Mobile Sensor Nodes Research Articles

Energy efficient routing for improving lifetime in MWSN: A clustering approach

A Mobile wireless sensor network (MWSN) consists of mobile sensor nodes, which can be deployed in any specific environment, and due to its’ mobility it can perform with rapid topological transformations of a network. The sensor nodes having limited battery power are used to collect specific data and this raw data is sent to a static sink node of the network. Under such a scenario, to avoid frequent disconnections due to topological change in the network and can avail more reliable data transmissions in energy awareness perspective, an energy efficient routing protocol for MWSNs to improve its lifetime is proposed in this paper by utilizing a clustering approach. A MWSN with random number of sensor nodes are initially considered and then, clustering algorithm K-means is used to find a predefined number of clusters with their initial cluster heads (CHs) and the centroid location of these clusters is also determined. The role of these CHs is to elect our DDBLACH (distance to sink and cluster centroid with battery level aware cluster head) nodes from each cluster, by sending and receiving intra-cluster messages among other member sensor nodes. A DDBLACH node is determined by using three factors, such as minimum distance from cluster centroid location, minimum distance from sink and the maximum battery level of the node from each cluster. These DDBLACH nodes are used to collect data from intra-cluster sensors and thereafter, send those towards sink node for further processing using tree based hierarchical routes. Finally, an energy efficient routing technique for MWSNs is proposed for data transmission from these DDBLACH nodes of clusters to a sink of the network. Here simulation results indicate the supremacy of our proposed scheme over other existing methods in various aspects, such as improving the presence of alive nodes and average network lifetime.

Development of Fused Recurrent Neural Network‐Gated Recurrent Unit with Improved Sandpiper Optimization‐based Intelligent Node Localization Framework in WSN

SummaryEmploying mobile nodes or Sensor Nodes (SN) in the Wireless Sensor Networks (WSNs) is a critical task within the coverage area. However, these techniques produce huge errors in the evaluated distance between source nodes and unknown nodes, which increases the average NL error of the unknown node. The usage of a Global Positioning System (GPS) for all the SNs needs high cost for implementation in large‐scale WSNs. The designing of NL in WSN with less energy requirements and less cost is very significant. Enhanced NL is performed with deep learning techniques together with the heuristic strategy for performance enhancement. The developed localization model is mainly utilized for providing fast, scalable, and easily implementable results over heterogeneous and homogeneous networks. Moreover, it is applicable for sparse and dense deployment of nodes. Here, a hybridized deep structured approach is employed by integrating the Recurrent Neural Network (RNN) with a Gated Recurrent Unit (GRU) for calculating the distance among the source nodes to facilitate determining the optimal location for the unidentified node by analyzing the node information present in the received packets. Here, the optimization of constraints executed in the deep learning techniques with the Modified Exploration‐based Sandpiper Optimization Algorithm (ME‐SOA), and also it is utilized to find the optimal location of unknown nodes based on computed distance from the received packets that minimize the average localization error. The experimental results are validated and show the effectiveness of the proposed algorithm with a reduced error rate than other algorithms.

Auto-localization algorithm for mobile sensor nodes in wireless sensor networks

Auto-localization algorithm for mobile sensor nodes in wireless sensor networks

Enhanced Energy Proficient Clustering (EEPC) algorithm for minimizes the energy depletion in wireless sensor network

Recent developments in wireless sensor networks (WSNs) have generated interest in the area of sensor tracking events. The proposed work aims to decrease energy usage by identifying functional relay nodes utilizing the enhanced energy proficient clustering (EEPC) method. To minimize long-distance interaction between CH and BS, a power-efficient relay chosen technique is proposed using improved Grasshopper Optimization algorithm (IGOA). The network is constructed using both mobile and fixed nodes. Mobile nodes first choose cluster head (CH) among fixed nodes after broadcasting information. Depending on the related positioning and power density, mobile nodes choose their CH. CH receives information from mobile sensor nodes (SNs). Based on the nodes’ velocity and position, the EEPC method computes particle fitness value and chooses the relay nodes. Performance metrics include Throughput, End-to-End Delay, Packet Delivery Ratio (PDR), Quantity of Received Packets, Total Residual Energy, and Total Energy Consumption, network lifetime. The suggested technique enhances network lifetime and reduces energy consumption when compared to other existing protocols. After 200 simulation rounds, the suggested EEPC displays 98.87% PDR. However, during 200 simulation cycles, ANFISRS, ORNS and DTC-ORS show 97.82%, 96.03%, and 89.585% PDR, respectively.

