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.

     

3HA: Hybrid Hole Healing Algorithm in a Wireless Sensor Networks

In wireless sensor networks (WSNs), the appearance of coverage holes over a large target field is mostly possible. Those holes reduce network performance and may affect the network efficiency. Several approaches were proposed to heal coverage holes in WSNs, but they still suffer from some weaknesses. In this paper we suggest a distributed algorithm, named hybrid hole healing algorithm (3HA), to find the minimum effective patching positions to deploy additional nodes to cover the holes. A hole manager node of each hole is responsible for operating the 3HA algorithm which requires two phases. The first phase finds all candidate patching positions using a Voronoi diagram. It takes all Voronoi vertices within the hole as the initial patching positions list. The second phase reduces as much as possible this list based on integer linear programming and on a probabilistic sensor model. The 3HA algorithm repeats the above phases in rounds, until all Voronoi vertices are covered. Simulation results show that our solution offers a high coverage ratio for various forms and sizes of holes and reduces the number of additional sensors when compared to some algorithms like the Perimeter-based, the Delaunay triangulation-based, the Voronoi-based, and the Trees-based coverage hole healing methods.

     

Multi-Source Temporal Data Aggregation in Wireless Sensor Networks

Data aggregation has been emerged as a basic approach in wireless sensor networks (WSNs) in order to reduce the number of transmissions of sensor nodes.This paper proposes an energy-efficient multi-source temporal data aggregation model called MSTDA in WSNs. In MSTDA model, a feature selection algorithm using particle swarm optimization (PSO) is presented to simplify the historical data source firstly. And then a data prediction algorithm based on improved BP neural network with PSO (PSO-BPNN) is proposed. This MSTDA model, which helps to find out potential laws according to historical data sets, is deployed at both the base station (BS) and the node. Only when the deviation between the actual and the predicted value at the node exceeds a certain threshold, the sampling value and new model are sent to BS. The experiments on the dataset which comes from the actual data collected from 54 sensors deployed in the Intel Berkeley Research lab made a satisfied performance. When the error threshold greater than 0.15, it can decrease more than 80% data transmissions.     

A Location-less Energy Efficient Algorithm for Load Balanced Clustering in Wireless Sensor Networks

One of the biggest challenges in Wireless Sensor Networks (WSNs) is to efficiently utilise the limited energy available in the network. In most cases, the energy units of sensors cannot be replaced or replenished. Therefore, the need for energy efficient and robust algorithms for load balancing in WSNs is ever present. This need is even more pronounced in the case of cluster-based WSNs, where the Cluster Head (CH) gathers data from its member nodes and transmits this data to the base station or sink. In this paper, we propose a location independent algorithm to cluster the sensor nodes under gateways, as CHs into well defined, load balanced clusters. The location-less aspect also avoids the energy loss in running GPS modules. Simulations of the proposed algorithm are performed and compared with a few existing algorithms. The results show that the proposed algorithm shows better performance under different evaluation metrics such as average energy consumed by sensor nodes vs number of rounds, number of active sensors vs number of rounds, first gateway die and half of the gateways die.

     

Schedule-Based Cooperative Multi-agent Reinforcement Learning for Multi-channel Communication in Wireless Sensor Networks

Wireless sensor networks (WSNs) have become an important component in the Internet of things (IoT) field. In WSNs, multi-channel protocols have been developed to overcome some limitations related to the throughput and delivery rate which have become necessary for many IoT applications that require sufficient bandwidth to transmit a large amount of data. However, the requirement of frequent negotiation for channel assignment in distributed multi-channel protocols incurs an extra-large communication overhead which results in a reduction of the network lifetime. To deal with this requirement in an energy-efficient way is a challenging task. Hence, the Reinforcement Learning (RL) approach for channel assignment is used to overcome this problem. Nevertheless, the use of the RL approach requires a number of iterations to obtain the best solution which in turn creates a communication overhead and time-wasting. In this paper, a Self-schedule based Cooperative multi-agent Reinforcement Learning for Channel Assignment (SCRL CA) approach is proposed to improve the network lifetime and performance. The proposal addresses both regular traffic scheduling and assignment of the available orthogonal channels in an energy-efficient way. We solve the cooperation between the RL agents problem by using the self-schedule method to accelerate the RL iterations, reduce the communication overhead and balance the energy consumption in the route selection process. Therefore, two algorithms are proposed, the first one is for the Static channel assignment (SSCRL CA) while the second one is for the Dynamic channel assignment (DSCRL CA). The results of extensive simulation experiments show the effectiveness of our approach in improving the network lifetime and performance through the two algorithms.

