vidal et al.  providesCompetitive Paradigm —since the number of different object types that meet the condition is precisely 6. Note that, for a specific value of Recall the definition of signs (Equation ). With aIn this context, Malan & Engelbrecht  propose f without neutral steps. So we have only one group of optima, no plateaus, some rugged objects without neutral steps, some smooth objects without neutral steps and no objects of other type. In this case, if we start from For example, consider a trajectory Density-Basins Information Furthermore, it is possible to explore the smoothness of a fitness landscape from the perspective of basins of attraction, using groups of isolated optima, i.e., For instance, consider a trajectory m, the number of groups of optima as k and the total length of the trajectory as n. In In this context, we denote the number of peaks as h value grows, then k decreases (for a fixed m). So the number of groups of optima decreases and the density of peaks increases. Thus, Density-basin information provides an idea about the number of peaks per group of optima.As shown in Expression (14), fitness landscapes with different numbers of local optima could have the same Information Content, depending on the number of groups. Furthermore, Expression (15) only considers the probability of occurrence of smooth objects with neutral steps. In this way, if the In contrast, consider a similar fitness landscape, in which all the peaks are not isolated  estimates the proportion of feasible solutions within the ground solution space (the solution space of the unconstraint base problem). Given a random walk of n solutions generated by a random walk Ratio of Feasible Boundary Crossings (RFBC) estimates the dispersion of feasible areas, quantifying the transitions between feasible and infeasible zones observed in a random walk. The principal hypothesis is that if infeasible areas separate many feasible ones, then there will be many transitions in a random walk. Given a sequence of The literature associated with FLA in VRP is relatively scarce compared to that of TSP  considers that skewness :(19)SIC=Our research explores two main aspects of FLA in VRP: (1) to analyze the impact of the solution representation, local operator, and additional constraints on the properties of the underlying fitness landscapes and (2) to explain the difficulty for local search algorithms in terms of the fitness landscape’s properties. This section describes the main methodological aspects of the investigation.Q is the vehicle capacity. This setting implies a very high fleet capacity utilization.Whereas real-life vehicle routing problems very often deal with instances where the total capacity of the fleet is much larger than the total demand, the instances studied in the literature associated with CVRP usually fix the number of vehicles close to base instances taken from the literature , ,37, baseFor example, consider an instance of CVRP with an original capacity In the context of FLA, any change  in a fitness landscape’s structure affects an algorithm’s ability to search that space.k individual routes. Notice that, like in VRP formulations based on three indexes, the routes/vehicles are distinguishable: the sub-permutation of customers between the first two occurrences of the depot correspond to the first vehicle, the customers between the second and the third occurrence correspond to the second vehicle, etc. In addition, notice a natural bijection between the solution space obtained using the Delimiters representation for m-TSP and the set of feasible solutions of our m-TSP MILP formulation. The same holds for the subspace of feasible solutions for CVRP and the feasible solutions of our CVRP MILP formulation.Search algorithms for VRP can use many alternative solution encodings. A Delimiters representation corresponds directly to a set of k individual routes to get a solution for VRP with k vehicles. The transformation is performed with a Split algorithm [k sub-permutations, each consisting of customers that are consecutive on the Giant-Tour and maintaining the relative order of visits. If there are some ways of splitting the Giant-Tour into feasible routes , the Split algorithm chooses the one with the lowest cost. Otherwise, the Giant-Tour is infeasible. Notice that working with this representation implies that some of the feasible solutions for the MILP formulation of the corresponding VRP are not represented in the solution space. It can be easily seen by comparison with the Delimiters representation: given a fixed permutation of all customers, many different Delimiters representations are obtained by placing the In contrast, Giant-Tour representation corresponds to a TSP solution that needs to be partitioned into lgorithm : the perThe representations support application of the original operators developed for TSP (Giant-Tour) and their modifications for VRP (Delimiters). Furthermore, these types of representations are widely used in evolutionary and population-based methods.Our analyses are based on sequences of solutions obtained from random walks in fitness landscapes. To create