Coverage and Connectivity-Based 3D Wireless Sensor Deployment Optimization

The wireless sensor network technology of Internet of Things (IoT) senses, collects and processes the data from its interconnected intelligent sensors to the base station. These sensors help the IoT to understand the environmental change and respond towards it. Thus sensor placement is a crucial device of IoT for efficient coverage and connectivity in the network. Many existing works focus on optimal sensor placement for two dimensional terrain but in various real-time applications sensors are often deployed over three-dimensional ambience. Therefore, this paper proposes a vertex coloring based sensor deployment algorithm for 3D terrain to determine the sensor requirement and its optimal spot and to obtain 100% target coverage. Further, the quality of the connectivity of sensors in the network is determined using Breadth first search algorithm. The results obtained from the proposed algorithm reveal that it provides efficient coverage and connectivity when compared with the existing methods.

     

A distributed self-deployment algorithm for the coverage of mobile wireless sensor networks

This letter addresses a coverage problem through the use of a self-deployed mobile wireless sensor network. We propose a distributed motion coordination algorithm for the mobile sensors to autonomously form a sensor barrier between two given landmarks to achieve the barrier coverage. The algorithm is developed based on some simple rules that are computationally efficient and require less communication overhead.     

Efficient Placement and Dispatch of Sensors in a Wireless Sensor Network

Sensor deployment is a critical issue because it affects the cost and detection capability of a wireless sensor network. In this work, we consider two related deployment problems: sensor placement and sensor dispatch. The former asks how to place the least number of sensors in a field to achieve sensing coverage and network connectivity, and the latter asks how to determine from a set of mobile sensors a subset of sensors to be moved to an area of interest with certain objective functions such that the coverage and connectivity properties are satisfied. This work is targeted toward planned deployment. Our solution to the placement problem allows an arbitrary-shaped sensing field possibly with arbitrary-shaped obstacles and an arbitrary relationship between the communication distance and sensing distance of sensors and, thus, significantly relaxes the limitations of existing results. Our solutions to the dispatch problem include a centralized one and a distributed one. The centralized one is based on adopting the former placement results and converting the problem to the maximum-weight maximum-matching problem with the objective of minimizing the total energy consumption to move sensors or maximizing the average remaining energy of sensors after movement. Designed in a similar way, the distributed one allows sensors to determine their moving directions in an autonomous manner.     

Connectivity-Preserved and Force-Based Deployment Scheme for Mobile Sensor Network

This paper considers the self-deployment of wireless sensor networks. Conventional deployment problem usually focuses on enhancing the coverage, while the conditions for network connectivity are largely simplified. We present a deployment scheme to enhance the coverage while keeping the network connected at each step of the deployment. Our scheme contains two parts. The coverage improvement part proposes an improved force-based mechanism. A limit is provided to determine the sensors which should attractive each other, so the wasted overlap and communication resource can be reduced. The connectivity preservation part provides constrains for the movement distance of each sensor, in order to take account of both connectivity and coverage enhancement. Some simulation results are presented to show the connectivity preservation and coverage maximization properties of our mechanism.     

Connected sensor cover for area information coverage in wireless sensor networks

Coverage is an important issue in wireless sensor networks (WSNs) and is often used to measure how well a sensor field is monitored by the deployed sensors. If the area covered by a sensor can also be covered by some other sensors, this sensor can go into an energy‐saving sleep state without sacrificing the coverage requirement. In this paper, we study the problem of how to select active sensors with the constraints that the selected active sensors can provide complete field coverage and are completely connected. We propose to use the notion of information coverage, which is based on estimation theory to exploit the collaborative nature of WSNs, instead of using the conventional definition of coverage. Owing to the use of information coverage, a point that is not within the sensing disk of any sensor can still be considered to be covered without loss of estimation reliability. We propose a heuristic to approximately solve our problem. The basic idea is to grow a connected sensor tree to maximize the profit from the covered points of the selected sensors in each step. Simulations are used to validate the effectiveness of the proposed algorithm and the results illustrate that the number of active sensors to provide area coverage can be greatly reduced by using the notion of information coverage compared with that by using the conventional definition of coverage. Copyright © 2008 John Wiley & Sons, Ltd.     

