Connectivity In Wireless Sensor Networks Research Articles

Gram Matrix Completion for Cooperative Localization in Partially Connected Wireless Sensor Network

Gram Matrix Completion for Cooperative Localization in Partially Connected Wireless Sensor Network

Sink Node Placement and Partial Connectivity in Wireless Sensor Networks.

This research delves into the aspects of communication and connectivity problems within random Wireless Sensor Networks (WSNs). It takes into account the distinctive role of the sink node, its placement, and application-specific requirements for effective communication while conserving valuable network resources. Through mathematical modeling, theoretical analysis, and simulation evaluations, we derive, compare, and contrast the probabilities of partial and full connectivity within a random WSN, factoring in network parameters and the maximum allowable hop distance/count hmax. hmax captures the diverse range of delay-sensitive requirements encountered in practical scenarios. Our research underscores the significant impact of the sink node and its placement on network connectivity and the sensor connection rate. The results exemplify a noteworthy decline in the sensor connection rate, dropping from 98.8% to 72.5%, upon relocating the sink node from the network center to the periphery. Moreover, as compared with full connectivity, partial connectivity and the sensor connection rate are more suitable metrics for assessing the communication capability of random WSNs. The results illustrate that 1.367 times more energy is required to connect less than 4% of the remote sensors, based on the examined network settings. Additionally, to increase the sensor connection rate slightly from 96% to 100%, an additional 538% more energy is required in multipath fading based on the widely adopted energy consumption model. This research and its outcomes contribute to establishing appropriate performance metrics and determining critical network parameters for the practical design and implementation of real-world wireless sensor networks.

Efficient target monitoring with fault‐tolerant connectivity in wireless sensor networks

AbstractSensing coverage with fault‐tolerant connectivity is considered as a recent research challenge, which measures the Quality of Service (QoS) in wireless sensor networks. QoS is determined based on the set of deployed sensor nodes that monitor all available discrete targets lying at the given point of interest and at the same time have to retain reliable connectivity with the base station. In this article, we focus on two phases namely, monitoring targets in the given sensing area by splitting the sensor network into set covers, which is considered as a nondeterministic polynomial‐time complete problem and optimal deployment of intelligent nodes. At first, achieving full coverage with deployed sensors by covering all discrete targets to prolong the network lifetime. On the other hand, a novel S2 (sensors‐spine) node approach was used to place an optimal number of intelligent nodes using a nature‐inspired algorithm named moth flame optimization to overcome the fault among the disjoints sets such that the entire network was fully connected and ensure that sensed data from each target points are collected by the sink node. To evaluate our proposed design, extensive simulations were conducted to demonstrate the efficacy of our proposed mechanism that outperforms other existing techniques found in the literature in terms of prolonged network lifetime and there is no failure in the transmission path during data transmission to the sink node at every t s, such that the entire network was fully connected.

Hybrid Algorithm for Node Deployment with the Guarantee of Connectivity in Wireless Sensor Networks

In this paper, the problem of deployment in wireless sensor networks is investigated. The authors propose a Hybrid Modified Crow Search Bee Algorithm (HMCSBA) for coverage maximization with the guarantee of connectivity between the deployed sensors. Firstly, a Modified Crow Search Algorithm (MCSA) is proposed based on the basic CSA algorithm to form a connected network after initial random deployment. The position equation of the original CSA was updated by introducing a linear flight length that increases throughout iterations to force the sensors to join the network. Then, the Bees Algorithm (BA) is applied to optimize the network coverage without losing connectivity between the deployed sensors. Simulations and comparative studies were carried out to prove the relevance of the proposed algorithm. Results demonstrate that the proposed algorithm can optimize the coverage and guarantee network connectivity.

The Use of Computational Geometry Techniques to Resolve the Issues of Coverage and Connectivity in Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) enhance the ability to sense and control the physical environment in various applications. The functionality of WSNs depends on various aspects like the localization of nodes, the strategies of node deployment, and a lifetime of nodes and routing techniques, etc. Coverage is an essential part of WSNs wherein the targeted area is covered by at least one node. Computational Geometry (CG) -based techniques significantly improve the coverage and connectivity of WSNs. This paper is a step towards employing some of the popular techniques in WSNs in a productive manner. Furthermore, this paper attempts to survey the existing research conducted using Computational Geometry-based methods in WSNs. In order to address coverage and connectivity issues in WSNs, the use of the Voronoi Diagram, Delaunay Triangulation, Voronoi Tessellation, and the Convex Hull have played a prominent role. Finally, the paper concludes by discussing various research challenges and proposed solutions using Computational Geometry-based techniques.