High uncertainty aware localization and error optimization of mobile nodes for wireless sensor networks

<p>The localization of mobile sensor nodes in a wireless sensor network (WSN) is a key research area for the speedy development of wireless communication and microelectronics. The localization of mobile sensor nodes massively depends upon the received signal strength (RSS). Recently, the least squared relative error (LSRE) measurements are optimized using traditional semidefinite programming (SDP) and the location of the mobile sensor nodes was determined using the previous localization methods like least squared relative error and semidefinite programming (LSRE-SDP), and approximate nonlinear least squares and semidefinite programming (ANLS-SDP). Therefore, in this work, a novel high uncertainty aware-localization error correction and optimization (HUA-LECO) model is employed to minimize the aforementioned problems regarding the localization of mobile sensor nodes and enhance the performance efficiency of root mean square error (RMSE) results. Here, the position of target mobile sensor nodes is evaluated based on the gathered measurements while discarding faulty data. Here, an iterative weight updation approach is utilized to perform localization based on Monte Carlo simulations. Simulation results show significant improvement in terms of RMSE results in comparison with traditional LSRE-SDP and ANLS-SDP methods under high uncertainty.</p>

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

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.

Mobile Structural Health Monitoring Based on Legged Robots

With the advancements in information, communication, and sensing technologies, structural health monitoring (SHM) has matured into a substantial pillar of infrastructure maintenance. In particular, wireless sensor networks have gradually been incorporated into SHM, leveraging new opportunities towards reduced installation efforts and enhanced flexibility and scalability, as compared to cable-based SHM systems. However, wireless sensor nodes are installed at fixed locations and need to be employed at high density to reliably monitor large infrastructure, which may cause high installation costs. Furthermore, the limited power autonomy of wireless sensor networks, installed at fixed locations for unattended long-term operation, still represents a significant constraint when deploying stationary wireless sensor nodes for SHM. To resolve the critical constraints stemming from costly high-density deployment and limited power autonomy, a mobile structural health monitoring concept based on legged robots is proposed in the study reported in this paper. The study explores the accuracy and cost-efficiency of deploying legged robots in dense measurement setups for wireless SHM of civil infrastructure, aiming to gain insights into the advantages of mobile wireless sensor nodes in general and of legged robots in particular, in terms of obtaining rich information on the structural condition. As is shown in this paper, the legged robots, as compared to stationary wireless sensor nodes, require a smaller number of nodes to be deployed in civil infrastructure to achieve rich sensor information, entailing more cost-efficient, yet accurate, SHM. In conclusion, this study represents a first step towards autonomous robotic fleets advancing structural health monitoring.

AutoBar: Automatic Barrier Coverage Formation for Danger Keep Out Applications in Smart City.

Barrier coverage is a fundamental application in wireless sensor networks, which are widely used for smart cities. In applications, the sensors form a barrier for the intruders and protect an area through intrusion detection. In this paper, we study a new branch of barrier coverage, namely warning barrier coverage (WBC). Different from the classic barrier coverage, WBC has the inverse protect direction, which moves the sensors surrounding a dangerous region and protects any unexpected visitors by warning them away from the dangers. WBC holds a promising prospect in many danger keep out applications for smart cities. For example, a WBC can enclose the debris area in the sea and alarm any approaching ships in order to avoid their damaging propellers. One special feature of WBC is that the target region is usually dangerous and its boundary is previously unknown. Hence, the scattered mobile nodes need to detect the boundary and form the barrier coverage themselves. It is challenging to form these distributed sensor nodes into a barrier because a node can sense only the local information and there is no global information of the unknown region or other nodes. To this end, in response to the newly proposed issue of the formation of barrier cover, we propose a novel solution AutoBar for mobile sensor nodes to automatically form a WBC for smart cities. Notably, this is the first work to trigger the coverage problem of the alarm barrier, wherein the regional information is not pre-known. To pursue the high coverage quality, we theoretically derive the optimal distribution pattern of sensor nodes using convex theory. Based on the analysis, we design a fully distributed algorithm that enables nodes to collaboratively move toward the optimal distribution pattern. In addition, AutoBar is able to reorganize the barrier even if any node is broken. To validate the feasibility of AutoBar, we develop the prototype of the specialized mobile node, which consists of two kinds of sensors: one for boundary detection and another for visitor detection. Based on the prototype, we conduct extensive real trace-driven simulations in various smart city scenarios. Performance results demonstrate that AutoBar outperforms the existing barrier coverage strategies in terms of coverage quality, formation duration, and communication overhead.

