The integration of Internet of Things (IoT) technologies into smart agriculture has significantly enhanced automation, monitoring, and productivity. However, these systems introduce critical cybersecurity vulnerabilities and generate heterogeneous data, including structured network traffic and image-based inputs. This requires an intrusion detection system (IDS) capable of handling multimodal data, particularly since adversarial attacks can manipulate inputs to evade traditional detection models. To address these challenges, this paper proposes a novel image-based IDS for smart agriculture environments. The system transforms network traffic into images and employs VGG16 for feature extraction, Binary Greylag Goose Optimization for feature selection, and a random forest for classification. It further integrates a dual defense strategy that combines a Convolutional Autoencoder Denoising (CAED) module with Adversarial Training (AT) to improve robustness against adversarial perturbations. The proposed solution is evaluated on the CICIoT2023 dataset in eight traffic classes under three white-box adversarial attacks. The IDS demonstrates strong resilience across all perturbation levels. For weak perturbations (ε=0.01), the dual defense achieves accuracies of at least 99.46%. Under moderate perturbations (ε=0.1), it maintains high performance with macro-averaged accuracies of at least 99.39%. Even under strong perturbations (ε=0.3), the system remains robust, attaining an accuracy of at least 96.50%. To assess generalization to real agricultural settings, the IDS is also tested using native crop images from the agricultural dataset. Under severe adversarial distortion (ε=0.3), the system maintains robustness, achieving a macro-averaged accuracy of at least 96.71%. These results confirm that the proposed multimodal IDS provides a resilient, adaptive security solution for smart agriculture networks facing advanced adversarial threats.

One of the most challenging tasks in a disaster scenario is the detection and localization of victims with high accuracy and minimum delay, especially in out-of-coverage areas. In the event of a disaster that disrupts the cellular network infrastructure, emergency calls can be relayed to the core network via multi-hop D2D communications. In this paper, a localization system is proposed that uses radio measurements obtained through such D2D multi-hop assisted emergency calls to localize in-coverage and out-of-coverage devices. To address the uncertainty and gradual reception of data in real-time in this scenario, a dynamic constraint satisfaction-based Multi Victim Localization Algorithm(MVLA)is proposed. This algorithm locates multi-hop devices in a progressive propagation manner to provide fast and accurate updates on victim locations. Additionally, three modes of$\mathit{MVLA}$, namely$\mathit{MVLA}_{recent}$,$\mathit{MVLA}_{seq}$, and$\mathit{MVLA}_{all}$are proposed. Simulation results demonstrate that$\mathit{MVLA}_{all}$has a lower localization error compared to$\mathit{MVLA}_{recent}$and$\mathit{MVLA}_{seq}$. Moreover,$\mathit{MVLA}_{all}$, is compared with an existing particle filtering-based localization algorithm called RSSI Monte-Carlo Boxed Localization (RSSI-MCL) under an increasing number of emergency user devices and functional gNodeBs. Results show that$\mathit{MVLA}_{all}$significantly outperforms theRSSI-MCLmethod in terms of localization accuracy and computational delay.

The Internet of Things (IoT) represents a pervasive system that continuously demonstrates an expanded application in various domains. The energy-efficiency problem has always been a crucial issue linked to this type of network where the system lifetime strongly depends on devices’ batteries. Numerous energy-efficient networking protocols have been proposed in the literature to increase the system lifetime. However, most of the proposed approaches deal with the short-term vision of energy consumption and omit to consider the rechargeable battery degradation when evaluating the network lifetime. Indeed, the major parts of the network devices use rechargeable batteries that age and degrade over time due to several factors (temperature, voltage, charging/discharging cycle, etc.). Therefore, it is essential to promptly detect these internal and environmental degradation factors to avoid network failures. Clustering represents one of the main wireless network protocols and plays an essential role in network self organizing. In this work, we propose a novel long-term energy optimization clustering approach based on battery State of Health (SoH) prediction, called LECA_SOH. The objective is to predict the impact of cluster heads election on the rechargeable batteries SoH before applying the clustering. LECA_SOH fosters the selection of the nodes, which will less suffer from battery degradation during the future rounds, leading to extend the system lifetime. The obtained results demonstrate that the proposed clustering approach improves the network lifetime in the long term and extends the number of recharging cycles compared to the conventional energy-efficient approaches.

