Energy constraints and spectrum scarcity are two main challenges hindering the widespread adoption of wireless body area networks (WBANs) for the Medical Internet of Things (MIoT). This paper tackles these issues by proposing a novel cooperative cognitive radio (CR) intra-WBAN system that effectively integrates ambient backscatter communication (AmBC) and radio-frequency energy harvesting (RFEH). In the proposed design, body sensors (BSNs) are categorized into primary BSNs and cognitive BSNs based on their data latency requirements. A hybrid BSNs coexistence/cooperative energy and information transmit protocol is established, which enables cognitive BSNs to: 1) Opportunistically backscatter their data onto the primary source signal, thereby improving spectral efficiency, 2) Harvest RF energy from the primary signal for sustainable operation, and 3) Act as amplify-and-forward relays for the primary network in exchange for dedicated spectrum access. We derive analytical expressions for the outage probabilities of both primary and cognitive networks, and analyze the impact of key parameters, including the backscattering coefficient, spectrum-sharing power allocation factor, and time segmentation factor, on the outage probability of the network. Simulation results validate our analysis and demonstrate that the proposed scheme achieves a remarkable improvement in both network throughput and energy efficiency compared to existing work and other benchmark schemes, showcasing its significant potential for sustainable MIoT.

Dynamic spectrum access (DSA) is one of the pivotal technologies that is widely recognized to be able to cope with the massive demand for limited spectrum resources by massive data in 5G/B5G networks. To address spectrum fragmentation and sharing in 5G/B5G cognitive radio ad hoc networks (CRAHNs), based on the DSA technique, this paper proposes an optimal matched channel selection strategy with finite buffer (OMCS-FB). In the OMCS-FB, a cognitive user (CU) with the transmission request selects the channel whose idle time optimally matches its transmission time rather than selecting the channel with the longest idle time; if the CU fails to access the channel, the CU enters the buffer and waits for the next transmission opportunity. A (K+1)-layer continuous-time Markov chain (CTMC) with the number of primary users (PUs) and CUs in primary channels and the number of CUs in the buffer as 3-D metrics is established, which can effectively portray the activity behavior of users and the occupancy states of primary channels under the OMCS-FB. The CTMC rate steady-state equations are then solved using the successive over-relaxation (SOR) iterative algorithm to obtain the system steady-state probability distributions and performance metrics. The results show that the OMCS-FB effectively suppresses spectrum fragmentation of the MAC layer in the time dimension and enables efficient spectrum sharing among CUs and PUs, as verified by Monte Carlo simulation.

This paper presents a decentralized and automated framework for microgrid management using blockchain and smart contract technologies. More precisely, a decentralized energy trading protocol using smart contracts to automate transaction processing with energy tokens is proposed to monitor and complete energy delivery between producers and consumers. We defined an energy token, WattCoin, for payments and a dynamic price negotiation algorithm between producers and consumers in microgrid (PN-MG), which, via multiple rounds of price negotiations, should converge to a transaction price that optimizes their profits and energy savings. Conducted simulations show that the energy transactions between producers and consumers completed under our framework increased producers’ profits by 5.26% and decreased consumers’ costs by 6.73% compared to fixed-price transactions.

The deployment of new technical solutions involves significant costs, necessitating a thorough evaluation of potential benefits to determine their relevance. In telecommunications, for example, transitioning between cellular technology genera-tions-such as from 3 G to $4 \mathrm{G}, 4 \mathrm{G}$ to 5 G, or beyond-entails substantial infrastructure investments. Currently, decision-making relies on historical data and limited simulation capabilities, resulting in uncertain assessments of service quality improvements and deployment costs. This challenge is mirrored across various fields, including healthcare, where evaluating the benefits of treatments prior to administration remains complex. This paper highlights the critical need for methodologies that enable precise, a priori estimation of performance gains associated with technological upgrades. Developing such approaches would enhance decisionmaking processes, optimize resource allocation, and facilitate more informed deployment strategies across diverse sectors.