Modified Floyd–Warshall’s Algorithm for Maximum Connectivity in Wireless Sensor Networks under Uncertainty

The main objective of this paper is to find the duration of maximum time connectivity of sensor nodes under uncertainty utilizing the prespecified voltage/power of each sensor node. Wireless sensor networks (WSNs) are composed of nodes that transmit data between each other over routing. A variety of routing protocols and algorithms exist, each related to a particular set of conditions. There are a variety of routing algorithms available, some of which can be used in WSNs for routing. The goal of the fastest distance routing algorithms in a WSN is to use the least amount of energy possible. In a WSN, Dijkstra’s algorithm is typically used for shortest path routing. The Floyd–Warshall’s algorithm is used to compute the shortest paths between distinct nodes in a regular graph, but due to the absence of a communication mode, this algorithm is not ideal for routing in wireless networks. In this research work, we have considered a WSN to find out the maximum connectivity time utilizing optimum voltage. On the other hand, duration of connectivity and energy/voltage are two vital parameters that are difficult to manage. Because of limited resources and safety concerns, safety implementation is limited. Also, due to the irregular/hazardous environmental situations, the distance between sensor nodes and its voltage to link up the nodes are totally unpredictable. In this work, we employ triangular fuzzy numbers to express unpredictability. Then, utilizing defuzzification of fuzzy numbers, the associated WSN problem was transformed into a crisp one. The widely used signed distance approach has been applied for the defuzzification of fuzzy numbers in this case. To determine the best outcome and to illustrate the usefulness of the suggested technique, a numerical example has been solved using the modified Floyd–Warshall’s algorithm. Finally, concluding remarks on the proposed approach as well as future studies have been provided.

An Adaptive Topology Management Scheme to Maintain Network Connectivity in Wireless Sensor Networks.

The roots of Wireless Sensor Networks (WSNs) are tracked back to US military developments, and, currently, WSNs have paved their way into a vast domain of civil applications, especially environmental, critical infrastructure, habitat monitoring, etc. In the majority of these applications, WSNs have been deployed to monitor critical and inaccessible terrains; however, due to their unique and resource-constrained nature, WSNs face many design and deployment challenges in these difficult-to-access working environments, including connectivity maintenance, topology management, reliability, etc. However, for WSNs, topology management and connectivity still remain a major concern in WSNs that hampers their operations, with a direct impact on the overall application performance of WSNs. To address this issue, in this paper, we propose a new topology management and connectivity maintenance scheme called a Tolerating Fault and Maintaining Network Connectivity using Array Antenna (ToMaCAA) for WSNs. ToMaCAA is a system designed to adapt to dynamic structures and maintain network connectivity while consuming fewer network resources. Thereafter, we incorporated a Phase Array Antenna into the existing topology management technologies, proving ToMaCAA to be a novel contribution. This new approach allows a node to connect to the farthest node in the network while conserving resources and energy. Moreover, data transmission is restricted to one route, reducing overheads and conserving energy in various other nodes’ idle listening state. For the implementation of ToMaCAA, the MATLAB network simulation platform has been used to test and analyse its performance. The output results were compared with the benchmark schemes, i.e., Disjoint Path Vector (DPV), Adaptive Disjoint Path Vector (ADPV), and Pickup Non-Critical Node Based k-Connectivity (PINC). The performance of ToMaCAA was evaluated based on different performance metrics, i.e., the network lifetime, total number of transmitted messages, and node failure in WSNs. The output results revealed that the ToMaCAA outperformed the DPV, ADPV, and PINC schemes in terms of maintaining network connectivity during link failures and made the network more fault-tolerant and reliable.

