Medical equipment predictive maintenance is essential to maintaining consistent and dependable healthcare services. Wireless Sensor Networks are essential for keeping an eye on medical devices and anticipating malfunctions before they happen. In this work, we provide a predictive maintenance strategy for medical WSNs based on LSTMs and use Local Interpretable Model-Agnostic Explanations to improve its interpretability. Our method increases the accuracy of fault predictions while providing decision-making transparency. Results from experiments show how well our model works in real-time to explain contributing elements and spot possible failures..

Wireless Sensor Networks are becoming more and more crucial to the advancement of numerous technologies, particularly when combined with Big Data platforms. Although this connection has a lot of potential, there are challenging security challenges as well. Even though WSNs have been the subject of a lot of research, the security requirements for WSNs functioning in Big Data environments have not yet been thoroughly examined. It is also a crucial use of IoT, allowing sensors to exchange a variety of data. However, because of its inherent unreliability and natural surroundings, such a network is susceptible to numerous types of attacks, including insider attacks. Intrusion detection systems (IDSs) are commonly used in WSNs to protect against insider assaults by putting in place the right procedures and techniques. However, sensors may produce too much data in the big data era, which could reduce the efficiency of WSN computing. An overview of the security concerns and difficulties facing WSNs in the big data era is provided in this study. In order to improve the detection of insider threats and the overall security posture of WSNs, a literature review on cybersecurity IDS on WSN in the context of big data is finally suggested. It highlights advancements in IDS methodologies, including federated learning, machine learning, deep learning, and big data techniques.

The Internet of Things and Wireless Sensor Networks have become indispensable for a variety of applications. Scaling these networks requires efficient clustering and cluster head selection in order to manage energy consumption and maintain network performance. This paper proposes a novel clustering framework called Federated Learning for Head Selection and Spectral Clustering (FLHC-SC), which uniquely combines Federated Learning for decentralized cluster head (CH) selection and spectral clustering for graph-based clustering. Unlike conventional methods in which clustering is performed before CH selection, FLHC-SC reverses this process, making it possible to select CHs intelligently based on local learning prior to spatial partitioning. As a result of this decoupled, two-phase strategy, FLHC-SC can adapt to dynamic network conditions while maintaining energy and structural efficiency. IoT simulations were conducted to compare the proposed method with three recently proposed clustering methods: a modified version of Low-Energy Adaptive Clustering Hierarchy (LEACH Distributed Clustering), a hybrid PSO-based approach and a K-means–fuzzy logic clustering method. Results show that FLHC-SC outperforms other methods in several key performance metrics. It maintains 60% of active nodes at the end of simulation rounds while preserving a higher average residual energy. Furthermore, it achieves a maximum silhouette coefficient of 0.998 and a lower intra-cluster compactness value. Statistical validation with ANOVA test was used to confirm the superior performance of the proposed method.

Strong security measures are now more important than ever due to the quick growth of wireless sensor networks in sectors like smart cities, healthcare, and agriculture. In order to protect these networks from malevolent assaults, intrusion detection systems (IDS) are essential. However, network operators find it challenging to comprehend the system’s decision-making process due to the lack of transparency in traditional IDS. To address this issue, we propose an explainable artificial intelligence based intrusion detection method for large-scale WSNs. The goal of this research is to combine machine learning models with explain-ability frameworks like SHAP and LIME in order to efficiently identify and clarify anomalies in network data. In order to detect different kinds of attacks, we train and assess a variety of machine learning classifiers, such as decision trees, support vector machines, and ensemble approaches, using the popular KDD Cup 99 dataset. The XIA frameworkS assist discover important aspects that aid in attack detection and offer insights into the model’s decision-making process. Additionally, the model’s durability and scalability make it a feasible option for implementation in extensive WSNs, particularly in agriculture, where network security is essential to guaranteeing the dependability of IoT-based monitoring systems. The results have demonstrate the efficacy of the XAI-based intrusion detection system in attaining high accuracy, precision, recall, and F1-score while maintaining decision-making transparency. This method not only enhances intrusion detection but also provides network administrators with the means to decipher and verify the system’s predictions, increasing confidence in automated security systems.