A new spectrum sharing protocol with simultaneous wireless information and power transfer (SWIPT) is proposed to cope with the increasingly prominent problem of spectrum and energy scarcity. It operates within a cognitive radio network (CRN) in the context of cellular IoV (C‐IoV), enabling bidirectional communication between two vehicles parked within the base station coverage (VnBSs) while facilitating cooperation for a pair of primary users (PUs), i.e., VnBSs can act as relays to provide cooperation communication for the cell–edge vehicle user (eVU). Unlike most existing work, both VnBSs can use time switching (TS) to obtain energy from radio frequency (RF) signals emitted from the base station. In order to enhance the reliability of the network, this study incorporates the automatic repeat request (ARQ) technique in the CRN supported by the nonorthogonal multiple access (NOMA) and SWIPT, which has not been performed in other works. Based on this, the transmission is divided into one energy harvesting (EH) phase and three information processing (IP) phases. A new packet for PUs is transmitted in the first IP phase and is allowed to be retransmitted twice in the last two IP phases depending on the decoding. VnBSs act as relays to obtain energy in the EH phase to assist in retransmitting the PU’s packets and sending their own packets in the last two IP phases. The system states are analyzed by building a one‐dimensional Markov chain, and the end‐to‐end outage probability (OP) is calculated for each state under the Nakagami‐m fading channel. Using these two results, the OP of the primary and secondary networks, system throughput and energy efficiency (EE) are derived. Finally, the validity of the derived results is verified by Monte Carlo simulation using MATLAB and compared with the protocol without ARQ, and the protocol proposed shows a better performance.

Federated Learning (FL) is a powerful Machine Learning (ML) technique that allows multiple clients to collaborate on training models while keeping their data private. Unlike traditional centralized methods, FL ensures that data are kept separate, which helps to protect privacy. However, an important area that needs more research while using the FL system is detecting harmful models within the Internet of Things (IoT) context. For example, poisoning attacks, where compromised clients introduce harmful data, can degrade the model’s overall performance or lead to incorrect predictions. This paper comprehensively reviews of recent attacks in FL within IoT networks, along with defense mechanisms and common FL frameworks. It begins by highlighting the significance of FL in IoT networks, exploring its applications, benefits, and inherent security challenges. It then explores specific attacks targeting FL in IoT networks. The defensive strategies are evaluated, including their performance metrics, datasets used, and related work, providing a comparative analysis of these techniques. Common FL frameworks and their criteria are reviewed. Our goal is to offer a detailed understanding and solutions to enhance the strength and resilience of FL systems in IoT networks.

Load forecasting is considered to be the core of an efficient predictive energy management for buildings. In this context, the deployment of smart meters and sensors enabled continuous energy usage monitoring in modern buildings. This streaming data led to the development of a new Online Home Energy Management System (OHEMS). The aim of this study is to develop an advanced smart metering infrastructure for online power forecasting, using embedded hardware with low computing power and real time constraints. As a benchmark, we applied both online and offline load forecasting modes to assess three prediction approaches in terms of accuracy and computational time. A single Machine learning algorithm using Long Short-Term Memory (LSTM) and hybrid Machine learning algorithms (CNN-LSTM), and ensembles machine learning approaches including eXtreme Gradient Boosting Machine (XGBoost) and Random Forest (RF). Furthermore, a novel practical stacking method for Short-Term Load Forecasting (STLF) using a stacked generalization ensemble method, which combines XGBoost and RF methods has been proposed. In online mode, the proposed stacking model achieved the best forecasting performance with a sMAPE of 1.15%, followed by RF (1.22%), XGBoost (1.23%), CNN-LSTM (1.97%) and LSTM (2.20%). In offline mode, the CNN-LSTM model outperformed all other methods with a sMAPE of 1.01%, demonstrating the advantage of deep feature extraction and complete data availability in batch forecasting. Performance-based retraining was shown to be more effective than periodic retraining, which might still be useful in fog computing scenarios. In general, offline CNN-LSTM is preferable for scenarios demanding maximum accuracy, while the stacking model is more suitable for scalable, real-time online forecasting in constrained environments.

Federated Learning (FL) is a technique used in Internet of Things (IoT) networks to enhance data privacy through decentralised Machine Learning (ML). However FL faces challenges due to the Non-Independent and Identically Distributed (Non-IID) data that is stored on various devices. Each device typically has a unique Non-IID subset of data from its local environment. This Non-IID distribution can be manipulated by poisoning attacks, where malicious modifications disrupt the global model. To addresses these complex in both IID and Non-IID data environments, we introduce AM2DN-FL. This adaptive approach identifies and removes malicious models in FL system, using a dual-sided defense strategy that leverages server and client components to combat Label-Flipping (LF) and backdoor attacks. AM2DN-FL employs an refined Local Outlier Factor (LOF) algorithm with an adaptive threshold based on Genetic Algorithms (GA) to fine-tuning the optimal threshold selection. Our simulation outcomes, utilizing the MNIST and CIFAR10 datasets for IID and Non-IID scenarios, demonstrate that our innovative approach outperforms other previously examined approaches in the literature across various performance metrics, such as Accuracy Rate (ACC), Attack Success Rate (ASR), Recall, Precision, and CPU run-time.

