Special Issue on “Wireless Sensor Networks”

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.

Target Coverage and Network Connectivity in Wireless Sensor Networks (WSNs) plays a momentous role in the field of monitoring environment, habitant observing, disaster recovery, surveillance, etc. Coverage and Network Connectivity collectively can be considered as a proportion of Quality of Service (QoS) in a sensor network. It implies the coverage of each point in the detecting field while in the meantime fulfilling the measure that each node is surrounded by the equivalent neighbourhood of at least one other node. Many primary issues influence the outline and performance of WSN. A Comprehensive survey is made based on the scheduling algorithm and set cover approach, in which we analyze various approaches and their algorithm complexity. Firstly, the scheduling algorithm intermittently provides the required data service with guaranteed efficiency. This methodology can efficiently reduce the entire number of active nodes in the network, resulting in maximizing the network lifetime. Secondly, the set cover approach plays a major role in solving target coverage problem which group sensors into each cover set thereby, target coverage was achieved. Typical issues and open research challenges are addressed by classifying the approaches available in literature into three broad categories namely, adaptable coverage radius, coverage deployment strategy, and sleep scheduling. This survey aims to provide a better perceptive of the existing open challenges and possible futuristic approaches for target coverage and network connectivity in wireless sensor networks.

Notice of Violation of IEEE Publication Principles Critical Density for Coverage and Connectivity in Two-Dimensional Aligned-Orientation Directional Sensor Networks Using Continuum Percolation, by Mohammad Khanjary, Masoud Sabaei, and Mohammad Reza Meybodi in IEEE Sensors Journal, vol. 14, no. 8, pp. 2856-2863, Aug. 2014 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. Integrated Coverage and Connectivity in Wireless Sensor Networks: A Two-Dimensional Percolation Problem, by H. M. Ammari and S. K. Das, in IEEE Transactions on Computers, vol. 57, no. 10, pp. 1423-1434, Oct. 2008 Sensing coverage is one of the fundamental design issues in wireless sensor networks, which reflects the surveillance quality provided by them. Moreover, network connectivity enables the gathered data by sensors to reach to the sink node. Given an initially uncovered field, and as more and more directional sensors are continuously added to the sensor network, the size of partial covered areas increases. At some point, the situation abruptly changes from small fragmented covered areas to a single large covered area. We call this abrupt change the sensing-coverage phase transition (SCPT). Likewise, given an originally disconnected sensor network, as more and more sensors are added, the number of connected components changes such that the sensor network suddenly becomes connected at some point. We call this sudden change the network connectivity phase transition (NCPT). The nature of such phase transitions is a central topic in the percolation theory. In this paper, we introduce aligned-orientation directional sensor networks in which nodes are deployed based on Poisson point process and the orientation of all sensor nodes is the same. Then, we propose an approach to compute density of nodes at critical percolation for both of the SCPT and NCPT problems in such networks, for all angles of field-of-view between 0 and π by using continuum percolation. Due to percolation theory, the critical density is infimum density that for densities above it SCPT and NCPT almost surely occur. In addition, we propose a model for percolation in directional sensor networks, which provides a basis for solving the SCPT and NCPT problems together.

It is our great pleasure to welcome you to the 3rd ACM Workshop on Security of Ad Hoc and Sensor Networks (SASN'05), held in conjunction with the 12th ACM Conference on Computer and Communications Security (CCS'05), November 7, 2005, in Alexandria, Virginia.This year we received a total of 39 submissions on various aspects of ad-hoc and sensor network security. The program committee selected 6 as regular papers that cover a variety of topics, including trust establishment in Mobile Ad-Hoc Networks (MANETs), security of vehicular ad-hoc networks, secure group communication, key pre-distribution for sensor networks, jamming attacks against sensor network MAC protocols, and defense against Denial-of-Service (DoS) attacks. This year we continue the tradition of SASN'03 and SASN'04 to encourage the presentation and discussion of work-in-progress on ad-hoc and sensor network security. Among the 39 submissions, the program committee selected 7 papers to present in work-in-progress sessions, covering topics on anonymous routing for MANETs, key distribution for inter-group multicast in ad-hoc networks, improved topology assumptions for threshold cryptography in MANETs, RFID security, tracing in mobile wireless sensor networks, time synchronization attacks in sensor networks, and coordinated deployment for improving secure communications and sensing coverage in wireless sensor networks. In addition, the program includes a panel on Advances in Sensor and Ad-hoc Network Security and an invited keynote talk by Radha Poovandran (University of Washington) on Information Assurance in MANETs and Wireless Sensor Networks. We hope that these proceedings will serve as a valuable reference for security researchers and developers.