Modular Robots Self Reconfiguration (MRSR) is one of the most challenging problems in nowadays robotics field. This problem consists in the determination of how a set of identical modular robots, with local knowledge of the system and limited energy and computational capacities, can reorganize themselves into a target topology or shape. MRSR has received great attention from the research community. Therefore, a lot of centralized and decentralized algorithms were designed to answer this problem. Unfortunately, the analysis of why and when an algorithm is better than another is less studied. In this paper, we proposed a hybrid centralized/distributed modular robots reconfiguration approach. In this approach, a convolution neural network system is used to estimate the most adapted distributed reconfiguration algorithm according to the initial shape formed by the modular robots and the target shape. Two distributed algorithms are studied: C2SR and TBSR. The designed CNN model allows determining which option is the best for a given reconfiguration problem: use of the C2SR algorithm, use of the TBSR algorithm, or both algorithms are equivalent. The obtained results show that the ML tool succeeds 97.25% of the time to determine the suitable algorithm based on the initial and the final shapes. In addition, the system can be extended to any number of algorithms. Our contribution is the production of a neural network built for the selection of the best modular robots self-reconfiguration algorithm.

Lifetime improvement is a major concern for energy constrained wireless networks. Clustering the network topology is widely utilized for managing and enhancing the system duration. With conventional clustering mechanism Cluster Heads (CHs) close to the Base Station (BS) utilize higher power resource for relaying data packets of the other network CHs. This scenario obstruct the network performance as nodes close to the BS attend an earlier death than their desired durability due to the overloaded routing task. This scenario unbalanced energy consumption and is designated as the hot spot problem. The interest in this work is to carry the intra clustering topology in a vast scale contexts to support the network rising and fairly power balance the energy consuming. In this context, we present an Improved Lifetime Optimization Clustering (ILCK) approach that uses the Kruskal minimal spanning tree heuristic (MST) and consider the state of health (SOH) of devices batteries for the network life maximization. ILCK appeal the Kruskal algorithm in a distributed trend to achieve a minimal MST tree inside wide cluster to consolidate the intra cluster routing topology and mitigate the energy allocated to wireless communications. To the best of our awareness, this is a primary solution that merge the Kruskal approach within an uneven clustering to prolong the objects battery endurance and ease the energy hot spot routing issues. The complexity proof of the proposed approach is provided and simulation results denote that ILCK can adequately scale down the power consumption and lengthen the execution time of the deployed network.

Energy optimization is an imminent worldwide issues for green computing, it constitutes a major concern and a critical aspect especially for energy constrained wireless networks. To overcome this issue, clustering techniques were introduced as a prominent method that arranges the system operation in correlated manner to attend the energy preservation and prolong the network lifespan. However, existing clustering works only focus on preserving the battery charge to operate until it drains out. This approach is most appropriate for non-rechargeable batteries. However, rechargeable batteries become commonly used and need to be considered. The full discharge of rechargeable battery does not mean the device obsolescence. Therefore, the system lifetime optimization should take into consideration the degradation of the rechargeable batteries performances. In this context, we proposed an improved long-term energy efficient unequal clustering approach based on the battery state of health for IoT networks (ILEC_SOH). This work represents an initial step in the integration of the battery health degradation into the unequal network clustering. The obtained results show that the consideration of battery state of health (SOH) significantly improve the network lifespan in the long term compared to the conventional energy efficient approaches.

Modular robot network architectures are experiencing growing popularity. The problem of automatically reconfiguring a set of modular robots into a given target shape presents a real challenge to distributed computing.Many works on the subject restrict the nature of the constructed target forms. The bolder approaches focus on reducing the number of overall required movements. In this work, we propose a distributed asynchronous self-reconfiguration algorithm allowing to distribute the effort made by each robot to reach the final shape. This makes it possible to extend the life of the network of micro-robots.We compare our TBSR algorithm with C2SR self-reconfiguration algorithm using VisibleSim simulator. The obtained results show that globally TBSR outperforms C2SR except for rare cases. The TBSR algorithm allows reducing the number of required moves up to 17%. Besides, the ability of TBSR to balance the number of moves over the modular robots makes that the maximum number of moves per robot is reduced up to 40%.