Federated Learning (FL) is a crucial technology in decentralized Machine Learning (ML), prominently used within Internet of Things (IoT) networks to enhance data privacy. However, it is threatened by poisoning attacks, where harmful data alterations can significantly disrupt learning processes. This paper introduces a novel solution, Entropy-based Robust Defense Federated Learning (ERDFL), to counteract these disruptions. Our approach leverages entropy information for enhanced detection of malicious models and also innovatively adjusts detection thresholds in real-time, thereby effectively identifying and excluding potentially malicious clients within the FL process. Our simulation results, using the Mnist, Fashion-Mnist, and IMDB datasets, demonstrate that our novel approach surpasses other previously studied approaches in the literature across multiple performance metrics, including Accuracy Rate (ACC), Attack Success Rate(ASR), Loss Rate (LR) and CPU aggregation run-time.

Federated learning (FL) enables collaborative machine learning among multiple devices without sharing private data. However, FL systems are vulnerable to poisoning attacks where malicious participants send malicious model updates to compromise the global model's accuracy. To enhance malicious model detection, we propose an EMDG-FL approach that optimizes the threshold used to identify attacks through a Genetic Algorithm (GA). The threshold indicates the degree of divergence between benign and malicious model updates. A tightly tuned threshold improves detection efficiency by reducing false positives and negatives. Our approach also includes a comparison study evaluating EMDG-FL against other defenses from literature across metrics like Accuracy Rate (ACC), Attack Success Rate (ASR) and Loss Rate (LR). Simulation results using two datasets demonstrate that EMDG-FL outperforms prior works in detecting poisoning attacks in FL. The optimized threshold calculation enables more precise and efficient identification of malicious models.

In many practical communication environments, traditional feature extraction methods in spectrum sensing fail to fully exploit the information of primary users. Additionally, conventional machine learning methods have weak learning capabilities, making it difficult to maintain efficient and stable spectrum sensing performance in complex noise environments. Furthermore, non‐Gaussian noise can significantly affect the detection performance of spectrum sensing. To address these issues, this paper first proposes a feature extraction method based on Hierarchical Fuzzy Dispersion Entropy (HFDE) to better extract high‐frequency and low‐frequency information from signal samples, providing more comprehensive features for subsequent models to optimize feature extraction effectiveness. Then, a parallel model combining Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) is constructed to enhance learning ability. While CNN extracts local features, GRU processes temporal relationships, and the features output by both are concatenated to achieve effective feature learning and temporal modeling of primary user signal data represented by HFDE. Finally, using the feature vectors output by the CNN‐GRU model, detection statistics and detection thresholds for spectrum sensing are constructed for online detection. Simulation results validate the effectiveness and robustness of this method in spectrum sensing under non‐Gaussian noise. In the presence of significant non‐Gaussian noise intensity and a signal‐to‐noise ratio of −14 dB, the detection probability can reach 97.1%. Additionally, for the detection of unknown signals, the model can still maintain a detection probability of over 90%.

The programmable matter has paved the way for the emergence of new paradigms in the fields of computer science, design, Haptics, and material science. Several kinetic-based approaches have been developed to represent an object via surface deformation using a set of patterned actuators. Therefore, a loss of shape is noticed in the physical rendering as the actuation is applied in a single direction. This work considers a deformable interface having a chained architecture, in which smart materials such as shape memory alloy are used as a controllable hinge mechanism allowing to perform bidirectional self-folding capabilities. Such an interface can render 3D models through two main operations: NURBS slicing and segment fitting operations. More precisely, models are downscaled to match the configuration of the interface (chains × hinges per a chain), then angles are exported and replicated by the controllable shape memory effect of shape memory alloy using the Joule effect. Unlike the existing architectures, this approach affords to render a physical model with a low digital to physical conversion loss by means of its geometric complexity (e.g., cavities, lateral shape variation). The proposed approach has been modeled and validated through numerical simulation using COMSOL Multiphysics® software.

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.

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%.

Cloud computing technology hosts application for users to accesses computing as services. Its application has been widely used and increases to become a part of enterprises' computing infrastructures. However, Cloud computing latency and request deadline fulfillment issues are among the major problems. Therefore, there is a need for a solution to synchronize a request states in Cloud computing. In this paper, the resource allocation and scheduling problem for services in the cloud are addressed and a solution model is presented. Our approach is based on MAX-Plus algebra, to provide a deterministic and exact solution to the minimization of services queries response times. Our proposal is tested on various problems sizes to evaluate its performance and scalability.

