Analysis of the redundancy in coverage of a heterogeneous wireless sensor network

Coverage has recently emerged as a premier research topic. More and more research focused on coverage in wireless sensor networks using Voronoi. While most existing research efforts in the area of wireless sensor networks have focused on networks with nodes which have the same capabilities. But for Heterogeneous Wireless Sensor Networks (HWSN), which is more universal, there is a lack of study for using Voronoi solves the problem of coverage. In this paper we address on X-Voronoi for solve coverage in heterogeneous networks. Simulations confirm that the X-Voronoi is able to solve the problem of analyzing coverage performance in heterogeneous networks. To our knowledge, this is the first study on Voronoi for Heterogeneous Wireless Sensor Networks.

Due to the increase of Wireless Sensor Network (WSN) technologies demand, the optimal sensor node deployment is considered as one of the most important factors that directly affect the network coverage. Most researches in WSNs that solved the problem of coverage in homogeneous and heterogeneous cases are suffering from many drawbacks such as consumed energy and high cost. In this paper, we propose an efficient deployment model based on probabilistic sensing model (PSM) and harmony search algorithm (HSA) to achieve the balance between the network coverage performance and the network cost in a heterogeneous wireless sensor network (HEWSN). The HSA is used for deployment optimization of HEWSN nodes which makes a balance between the coverage and financial cost. The PSM is used to solve the overlapping problem among the sensors. The performance of the proposed model is analyzed in terms of coverage ratio and cost evaluations. The simulation results showed the capability of the proposed heterogeneous deployment model to achieve maximum coverage and a minimum number of sensors compared to homogeneous deployment. Furthermore, a comparative study with a meta-heuristic genetic-based algorithm in HEWSN has also been conducted, and its results confirm the superiority of the proposed model.

Coverage in wireless sensor networks is a critical issue since it reflects the cost and detection capability of the network. Sensor coverage model is closely to the sensing functions of different types of sensors while network coverage is a collaborative measure from geographically distributed sensors. The coverage problems in wireless sensor networks have been formulated in various ways with different assumptions, constraints and objectives. In this work, a collaborative coverage model is proposed in the target surveillance application, while sensor sensing probabilistic model is introduced. Furthermore, a collaborative target coverage optimization algorithm (CTCOMSN) is presented to prolong the network lifetime in energy constraint mobile sensor networks. Simulation results show that CTCOMSN can prolong the lifetime greatly while improving the sensing quality.

During these years, the 3D node coverage of heterogeneous wireless sensor networks that are closer to the actual application environment has become a strong focus of research. However, the direct application of traditional two-dimensional planar coverage methods to three-dimensional space suffers from high application complexity, a low coverage rate, and a short life cycle. Most methods ignore the network life cycle when considering coverage. The network coverage and life cycle determine the quality of service (QoS) in heterogeneous wireless sensor networks. Thus, energy-efficient coverage enhancement is a significantly pivotal and challenging task. To solve the above task, an energy-efficient coverage enhancement method, VKECE-3D, based on 3D-Voronoi partitioning and the K-means algorithm is proposed. The quantity of active nodes is kept to a minimum while guaranteeing coverage. Firstly, based on node deployment at random, the nodes are deployed twice using a highly destructive polynomial mutation strategy to improve the uniformity of the nodes. Secondly, the optimal perceptual radius is calculated using the K-means algorithm and 3D-Voronoi partitioning to enhance the network coverage quality. Finally, a multi-hop communication and polling working mechanism are proposed to lower the nodes’ energy consumption and lengthen the network’s lifetime. Its simulation findings demonstrate that compared to other energy-efficient coverage enhancement solutions, VKECE-3D improves network coverage and greatly lengthens the network’s lifetime.

A wireless sensor network is a correspondence framework which detects and accumulates data from a specific zone and sends it to the base station or sink node. WSN can be very exceptionally valuable for target detection and recognizable proof by military and ecological applications like agrarian farm monitoring and woodland fire supervising. A genetic algorithm is a meta-heuristic approach propelled by the process of natural selection that belongs to the class of evolutionary algorithms. It is utilized to create high-quality solutions to optimization and search problem by relying on bio-inspired operators such as mutation, crossover, and selection. The goal is deploying a minimum number of sensor element nodes without overlapping each other based on antenna height and separation between sensor nodes. The experimental result shows that better coverage in heterogeneous wireless sensor network using NS2 software simulation.