Secure and efficient blockchain-based consensus scheme for MWSNs with clustered architecture

Blockchain has proven in sensor networks as a distributed solution for transparent and secure storage, which allows its application in mobile wireless sensor networks (MWSNs). The consensus mechanism, an essential aspect of blockchain technology, must concern the high mobility, resource-constrained nature, and weak physical defenses of sensor nodes in MWSNs. To secure MWSN data storage in clustered communication, we design a proof-of-information (PoI) variant for fair miner campaigning via the amount of valid data generated from environmental information, including a dynamic adjustment of the data volume threshold to detect malicious nodes and prevent them from misreporting information. Additionally, we introduce a filtering mechanism through the dynamic integrated trust (DIt) of nodes, which integrates the trust evaluation of peer nodes across the network combining objective performance to prevent malicious nodes from infiltrating the final consensus group. The multi-level filtering technique improves the campaign fairness while isolating malicious nodes, ensuring complexity-sensitive PBFT algorithm efficiency in large-scale networks. Simulation results show that the scheme isolates 90% of the malicious nodes and screens 20% of members to produce a smaller final consensus group. Further analysis of impacts on the performance considering network topology and mobility patterns and comparisons of the relevant solutions are presented.

Lightweight APIT with Bat Optimization with Simulated Annealing Localization for Resource-Constrained Sensor Networks

In a wireless sensor network, information processing, and information acquisition, localization technology is the key to making it practically possible application. Approximate Point-in-Triangulation (APIT) is the most widely used localization estimation which has high accuracy in localizing the nodes and ease of deployment of nodes in the real-time environment. Though it has numerous key advantages, some of the drawbacks which make it a little setback in preference are the unevenness in the distribution of nodes. Tracking is more appropriate for mobile sensor nodes than tracking is for static sensor nodes. The two main types of localization algorithms are range-based and range-free techniques. In an indoor setting, the projected range (distance) between an anchor and an unknown node is very inaccurate. By utilizing a large number of already existing access points (APs) in the range-free localization approach, this issue can be overcome to a great extent. The utilization of multisensor data, such as magnetic, inertial, compass, gyroscope, ultrasonic, infrared, visual, and/or odometer, is stressed in recent research to further increase localization accuracy. The tracking system also makes location predictions for the future based on historical location data. To overcome this issue, the proposed localization algorithm of APIT with Bat-SA proves its efficiency. Due to its low localization error, the traditional Bat method is more accurate than APIT. The proposed Bat using the SA algorithm is found to perform better than the traditional APIT algorithm in terms of convergence of computing rate and success rate. In order to mimic the suggested APIT method, it is paired with the Bat-SA localization technique. Simulation evaluation proves the performance efficiency of the proposed algorithm. The performance metrics parameters are latency, node distribution map, positioning error map, and neighbor relationship diagram which are used to evaluate the proposed method.

Environmental Modeling and Traffic Simulation: A multivariate approach to monitor urban air pollutant agents.

This work presents an interdisciplinary assessment examining air quality tracking in urban environments. This application is well suited to be approached with wireless sensor networks' paradigm in their overall variations. The proposed modeling application takes advantage of Vehicle Sensor Networks (VSN) by embedding sensor nodes in public transportation, addressing this study case with bus lines so that the mobiles spread the sampling activity through many different places visited during the route. Simultaneously, it alleviates power management restrictions, packaging dimensions (size and weight), and general maintenance issues. We perform environmental modeling based on real data considering temporal and spatial multivariate behavior on observed phenomena. We consider the city of São Paulo in our case study and parse the asserted data to create a multivariate map of samples, showing the behavior of five different air pollutants from fossil-fueled vehicles (CO, O3, PM10, NO2 and SO2) simultaneously while it also varies in time. Furthermore, the experiment considers a detailed description of roads, bus lines, vehicle itineraries, and general traffic information. The input data that has unformatted or missing information due to being sourced from real sensors is handled to create the map mentioned above. Our methodology addresses the following: 1) the mentioned environmental simulation, 2) the deployment of mobile sensor nodes and performing sensing process, 3) the implementation of network activity and delivery of collected data, 4) visualization of monitored environment based on gathered data using Voronoi Diagrams to fill blank data at non-reached areas. Finally, our VSN-based approach improved 126 times lower error and 11 times higher coverage compared to conventional monitoring with air quality stations.