Global pandemics such as Covid-19 have led to massive loss of human lives and strict lockdown measures worldwide. To return to a certain level of normalcy, community awareness on avoiding high population density areas is significantly important for infection prevention and control. With the availability of new telecommunication technologies, it is possible to provide highly informative population clustering data back to people using wireless aerial agents (WAAs) placed in a local area. Hence, a service architecture that allows users to access the localization of population clusters is proposed. Further, a convex hull-based clustering method, enhanced population clustering (E-PC), is proposed. This method refined the result of conventional clustering methods such as K-means and Gaussian mixture model (GMM). Moreover, the potential in E-PC to achieve the same or higher results compared to the original K-means and GMM, while consuming lesser data points, is demonstrated. On average, E-PC improved the cluster detection performance in both K-means and GMM by 18.93% under different environments such as remote, rural, suburban, and urban in terms of silhouette score. Further, E-PC allows a 15% data reduction which results in decreasing the computational cost and energy consumption of the WAAs.

Energy optimization is one of the major issues in the telecommunication field and particularly in wireless networks. This optimization is an essential condition for the ubiquity of wireless and mobile networks. Recent studies show that the Information and Communications Technology sector (ICT) (production, distribution, and use) amounts to 1.4% of overall global CO2e emissions.

Wireless mobile networks are widely used during large catastrophes such as earthquakes and floods where robust networking systems are indispensable to protect human lives. The objective of this paper is to present a self-adaptive emergency call protocol that allows keeping potential victims connected to the core network through the available functional stations, called gNBs in 5G, when a fraction of gNBs in a network area are fully destructed with no access to other gNBs or the core network due to the disaster. Nowadays, the density of mobile devices and progress in outband device to device (D2D) communication provide the framework for the extension of both mobile and network coverage. We propose a novel, 3GPP compatible and completely distributed protocol called M-HELP for emergency call service for 4G/5G enabled mobile networks. We assess M-HELP efficiency under various scenarios representing different degrees of network destruction and different emergency call conditions. The tests demonstrate the significant performance of M-HELP in terms of transmission success rate, energy management, latency and control traffic load.

Nanotechnology becomes reality paving the way for many new applications. In nanonetwork system, each nanosized device is equipped with limited capabilities and is dedicated to a basic task but the combination of the numerous devices actions results in high-level functions. In this context, large number of devices concentrated in a limited area must exchange data using wireless links. Spread in Time On-Off Keying (TS-OOK) protocol was proposed as a technique to share the radio channel over the different terahertz nano-devices. TS-OOK is based on a Femtosecond-Long pulse modulation where communication data are sent using a sequence of pulses interleaved by a constant duration randomly selected. In this paper we provide a critical analysis of the TS-OOK approach. We prove that the TS-OOK is not adaptive against the traffic load variation and induces an imbalance between the active nodes. This inequity is due to the dependency of a communication quality on the randomly chosen symbol rate. We propose a dynamic TS-OOK modulation approach, called SRH-TSOOK (Symbol rate Hopping TSOOK), where the duration between two consecutive pulses of the same transmission follows a pseudo-random sequence. We show that this approach performs better than the standard protocol when the number of active nodes increases and guarantees a better distribution of the channel capacity over the active communications. For instance, while the throughput of a communication within a TS-OOK protocol may falls bellow 105frames/s with 300 active nodes, the throughput in the SRH-TSOOK protocol stabilizes around 207frames/s for all the active communications. The comparison is made on the basis of probabilistic analysis allowing a numerical and accurate evaluation of the protocols performance.

Programmable matter i.e. matter that can change its physical properties, more likely its shape according to an internal or an external action is a good example of a cybermatics component. As it links a cyberized shape to real matter, it is a straight example of cyber-physical conjugation. But, this interaction between virtual and real worlds needs two elements. The first one is to find a way to represent the cyberized object using programmable matter and the second is to be able to adapt the matter to the cyberized changes. This article presents the progresses made in these two topics within the Claytronics project.