     

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.     

Exploiting Mobility for Efficient Coverage in Sparse Wireless Sensor Networks

Reliable monitoring of a large area with a Wireless Sensor Network (WSN) typically requires a very large number of stationary nodes, implying a prohibitive cost and excessive (radio) interference. Our objective is to develop an efficient system that will employ a smaller number of stationary nodes that will collaborate with a small set of mobile nodes in order to improve the area coverage. The main strength of this collaborative architecture stems from the ability of the mobile sensors to sample areas not covered (monitored) by stationary sensors. An important element of the proposed system is the ability of each mobile node to autonomously decide its path based on local information (i.e. a combination of self collected measurements and information gathered by stationary sensors in the mobile’s communication range), which is essential in the context of large, distributed WSNs. The contribution of the paper is the development of a simple distributed algorithm that allows mobile nodes to autonomously navigate through the field and improve the area coverage. We present simulation results based on a real sparse stationary WSN deployment for the coverage improvement scenario.     

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.     

An Energy Efficient Barrier Coverage Algorithm for Wireless Sensor Networks

Intrusion detection is one of the most important applications of wireless sensor networks. When mobile objects are entering into the boundary of a sensor field or are moving cross the sensor field, they should be detected by the scattered sensor nodes before they pierce through the field of sensor (barrier coverage). In this paper, we propose an energy efficient scheduling method based on learning automata, in which each node is equipped with a learning automaton, which helps the node to select best node to guarantee barrier coverage, at any given time. To apply our method, we used coverage graph of deployed networks and learning automata of each node operates based on nodes that located in adjacency of current node. Our algorithm tries to select minimum number of required nodes to monitor barriers in deployed network. To investigate the efficiency of the proposed barrier coverage algorithm several computer simulation experiments are conducted. Numerical results show the superiority of the proposed method over the existing methods in term of the network lifetime and our proposed algorithm can operate very close to optimal method.     

Power-Saving Scheduling for Multiple-Target Coverage in Wireless Sensor Networks

We address the multiple-target coverage problem (MTCP) in wireless sensor networks (WSNs). We also propose an energy-efficient sensor-scheduling algorithm for multiple-target coverage (MTC) that considers both the transmitting energy for collected data and overlapped targets. We introduce two algorithms: one optimal, the other heuristic. Simulation results show that the proposed algorithms can contribute to extending the lifetime of network and that the heuristic algorithm is more practical than the optimal algorithm with respect to complexity.     

多蜜源蜂群算法在无线传感器网络覆盖的优化

基于人工蜂群算法以及无线传感器网络相关技术,提出了一种基于互动策略的多蜜源蜂群算法。该算法采用灵敏度与信息素结合的选择策略代替轮盘赌选择方式以实现跟随蜂的开采过程并引入互动策略实现跟随蜂的邻域搜索。此外,在每次迭代结束时,根据OBL策略产生新蜜源以更新最差蜜源。仿真结果表明,该算法能够使检测区域内传感器节点的分布更加均匀,且通过较少次数的迭代,实现更优的网络覆盖率,这对于延长整个无线传感器网络的生命周期,降低网络的能耗,有着重要的影响。     

A QoS aware optimal node deployment in wireless sensor network using Grey wolf optimization approach for IoT applications