Wireless sensor network (WSN) represents a very important research that targets a very large number of possible applications in healthcare, smart cities, environmental monitoring, military, industrial automation and recently in smart grids. It consists of three main components: a large number of nodes, gateways and software. WSN workload needs an unlimited lifetime energy and it doesn't depend on a limit energy usage while sensor nodes are supplied by batteries or supercapacitors with limited energy. Particular algorithms must be employed so that energy consumption is reduced. In this paper, clustering protocols are investigated and a new approach is proposed to increase the lifetime of the wireless sensor network. Simulation results show that its performance is better in terms of prolonging the lifetime of the network and increasing the number of data packets received by the Base Station (BS).

This paper presents a novel wearable biomedical Network on Chip (NoC) concept development to monitor and predict irregular brain waves as advanced sensitive portable for an electroencephalogram (EEG) analysis device. The proposed device will monitor brain's spontaneous electrical activity in normal and abnormal situations for specific patients suffering from different types of epilepsy. This NOC would be able to predict the severity of the forthcoming epileptic attack. Meanwhile, this device will alert epileptic patients by giving an alarm on detection of any kind of abnormal brain electrical activity. The EEG NoC reads brain signals from a wireless sensor on the located on the patient's scalp, and runs parallel processing and filtering for the brainwaves. This process makes the detection of brain abnormalities possible, and many patients can be saved by predicting the time of epileptic seizure. When such a prediction occurs, alarm signals are sent to the patient to take protective measures. In this way, it will help patients to prevent themselves from different kinds of injuries and risky behaviors occurring during epilepsy attack or after that.

The development of ubiquitous and pervasive computing systems requires new approaches and paradigms. Mobile agent based approaches have received a great attention for developing distributed applications. Agents are programs that can migrate from a machine to another in a network and perform tasks on distant machines. However, it is difficult to estimate a priori the appropriate number of agents allowed to be spawned in the network without any global information or controller. Indeed, increasing agent population size, with cloning operation, will increase resource demands in the network, which would indirectly affect the network performance. This paper focuses on the problem of dynamic regulation of mobile agent population size in a distributed system and proposes an approach that takes inspiration from the immune system concepts. Simulations have been conducted and results are reported to show the effectiveness of the proposed approach.

High-performance reconfigurable computing involves acceleration of significant portions of an application using reconfigurable hardware. Mapping application task graphs onto reconfigurable hardware is, therefore, of rising attention. In this work, we approach the mapping problem by incorporating multiple architectural variants for each hardware task; the variants reflect tradeoffs between the logic resources consumed and the task execution throughput. We propose a mapping approach based on the genetic algorithm, and show its effectiveness for random task graphs as well as an N-body simulation application, demonstrating improvements of up to 78.6 percent in the execution time compared with choosing a fixed implementation variant for all tasks. We then validate our methodology through experiments on real hardware, an SRC-6 reconfigurable computer.

Rapid advances in technology and design tools enabled today engineers to design system-on-chip containing large number of cores. These systems have limited resources and should be implemented with very little silicon area overhead. Several studies have demonstrated that buffers inside switches of the on-chip interconnect take a significant portion of the system silicon area that can affects the performance and the energy consumption. Therefore, their size should be carefully customized to match communication patterns of a target application. In this paper, a compartmental Fluid-flow based modeling approach is presented to allocate required resource for each buffer based on the application traffic pattern. Simulations are conducted and preliminary results are reported to show the efficiency of the Fluid-flow based modeling method for a buffer space allocation.

Ubiquitous and pervasive computing deals with the design of autonomous and adaptive systems and services that interact with the closest environment enhanced by context awareness and emergence functionalities. In this article, we investigate the relationships between the environment, the actions (services), and the selection algorithm that is guaranteed to take the system to a state that suits a stochastically changing environment. Making the assumption that peering relationships between potential actions can be specified by an affinity network, the action selection mechanism is translated into an iterative algorithm that lets each activity update its strength until it converges to a solution. In pervasive environments, where services and devices interfere with each other, the proposed action selection approach prevents unexpected and undesirable behaviors or oscillating loops in a such dynamic environment.

