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"wireless sensor networks"
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Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
Battery-powered sensor nodes encounter substantial energy constraints, especially in linear wireless sensor network (LWSN) applications like border surveillance and road, bridge, railway, powerline, and pipeline monitoring, where inaccessible locations exacerbate battery replacement challenges. Addressing these issues is crucial for extending a network’s lifetime and reducing operational costs. This paper presents a comprehensive analysis of the factors affecting WSN energy consumption at the node and network levels, alongside effective energy management strategies for prolonging the WSN’s lifetime. By categorizing existing strategies into node energy reduction, network energy balancing, and energy replenishment, this study assesses their effectiveness when implemented in LWSN applications, providing valuable insights to assist engineers during the design of green and energy-efficient LWSN monitoring systems.
Journal Article
Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges
Localization is an important aspect in the field of wireless sensor networks (WSNs) that has developed significant research interest among academia and research community. Wireless sensor network is formed by a large number of tiny, low energy, limited processing capability and low-cost sensors that communicate with each other in ad-hoc fashion. The task of determining physical coordinates of sensor nodes in WSNs is known as localization or positioning and is a key factor in today’s communication systems to estimate the place of origin of events. As the requirement of the positioning accuracy for different applications varies, different localization methods are used in different applications and there are several challenges in some special scenarios such as forest fire detection. In this paper, we survey different measurement techniques and strategies for range based and range free localization with an emphasis on the latter. Further, we discuss different localization-based applications, where the estimation of the location information is crucial. Finally, a comprehensive discussion of the challenges such as accuracy, cost, complexity, and scalability are given.
Journal Article
A modified cluster-head selection algorithm in wireless sensor networks based on LEACH
In order to overcome drawbacks of unreasonable cluster-head selection and excessive energy consumption in wireless sensor networks (WSNs), a modified cluster-head selection algorithm based on LEACH (LEACH-M) was proposed. Based on distributed address assignment mechanism (DAAM) of ZigBee, both residual energy and network address of nodes were taken into account to optimize cluster-head threshold equation. Furthermore, by leveraging a cluster-head competitive mechanism, LEACH-M successfully balanced the network energy burden and dramatically improved energy efficiency. The simulation results in NS-2.35 show that the proposed algorithm can prolong the network lifetime, minimize the energy consumption, and increase the amount of data received at base station whether region is in a 100 × 100m2or in a 300 × 300m2.
Journal Article
Artificial Intelligence-Driven Intrusion Detection in Software-Defined Wireless Sensor Networks: Towards Secure IoT-Enabled Healthcare Systems
Wireless Sensor Networks (WSNs) are increasingly deployed in Internet of Things (IoT) systems for applications such as smart transportation, telemedicine, smart health monitoring and fall detection systems for the elderly people. Given that huge amount of data, vital and critical information can be exchanged between the different parts of a WSN, good management and protection schemes are needed to ensure an efficient and secure operation of the WSN. To ensure an efficient management of WSNs, the Software-Defined Wireless Sensor Network (SDWSN) paradigm has been recently introduced in the literature. In the same vein, Intrusion Detection Systems, have been used in the literature to safeguard the security of SDWSN-based IoTs. In this paper, three popular Artificial Intelligence techniques (Decision Tree, Naïve Bayes, and Deep Artificial Neural Network) are trained to be deployed as anomaly detectors in IDSs. It is shown that an IDS using the Decision Tree-based anomaly detector yields the best performances metrics both in the binary classification and in the multinomial classification. Additionally, it was found that an IDS using the Naïve Bayes-based anomaly detector was only adapted for binary classification of intrusions in low memory capacity SDWSN-based IoT (e.g., wearable fitness tracker). Moreover, new state-of-the-art accuracy (binary classification) and F-scores (multinomial classification) were achieved by introducing an end-to-end feature engineering scheme aimed at obtaining 118 features from the 41 features of the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) dataset. The state-of-the-art accuracy was pushed to 0.999777 using the Decision Tree-based anomaly detector. Finally, it was found that the Deep Artificial Neural Network should be expected to become the next default anomaly detector in the light of its current performance metrics and the increasing abundance of training data.
Journal Article
Physical layer security for wireless sensing and communication
Wireless physical layer (PHY) security has attracted much attention due to the broadcast nature of the wireless medium and its inherent vulnerability to eavesdropping, jamming, and interference. Physical Layer Security for Wireless Sensing and Communication covers both communication and sensing security from a broad perspective. The main emphasis is on PHY security, although other security measures are covered for the sake of completeness and as a step towards cross-layer security and cognitive security vision. After discussing the features of wireless channels from both the communication and sensing perspectives, the book details their exploitation for secure transmission utilizing various approaches. Wireless sensing and radio environment concepts are also addressed, along with the related security implications in terms of eavesdropping, disruption, manipulation, and, in general, the exploitation of wireless sensing by unauthorised users. Several solutions for these threats from the domains of wireless communication, military radars, and machine learning, are discussed. The book provides valuable information to researchers in academia and industry, as well as engineers, developers, and advanced students in the field of cybersecurity.
Book
Heuristic Algorithms and Linear Programming for Energy Optimization in Three‐Dimensional Wireless Sensor Networks
Optimizing the energy consumption of three‐dimensional wireless sensor networks (3D‐WSNs) is significantly more difficult than optimizing that of two‐dimensional WSNs. This is primarily because of the additional spatial dimension, which introduces further computational and operational complexity to the network. Recently, a new method based on mixed‐integer linear programming (MILP) was introduced to address the energy efficiency problem of 3D‐WSNs. However, this method relies on unrealistic assumptions, which limit its practical applicability. Specifically, it is assumed that networks consist of randomly deployed unlimited energy sink nodes. The random placement of sink nodes degrades the network performance, and the assumption of infinite energy for the sink nodes makes the model impractical for real‐world scenarios. To address these shortcomings, this paper proposes a novel method that combines the differential evolution (DE) algorithm with MILP, referred to as hybrid DE‐MILP, to optimize energy usage and extend the lifetime of 3D‐WSNs. Unlike the MILP approach, the proposed method eliminates the need for sink nodes. Instead, it identifies cluster heads (CHs) from among finite‐energy sensor nodes using a heuristic strategy. Once the CHs are selected, linear programming is employed to determine the most energy‐efficient communication path among the nodes. Our approach is more practical and better suited for real‐world deployment because it relies solely on homogeneous sensor nodes with limited energy. The proposed method was evaluated and compared with MILP and multihop low‐energy adaptive clustering hierarchy (MH‐LEACH) algorithms. Simulation results demonstrated that the proposed approach significantly outperforms both the MILP and MH‐LEACH methods. Specifically, our method reduces the energy consumption by at least 13% and 48% compared with the MILP and MH‐LEACH methods, respectively. This reduction in network energy consumption improved the network lifetime by at least 28% and 70%, respectively. The results of this study demonstrate that the hybrid DE‐MILP approach is a robust and scalable framework for practical 3D‐WSN deployment.
Journal Article