The programmable material is one of the most challenging problems in micro-robot networking. In addition to the problems that arise by the miniaturization of millimeter-scale mobile devices, the conception of the distributed asynchronous algorithms allowing the coordination of large number of robots remains a very complex task. Micro-robot network represents one of the implementations of the Internet of things, where a set of micro-robots react to an order submitted on a wireless downlink channel specifying a global goal. This goal corresponds to a target shape in the case of shape-shifting problem. Programmable materials have many applications in the field of paintable displays, prototyping, locomotion, etc. We propose in this paper an original flexible distributed algorithm allowing to reorganize a modular micro-robot network into a desired target shape (physical topology). The efficiency of such an algorithm is assessed on the basis of the memory requirements, the communication load, and the number of performed movements to reach the final shape. The proposed algorithm shows a great flexibility concerning the range of target shapes that can be achieved, in part because there is no need for an explicit description of the final shape. To assess the computational performances of the presented algorithm, we proposed a linear programming model of the shape-shifting problem that provides a lower bound of optimized criteria. The comparison of our results with those given by the relaxed linear programming proves the efficiency of our approach.

In this paper we propose a distributed and robust parallel redeployment algorithm for MEMS micro robots. MEMS micro robots are low-power and low-memory capacity devices that can sense and act. To deal with the MEMS micro robots characteristics, in this paper, we present an efficient redeployment algorithm without predefined positions of the target shape, which reduces the memory usage to a constant complexity. This algorithm optimizes the energy consumption by minimizing the amount of displacement and the number of messages. This solution improves the memory usage (number of states), the execution time and the number of movements by using movement of different micro robots at the same time. In addition, we show how to predict the number of movement for each node to make the algorithm robust.

Novel platforms of modular robot systems have been developed with important applications in safety, transportation and sensing domains. In such systems, modular robots are able to change their organization in order to obtain different shapes. The conception of distributed programs allowing the "optimal" reorganization of a set of robots into a specific shape appears as a very challenging problem. In this paper we present an original distributed meta-algorithm for micro-robots shape-shifting problem. We show that this meta-algorithm, described as a general functioning schema, presents a good framework to easily conceive distributed algorithms for shape-shifting problems. We also prove the facility to instantiate the algorithm for special target shapes and we give an adaptation of the algorithm to reach any horizontally convex form. The presented meta-algorithm presents two main advantages: first, there is no need to exact positioning of the robots and secondly, the memory storage and communication requirements are significantly reduced.

In this paper we propose a distributed and efficient parallel self-reconfiguration algorithm for MEMS microrobots. MEMS microrobots perform various missions and tasks in a wide range of applications including odor localization, firefighting, medical service, surveillance and security, and search and rescue. To achieve these tasks the self-reconfiguration for MEMS microrobots is required. The self-reconfiguration with shared map does not scale. Because with the map (predefined positions of the target shape) each node should store all predefined positions of the target shape, therefore this is not always possible as MEMS nodes have a low-memory capacity. In this paper, we present an efficient self-reconfiguration algorithm without predefined positions of the target shape, which reduces the memory usage to a constant complexity. This algorithm improves the energy consumption by minimizing the amount of displacement and the number of messages.

Multimedia Broadcast Multicast Service system supports efficient diffusion of multicast multimedia services in cellular networks. Our previous work shows that the radio resource management problem for MBMS can be modeled as a combinatorial optimization problem which tries to find optimal assignment of power and channel codes [1]. In this paper, we propose to solve such problem by using metaheuristic algorithm: Tabu Search (TS). In our work, we modify the general TS algorithm and map it onto our model. We also extend the classic TS procedure by proposing a tabu repair mechanism, which helps to explore new candidate solutions. The proposed algorithm is compared with two other metaheuristics: Greedy Local Search (GLS) and Simulated Annealing (SA). Simulations show that, within acceptable amount of time, TS can find better solution than GLS and SA.

Multimedia Broadcast Multicast Service (MBMS) system supports efficient diffusion of multicast multimedia services in cellular networks. Our previous work shows that the radio resource management (RRM) problem for MBMS can be modeled as an optimization problem which tries to find optimum assignment solution of power and channel codes in a given search space [1]. In this paper, based on the proposed model, we design a resource assignment approach by using the tabu search (TS) algorithm. Based on the model characteristics, we define three tabu memory structures and evaluate their search performance. We also extend the classic TS by proposing a tabu repair mechanism, which helps to avoid local optimum and improve the search efficiency. Simulation results show that the proposed TS algorithm outperforms the existing algorithms.