Evolutionary algorithms with limited fitness evaluations may find it difficult to solve optimization issues requiring significant computational resources. The performance of the majority of current evolutionary multi-objective optimization methods often degrades with the number of objectives, especially when the number of objectives is greater than three. These problems are called many-objective optimization problems (MaOP). The MOEA is one of the most active areas in the field of evolutionary computer science because of its relevance and wide variety of applications as in the fields of engineering, finance, biology and computer science. In this paper, we provide an overview of the state of the art in the different categories of MaOEAs, mainly in the last three years. It covers four categories, including decomposition-based algorithms, indicator-based algorithms, surrogate-based algorithms and dominance-based ones, as well as current research and future potential developments for this family of algorithms.

Bluetooth Low Energy (BLE) has revealed for decades many interesting opportunities especially in the field of positioning that made of it one of the most used technologies in indoor localization solutions. The system we propose is a combination of BLE, Acoustic and LiFi technologies. Our approach is not just arithmetic but using real time computed metrics that act precision indicators or "Dilution of precision" (DOP) weights of the results given by each technology. Depending on these DOP-like indicators, we were able to judge how much precise and accurate the returned results are. Our new approach was confirmed during experiments showing that the precision of this new hybrid system is much better when using these new precision metrics.

The 3D indoor redeployment of connected objects in IoT collection networks is a complex problem that influences the overall performance of the network. In this paper, we aim to resolve this problem using a real prototyping system based on a real-world deployment. The aim is to choose the best positions to add a set of connected objects while optimizing a set of objectives. The used approach is based on a new hybrid optimization algorithm that combines a strategy of incorporation of user preferences (PI-EMO-VF) with a many-objective recent variant of the genetic algorithms (NSGA-III). The obtained numerical results and the real experiments on our testbeds prove the effectiveness of the proposed approach compared with another recent optimization algorithm (MOEA/DD).

The use of real prototyping systems allows implementing real-world deployments which permit evaluating new protocols, algorithms and network solutions. This study investigates the problem of 3D indoor redeployment of connected objects in IoT collection networks. The objective is to choose the right positions in which connected objects are added to an initial configuration, while optimizing a set of objectives. To solve this problem, a novel bird's dialect-based particle swarm optimization algorithm (named acMaPSO) is introduced. The new concept of bird's dialect is based on a set of birds which are separated into different dialect groups by their regional habitation and are classified into groups according to their common manner of singing. The obtained numerical results and the real experiments on our testbed prove the effectiveness of the two proposed variants compared with the standard PSO algorithm and a recent state of art of many-objective evolutionary algorithms: the NSGA-III.

Coverage is one of the most important performance metrics for sensor networks that reflects how well a sensor field is monitored. In this paper, we are interested in studying the positioning and placement of sensor nodes in a WSN in order to maximize the coverage area and to optimize the audio localization in wireless sensor networks. First, we introduce the problem of deployment. Then we propose a mathematical formulation and a genetic based approach to solve this problem. Finally, we present the results of experimentations. This paper presents a genetic algorithm which aims at searching for an optimal or near optimal solution to the coverage holes problem. Compared with random deployment as well as existing methods, our genetic algorithm shows significant performance improvement in terms of quality.

A mobile Ad-hoc Network (MANET) become the one of top area of research, due to their simplicity of deployment. No infrastructure is required for nodes to connect with each other in the network, and with out aid of centralized administration. The nodes with MANET are quickly deployable, independent and self repair. Despite these advantages routing protocols suffering from many problems like mobility, synchronization, localization, long route and other while routing. Therefore this protocols should be study in depth, simulated in different conditions and classified. this classification and simulation helps in understanding, comparing performances and assist researchers to differentiate the characteristics and define the pros and cons of routing protocols. A detailed study and simulation model using Network Simulator (NS-2.34) with different traffic models are presented in this paper. The simulation and the performance study will focus on the impact of the network size, average energy consumption per received packet and the network density.

A Wireless Sensor Network (WSN) is a particular ad hoc network which consists of a set of small size devices called sensors exploited in a defined geographical zone. The communication between sensors is carried out through using a radio communication. WSNs are employed in various applications requiring the use of fixed nodes (military applications) or mobile nodes (medical applications). In this paper, three energy-efficient clustering algorithms (EECA) are proposed to minimize WSNs energy consumption. The first contribution EECA-F (EECA for Fixed WSNs) is designed for the WSNs which have fixed nodes. Then, we will define two techniques for the mobile WSNs; EECA-M1 (EECA for Mobile WSNs) which is used when the nodes mobility is constant and EECA-M2 which is defined for the WSNs having variable nodes mobility. By comparing our contributions to the recent clustering algorithms, simulation results show that our algorithms are effective in saving energy and reduce up to 25% of power consumption.