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.

Prolonging lifetime is a key challenge in wireless sensor networks comprising unattended, limited resource sensor nodes. As sensors are usually densely deployed, redundant data is produced by multiple observations on the same event, which decreases the network lifetime. With each observing sensor acting as a source of data, we schedule the time when these sources are allowed to send data to prolong the network lifetime with the guarantee that the total data provided satisfies the application requirement at any time. Considering k-coverage of each target as the application requirement, we propose the observation time scheduling for network lifetime maximization (OSLM) problem and solve it for two observation scenarios: a) a sensor node can distinguish the targets in its sensing range and selects a subset of targets to observe and b) a sensor node has to simultaneously observe all the targets in its sensing range as it cannot distinguish targets. We develop an optimal solution using a linear programming (LP) problem formulation for OSLM problem for the first observation scenario and solve it using CPLEX package. We prove that OSLM problem for the second observation scenario is NP-complete. A heuristic algorithm for the second scenario is then proposed. The performance is evaluated by the numerical results obtained from CPLEX and extensive simulation results

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.

Finding an optimal node deployment strategy in wireless sensor networks (WSNs) that would reduce cost, be robust to node failures, reduce computation, and communication overhead, and guarantee a high level of coverage along with network connectivity is a difficult problem. In fact, sensing coverage and network connectivity are two of the most fundamental problems in WSNs as they can directly impact the network lifetime and operation. In this paper, we consider deriving optimal conditions for connectivity with coverage in WSNs. Most versions of this problem are (NP-complete), while approximation algorithms cannot be developed for some versions of polynomial time, unless P = NP. Hence, we also develop a heuristic for some versions of the problem and the efficacy of the heuristic will be evaluated through extensive simulations. We are also interested in determining the probability of finding a path between a given pair of nodes over a given topology of WSNs. This will serve as a measure of connectivity with coverage of the network. Hence, we derive necessary and sufficient conditions for connectivity with coverage over a clustered structure in WSNs. Then, employing queuing networks modeling techniques, we present a dynamic programming study of the connectivity with coverage of clustered structure and its effect on routing in generalized WSNs. The performance evaluation of the proposed schemes shows that availability of nodes, sensor node coverage, and the connectivity were sufficiently enhanced to maximize network lifetime.

Wireless sensor networks are a collection of specially designed nodes to provide support to sensor information propagation. Sensor nodes are basically designed to sense data as sensor and distribute to servers for further analysis. Based on the specific requirements, the numbers of sensor nodes can range from hundreds to thousands. All the communication will take place through predefined network characteristics. WSNs generally face energy issues. LEACH is designed for efficient cluster formation. It provides an efficient energy solution for data transmission from node to base station. Various enhanced version of LEACH has been proposed by Assuming LEACH as base protocol. The energy constraints are a critical aspect in developing the routing protocols for WSNs. Designers and researchers have contributed their effort through enhancement in energy saving procedure into WSNs. In this work, the LEACH based protocols and their performance in WSN has been analysed. This research paper evaluates recent advancement in LEACH based approaches for Wireless Wide Area Sensor Network with comparison table.

Wireless sensor networks are often deployed for data-gathering or monitoring in a geographical region. This paper explains an important issue to maintain the fidelity of the sensed data while minimizing energy usage in the network. Nature inspired computation like evolutionary computation, swarm intelligence etc., which offers practical advantages to the researcher facing difficult optimization problems. The genetic algorithms are used for efficient connectivity and coverage. Single Objective Genetic Algorithms (SOGA) method is used to yield good results in terms of Coverage, but the objective's graph had shown Pareto optimal designs with differing Endurance. However it is attractive to offer Pareto optimal designs to a user willing to settle for a poorer Coverage in order to gain in Endurance, so that the sensor network lasts longer. This explains concept of Multiple Objective Genetic Algorithm (MOGA) and its implementation and results which are compared to those of the SOGA. Endurance and Robustness to deployment inaccuracy tend to work in the same direction. A MOGA was conducted with the Coverage and Robustness as objectives. The main objective of this paper is to propose new Strength Perito Evolutionary Algorithm (SPEA) method along with clustering, this will reduce the distances between the sensor nodes that increase the efficiency of the nodes and also increase the connectivity. This will increase lifetime of sensors and connectivity.