一种无线传感器网络全连通群的休眠调度算法

该文针对无线传感器网络的覆盖性和连通性问题,在假设传感器节点地理位置信息已知的条件下,设计了一种包含全连通群的建立和维护以及群内节点休眠调度的全新算法。该算法采用保证群内节点彼此一跳可达的全连通群分群方法,以及分布式节能的休眠调度策略,最大程度上减少传感器网络的能量消耗,延长了网络寿命。仿真结果表明:该算法能较好地保证无线传感器网络的覆盖性和连通性,且能耗较低。     

Connectivity maintenance and coverage preservation in wireless sensor networks

In wireless sensor networks, one of the main design challenges is to save severely constrained energy resources and obtain long system lifetime. Low cost of sensors enables us to randomly deploy a large number of sensor nodes. Thus, a potential approach to solve lifetime problem arises. That is to let sensors work alternatively by identifying redundant nodes in high-density networks and assigning them an off-duty operation mode that has lower energy consumption than the normal on-duty mode. In a single wireless sensor network, sensors are performing two operations: sensing and communication. Therefore, there might exist two kinds of redundancy in the network. Most of the previous work addressed only one kind of redundancy: sensing or communication alone. Wang et al. [Intergrated Coverage and Connectivity Configuration in Wireless Sensor Networks, in: Proceedings of the First ACM Conference on Embedded Networked Sensor Systems (SenSys 2003), Los Angeles, November 2003] and Zhang and Hou [Maintaining Sensing Coverage and Connectivity in Large Sensor Networks. Technical report UIUCDCS-R-2003-2351, June 2003] first discussed how to combine consideration of coverage and connectivity maintenance in a single activity scheduling. They provided a sufficient condition for safe scheduling integration in those fully covered networks. However, random node deployment often makes initial sensing holes inside the deployed area inevitable even in an extremely high-density network. Therefore, in this paper, we enhance their work to support general wireless sensor networks by proving another conclusion: “the communication range is twice of the sensing range” is the sufficient condition and the tight lower bound to ensure that complete coverage preservation implies connectivity among active nodes if the original network topology (consisting of all the deployed nodes) is connected. Also, we extend the result to k-degree network connectivity and k-degree coverage preservation.     

Enhanced algorithms for deploying the minimum sensors to construct a wireless sensor network having full coverage of critical square grids

With the rapid technological development of sensors, many applications have been designed to use wireless sensor networks to monitor a certain area and provide quality-of-service guarantees. Therefore, the coverage problem had an important issue for constructing wireless sensor networks. Recently, a coverage problem of constructing a minimum size wireless sensor network to fully cover critical squares in a sensor field, termed CRITICAL-SQUARE-GRID COVERAGE, has received much attention. CRITICAL-SQUARE-GRID COVERAGE is shown to be NP-Complete, and an approximation algorithm, termed Steiner-tree-based critical grid covering algorithm (STBCGCA), is proposed accordingly. In STBCGCA, a sensor is selected to cover critical squares only if at least one of the critical squares is fully covered by the sensor. However, a critical square grid can be cooperatively covered by two or more sensors; that is, one sensor covers one part of the critical square, and the other sensors cover the other part of the critical square. This motivates us to propose two efficient algorithms based on STBCGCA, termed critical-grid-partitioned (CGP-STBCGCA) and reference-point-covered (RPC-STBCGCA), that select sensors that can cooperatively cover critical squares in an attempt to minimize the size of the wireless sensor network. The theoretical analysis shows that sensors deployed by CGP-STBCGCA and RPC-STBCGCA can form a connected wireless sensor network that fully covers all critical grids. In addition, a performance guarantee for CGP-STBCGCA is provided. Simulation results show that the ratio of the average number of deployed sensors in STBCGCA to that in CGP-STBCGCA and RPC-STBCGCA in about 90 % of the cases was between 1.08 and 2.52 for CRITICAL-SQUARE-GRID COVERAGE.     