UAV-assisted connectivity enhancement algorithms for multiple isolated sensor networks in agricultural Internet of Things

In recent years, Wireless Sensor Networks (WSNs) are used in agricultural Internet of Things (IoT) to observe crop data, that has improved the output and quality of agricultural product. However, the connectivity of WSNs in agricultural IoT will encounter new challenges when a farmland is segmented into multiple isolated sub-sensor networks (MISN) due to natural environmental factors. In order to tackle this issue, a new definition of connectivity in MISN is proposed, and the Unmanned Aerial Vehicle (UAV) assisted connectivity enhancement algorithms (UCE) are designed. Our target is to minimize energy consumption of the UAV while satisfying MISN connectivity requirements. Firstly, the destination selection ant colony optimization algorithm (DSACO) and the normalized ant colony optimization algorithm (NACO) are proposed to connect all the sub-sensor networks. Through comparative analysis of them, the optimal path for the UAV to solve the above optimization problem is found. Secondly, autonomously generated optimal point ant colony optimization algorithm (AGOP) is proposed to connect non-communicable nodes within each sub-sensor network. Simulation results show that the complexity of the three algorithms is low, and they can complete the connectivity enhancement task of a large outdoor MISN with reduced energy consumption of the UAV, and the connectivity of the MISN has been significantly improved.

Symmetric Connectivity in Wireless Sensor Networks with π/3 Directional Antennas

Directional Wireless Sensor Networks (DWSNs) have been a very active research topic as they have a wide range of applications in both military and civilian areas. In this paper, we study the Antenna Orientation (AO) and Antenna Orientation and Power Assignment (AOPA) problems, two important issues in DWSNs. In the AO problem, given a set [Formula: see text] of sensors equipped with directional antennas with beam-width [Formula: see text], the objective is to determine an orientation of the antennas and a minimum transmission range [Formula: see text] such that the induced symmetric communication graph is connected. We prove that the AO problem is NP-hard. We then extend our proof technique and show that the AOPA problem for DWSNs equipped with [Formula: see text] directional antennas is also NP-hard where we have to determine an orientation of the antennas as well as a power assignment [Formula: see text] to the nodes such that the resulting symmetric communication graph is connected, and (2) [Formula: see text] is minimized ([Formula: see text] is the distance-power gradient, [Formula: see text]). We propose an [Formula: see text] heuristic algorithm and demonstrate by simulation that our method is better than the Ice-cream method.

ДОСЛІДЖЕННЯ ЗАДАЧ ПОБУДОВИ БЕЗДРОТОВИХ СЕНСОРНИХ МЕРЕЖ

The research of problems of construction of wireless sensor networks is carried out in the work, the main stage of their decision is development of models of the description of a sensor network and the corresponding methods, technologies of maintenance of necessary parameters of quality of functioning of a sensor network. The study involves the consideration of basic technologies and standards in which there is an opportunity to implement the creation of wireless sensor networks. The basis of wireless sensor networks are communication channels organized between network elements using appropriate wireless technology. The main tasks of building wireless sensor networks: determining traffic parameters; determining the list of services; development or selection of a network model; determination of network functioning indicators; solving the problem of compromise between the quality of operation, traffic and resources. A number of wireless networking technologies are widespread in corporate networks, use an unlicensed frequency band, and in private networks, which allows their use in a number of tasks, due to the density of devices that support them and the availability of technology. A number of technologies do not support the functions of self-organization of the communication network. Such mechanisms can be implemented using software and related protocols at the network level. Existing technologies have the ability to organize communication from tens to tens of thousands of meters. For specific applications, it is one of the essential factors in choosing the appropriate technology. The probability of connectivity in wireless sensor networks depends on the structure and parameters that determine the network: characteristics of the service area (volume, flat, configuration, geometric dimensions), number of network nodes, characteristics of communication capabilities of network elements, antenna patterns, standards and protocols, communication range, transmitter power), features of the involved routing protocols. Features of providing connectivity in wireless sensor networks lie in the possibility of using gateways built using various technologies, which can significantly expand the capabilities of wireless networks in increasing the likelihood of connectivity. A feature of the construction of wireless sensor networks are significant differences in traffic servicing in the networks of different technologies used. Indicators of the quality of wireless networks, such as bandwidth, delay, probability of loss are interdependent and show how effectively the sensor network works. When you change one of the performance indicators of wireless sensor networks, it affects other quality indicators.

