Mobile Sensor Networks Research Articles

Ad Hoc Mesh Network Localization Using Ultra-Wideband for Mobile Robotics.

This article explores the implementation of high-accuracy GPS-denied ad hoc localization. Little research exists on ad hoc ultra-wideband-enabled localization systems with mobile and stationary nodes. This work aims to demonstrate the localization of bicycle-modeled robots in a non-static environment through a mesh network of mobile, stationary robots, and ultra-wideband sensors. The non-static environment adds a layer of complexity when actors can enter and exit the node's field of view. The method starts with an initial localization step where each unmanned ground vehicle (UGV) uses the surrounding, available anchors to derive an initial local or, if possible, global position estimate. The initial localization uses a simplified implementation of the iterative multi-iteration ad hoc localization system (AHLos). This estimate was refined using an unscented Kalman filter (UKF) following a constant turn rate and velocity magnitude model (CTRV). The UKF then fuses the robot's odometry and the range measurements from the Decawave ultra-wideband receivers stationed on the network nodes. Through this position estimation stage, the robot broadcasts its estimated position to its neighbors to help the others further improve their localization estimates and localize themselves. This wave-like cycle of nodes helping to localize each other allows the network to act as a mobile ad hoc localization network.

Fuzzy cluster-based multi-node charging strategy in mobile sensor networks

Energy is crucial for the continuous operation of rechargeable sensor networks, and efficient energy replenishment strategies are vital for improving network performance. However, existing solutions primarily focus on stationary sensor networks, and there are fewer strategies for energy replenishment in mobile networks. Typically, a Mobile Car (MC) charges individual mobile nodes one to one. To address the mobility of sensor nodes and enhance the energy replenishment efficiency of the network, this paper proposes a fuzzy clustering-based multi-node charging strategy (MMCS) in Mobile Sensor Network (MSN). Firstly, an improved multi-factor fuzzy C-mean clustering algorithm (MFCM++) is designed to cluster nodes with similar moving directions and remaining energy. Secondly, the cuckoo search algorithm (CS) is utilized to determine the optimal charging position for each cluster based on the varying energy demand levels of mobile nodes, thereby reducing the cluster charging waiting time. Finally, a spatio-temporal factor-based cluster selection algorithm (STCS) is developed to determine the order of cluster charging and select the next cluster to be charged. Extensive simulation results demonstrate that MMCS effectively enhances network performance, reduces energy costs, and addresses the energy problem in large-scale MSNs

Mechanism analysis of regulating Turing instability and Hopf bifurcation of malware propagation in mobile wireless sensor networks

A dynamical model is constructed to depict the spatial-temporal evolution of malware in mobile wireless sensor networks (MWSNs). Based on such a model, we design a hybrid control scheme combining parameter perturbation and state feedback to effectively manipulate the spatiotemporal dynamics of malware propagation. The hybrid control can not only suppress the Turing instability caused by diffusion factor but can also adjust the occurrence of Hopf bifurcation induced by time delay. Numerical simulation results show that the hybrid control strategy can efficiently manipulate the transmission dynamics to achieve our expected desired properties, thus reducing the harm of malware propagation to MWSNs.

Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science

Wireless networks play a pivotal role in various domains, including industrial automation, autonomous vehicles, robotics, and mobile sensor networks. This research investigates the critical issue of packet loss in modern wireless networks and aims to identify the conditions within a network’s environment that lead to such losses. We propose a packet status prediction model for data packets that travel through a wireless network based on the IEEE 802.15.4 standard and are exposed to five different types of interference in a controlled experimentation environment. The proposed model focuses on the packetization process and its impact on network robustness. This study explores the challenges posed by packet loss, particularly in the context of interference, and puts forth the hypothesis that specific environmental conditions are linked to packet loss occurrences. The contribution of this work lies in advancing our understanding of the conditions leading to packet loss in wireless networks. Data are retrieved with a single CC2531 USB Dongle Packet Sniffer, whose pieces of information on packets become the features of each packet from which the classifier model will gather the training data with the aim of predicting whether a packet will unsuccessfully arrive at its destination. We found that interference causes more packet loss than that caused by various devices using a WiFi communication protocol simultaneously. In addition, we found that the most important predictors are network strength and packet size; low network strength tends to lead to more packet loss, especially for larger packets. This study contributes to the ongoing efforts to predict and mitigate packet loss, emphasizing the need for adaptive models in dynamic wireless environments.