CWSC: Connected k-coverage working sets construction algorithm in wireless sensor networks

One of the most important issues for wireless sensor networks is to get a long network lifetime without affecting either communication connectivity or sensing coverage. Many sensors that are deployed randomly in a dense sensor network in a redundant way waste a lot of energy. One effective way to save energy is to let only a subset of sensors work at any given time. In this paper, we mainly consider such a problem. Selecting the minimum number of connected sensor nodes that can provide k-coverage (k ≥ 1), i.e., selecting a subset S of working sensors, such that almost every point in the sensing region can be covered by at least k sensors and the sensors in S can form a connected communication subgraph. We propose a connected k-coverage working sets construction algorithm (CWSC) based on Euclidean distance to k-cover the sensing region while minimizing the number of working sensors. CWSC can produce different coverage degrees according to different applications, which can enhance the flexibility of the sensor network. Simulation results show that the proposed algorithm, which can conserve energy and prolong the lifetime of the sensor network, is better than the previous algorithms.     

A Pre-Determined Nodes Deployment Strategy of Two-Tiered Wireless Sensor Networks Based on Minimizing Cost

Nodes deployment is a fundamental factor in determining the connectivity, coverage, lifetime and cost of wireless sensor networks. In this paper, a two-tiered wireless sensor networks consisting of sensor clusters and a base station is considered. Within a sensor cluster, there are many sensor nodes and a relay node. We focus on the deployment strategy for sensor nodes and relay nodes to minimize cost under some constraints. Several means are used. The regular hexagonal cell architecture is employed to build networks. Based on the analysis of energy consumption of sensors and cost of network, an integer programming model is presented to minimize the cost. By the model, number of layers of sensor cluster is determined. In order to balance the energy consumption of sensors on the identical layer, a uniform load routing algorithm is used. The numerical analysis and simulation results show that the waste of energy and cost of wireless sensor networks can be effectively reduced by using the strategy.     

Hybrid Wireless Sensors Deployment Scheme with Connectivity and Coverage Maintaining in Wireless Sensor Networks

With the rapid growth of the internet of things (IoT), an impressive number of IoT’s application based on wireless sensor networks (WSNs) has been deployed in various domain. Due to its wide ranged applications, WSNs that have the capability to monitor a given sensing field, became the most used platform of IoT. Therefore, coverage becomes one of the most important challenge of WSNs. The search for better positions to assign to the sensors in order to control each point of an area of interest and the collection of data from sensors are major concerns in WSNs. This work addresses these problems by providing a hybrid approach that ensures sensors deployment on a grid for targets coverage while taking into account connectivity. The proposed sequential hybrid approach is based on three algorithms. The first places the sensors so as to all targets are covered. The second removes redundancies from the placement algorithm to reduce the number of sensors deployed. The third one, based on the genetic algorithm, aims to generate a connected graph which provide a minimal path that links deployed sensors and sink. Simulations and a comparative study were carried out to prove the relevance of the proposed method.

     

Optimal barrier coverage for critical area surveillance using wireless sensor networks

Intrusion detection using barrier coverage is one of many applications existed in wireless sensor networks. The main purpose of using barrier coverage is to monitor the borders of a specific area against the intruders that are trying to penetrate this critical area by ensuring the total coverage with a low cost and extending the lifetime of the network, many solutions have been proposed in the literature in order to solve the coverage problem in wireless sensor networks, which became a vital field of research. In this paper, we present a new technique based on geometric mathematical models, in order to guarantee the total coverage of our deployed barriers with a minimum possible number of sensors. The idea is to calculate the number of sensors adequate to cover a barrier before deployment. We then formulate the problem to minimize the number of sensors to be deployed to properly cover a barrier; the calculation makes it possible to solve this problem in polynomial using our own heuristic. Additionally, we propose a new mechanism for ensuring a fault‐tolerant network by detecting the faulty sensors and select other suited sensors to close the existing gaps inside the barriers and detecting the sensors whose energy is low before the failure. The obtained simulation results prove the effectiveness of the proposed algorithms.     