This paper considers a heterogeneous wireless sensor network that consists of several resource-rich supernodes used for data relaying and a large number of energy constrained wireless sensor nodes. Sensor nodes are deployed randomly to monitor a number of targets. Since targets are redundantly covered by more sensors, we organize the sensors in set covers that are activated successively to conserve energy. In this paper we introduce the Heterogeneous Connected Set Covers (HCSC) problem that has the objective to find a maximum number of set covers such that each set cover monitors all targets and each active sensor is connected to at least one supernode. A sensor can participate in multiple set covers, but the total energy spent in all sets is constrained by the battery capacity. In this paper we show that HCSC is NP-complete and we propose two algorithms for solving this problem, an Integer Programming approach and a distributed and localized protocol. Simulation results are presented to evaluate these solutions.

As the Internet of Things (IoT) evolves, more and more Wireless Sensor Networks (WSNs) are being deployed in the real world. Connected vehicles, smart grids, smart cities, smart healthcare, networks of robots, and disaster recovery networks are some examples. In WSNs, the area coverage is one of the most important quality of service metrics. A WSN without enough area coverage yields incorrect results. So calculating the covered area of a WSN is mandatory. Previous studies have used a simple approach: all nodes send their location to the sink, and it calculates the covered area centrally which makes huge unnecessary communication overhead. In our previous work titled Distributed Exact Coverage Rate Calculation, we calculated the covered area of a homogenous WSN in a distributed manner. In this paper, we provide a Heterogeneous Distributed Precise Coverage Rate (HDPCR) mechanism that calculates the covered area of a Heterogeneous Wireless Sensor Network by using a localized mechanism. With the use of boundary detection mechanisms, the HDPCR detects the boundary of the network and calculates its area. HDPCR also detects holes and calculates their area precisely. By subtracting these two calculated values, the covered area of the network can be computed. Many related studies have evaluated the coverage rate approximately with error and require more calculations to reduce the error rate. HDPCR calculates the coverage rate precisely without an error rate using simple arithmetic calculations. The exhaustive simulation also shows the superiority of HDPCR as compared to the previous approaches.

Exploring coverage and security challenges in wireless sensor networks: A survey

Although most of the studies on coverage and connectivity in wireless sensor networks (WSNs) considered two-dimensional (2D) settings, such networks can in reality be accurately modeled in a three-dimensional (3D) space. The concepts of continuum percolation theory best fit the problem of connectivity in WSNs to find out whether the network provides long-distance multihop communication. In this paper, we focus on percolation in coverage and connectivity in 3D WSNs. We say that the network exhibits a coverage percolation (respectively, connectivity percolation) when a giant covered region (respectively, giant connected component) almost surely spans the entire network for the first time. Because of the dependency between coverage and connectivity, the problem is not only a continuum percolation problem but also an integrated continuum percolation problem. Thus, we propose an integrated-concentric-sphere model to address coverage and connectivity in 3D WSNs in an integrated way. First, we compute the critical density lambdaC con above which coverage percolation in 3D WSNs will almost surely occur. Second, we compute the critical density lambdac con above which connectivity percolation in 3D WSNs will almost surely occur. Third, we compute the critical density lambdac cov-con above which both coverage and connectivity percolation in 3D WSNs will almost surely occur. For each of these three problems, we also compute their corresponding critical network degree. Our results can be helpful in the design of energy-efficient topology control protocols for 3D WSNs in terms of coverage and connectivity.

As more and more innovative applications of wireless sensor networks emerge, the need for deploying heterogeneous sensors with different functionalities and capabilities rise rapidly. In this study, we consider a heterogeneous sensor network containing at least two types of sensors: powerful sensors and normal sensors. Powerful sensors have higher energy and computing capacities, and can communicate with normal sensors using the same network interface. Communications between powerful sensors may thus be facilitated by building an overlay on top of the normal sensors. The topology of the overlay is critical. The overlay must have a low diameter to efficiently support upper layer applications such as resource discovery. It must also consider the energy consumption of the relaying normal sensors. In this paper, we propose a distributed overlay formation protocol to achieve the above goals. Through simulation, we compare our protocol with two overlay formation protocols, fully connected graph and minimum spanning tree. The results show that our proposed protocol can achieve better performance both in message latency and energy consumption.

We develop new algorithms for spatial field reconstruction, exceedance level estimation and classification in heterogeneous (mixed analog & digital sensors) Wireless Sensor Networks (WSNs). We consider spatial physical phenomena which are observed by a heterogeneous WSN, meaning that it consists partially of sparsely deployed high-quality sensors and partially of low-quality sensors. The high-quality sensors transmit their (continuous) noisy observations to the Fusion Centre (FC), while the low-quality sensors first perform a simple thresholding operation and then transmit their binary values over imperfect wireless channels to the FC. The resulting observations are mixed continuous and discrete (1-bit decisions) observations, and are combined in the FC to solve the inference problems. We first formulate the problem of spatial field reconstruction, exceedance level estimation and classification in such heterogeneous networks. We show that the resulting posterior predictive distribution, which is key in fusing such disparate observations, involves intractable integrals. To overcome this problem, we develop an algorithm that is based on a multivariate series expansion approach resulting in a Saddle-point type approximation. We then present comprehensive study of the performance gain that can be obtained by augmenting the high-quality sensors with low-quality sensors using real data of insurance storm surge database known as the Extreme Wind Storms Catalogue.