A self-adaptive attraction and repulsion-based naked mole-rat algorithm for energy-efficient mobile wireless sensor networks

Naked mole-rat algorithm (NMRA) is a swarm intelligence-based algorithm that draws inspiration from the mating behaviour of mole rats (workers and breeders). This approach, which is based on the ability of breeders to reproduce with the queen, has been utilized to tackle optimization problems. The algorithm, however, suffers from local optima stagnation problem and a slower rate of convergence in order to provide gobal optimal solution. This study suggests attraction and repulsion strategy based NMRA (ARNMRA) along with self-adaptive properties to avoid trapping of solution in local optima. This strategy is utilized to create new breeder rat solutions and mating factor (λ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(\\lambda )$$\\end{document} is made self-adaptive using simulated annealing (sa) based mutation operator. ARNMRA is evaluated on CEC 2005 numerical benchmark problems and found to be superior to other algorithms, including well-known ones like selective operation based GWO (SOGWO), opposition based laplacian equilibrium optimizer (OB-L-EO), improved whale optimization algorithm (IWOA), success-history based adaptive DE (SHADE) and original NMRA. Further, according to experimental results, the performance of ARNMRA is likewise superior to the NMRA for the CEC 2019 and CEC 2020 numerical problems. Convergence profiles and statistical tests (rank-sum test and Friedman test) are employed further to validate the experimental results. Moreover, this article extends the application of ARNMRA to address the data gathering aspect in mobile wireless sensor networks (MWSNs) with the goal of prolonging network lifetime and enhancing energy efficiency. In this MWSN-based protocol, a sensor node is elected as a cluster head based on factors like mobility, residual energy, and connection time. The protocol aims to maximize the system lifetime by efficiently collecting data from all sensors and transmitting it to the base station. The study emphasizes the significance of considering dynamic node densities and speed when designing effective data-gathering protocols for MWSNs.

Enhancing Location Accuracy by Minimizing RMS Using RSS-AMLE in WSN

Over the past decade, there has been significant growth in wireless sensor networks, particularly in the context of industrial applications. Mobile sensor networks have garnered research interest due to their ability to facilitate communication between various devices. Still, the mobility of these nodes gives rise to challenges such as network coverage and connectivity issues. Addressing these challenges necessitates accurate estimation of sensor node locations, a critical factor in network performance. Numerous methods, such as Angle of Arrival (AOA) and Time of Arrival (TOA), have been proposed for node localization. Still, these methods are plagued by localization errors and high implementation costs. To overcome these localization errors in wireless sensor networks, we present an adaptive approach based on the Received Signal Strength (RSS) model. This model views localization as a non-convex problem and employs an adaptive maximum likelihood estimation to minimize localization errors. An extensive simulation study is carried out to measure the performance of the intended approach in minimizing the localization error. The results unequivocally demonstrate that our localization scheme achieves higher accuracy in locating sensor nodes while reducing deployment costs. Comparative analysis against existing methods further underscores the significance of our approach.

Task Optimization for a Class of Mobile Depth Sensor Network in Unknown Environments

Task Optimization for a Class of Mobile Depth Sensor Network in Unknown Environments

Coverage Control for Mobile Sensor Networks with Multiplicative Measurement Errors and Limited Interaction Ranges

Coverage Control for Mobile Sensor Networks with Multiplicative Measurement Errors and Limited Interaction Ranges

RBFNN-Based Distributed Coverage Control on an Unknown Region

In this paper, we investigate the problem of achieving distributed coverage control of a mobile sensor network on an unknown region using local measurements. To accomplish this objective, each sensor is equipped with two-layer dynamics. The upper layer dynamic employs a completely distributed observer algorithm on the target region for state estimation of the density function. The lower layer dynamic utilizes a radial basis function neural network-based motion algorithm, which involves only the estimated state obtained by the upper layer dynamics, to guide the sensors towards an optimal coverage configuration. We demonstrate that with only the joint detectability of the partial outputs measurement, it is possible to achieve distributed coverage control in the unknown region without requiring additional information about the density function, communication topology associated with the sensors, or coupling gains. Finally, two examples are used to validate the theoretical findings.