Formation of wireless sensor network protection system parameters for intrusion detection in the form of false event flows

Wireless sensor networks with stationary and mobile sensor nodes are studied. For mobile nodes, in addition to sensor nodes, the influence of node movement speed on the duration of the network life cycle for mobile AdHoc networks was also studied. When studying the impact of erroneous events on the sensor field, it was established that providing sensor nodes with mobility allows increasing the life cycle of the network. A model of intrusion into a wireless sensor network with the aim of shortening its life cycle has been developed, which differs from known models in that false event streams are used to achieve this goal. The model is developed based on typical geometric, quantitative and energy parameters of wireless sensor networks using a basic clustering algorithm for a homogeneous mobile sensor network under conditions of Poisson network intrusion and deterministic error event flows. It is established that the duration of the life cycle of a wireless sensor network can significantly depend on the type of the flow of erroneous events and, other things being equal, under the influence of a deterministic flow can be almost half as long as under the influence of a flow of erroneous events. the impact of the flow of false events. the Poisson flow effect. Detection of false events in a wireless sensor network can be considered as a target tracking task, and to detect false events with a given probability, taking into account the limited capabilities of sensor nodes, it is advisable to use the architectural characteristics of the network, the distribution of the density of nodes on the sensor field.

Improved 3D localization algorithm for large scale wireless sensor networks

As localization represents the main core of various wireless sensor network applications, several localization algorithms have been suggested in wireless sensor network research. In this article, we put forward an iterative bounding box algorithm enhanced by a Kalman filter to refine the unknown node’s estimated position. In fact, several research efforts are currently in progress to extend the 2D positioning algorithm in WSNs to 3D that reflects reality and the most practical applications. Subsequently, we replace a large number of GPS-equipped anchors with a single mobile anchor. In our studies, we consider the type of range-free sensor network exploiting the wireless sensors connectivity. We assess the performance of our algorithm using exhaustive experiments on several isotropic and anisotropic topologies. Our proposed algorithm can fulfill the joint goals of algorithm transparency and accuracy for various scenarios by evaluating parameters such as localization accuracy whilst changing other simulation parameters such as the effect of communication range, mobile anchor node position and sensor node deployment topology. It has been proven by the results of the experiments that the proposed algorithm effectively reduces the location error without requiring more equipment or increasing the communication cost.

An efficient routing protocol for coherent energy using mayfly optimization algorithm in heterogeneous wireless sensor networks

AbstractHeterogeneous wireless sensor network (WSN) is used in infrastructure monitoring, animal monitoring, smart building and environmental monitoring. Several research has been done to detect energy efficiency concerns, but none of them has been successful. In this proposed model, the clusters are formed using K‐means clustering algorithm for the sensor nodes. MOA is used in combination with an energy efficient routing protocol to select the CH. Sub‐clusters are formed because of the nodes that cannot survive with the CH. To transfer the data from CH to the base station, each and every sub‐cluster selects a separate CH. When the energy level of the CH becomes low, the selection process of CH will start again and a new CH will be selected according to the fitness evaluation of the efficient routing protocol. The performance of the mobile sensor nodes is determined using the residual energy, network lifetime, number of data packets sent to the sink and number of dead nodes. The proposed model performed better than the existing techniques such as LEACH, LEACH‐PSO and SEECP. Simulation results illustrate that the proposed model enhances the life time of networks, nodes' energy consumption rate and exhaustion ratio for every node by more than 4.12%, 5.76% and 6.18%.

Energy-aware wireless mesh network deployment using optimization mechanism

Wireless mesh networks are widely used to create network infrastructure in rural areas due to their flexible properties, such as self-healing mechanisms and associated redundant paths. For example, a wireless mesh network can suitably operate with limited battery power in wildlife monitoring applications. The locations of mobile routers to support mobile sensor nodes are essential for extending the system lifetime. This study proposes an energy-aware wireless mesh network deployment optimization mechanism. The goal is to determine a suitable location for the mesh routers with the aim of maximizing network lifetime. After the location solution is obtained from the proposed mechanism, it is then evaluated for system lifetime and network performance by the network simulator (ns-3). The proposed method outperforms the brute-force method in terms of computation time for all amounts of mesh clients. For example, for 30 mesh clients, the proposed method uses only a few minutes, while the brute-force mechanism requires more than 200 minutes to complete the process. Furthermore, compared to the brute-force method, it achieves nearly the same system lifetime and other performance parameters, such as throughput, packet delivery ratio, and packet inter-arrival time. In the real implementation, in which the sensor node placements can be changed during the installation period owing to the environmental status or the recommendation of the installers, the results can be recalculated in a short period.