Many wireless sensor network applications must resolve the inherent conflict between energy efficient and nodes of coverage in wireless sensor networks to achieve desired quality of service (QoS). The wireless sensor networks (WSNs) have been the main focus in the past few years, and it is becoming a special issue. We study the problem of controlling the node locations and energy within ad-hoc sensor networks. Network survivability is a very important measure for deciding the efficiency of network protocols. It includes a measure of the network lifetime as well as the quality of service. In fact, these dynamics are very important in evaluating networks. The wireless network applications can be found in battlefield communication, homeland security, pollution sensing and traffic monitoring. As such, there has been an increasing need for defining and developing simulation frameworks for carrying out high-fidelity WSN simulation. In this cause, we present a simulation by including factors such as energy-consumption, Bandwidth, nodes, and coverage and provides an object-oriented definition of target, sensor and sink nodes, sensor and wireless communication nodes, and physical radio such as relating of nodes, mobility model and power model both energy-producing and energy-consuming components, the simulation was developed using C#.

In the precondition of the basic connectivity of wireless sensor networks, how to control spreading of the virus infection is the most challenging security problem in a large number of sensor nodes deployed randomly. Existing research results show that network topology is complex and spreading of virus infection heavily. In order to constitute the reasonable immune strategy and control the spreading of virus infection effectively in sensor networks. in this paper, a virus spreading model with average degree constraint was proposed. For this model, With increasing communication radius and constraining node degree, the inconsistency of network topology connectivity and sparseness can be solved. Our scheme can decrease working nodes, guarantee network topology sparseness; the effect of activity nodes-density d, spread efficiency lambda on the spreading of virus infection was studied. The theoretical analysis and analog simulation show that there is a critical value in this model based on average degree constraint, and only when the product of spread efficiency and activity nodes-density goes beyond the critical value that the virus infection can spread continuously.

In this paper, we study the barrier lifetime maximisation (BLM) and barrier gap mending (BGM) problem in a sensor network, where sensors can choose to use different sensing ranges when constructing a sensor barrier. In the BLM problem, for small–scale sensor networks, we propose a linear programming optimisation method. For large–scale sensor networks, we propose two distributed heuristics to select a next node to construct barriers. Simulation results show that compared with the randomised one, the greedy scheme can achieve longer lifetime. In the BGM problem, we study how to efficiently mend barrier gaps in a hybrid sensor network consisting of both stationary and mobile sensors. We propose two gap mending schemes: the min–max scheme to minimise the maximal energy consumption to move sensors, and the max–lifetime scheme to maximise the lifetime of barrier coverage after mending all gaps. Simulation results show that the min–max scheme can achieve a lower maximal moving distance and the max–lifetime scheme can efficiently extend the barrier lifetime.

Some problems about the connectivity of wireless sensor networks (WSNs) are always important and difficult topics in research, especially the trade-off between connectivity and energy control. In this paper, we present a novel and effective method to calculate nearest neighbor nodes in three-dimensional WSNs using Poisson point field theory, which enables each node to find the kth nearest neighbor node and adjust transmitting range according to local requirement. Moreover, we derive the probability expression of the k-connected network. Finally, we give corresponding simulation results and discussions about the practical value for node energy control and design of WSNs.

In recent times although there is lot of research in field of localization application but very few aims at localization module with optimal energy consumption. Localization, being inevitable for a range of applications, is also a basis for many network operations such as data packet forwarding i.e. routing and sensing coverage in wireless sensor networks. With a range of application scenario, it is a challenging task to design a localization module for a wireless sensor network consisting of sensor nodes with limited capacity in terms of power, processing and storage etc. This paper presents a simple model of a range based localization system that employs distributed computing in a collaborative and cooperative manner. The aim of this work is to modify a range based localization algorithm to utilize scarce resources optimally along with improved performance by changing the inherent methodologies of position estimation. In this research work basically the role of a media access control scheme (MAC) in the localization module is investigated. A MAC is basically used to save the energy by reducing collisions and retransmission of range packets. Further, energy is conserved by restricting the coverage to a smaller part containing the neighbours up to certain hops and that is formed by dividing the whole network area into clusters. To analyse the performance spectrum, we have simulated the localization method under a variety of application scenarios and lead to the conclusion that this approach increases the efficiency of localization in wireless sensor networks. In the simulation process, every minute aspect of sensor network localization model including the topology, range, mobility, channel, propagation schemes etc. were considered and the performance was finally analyzed.