Zigbee Based Mobile Sensing for Wireless Sensor Networks

Wireless sensor networks, have drawn a lot of interest because of their adaptability and range of uses in different industries. WSNs face significant challenges when it comes to energy efficiency because sensor nodes are usually battery-powered and have limited resources. Several energy-efficient methods and protocols, such as duty cycling, data aggregation, and topology management, have been put forth to address this problem. Moreover, new potential for mobile wireless sensor networks are presented by the integration of WSNs with mobile and static devices, such as drones, tablets, and smartphones. In this study, we suggest utilizing Zigbee technology to establish a robust and flexible monitoring system for both stationary and mobile sensors. Numerous industries, including healthcare, smart agriculture, asset tracking, energy management, smart home automation, and industrial monitoring and control, have made extensive use of Zigbee. By leveraging Zigbee's capabilities, we hope to improve the protocol's performance while establishing dependable communication links between nodes, analyzing the communication range, and assessing the influence of environmental conditions. In this study, a system model for Zigbee deployment in mobile robots will be presented. It will address the basics of Zigbee, communication difficulties, networking with Zigbee, and simulations or real-world outcomes. We will learn about the strengths and weaknesses of Zigbee-based systems in terms of creating reliable communication links in mobile wireless sensor networks by looking at their architecture and functionality. The results of this study will help us comprehend Zigbee's potential to improve monitoring systems and make better decisions across a range of industries. The study's emphasis on mobile monitoring systems signifies a step forward in addressing the evolving needs of wireless sensor networks in dynamic environments.

Efficient and Low-Complex Signal Detection with Iterative Feedback in Wireless MIMO-OFDM Systems.

To solve error propagation and exorbitant computational complexity of signal detection in wireless multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, a low-complex and efficient signal detection with iterative feedback is proposed via a constellation point feedback optimization of minimum mean square error-ordered successive interference cancellation (MMSE-OSIC) to approach the optimal detection. The candidate vectors are formed by selecting the candidate constellation points. Additionally, the vector most approaching received signals is chosen by the maximum likelihood (ML) criterion in formed candidate vectors to reduce the error propagation by previous erroneous decision, thus improving the detection performance. Under a large number of matrix inversion operations in the above iterative MMSE process, effective and fast signal detection is hard to be achieved. Then, a symmetric successive relaxation iterative algorithm is proposed to avoid the complex matrix inversion calculation process. The relaxation factor and initial iteration value are reasonably configured with low computational complexity to achieve good detection close to that of the MMSE with fewer iterations. Simultaneously, the error diffusion and complexity accumulation caused by the successive detection of the subsequent OSIC scheme are also improved. In addition, a method via a parallel coarse and fine detection deals with several layers to both reduce iterations and improve performance. Therefore, the proposed scheme significantly promotes the MIMO-OFDM performance and thus plays an irreplaceable role in the future sixth generation (6G) mobile communications and wireless sensor networks, and so on.

Distributed estimation and control over mobile sensor networks with jointly connected topology: Event-triggered approach

This article deals with the problem of distributed event-triggered tracking control in mobile sensor networks (MSNs) with a jointly connected topology (JCT). Two schemes are proposed for linear and Lipschitz nonlinear MSNs to estimate and track a mobile target. The proposed schemes are established using an event-triggered method to avoid continuous exchange of information between sensor nodes. In comparison with the other research under event-triggered communication strategies where states of the target are available, this paper considers that the states of the target are not available and two event-triggered algorithms are established for sensor nodes to estimate and follow the states of the continuous-time targets that can be seen in various real-world applications. Also, the proposed schemes are designed for the JCT with disconnected graphs which means the communication topology of the MSN is not required to be connected for all time instants. By employing the Cauchy convergence criterion and a common Lyapunov function, sufficient conditions are also established to ensure event-based tracking control subject to JCT. The effectiveness of the proposed work is verified by presenting simulation examples.

Cooperative Localization and Target Tracking in Mobile Sensor Networks With Uncertainties in Measurement Models

Cooperative Localization and Target Tracking in Mobile Sensor Networks With Uncertainties in Measurement Models

A comparative study of Gaussian mixture algorithm and K-means algorithm for efficient energy clustering in MWSN

Wireless sensor networks (WSNs) represent one of the key technologies in internet of things (IoTs) networks. Since WSNs have finite energy sources, there is ongoing research work to develop new strategies for minimizing power consumption or enhancing traditional techniques. In this paper, a novel Gaussian mixture models (GMMs) algorithm is proposed for mobile wireless sensor networks (MWSNs) for energy saving. Performance evaluation of the clustering process with the GMM algorithm shows a remarkable energy saving in the network of up to 92%. In addition, a comparison with another clustering strategy that uses the K-means algorithm has been made, and the developed method has outperformed K-means with superior performance, saving energy of up to 92% at 4,500 rounds.