Cluster head replacement strategy for wireless sensor networks connectivity repairing

Cluster head replacement strategy for wireless sensor networks connectivity repairing

Cluster head replacement strategy for wireless sensor networks connectivity repairing

Cluster head replacement strategy for wireless sensor networks connectivity repairing

Low-Latency and High-Concurrency 5G Wireless Sensor Network Assists Innovation in Ideological and Political Education in Colleges and Universities

This paper first analyzes the existing optimization models and methods based on low-latency and high-concurrency 5G wireless sensor networks and focuses on some research results based on the physical layer, data link layer, and network layer. After that, the energy-constrained cross-layer optimization algorithm of the joint physical layer and network layer and the cross-layer optimization algorithm based on reliable transmission are analyzed and compared. According to the ideological and political education cycle and network connectivity of wireless sensor networks, the existing definition of the ideological and political education cycle of wireless sensor networks was questioned and its irrationality was demonstrated. Firstly, we introduce the system model, energy capture model, and energy consumption model of this scheme; then, calculate the energy consumption of the node sending and receiving data and analyze the maximum throughput that the node can provide for data transmission to perform mathematical modeling. In a balanced ideological and political education cycle, the optimal solution is given. Finally, it introduces distributed high-throughput routing for EH-WSNs based on low latency and high concurrency and gives a performance comparison with the shortest path routing scheme. The energy consumption of each part of the node is analyzed in a comprehensive and in-depth manner, and the direction of the energy optimization strategy of the network node and its sensor network is pointed out. On this basis, this article proposes a new definition of the network ideological and political education cycle: the ideological and political education cycle of the wireless sensor network is equal to the ideological and political education cycle of the first joint in the network that fails due to energy exhaustion. Based on this definition, the wireless sensor network is analyzed, and the cross-layer (physical layer, data link layer, and network layer) optimization mathematical model of the wireless sensor network ideological and political education cycle is established. The two data collection schemes proposed in this paper are simulated under the conditions of no-delay ideological and political education cycle and different time-delayed ideological and political education cycles. The simulation results show that the model is better than both in terms of network life and data integrity.

PINC: Pickup Non-Critical Node Based k-Connectivity Restoration in Wireless Sensor Networks.

A Wireless Sensor Network (WSN) is connected if a communication path exists among each pair of sensor nodes (motes). Maintaining reliable connectivity in WSNs is a complicated task, since any failure in the nodes can cause the data transmission paths to break. In a k-connected WSN, the connectivity survives after failure in any k-1 nodes; hence, preserving the k-connectivity ensures that the WSN can permit k-1 node failures without wasting the connectivity. Higher k values will increase the reliability of a WSN against node failures. We propose a simple and efficient algorithm (PINC) to accomplish movement-based k-connectivity restoration that divides the nodes into the critical, which are the nodes whose failure reduces k, and non-critical groups. The PINC algorithm pickups and moves the non-critical nodes when a critical node stops working. This algorithm moves a non-critical node with minimum movement cost to the position of the failed mote. The measurements obtained from the testbed of real IRIS motes and Kobuki robots, along with extensive simulations, revealed that the PINC restores the k-connectivity by generating optimum movements faster than its competitors.

LDCA: Lightweight Dynamic Clustering Algorithm for IoT-Connected Wide-Area WSN and Mobile Data Sink Using LoRa

Wide-area monitoring applications of the Internet-of-Things (IoT) connected wireless sensor network (WSN) consists of sensor nodes (SNs) with limited hardware and energy sources. The distributed nature of such a network and the difficulty of remote access make it more demanding to design an energy-efficient WSN. Moreover, long-range and low-power wireless connectivity is a challenge in IoT-connected applications. Present WSN topologies deal mainly with fixed SNs, SN distribution, and fixed data sink (DS). The majority of the control layer is implemented in the lower hierarchical layer or in a virtual middle layer, which reduces the network lifetime due to excessive processing and data transmission activities. This article proposes a real-time lightweight dynamic clustering algorithm (LDCA) for a WSN that supports the following two scenarios with limited processing resources: 1) with mobile DSs and static SNs (such as DS and SNs mounted on unmanned aerial vehicles, autonomous vehicles) and 2) with mobile SNs and static DSs (such as livestock monitoring or autonomous robots in smart farming, and urban monitoring). The proposed algorithm is based on the received signal strength indicator and signal-to-noise ratio of a long-range (LoRa) interface and its residual energy. Mathematical models were derived for real-time clustering using LoRa. Memory requirement and clustering efficiency of the constrained SN and DS for various mobility scenario were evaluated. The proposed LDCA reduces the energy requirement to 33% compared to static clustering, by reducing the number of concurrent clusters and hops. In addition, a hardware-based approach was used to validate the LDCA algorithm, and evaluate its performance in terms of energy efficiency, packet delivery rate, and network lifetime.