The growth of Wireless Sensor Networks (WSN) becomes the backbone of all smart IoT applications. Deploying reliable WSNs is particularly significant for critical Internet of Things (IoT) applications, such as health monitoring, industrial and military applications. In such applications, the WSN’s inability to perform its necessary tasks and degrading QoS can have profound consequences and can not be tolerated. Thus, deploying reliable WSNs to achieve better Quality of Service (QoS) support is a relatively new topic gaining more interest. Consequently, deploying a large number of nodes while simultaneously optimizing various measures is regarded as an NP-hard problem. In this paper, a Grey wolf-based optimization technique is used for node deployment that guarantees a given set of QoS metrics, namely maximizing coverage, connectivity and minimizing the overall cost of the network. The aim is to find the optimum number of appropriate positions for sensor nodes deployment under various p-coverage and q-connectivity configurations. The proposed approach offers an efficient wolf representation scheme and formulates a novel multi-objective fitness function. A rigorous simulation and statistical analysis are performed to prove the proposed scheme’s efficiency. Also, a comparative analysis is being carried with existing state-of-the-art algorithms, namely PSO, GA, and Greedy approach, and the efficiency of the proposed method improved by more than 11%, 14%, and 20%, respectively, in selecting appropriate positions with desired coverage and connectivity.

     

传感器网络中基于数据融合的栅栏覆盖控制研究

该文采用概率性感知模型,并利用数据融合技术构造虚拟节点来增加节点覆盖区域。在此基础上,提出一种栅栏覆盖控制算法。算法借助分治法构造栅栏,以减少节点间通信开销;并调度传感器使冗余节点睡眠,达到减少能耗和延长网络寿命的目的。分析和实验结果表明,针对所提问题设计的模型和算法可有效增加节点覆盖范围及节点间最大间隔距离,且在栅栏数、网络寿命等性能上均优于基于节点监测数据未融合的栅栏覆盖控制算法。     

Maximal coverage hybrid search algorithm for deployment in wireless sensor networks

A vital design aspect in the setting up of a wireless sensor network is the deployment of sensors. One of the key metrics of the quality of deployment is the coverage as it reflects the monitoring capability of the network. Random deployment is a sub-optimal method as it causes unbalanced deployment and requires sensors in excess of the planned deployment to achieve the same level of coverage. To achieve maximum coverage with a limited number of sensors, planned deployment is a preferred choice. Maximizing the coverage of the region of interest with a given number and type of sensors is an optimization problem. A novel maximal coverage hybrid search algorithm (MCHSA) is proposed in this paper to solve this problem. The MCHSA is a hybrid search algorithm that achieves the balance between exploration and exploitation by applying the particle swarm optimization as a global search technique and using the Hooke–Jeeves pattern search method to improve the local search. The algorithm starts with a good initial population. The proposed MCHSA has low computational complexity and fast convergence. The performance of the MCHSA is analyzed by performing a comparison with the existing algorithms in the literature, in terms of coverage achieved and number of fitness function evaluations. The paper also discusses the tuning of parameters of the proposed algorithm.

     

Extending the Lifetime of Wireless Sensor Networks Through Mobile Relays

We investigate the benefits of a heterogeneous architecture for wireless sensor networks (WSNs) composed of a few resource rich mobile relay nodes and a large number of simple static nodes. The mobile relays have more energy than the static sensors. They can dynamically move around the network and help relieve sensors that are heavily burdened by high network traffic, thus extending the latter's lifetime. We first study the performance of a large dense network with one mobile relay and show that network lifetime improves over that of a purely static network by up to a factor of four. Also, the mobile relay needs to stay only within a two-hop radius of the sink. We then construct a joint mobility and routing algorithm which can yield a network lifetime close to the upper bound. The advantage of this algorithm is that it only requires a limited number of nodes in the network to be aware of the location of the mobile relay. Our simulation results show that one mobile relay can at least double the network lifetime in a randomly deployed WSN. By comparing the mobile relay approach with various static energy-provisioning methods, we demonstrate the importance of node mobility for resource provisioning in a WSN.      