Recent Investigations in Wireless Sensor Networks on Distributed Defense Mechanism for Clone Attacks

Regarding accelerating development of mobile sensor nodes technology, increasing the utilization of them, and also facing with security challenges in these networks; specially clone nodes attack, this paper focuses on exploiting optimum criteria of node clone intrusion detection procedures in mobile wireless sensor networks by using experimental analysis of procedures. Since many of recommended protocols in this area have not been experimentalised, also no comprehensive study has been performed on the possibility and capability of these procedures; in this paper types of sensor network architecture, with the presence of mobile sensor node, are analyzed. Then according to the type of architecture, the procedures of clone node intrusion detection is classified and meticulously scrutinized. Besides, due to measuring the efficiency, exploiting the optimum parameters and also appraising the expenses of procedures, Finally, the conclusion based on theoretical analysis and simulation is presented

Retracted: Study of Node Distribution and Density Optimization in Mobile Sensor Network 3D Space

Retracted: Study of Node Distribution and Density Optimization in Mobile Sensor Network 3D Space

Framework with Enhanced Routing algorithm for Mobile Sensor Network

Framework with Enhanced Routing algorithm for Mobile Sensor Network

A Proficient Obtrusion Recognition Clustered Mechanism for Malicious Sensor Nodes in a Mobile Wireless Sensor Network

A collection of wireless nodes that may be installed at any location and at any time without requiring an established network structure is called a mobile wireless sensor network. The problem of network performance arises from the mobility of nodes and their misbehaviour. Network performance is negatively impacted by data loss and sensor node misbehaviour. In certain cases, there are malicious sensor nodes that are designed to destroy the network’s capacity. This work aims to identify hostile nodes using an irregular set technique. The route entry table’s broadcasting metadata helps identify rogue nodes. Every sensor node in the network broadcasts information about adjacent nodes and maintains a sentry table. Premeditated data delivery proportion, throughput, delay, packet drop, and fault rate are used to estimate broadcasting record parameters. In the NS2 environment, mobile nodes with varying velocities are simulated. To generate an information table, mobile nodes with varying speeds are examined based on their broadcasting records. On the basis of guidelines taken from the irregular set tactic table, good and bad nodes are distinguished. Packets are disseminated along the shortest path that doesn’t contain any malicious nodes. The results of the proposed technique show that an irregular set tactic increases throughput, network capability, data delivery percentage, and end-to-end delay reduction in mobile sensors.

Coverage control for mobile sensor networks with unknown terrain roughness and nonuniform time-varying communication delays

Coverage control for mobile sensor networks with unknown terrain roughness and nonuniform time-varying communication delays

Considering Performance Issues in Ad Hoc Networking : A Comprehensive Study

Ad hoc networks have emerged as a versatile and dynamic communication paradigm with applications spanning from mobile devices and sensor networks to emergency response and military operations. However, their inherent decentralized and self-organizing nature gives rise to a myriad of performance challenges that must be comprehensively addressed to ensure reliable and efficient communication. This paper presents a comprehensive study of performance issues in ad hoc networking, aiming to provide a holistic understanding of the various factors impacting network performance. The study begins by examining the fundamental characteristics of ad hoc networks, highlighting their unique advantages and challenges. We delve into critical performance metrics such as throughput, latency, scalability, and energy efficiency, dissecting the factors that influence each of these metrics in ad hoc environments. The role of routing protocols, MAC layer design, and network topologies in shaping network performance is thoroughly analyzed. Furthermore, this study explores the impact of mobility patterns, traffic models, and interference on ad hoc network performance. It delves into the challenges posed by dynamic network topologies, node failures, and the need for adaptive mechanisms to maintain robust connectivity. Quality of service (QoS) considerations and their implications on performance are also discussed in detail. To provide a comprehensive view, we survey state-of-the-art solutions and techniques proposed in the literature to mitigate performance issues in ad hoc networks. These include adaptive routing algorithms, cross-layer optimization approaches, and novel communication paradigms like cognitive radio networks. Finally, we present a roadmap for future research directions in the field of ad hoc networking performance optimization. The goal is to inspire further investigation and innovation in addressing the evolving challenges faced by ad hoc networks, making them more reliable, efficient, and adaptable for a wide range of applications.