A reinforcement learning-based metaheuristic algorithm for solving global optimization problems

The purpose of this study is to utilize reinforcement learning in order to improve the performance of the Sand Cat Swarm Optimization algorithm (SCSO). In this paper, we propose a novel algorithm for the solution of global optimization problems that is called RLSCSO. In this method, metaheuristic algorithm is combined with reinforcement learning techniques to form a hybrid metaheuristic algorithm. This study aims to provide search agents with the opportunity to perform efficient exploration of the search space in order to find a global optimal solution by using efficient exploration and exploitation to find optimal solutions within a given search space. A comprehensive evaluation of the RLSCSO has been conducted on 20 benchmark functions and 100-digit challenge basic test functions. Additionally, the proposed algorithm is applied to the problem of localizing mobile sensor nodes, which is NP-hard (nondeterministic polynomial time). Several extensive analyses have been conducted in order to determine the effectiveness and efficiency of the proposed algorithm in solving global optimization problems. In terms of cost values, the RLSCSO algorithm provides the optimal solution, along with tradeoffs between exploration and exploitation.

Self-Organized Ad Hoc Mobile (SOAM) Underwater Sensor Networks

The need of underwater wireless sensor networks (UWSNs) having mobile sensor nodes has been there for a long time in form of underwater warfare or explorations by autonomous underwater vehicles (AUVs) or remote unmanned vehicles (ROVs). There are very few protocols for ad hoc mobile UWSNs (AMUWSNs). Designing a protocol for AMUWSN is quite challenging because of continuous random movement of the sensor nodes. In addition to random movement, the challenges to design a routing protocol for AMUWSN are more demanding than terrestrial ad hoc networks due to acoustic communications, which has large propagation delay in water. In this article, we present a self-organized ad hoc mobile (SOAM) routing protocol for AMUWSN. The sensor nodes may need to communicate with each other to the gateway (GW). The protocol, which we also refer to as SOAM, is a reactive, self-configuring, and self-organizing cluster-based routing protocol that uses received signal strength (RSS) for distance estimation. A beacon (BCN) packet will be sent by the GW, which will traverse through all the cluster heads (CHs) to form forwarding paths between the GW and the CHs. The ordinary sensor nodes (OSNs) will select the CHs every time they intend to forward a packet based on the BCN and they will receive from CHs. The formation of the forwarding path between the GW and the CHs and the selection CHs by OSN is explained in <xref ref-type="sec" rid="sec4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Section IV</xref> .

Mobile Sensor Nodes Traversal Schemes to Attend Events at Random Locations With Minimal Energy Depletion

A group of homogeneous mobile sensor nodes (MSNs) with fixed amount of energy are deployed randomly over the region of interest (ROI). Events might happen at a random location within the ROI. MSNs must travel a certain distance to reach the random location for information collection. The modified Min-Heap approach helps to identify a suitable MSN from the group of MSNs. The selected MSN will reach the random location by traveling a shorter distance than its counterparts. The proposed mathematical models are helpful in uniformly distributing the workload among the MSNs, to ensure that all the MSNs have an approximately equal amount of residual energy.

Hopping-Aware Cluster Header Capability for Sensor Relocation in Mobile IoT Networks

Mobile sensor nodes such as hopping sensors are of critical importance in data collection. However, the occurrence of sensing holes is unavoidable due to the energy limitation of the nodes. Thus, it is evident that the relocation of mobile sensors is the most desirable method to recover the sensing holes. The previous research conducted by the authors so far demonstrated the most realistic hopping sensor relocation scheme, which is suitable for the distributed environment. In previous studies, the cluster header plays an essential role in detecting the sensing hole and requesting the neighboring cluster to recover the sensing hole that occurred in the sensor node. However, the limitations of the cluster header in the previously proposed relocation protocol are not fully considered. Because the cluster header jumps more frequently than non-header nodes, its energy consumption is relatively high compared to other nodes. Therefore, it is most likely to lead to header node failure and can lead to data loss on the network. In this paper, the jumping ability and energy consumption of the cluster header are seriously considered. Additional ability to replace cluster headers in case of failure is also implemented. Simulation results show that the data collection time can be further increased, which demonstrates the validity of the proposed algorithms.