a sequence of solutions of the required length, we start with an empty sequence. First, we obtain the first solution by a random choice in the entire solution space and add it to the sequence. Then, we iteratively take the last solution from the sequence, choose one of its neighbors at random and add it to the sequence until the required length is achieved.The procedure for obtaining the neighbor solution is quite different in the two representations. For Delimiters, given the current VRP solution, the local operator is applied at random to obtain a new VRP solution. If the new solution is feasible, then it is kept. Otherwise, we choose another solution at random. The choice is repeated until we find a feasible solution. We use this procedure for statistical and information analysis. In contrast, for the feasibility analysis, we accept all solutions. With the Giant-Tour representation, we apply the local operator in the TSP space, and the Split algorithm looks for the best feasible VRP solution using the new Giant-Tour.Complete Dataset of Original FLA Measures, we consider each experimental instance of CVRP  with all combinations of representation and local operator, obtaining the total of 3870 fitness landscapes. For each fitness landscape, the corresponding FLA measures are extracted. We base statistical and information analysis on 30 random walks of 20,000 iterations. In contrast, the feasibility analysis considers a single large random walk of 100,000 iterations. Furthermore, we calculate SIC using a single random walk of 100,000 iterations.To create the One of the main objectives in FLA is to find patterns or properties of a problem or instance that can be exploited to improve the performance of algorithms. In order to evaluate the performance of a particular algorithm, we need to have a close to optimum solution for each instance. To get them, we use an extension of Lin-Kernighan-Helsgaun  for m-TSSince the objective here is not to design a state of the art algorithm, but instead to explore the properties of fitness landscapes that influence the performance of local search based methods, we use one of the most straightforward and widely used algorithms for VRP: to evaluate the difficulty of instances for local search-based algorithms, we study the behavior of Simulated Annealing (SA).SA converges towards an optimal solution, but it reaches this optimum only after an infinite number of search iterations. The approximation of the asymptotic behavior requires many iterations whose order of magnitude is equal to the state space’s cardinality. Nevertheless, the literature reports that good results can be obtained with a much smaller number of iterations.Our simulations generate the initial solution from a permutation of the customers chosen uniformly at random (TSP solution space), by applying the Split algorithm. We propose an SA parametrization that uses an initial temperature te t=0.9 . For eacTwo objectives are essential for this proposal: (1) to explore differences between the fitness landscapes generated by alternative representations, local operators and levels of fleet utilization and (2) to find properties (or patterns of characteristics) of fitness landscapes that give clues about instances hard to solve with SA. In addition, we believe that lessons learned in our particular setting will help to design methodologies for other contexts.Towards the first objective, we develop classification models based on Multinomial Logistic Regression (MLR)  Relocate Dataset, Delimiters  Exchange Dataset and Delimiters  2-Opt Dataset.Task #2: Predict the performance of SA: Consider a partition based on local operators. Both, the Delimiters and Giant-Tour Datasets are subdivided into the To discover the local operator and predict the performance of simulated annealing, we decided to use grouped datasets based on the original variables to directly assess the importance of each metric proposed in the literature. A MLR model is built based on backward elimination as a feature selection technique.In order to use classification models, we propose a discretization of the performance of SA in each grouped dataset. Consequently, we split the set of experimental instances into two equal-sized classes, based on the average Gap obtained by SA: Low (≤median) and High (>median). These categories let us capture performance behavior and explore underlying patterns associated with statistics, information and feasibility FLA measures.sample datasets that contain only one occupancy rate for each base instance. In this way, we apply MLR with each sample and based on all the samples we obtain confidence intervals for the performance evaluation metrics in the classification problem.As shown in principal components while retaining as much of the variation present in the datasets as possible. In this context, we select the principal components that capture at least 70% of the variability present in the data. Then, the projections onto the principal components are used as a transformed version of the grouped datasets. This procedure was performed previous to the MLR, to explore the relationships