A Coverage-Based Scheduling Algorithm for WSNs

Node scheduling in wireless sensor networks (WSNs) plays a vital role in conserving energy and lengthening the lifetime of networks, which are considered as prime design challenges. In large-scaled WSNs, especially where sensor nodes are deployed randomly, 100 % coverage is not possible all the times. Additionally, several types of applications of WSNs do not require 100 % coverage. Following these facts, in this paper, we propose a coverage based node scheduling algorithm. The algorithm shows that by sacrificing a little amount of coverage, a huge amount of energy can be saved. This, in turns, helps to increase the lifetime of the network. We provide mathematical analysis, which verifies the correctness of the proposed algorithm. The proposed algorithm ensures balanced energy consumption over the sensor networks. Moreover, simulation results demonstrate that the proposed algorithm almost doubles the lifetime of a wireless sensor network by sacrificing only 5–8 % of coverage.     

Maximization of WSN Life Using Hybrid Evolutionary Programming

Of all the challenges faced by wireless sensor networks (WSN), extending the lifetime of the network has received the most attention from researchers. This issue is critically important, especially when sensors are deployed to areas where it is practically impossible to charge their batteries, which are their only sources of power. Besides the development and deployment of ultra low-power devices, one effective computational approach is to partition the collection of sensors into several disjoint covers, so that each cover includes all targets, and then, activate the sensors of each cover one at a time.. This maximizes the possible disjoint covers with an available number of sensors and can be treated as a set-K cover problem, which has been proven to be NP-complete. Evolutionary programming is a very powerful algorithm that uses mutation as the primary operator for evolution. Hence, mutation defines the quality and time consumed in the final solution computation. We have applied the self adaptive mutation strategy based on hybridization of Gaussian and Cauchy distributions to develop to develop a faster and better solution. One of the limitations associated with the evolutionary process is that it requires definition of the redundancy covers, and therefore, it is difficult to obtain the upper bound of a cover. To solve this problem, a redundancy removal operator that forces the evolution process to find a solution without redundancy is introduced. Through simulations, it is shown that the proposed method maximizes the lifespan of WSNs.     

Multi-Objective Evolutionary Algorithm Based on Decomposition for Energy Efficient Coverage in Wireless Sensor Networks

Wireless sensor networks (WSNs) have become a hot area of research in recent years due to the realization of their ability in myriad applications including military surveillance, facility monitoring, target detection, and health care applications. However, many WSN design problems involve tradeoffs between multiple conflicting optimization objectives such as coverage preservation and energy conservation. Many of the existing sensor network design approaches, however, generally focus on a single optimization objective. For example, while both energy conservation in a cluster-based WSNs and coverage-maintenance protocols have been extensively studied in the past, these have not been integrated in a multi-objective optimization manner. This paper employs a recently developed multi-objective optimization algorithm, the so-called multi-objective evolutionary algorithm based on decomposition (MOEA/D) to solve simultaneously the coverage preservation and energy conservation design problems in cluster-based WSNs. The performance of the proposed approach, in terms of coverage and network lifetime is compared with a state-of-the-art evolutionary approach called NSGA II. Under the same environments, simulation results on different network topologies reveal that MOEA/D provides a feasible approach for extending the network lifetime while preserving more coverage area.     

Composite Fault Diagnosis in Wireless Sensor Networks Using Neural Networks

Wireless sensor networks (WSNs) are spatially distributed devices to support various applications. The undesirable behavior of the sensor node affects the computational efficiency and quality of service. Fault detection, identification, and isolation in WSNs will increase assurance of quality, reliability, and safety. In this paper, a novel neural network based fault diagnosis algorithm is proposed for WSNs to handle the composite fault environment. Composite fault includes hard, soft, intermittent, and transient faults. The proposed fault diagnosis protocol is based on gradient descent and evolutionary approach. It detects, diagnose, and isolate the faulty nodes in the network. The proposed protocol works in four phases such as clustering phase, communication phase, fault detection and classification phase, and isolation phase. Simulation results show that the proposed protocol performs better than the existing protocols in terms of detection accuracy, false alarm rate, false positive rate, and detection latency.