Inter-Vehicles Networks (IVNs) have emerged as a new environment for intelligent transportation applications. The implementation of these applications requires dealing with many issues. The design and the implementation of an efficient and scalable architecture for information dissemination constitute one major issue. In this paper, a decentralized architecture inspired by stigmergy and direct communication between Ants is presented. The Ants colony has a set of organizing principles, such as scalability and adaptability that are useful for developing a decentralized architecture in highly dynamic networks. Simulations are conducted using Starlogo simulator and some metrics such as information relevance and drivers' awareness are evaluated. Preliminary results are reported to first show the effectiveness of the proposed information dissemination strategy.

Network-on-Chip (NoC) has been proposed as an alternative to bus-based schemes to achieve high performance and scalability in System-on-Chip (SoC) design. Performance evaluation of On-Chip Interconnect (OCI) architectures is widely based on simulation which becomes computationally expensive, especially for large-scale NoCs. In this paper, a performance analysis model using Network Calculus is presented to characterize and evaluate the performance of NoC-based applications. The 2D Mesh on-chip interconnect is analyzed and main performance metrics such as end-to-end delay and buffer size requirements are computed and compared against the results produced by a discrete event simulator. The results shed more light on the potential of this analytical technique as a useful tool for NoC design and performance analysis.

The objective of pervasive computing is to provide anytime and everywhere, computing and communication services in particular, for mobiles that interact through ad hoc connections. This paper presents centralized and distributed service based clustering approaches wherein ad hoc or composite services are represented by clusters of nodes that establish relationships based on affinities. The relationships are adaptive according to the limitation of the mobile nodes battery power and to the dynamic network topology changes.

Based on top of Web protocols and XML language, Web services are emerging as a framework to provide application-to-application interaction. An important challenge is their integration in order to provide new value-added composite services, allowing consequently Business-to-Business relationships. Therefore, many composition languages have been proposed in the past few years. However, a weakness of these languages is that they are difficult to use in early stages of development, such as specification. Thus, an extension to UML 2.0, named UML-S, was introduced to develop composite Web services conforming to the model-driven engineering vision. This paper introduces the necessary transformation rules between UML-S and low-level code to comply with MDE approach.

Mobile agent is a program that can migrate from a machine to another in a network and perform tasks on machines that provide agent hosting capability. The agent can clone itself in order to increase system robustness and efficiency. The clone operation creates multiple instances of an agent to run on different machines. However, increasing agent population size, with cloning operation, will increase resource demands in the network, which would indirectly affect network performance. When, the mobile agents operate in a dynamic and distributed environment, it is difficult to estimate a priori the appropriate number of agents allowed to be spawned in the network. This paper focuses on the problem of dynamic regulation of mobile agent population size in a distributed system, and proposes an approach that takes inspiration from the immune system concept

Very large scale networks such as the Internet require a new operational model to use resources efficiently and reduce the need for the administration necessary in client-server networks. In this paper, we present an autonomous decentralised system based on mobile agent paradigm and inspired by the immune system as an alternative to the traditional client-server paradigm. The immune system has a useful set of organising principles that guide the design of scale, adapt and efficient enough networking model to bring answers to some large scale networking challenges. This research is the part of an effort to develop a mobile agent-based middleware that can monitor the resources distributed over a large scale networks.

Distributed object-oriented computing allows efficient use of the Network Of Workstations (NOW) paradigm. However, the underlying middlewares used to develop and deploy such applications do not provide developers with any standard supervision mechanism so that they know exactly what happens during their applications execution. This paper analyzes distributed CORBA and JAVA-based applications to point out functional and management supervision information which has to be gathered from the objects. Developers will use this information to improve the Quality of Service (QoS) of their distributed object-oriented applications (DOA).

Association rule mining (ARM) is one of the data mining problems receiving a great deal of attention in the database community. The main computation step in an ARM algorithm is frequent itemset discovery. In this paper, a frequent itemset discovery algorithm based on the Hopfield model is presented.

A general data movement technique was described by D. Nassimi and S. Sahni (1981) which often leads to efficient parallel algorithms on distributed-memory architectures for a wide class of problems. In this paper, by using the same arguments that was used to prove correctness of this technique, we show that the data movement operations involved may be reduced by a constant factor under some assumptions. We show also that this technique and the optimization can be utilized to embed arbitrary trees in the hypercube and the q-dimensional mesh by using a similar algorithm of the randomized flip-bit algorithm, described by F.T. Leighton (1992). We show that these embedding algorithms embed any M-node tree in N-PEs hypercube or N-PE's q-dimensional mesh with load O(M/N), which is optimal for all M.