between the variables. However, these new components are not used in the classification models.Additionally, we perform a Principal Component Analysis (PCA) independently as a dimensionality reduction technique for each grouped dataset. For each dataset, PCA transforms the set of correlated variables into a smaller number of uncorrelated variables called To present the results (The reader is referred to explore the complete dataset ), we useWe do these analyses to know if different constraint levels lead to significantly different values of FLA measures. If the Fisher-test is significant (✓), it implies that at least two groups among those compared are significantly different  the potential of using standard FLA measures and the one that we propose to differentiate between fitness landscapes obtained by using different representations and local operators and (2) the possibility to use information, statistical and feasibility FLA measures to predict the difficulty of searching the fitness landscape of a VRP instance with Simulated Annealing.Some authors ,44 claimFor Delimiters Dataset, we obtain the following three principal components:Delimiters Principal Component 1 (DPC-1) is strongly correlated with IC, PIC and SIC. A positive value in PC-1 indicates a high IC, a high SIC and a low PIC. A high value of IC, which implies a high frequency or diversity of rugged objects, combined with a low value of PIC, which indicates in the context of high IC a high number of neutral steps, indicates a relatively large amount of rugged objects with neutral steps. On the other hand, the positive relationship with SIC indicates that as Delimiters Principal Component 2 (DPC-2) is positively related to CL and DBI, and negatively to MIC. A high value in DPC-2 indicates that, comparatively, an instance achieves a lower MIC value. Furthermore, the positive relationship with DBI confirms a more significant presence and diversity of non-rugged objects. In this context, the relationship with CL indicates that smoother Landscapes will have a higher value in DPC-2.Delimiters Principal Component 3 (DPC-3) is positively related to RFBC and negatively correlated to FSR. This indicates that instances with a high value in DPC-3 have many transitions between feasible and infeasible solutions and a low proportion of feasible solutions.In contrast, for Giant-Tour Dataset, we obtain the following four principal components:Giant-Tour Principal Component 1 (GPC-1) has the same interpretation as the DPC-2 component in the Delimiters case. However, the percentage of explained variance of the data is much higher (32.8%). The result indicates that unlike Delimiters, mainly explained by the behavior of rugged objects, the variability of Giant-Tour Dataset is focused on smooth objects. For Giant-Tour, objects without neutral steps are even more likely than for Delimiters  has a similar interpretation to the DPC-1 component of Delimiters representation. Nevertheless, it does not include SIC.Giant-Tour Principal Component 3 (GPC-3) is positively related to SIC and negatively to RFBC. In this way, as mentioned in the description of GPC-2, the information provided when studying the Information Content variations when epsilon increases is independent from IC and PIC  is related to FSR and RFBC, as is DPC-3 in the Delimiters case.The projection in This analysis is performed separately on 6 grouped datasets: Delimiters  Relocate Dataset, Delimiters  Exchange Dataset and Delimiters  2-Opt Dataset.As mentioned in We perform PCA on the grouped data sets proposed for the task #2.To classify the operators, we use MLR. In this task, this model is used to discover the local operator that was used to obtain a given fitness landscape, based on FLA measures.Using Delimiters and Giant-Tour Datasets and applying the re-sampling technique, we split each sample dataset . The results establish that for the representation of Delimiters, MLR can differentiate between the local operators, based on the FLA measures, with high accuracy. However, the quality of models decreases considerably when the Giant-Tour representation is analyzed, indicating that the FLA measures we consider might not be sufficient for the study of representations and operators that yield more complex neighborhood structures.We explore different encodings (Delimiters and Giant-Tour) and local operators  and verify that they lead to different characteristics in the fitness landscape. We find that FLA measures based on statistics, information and feasibility analyses can identify some patterns in the fitness landscape generated by each configuration (representation-operator) and relate them to the difficulty of obtaining good solutions with a local-search-based algorithm .Finally, an essential contribution of this paper is the unification of different techniques of Fitness Landscape Analysis in the context of Vehicle Routing Problems and the proposal of the SIC measure as a new way to describe the Information Content curve. Our results add new information about fitness landscapes’ structure that can be useful for developing new methods and understanding their behavior.