Over the last decades, research on microelectromechanical systems (MEMS) has focused on the engineering process which has led to major advances. Future challenges will consist in adding embedded intelligence to MEMS systems to obtain distributed intelligent MEMS. One intrinsic characteristic of MEMS is their ability to be mass-produced. This, however, poses scalability problems because a significant number of MEMS can be placed in a small volume. Managing this scalability requires paradigm-shifts both in hardware and software parts. Furthermore, the need for actuated synchronization, programming, communication and mobility management raises new challenges in both control and programming. Finally, MEMS are prone to faulty behaviors as they are mechanical systems and they are issued from a batch fabrication process. A new programming paradigm which can meet these challenges is therefore needed. In this article, we present CO2Dim, which stands for Coordination and Computation in Distributed Intelligent MEMS. CO2DIM is a common project between France and Hong-Kong building a new programming environment which includes a language, based on a joint development of programming and control capabilities, a simulator and real hardware.

MEMS micro robots are low-power and low memory capacity devices that can sense and act. One of the most challenges in MEMS micro robot applications is the self-reconfiguration, especially when the efficiency and the scalability of the algorithm are required. In the literature, if we want a self-reconfiguration of micro robots to a target shape consisting of P positions, each micro robot should have a memory capacity of P positions. Therefore, if P equals to millions, each node should have a memory capacity of millions of positions. Therefore, this is not scalable. In this paper, nodes do not record any position, we present a self-reconfiguration method where a set of micro robots are unaware of their current position and do not have the map of the target shape. In other words, nodes do not store the positions that build the target shape. Consequently, memory usage for each node is reduced to O(1). An algorithm of self-reconfiguration to optimize the communication is deeply studied showing how to manage the dynamicity (wake up and sleep of micro robots) of the network to save energy. Our algorithm is implemented in Meld, a declarative language, and executed in a real environment simulator called DPRSim.

In this paper we present a mathematical modeling of Radio Resource Management (RRM) for multicast service diffusion based on Multimedia Broadcast Multicast Service (MBMS) standard. In this model, a flexible allocation approach named F2R2M is proposed, combining three candidate transport channels with scalable video transmission technology. The allocation procedure is implemented based on simulated annealing algorithm with a two- dimensional optimization objective and lexicographic order evaluation criteria. Experiments prove that, comparing with existing channel allocation approaches, F2R2M obtains allocation solution with equal QoS and lower transmission power consumption. Moreover, it reduces the possibility of achieving saturation of power or channelization codes when simulation scenarios have more users and heavy traffic load.

Frequency assignment is one of the main issues in radio networks planning. The multiple interferences are seldom taken into account in literature. There is not a framework with their modeling. A hypergraph modeling of the network gives a more realistic representation of this phenomenon. We generalize theT-coloring problem for graphs to hypergraphs. We apply this new modeling to IEEE 802.11b/g wireless networks and study its interest.

In this article we address the frequency management during WLAN planning. Frequency management refers to channels interference and SINR computation. We propose a new approach where location selection and frequency assignment are tackled together during WLAN planning process. Two steps characterize this approach. Firstly we use all the available channels for frequency assignment. Secondly multiple signals are taken into account to compute the SINR. Several experimental results show the benefits of this new approach.

Standard tabu search methods are based on the complete exploration of current solution neighborhood. However, for some problems with very large neighborhood or time-consuming evaluation, the total exploration of the neighborhood is impractical. In this paper, we present an adaptive exploration of neighborhood using extension and restriction mechanisms represented by a loop detection mechanism and a tabu list structure. This approach is applied to the K-coloring problem and evaluated on standard benchmarks like DIMACS in comparison with more powerful recently published algorithms

Networks based on cellular concept suffer of bad performance when calls are done during handoff, i.e. during the transfers from one cell to another one. A part of these bad performances are due to the lack of management of overlapping area between cells. These imperfections find expression in cells discontinuity and distortion, in irregularity on inter-antenna distances, and in cells size variation. In this paper, we present several new criteria for cellular networks optimization based on geometrical computation on handoff and interference zones. The relevance of the model is studied on the basis of numerical and visual appreciation accorded to different networks. The huge impact of cell geometry improvement on radio quality of the network will be also discussed.