一种三角形网格空洞修复算法

无线传感器网络由大量传感器节点组成,在网络初始化时节点随机部署在目标区域中,导致某一区域未被覆盖而形成覆盖空洞.针对目标区域中存在覆盖空洞问题,设计了一种基于三角形网格的无需地理信息的空洞探测算法ATN和空洞修复算法TNR.利用ATN算法检测节点与其邻居形成的三角形网格是否被完全覆盖,TNR算法以ATN算法理论为基础,向三角形网格中添加节点使目标区域完全覆盖.理论与仿真实验分析表明,ANR算法能够探测出目标区域中所有空洞,TNR算法在部署密集的传感网络中能够快速完成空洞修复.     

Position and hop-count assisted full coverage control in dense sensor networks

In recent years, wireless sensor networks (WSN’s) have gained much attention due to its various applications in military, environmental monitoring, industries and in many others. All these applications require some target field to be monitored by a group of sensor nodes. Hence, coverage becomes an important issue in WSN’s. This paper focuses on full coverage issue of WSN’s. Based on the idea of some existing and derived theorems, Position and Hop-count Assisted (PHA) algorithm is proposed. This algorithm provides full coverage of the target field, maintains network connectivity and tries to minimize the number of working sensor nodes. Algorithm works for communication range less than root three times of sensing range and it can be extended for arbitrary relation between communication range and sensing range. By using hop-count value, three-connectivity in the network is maintained. Also, neighbors information is used to create logical tree structure which can be utilized in routing, redundant data removal and in other areas. Simulation results show that PHA algorithm outperforms layered diffusion-based coverage control algorithm by providing better area coverage and activating fewer nodes.     

Genetic Algorithm-based Adaptive Optimization for Target Tracking in Wireless Sensor Networks

In this paper, we address the problem of genetic algorithm optimization for jointly selecting the best group of candidate sensors and optimizing the quantization for target tracking in wireless sensor networks. We focus on a more challenging problem of how to effectively utilize quantized sensor measurement for target tracking in sensor networks by considering best group of candidate sensors selection problem. The main objective of this paper is twofold. Firstly, the quantization level and the group of candidate sensors selection are to be optimized in order to provide the required data of the target and to balance the energy dissipation in the wireless sensor network. Secondly, the target position is to be estimated using quantized variational filtering (QVF) algorithm. The optimization of quantization and sensor selection are based on the Fast and Elitist Multi-objective Genetic Algorithm (NSGA-II). The proposed multi-objective (MO) function defines the main parameters that may influence the relevance of the participation in cooperation for target tracking and the transmitting power between one sensor and the cluster head (CH). The proposed algorithm is designed to: i) avoid the problem lot of computing times and operation counts, and ii) reduce the communication cost and the estimation error, which leads to a significant reduction of energy consumption and an accurate target tracking. The computation of these criteria is based on the predictive information provided by the QVF algorithm. The simulation results show that the NSGA-II -based QVF algorithm outperforms the standard quantized variational filtering algorithm and the centralized quantized particle filter.     

Reliable orientation estimation for mobile motion capturing in medical rehabilitation sessions based on inertial measurement units

Fully mobile and wireless motion capturing is a mandatory requirement for undisturbed and non-reactive analysis of human movements. Inertial sensor platforms are used in applications like training session analysis in sports or rehabilitation, and allow non-restricted motion capturing. The computation of the required reliable orientation estimation based on the inertial sensor RAW data is a demanding computational task. Therefore, an analysis of the computational costs and achievable accuracy of a Kalman filter and a complementary filter algorithm is provided. Highly customized and thus low-power, wearable computation platforms require low-level, platform independent communication protocols and connectivity. State-of-the-art small sized commercial inertial sensors either lack the availability of an open, platform independent protocol, wireless connectivity or extension interfaces for additional sensors. Therefore, an extensible, wireless inertial sensor called Institute of Microelectronic Systems Inertial Measurement Unit (IM)²SU, featuring onboard inertial sensor fusion, for use in home based stroke rehabilitation is presented. Furthermore, a Quaternion based, singularity free orientation estimation accuracy error measure is proposed and applied. To evaluate orientation estimation accuracy an optical system is used as golden reference. Orientation estimation based on a Kalman filter and a complementary filter algorithm is evaluated. The proposed IMU provides high orientation estimation accuracy, is platform independent, offers wireless connection and extensibility and is low cost.     

