Mobile Wireless Sensor Networks Research Articles

An enhanced bio‐inspired energy‐efficient localization routing for mobile wireless sensor network

SummaryEnvironmental disasters such as earthquakes, tsunamis, floods, and landslides disrupt natural resources, human casualties, and so on. Early warning and alert of such events will help for preparedness to avoid human and economic causalities. Managing a disaster or emergency scenario is a difficult endeavor. These episodes of mass devastation, whether caused by natural disasters or man‐made disasters, result in massive losses of money, property, and lives as a result of inadequate response by the government and management authorities. As a result, actions must be taken to avoid these circumstances from occurring by discovering the causes of these catastrophes in advance and providing swift rescue procedures once the crisis happens. In a disaster situation, communication plays a vital role, and a lot of people turn towards it as a source of information conveyance/delivery for controlling the situation. Even these communication networks are damaged and incur tremendous losses during disasters. All the communication networks, including mobile wireless sensor networks (WSNs) deployed for communication, also suffer huge damage and face many challenges during disasters. Nature appears to deal with these issues well. WSN is inspired by several biological systems and processes that have intrinsically attractive properties such as adaptation, self‐organization, and self‐healing. In this paper, we proposed an enhanced bio‐inspired energy‐efficient localization (EBEEL) routing for Mobile WSN. The main focus of the proposed EBEEL algorithm is to overcome the challenges faced by peer‐existing routing protocols such as data aggregation, limited bandwidth, scalability, data redundancy, energy consumption, packet drops, load balancing, and Quality of Service (QoS).

AI Assisted Energy Optimized Sustainable Model for Secured Routing in Mobile Wireless Sensor Network

AI Assisted Energy Optimized Sustainable Model for Secured Routing in Mobile Wireless Sensor Network

A novel model for efficient cluster head selection in mobile WSNs using residual energy and neural networks

Wireless Sensor Networks (WSNs) are essential for monitoring operational environments. Efficient selection of cluster heads is crucial in minimizing energy consumption. This study introduces an innovative model for determining the positions of cluster heads in a WSN, using the residual energy of each sensor node as a selection metric. The sensor incorporates a neural network model that has been trained using different significant models to determine the best location for the cluster head. Our model is compared with the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm to showcase its effectiveness, particularly in mobile node scenarios. We employ a feed-forward multilayer network, specifically Multilayer Perceptrons (MLPs), as the neural network technique. The proposed method comprises two stages: the training stage, where the neural network is trained with appropriate models, and the execution stage, where the trained network suggests the cluster head's location based on the sensor's conditions. The results demonstrate that the proposed method can accurately identify suitable locations for cluster heads in a sensor network and is capable of adapting to new models despite environmental changes and variations in input configurations.

A heuristic node placement strategy for extending network lifetime and ensuring target coverage in mobile wireless sensor networks

A heuristic node placement strategy for extending network lifetime and ensuring target coverage in mobile wireless sensor networks

Energy-efficient mobile node localization using CVA technology and SAI algorithm

In the evolving landscape of the Internet of Things (IoT), Mobile Wireless Sensor Networks (MWSN) play a pivotal role, particularly in dynamic environments requiring mobile sensing capabilities. A primary challenge in MWSNs is achieving accurate node positioning with minimal energy consumption, as these networks often consist of battery-powered, mobile sensors where energy replenishment is difficult. This paper addresses the critical problem of energy-efficient node localization in MWSNs. We propose a novel positioning approach leveraging Cooperative Virtual Array (CVA) technology, which strategically utilizes the mobility of nodes to enhance positioning accuracy while conservatively using energy resources. The methodology revolves around optimizing the number of transceiver nodes, considering factors such as node moving speed, total energy consumption, and positioning errors. Central to our approach is the Signal Arrival and Interaction (SAI) algorithm, an innovative technique devised for efficient and precise mobile node localization, replacing traditional Time of Arrival (ToA) methods. Our simulations, conducted under various scenarios, demonstrate the significant advantages of the CVA-based positioning algorithm. Results show a marked reduction in energy consumption and robust performance in mobile node scenarios. Key findings include substantial improvements in localization accuracy and energy efficiency, highlighting the potential of our approach in enhancing the operational sustainability of MWSNs. The implications of this research are far-reaching for IoT applications, particularly those involving mobile sensors, such as in smart cities, industrial monitoring, and disaster management. By introducing a novel, energy-efficient positioning method, our work contributes to the advancement of MWSN technology, offering a sustainable solution to the challenge of mobile node localization.