Recent advancements in miniaturization and inter-operability of wireless technologies have enabled a variant of Wireless Sensor Network (WSN), called Heterogeneous Wireless Sensor Network (HWSN). This plays a significant role in most popular applications of WSN such as habitat monitoring, volcano monitoring, military applications, etc. Similar to WSN, power resources are of utmost importance in HWSN too. Hence, several techniques have already been proposed to achieve energy-efficient network operations in heterogeneous networks. Among all those proposed schemes, the notion of clustering has proved its worth in obtaining energy-efficient clusters throughout the network. However, most of such clustering-based schemes utilize a dynamic probabilistic approach to deal with multilevel energy heterogeneity, which consumes many nodes' energy in the cluster formation. Moreover, with the varying degree of energy heterogeneity, most probabilistic schemes underperform and fail to compete. In this work, a Metaheuristic Multilevel Heterogeneous Clustering Technique (MMHCT) for Heterogeneous Wireless Sensor Networks has been proposed. MMHCT not only reduces the energy consumption in cluster-formation over the network rounds but also distributes the load evenly among the clusters to prolong the network lifetime. The supremacy of the proposed scheme is shown through various experiments against a state-of-the-art scheme, DEBCRP (Differential Evolution Based Clustering Routing Protocol) and the results confirm gains of 50%, 29.69%, 30.22%, 65.65%, & 37.17% in terms of network lifetime under 1-level, 2-level, 3-level, 4-level, & 5-level of energy heterogeneity respectively.

Aiming at the uneven random coverage distribution of wireless sensor networks sensor nodes, the sparrow search algorithm (SSA) is used to optimize the node coverage of wireless sensor networks. To improve the global search capability of SSA, the algorithm is improved and applied to the wireless sensor networks of bridge monitoring. For enhancing the coverage of the wireless sensor networks, this article uses two improved methods. One is to use the good point set theory to make the initial population evenly distributed; another is to introduce a weight factor to speed up its convergence. The experiments have proved the reliability and rationality of the algorithm. The improved algorithm is superior to other meta-heuristic algorithms and provides a new idea for optimizing bridge monitoring wireless sensor networks coverage.

Distributed key management is a major challenge in the design and deployment of secure wireless sensor networks (WSNs). In most existing distributed key management schemes, a common assumption is that all sensor nodes have the same capability. However, recent studies have demonstrated that heterogeneities of the sensor nodes, in particular the heterogeneities of energy capacity and transmission capability, can significantly improve the connectivity and lifetime of the WSNs. Consequently, it is important to design key management schemes for heterogeneous WSN (HWSN). In this paper, we provide a distributed key management scheme for HWSNs. The main contribution of this paper is to develop analytical models for evaluating the performance of the proposed scheme in terms of connectivity, reliability, and resilience. Extensive simulation results show that, with a small number of heterogeneous nodes, the WSN can achieve higher key connectivity, better reliability, and higher resilience. The results also show that our analytical models can accurately predict the performance under a variety of conditions

Data aggregation processes aim to reduce the amount of exchanged data in wireless sensor networks and consequently minimize the packet overhead and optimize energy efficiency. Securing the data aggregation process is a real challenge since the aggregation nodes must access the relayed data to apply the aggregation functions. The data aggregation security problem has been widely addressed in classical homogeneous wireless sensor networks, however, most of the proposed security protocols cannot guarantee a high level of security since the sensor node resources are limited. Heterogeneous wireless sensor networks have recently emerged as a new wireless sensor network category which expands the sensor nodes’ resources and capabilities. These new kinds of WSNs have opened new research opportunities where security represents a most attractive area. Indeed, robust and high security level algorithms can be used to secure the data aggregation at the heterogeneous aggregation nodes which is impossible in classical homogeneous WSNs. Contrary to the homogeneous sensor networks, the data aggregation security problem is still not sufficiently covered and the proposed data aggregation security protocols are numberless. To address this recent research area, this paper describes the data aggregation security problem in heterogeneous wireless sensor networks and surveys a few proposed security protocols. A classification and evaluation of the existing protocols is also introduced based on the adopted data aggregation security approach.