Node placement optimization using particle swarm optimization and iterated local search algorithm in wireless sensor networks

SummaryCoverage and connectivity are the two most challenging issues in target‐based wireless sensor networks (WSNs). For that, node placement is one of the fundamental concerns that affect the performance of coverage and connectivity in WSN. This paper introduces a new approach by combining particle swarm optimization and iterated local search (PSO‐ILS) to have an optimum coverage and connectivity rate with the minimum number of nodes. In one side, to maintain the full coverage of targets, the PSO‐ILS is used to deploy the minimum number of sensor nodes. In other side, to achieve the full connectivity, the optimal position determination (OPD) algorithm was conceived to identify the optimal candidate positions which can be used by the PSO‐ILS to place the minimum number of relay nodes. The obtained results considered over a number of runs are compared with canonical PSO, differential evolution (DE), and genetic algorithms (GAs). The outcomes derived from this comprehensive analysis determine that PSO‐ILS provides an effectual improvement in contrary to the methods PSO, DE, and GA in terms of the selected potential positions to ensure full coverage of target points and the number of relay nodes required to achieve full connectivity.

Unknown hostile environment-oriented autonomous WSN deployment using a mobile robot

In this study, we consider the Internet of Things (IoT) with an autonomous deployment framework and seek optimal localizable k-coverage (OLKC) strategies to preserve the connectivity and robustness in IoT networks to assist robots during disaster recovery activities. Therefore, we define localizable k-coverage as the covered region within which a mobile robot can localize itself aided by k neighboring beacon nodes (BNs) in a wireless sensor network (WSN). To this end, we first propose the optimal localizable k-coverage WSN deployment problem (OLKWDP) and present a novel framework that preserves WSN connectivity and robustness for mobile robots. To localize a mobile robot with at least k BNs and overcome the network hole problem that can occur in unknown hostile environments, we propose a hole recovery method for the OLKC achieved by a mobile robot that knows the concurrent mapping, deployment and localization of the WSN. We then present a mapping-to-image transformation method to reveal the interactions between the WSN deployment and the network holes for the OLKC while constructing the online mapping. To solve the OLKWDP, we also develop two optimality conditions to achieve maximum coverage by the proposed OLKC in the unknown hostile environment using the minimum number of sensors. Moreover, we analyze the factors that influence the probability of success of the OLKC and the factors that influence the performance of a mobile robot when determining the WSN deployment. The simulation results illustrate that our framework outperforms the trilateration and spanning tree (TST) method in unknown hostile environment exploration and can achieve the OLKC in a WSN. In 27 simulated situations, our framework achieved average rates of nearly 100% 1-coverage, 91.34% 2-coverage and 89.00% 3-coverage.

A fault tolerant algorithm for integrated coverage and connectivity in wireless sensor networks

There are two major requirements for any surveillance application using wireless sensor networks (WSN) - coverage and connectivity. In the design of such an application, a major constraint is limit...

A fault tolerant algorithm for integrated coverage and connectivity in wireless sensor networks

A fault tolerant algorithm for integrated coverage and connectivity in wireless sensor networks

Energy-efficient coverage and connectivity of wireless sensor network in the framework of hybrid sensor and vehicular network

Hybrid Sensor and Vehicular Network (HSVN) merges both Vehicular Ad hoc Network (VANET) and Wireless Sensor Network (WSN) to create a framework of collaboration between the two networks. The main objective of this collaboration is to provide real-time detection of dangerous road conditions for the improvement of road safety. To create a reliable HSVN, coverage and connectivity problems of WSN represent the most fundamental issues to ensure an effective road sensing and a robust data communication in HSVN applications. In this respect, we have proposed a distributed protocol for ensuring both coverage and connectivity of WSN in the framework of HSVN. The proposed protocol ensures the detection of any event occurring on the road and the connectivity between sensor nodes even in the case of sensor node failure. Also, the energy consumption is reduced by keeping only a minimum number of sensor nodes in active state. The WSN lifetime is extended by balancing the energy consumption among all sensor nodes and by allowing the sensor nodes to serve alternatively as a gateway which is responsible for reporting any detected event to the passing vehicles. Simulation results show that the proposed protocol achieves 100% of road coverage and energy consumption is improved up to 14%. The proposed protocol is reliable in the case of sensor node failure; coverage and connectivity are improved up to 13% and 87%, respectively, in the case of 30% of failed sensor nodes. These results indicate that the proposed protocol is promising as it outperforms the existing ones.