A Mobility Based Approach to Strengthen the Network Lifetime of Wireless Sensor Networks in 3D Region

Background: In this era of emerging technologies, Mobile Wireless Sensor Network (MWSN) has emerged as a powerful tool for many applications. Applications such as battlefield and traffic surveillance, agriculture and environment monitoring, smart homes and smart cities require a specific protocol to fulfill a specific purpose. WSN is composed of numerous tiny Sensor Nodes (SNs) along with one or more sinks, where sinks have unlimited sources of energy and SNs are battery- operated. SN tasks are to sense the data and transmit it to sink through the formation of dynamic topology. The SNs nearer to the sink rapidly exhaust their energy due to the heavy burden. Due to this, SNs became dead affecting the performance of the network lifespan. To overcome this problem, the concept of MWSN has been proposed. In MWSN, the sink can move from one location to another, and collect data from SNs. With the help of MWSN, the problem of energy holes can be resolved. An energy hole is a problem in which nodes are alive but they are not able to send the data due to low energy left. To overcome this problem, MWSN plays an important role. MSWN can move around the region and collect the data from SNs. Methods: In this work, we have proposed a Mobile Sink (MS) that can move on fixed or random locations for data collection from SNs. The comparative analysis of various MS strategies such as MS on boundaries, 4 sojourn locations in the region, random position in the region and fixed path to collect the data has been done. Results: SNs become dead in 2246 rounds in static approach. In the MS boundary approach, all SNs are dead in 2593 rounds. In the sojourn location, it lasts up to 4827. But in MS random and fixed location approaches, all SNs are dead in 11568 and 11513 rounds, respectively. Conclusion: The simulation results depict that the MS strategies having fixed or random positions in the region enhanced the network lifetime 4 to 5 times more than the static sink.

A Lightweight Three Dimensional Redeployment Algorithm for Distributed Mobile Wireless Sensor Networks

A Lightweight Three Dimensional Redeployment Algorithm for Distributed Mobile Wireless Sensor Networks

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.

Socially Aware Network Clustering for Throughput Maximization in Mobile Wireless Sensor Networks

Socially Aware Network Clustering for Throughput Maximization in Mobile Wireless Sensor Networks

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.

Energy Efficient Clustering Mechanism for Malicious Sensor Nodes in IOT Based MWSN

A mobile wireless sensor network (MWSN) is a collection of wireless nodes that may be set up anywhere and at any time without the need for a pre-existing network structure. Network performance is challenging factor due to nature of mobility and misbehaving nodes. Data loss and misbehaving in sensor nodes degrades performance of network. Some circumstances some malevolent sensor nodes predisposed to eradicate capability of network. The goal of this study is to use an irregular set approach to distinguish hostile nodes. Broadcasting information in route entry table identifies malicious node. Each sensor node in network preserves entry table and broadcast record regarding nearby nodes. To estimate broadcasting record metrics are premeditated as data deliverance proportion, throughput, delay, packet drop, fault rate. Mobile nodes with diverse velocity are simulated in NS2 environment which contains variable node clusters. Based broadcasting records of mobile nodes with diverse velocity are considered to create information table. Fine and malicious nodes are recognized based on rules derived in table of irregular set tactic. Shortest path without malicious node is elected to broadcast packets in the environment. Proposed tactic consequences reveal irregular set tactic amplifies network capability data deliverance percentage, throughput and dwindled end-to-end delay in mobile sensors.

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.

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

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.