Monte Carlo localization of wireless sensor networks with a single mobile beacon

One of the most important tasks in sensor networks is to determine the physical location of sensory nodes as they may not all be equipped with GPS receivers. In this paper we propose a localization method for wireless sensor networks (WSNs) using a single mobile beacon. The sensor locations are maintained as probability distributions that are sequentially updated using Monte Carlo sampling as the mobile beacon moves over the deployment area. Our method relieves much of the localization tasks from the less powerful sensor nodes themselves and relies on the more powerful beacon to perform the calculation. We discuss the Monte Carlo sampling steps in the context of the localization using a single beacon for various types of observations such as ranging, Angle of Arrival (AoA), connectivity and combinations of those. We also discuss the communication protocol that relays the observation data to the beacon and the localization result back to the sensors. We consider security issues in the localization process and the necessary steps to guard against the scenario in which a small number of sensors are compromised. Our simulation shows that our method is able to achieve less than 50% localization error and over 80% coverage with a very sparse network of degree less than 4 while achieving significantly better results if network connectivity increases.     

A distributed reinforcement learning based sensor node scheduling algorithm for coverage and connectivity maintenance in wireless sensor network

The fundamental challenge for randomly deployed resource-constrained wireless sensor network is to enhance the network lifetime without compromising its performance metrics such as coverage rate and network connectivity. One way is to schedule the activities of sensor nodes and form scheduling rounds autonomously in such a way that each spatial point is covered by at least one sensor node and there must be at least one communication path from the sensor nodes to base station. This autonomous activity scheduling of the sensor nodes can be efficiently done with Reinforcement Learning (RL), a technique of machine learning because it does not require prior environment modeling. In this paper, a Nash Q-Learning based node scheduling algorithm for coverage and connectivity maintenance (CCM-RL) is proposed where each node autonomously learns its optimal action (active/hibernate/sleep/customize the sensing range) to maximize the coverage rate and maintain network connectivity. The learning algorithm resides inside each sensor node. The main objective of this algorithm is to enable the sensor nodes to learn their optimal action so that the total number of activated nodes in each scheduling round becomes minimum and preserves the criteria of coverage rate and network connectivity. The comparison of CCM-RL protocol with other protocols proves its accuracy and reliability. The simulative comparison shows that CCM-RL performs better in terms of an average number of active sensor nodes in one scheduling round, coverage rate, and energy consumption.

     

Coverage and connectivity aware critical target monitoring for wireless sensor networks: Novel NSGA‐II–based approach

As sensor nodes have limited sensing and transmission capability, their efficient deployment takes an important role in proper monitoring of the critical targets in various applications of wireless sensor networks (WSNs). The key issues that need to be taken care during deployment are the lesser number of deployed sensors, coverage of the targets, and connectivity between the sensor nodes. In this paper, we have proposed NSGA‐II with modified dominance to solve the node deployment problem with the aforementioned three conflicting objectives. The conventional domination method is modified for better performance of the NSGA‐II. An intelligent representation of chromosome is provided. Three conflicting objectives are derived to evaluate the chromosomes. Extensive simulation on the proposed algorithm and the statistical test, and analysis of variance (ANOVA) followed by post hoc analysis are performed.     

Target localization based on energy considerations in distributed sensor networks

Wireless distributed sensor networks are important for a number of strategic applications such as coordinated target detection, surveillance, and localization. Energy is a critical resource in wireless sensor networks and system lifetime needs to be prolonged through the use of energy-conscious sensing strategies during system operation. We propose an energy-aware target detection and localization strategy for cluster-based wireless sensor networks. The proposed method is based on an a posteriori algorithm with a two-step communication protocol between the cluster head and the sensors within the cluster. Based on a limited amount of data received from the sensor nodes, the cluster head executes a localization procedure to determine the subset of sensors that must be queried for detailed target information. This approach reduces both energy consumption and communication bandwidth requirements, and prolongs the lifetime of the wireless sensor network. Simulation results show that a large amount of energy is saved during target localization using this strategy.