TBDD: Territory-Bound Data Delivery for Large-Scale Mobile Sink Wireless Sensor Networks

The hierarchical structure-based data dissemination is the most popular technique in mobile sink wireless sensor networks (MS-WSNs). An ingenious virtual structure design combined with a precise routing management strategy is significant to attaining efficient data dissemination in hierarchical approaches. This paper proposes a hierarchical protocol called TBDD that divides the network into multiple partitions called Regions, and spots the location of the mobile sink (MS) according to these partitions. TBDD dynamically assigns a defined role to each division by adopting the mobility of the sink. Thus, the protocol takes advantage of the sink’s movement and the Regions’ flexible role in balancing energy consumption throughout the network. A Region is designated as active if it contains the sink node or passive otherwise. By using the territory of the active region as a temporal location of the MS, the proposed protocol hides the local movements (i.e., moves inside the active region) of the sink from the rest of the network. In such a way, regardless of the exact position of the sink, sensed data flows from different network ends to the sink’s temporal location. Therefore, TBDD reduces the query request and response burden employed to get the position of the sink. Besides, TBDD implements a spanning tree to report the location information of the MS. Last; we applied an opportunistic routing technique that captures multiple network criteria to elect packet forwarder nodes. The proposed protocol is mathematically analyzed and experimentally evaluated and shows outstanding performance in terms of the number of hops, energy consumption, delay, network lifetime, and success ratio.

Energy Proficient Secure Clustered Protocol in Mobile Wireless Sensor Network Utilizing Blue Brain Technology

Mobile Wireless Sensor Network (MWSN) guarantees copious applications in daily life of ordinary people, government bodies, commerce, defense and culture etc. Association along with the devices in blue brain to fetch diverse appliances in the real world is an exigent assignment. Several existed protocols proficiently indulgence both homogenous and heterogeneous devices. This research work conquers the gap between the physical wireless blue brain network surroundings and the factual heterogeneous network. This research work intention not only provide a proficient energy responsive clustering broadcasting protocol for blue brain network, excluding a real blue brain network design for probing the proposed protocol contrasted to existed tactics. Proficient cluster-head election amplifies the deployment of the blue brain nodes energy stuffing and therefore amplifies the network life-span and the packets relaying rate to the base station. Energy Proficient Secure Clustered (EPSC) exploits special weighted selection possibilities for electing a Cluster-head based on regions heterogeneity intensity. NS2 simulation demonstrates prolongs the network life-span, diminishes reclustering, enhanced security and amplifies throughput contrasted with BM-BWO. The results will be beneficial for the design process in WSN for blue brain application.

Mitigation of Sinkhole Attack in Dynamic Directional Routing for Mobile Wireless Sensor Networks

Mitigation of Sinkhole Attack in Dynamic Directional Routing for Mobile Wireless Sensor Networks

Energy-Efficient Improved Optimal K-Means: Dynamic Cluster Head Selection based on Delaying the First Node Death in MWSN-IoT

The Internet of Things (IoT), which attaches dynamic devices that can access the Internet to create a smart environment, is a tempting research area. IoT-based mobile wireless sensor networks (WSN-IoT) are one of the major databases from which the IoT collects data for analysis and interpretation. However, one of the critical constraints is network lifetime. Routing-based clustered protocols and cluster head (CH) selection are crucial in load balancing and sensor longevity. Yet, with clustering, sensor node mobility requires more overhead because the nodes close to the center may get far and thus become unsuitable to be a CH, whereas those far from the center may get close and become good CH candidates, influencing energy consumption. This paper suggests an energy-efficient clustering protocol with a dynamic cluster head selection considering the distance to the cluster center, remaining energy, and each node's mobility degree implementing a rotation mechanism that allows cluster members to be equally elected while prioritizing those with the minimum weight. The advised algorithm aims to delay the first node death (FND) and thus prolong the stability period and minimize energy consumption by avoiding re-clustering. The performance of the proposed protocol exceeds that achieved in our previous work, Improved OKmeans, by 11%, in first dead node lifetime maximization, 43% in throughput, and 44% in energy efficiency.