Sensor Deployment Problem Research Articles

An Energy-Balanced and Timely Self-Relocation Algorithm for Grid-Based Mobile WSNs

The sensor relocation problem (SR problem) is an important issue in wireless sensor networks (WSNs). The main difference between SR problem and sensor deployment (SD) problem is that SR problem has a strict response time requirement. Because the SD problem occurs during network initialization, so there is no strict response time requirement. The SD problem is solved as long as the required coverage level is met. However, the SR problem occurs during operation of WSNs, we need to minimize the impact on the surveillance task while replacing faulty sensors with redundant sensors. In this paper, we revisit the SR problem in mobile WSNs. In terms of response time and moving distance, the proposed algorithm moves redundant sensors to faulty sensors and coverage holes by cascaded movement. It also considers the estimated value of network lifetime in the planning of cascading schedule. Hence, the SR problems caused by inappropriate cascading schedules can be avoided. Overall, the proposed SR algorithm has the following features: 1) shorter response time; 2) shorter moving distances; 3) smaller number of requesting places; and 4) longer network lifetime. The performance evaluation done in this paper has confirmed its superior performances in all these aspects.

Improving Coverage Deployment for Dynamic Nodes using Genetic Algorithm in Wireless Sensor Networks

Wireless Sensor Network is a group of sensor nodes aimed to monitor various environmental conditions at the field locations. Eventually the sensed data is sent to a processing center. Each sensor node consists of a different number of sensors for sensing different parameters in the field area. The sensor nodes are usually equipped with a pair of limited alkaline battery. Achieving maximum coverage deployment is a major problem in wireless sensor networks. In the Existing system, by using the Random Deployment some nodes may get overlapping this will cause unbalanced structure. By using maximum coverage sensor deployment problem, the Coverage achieved only for static nodes. In the proposed system, a Genetic Algorithm is used to deploy Sensor Nodes for the Maximum coverage within the area, where the sensors are of different types. In this work, first analyze the total coverage area the WSN, identify the types of Sensor nodes and Coverage sensing distance, and calculate the coverage sensing distance for the combination of all sensor types based on radius of each node. Improving the deployment of Dynamic nodes for achieving maximum coverage deployment by using Genetic Algorithm. As a result, we were implemented this work in Java. This will show the best performance in coverage and network lifetime.

Minimizing Movement for Target Coverage and Network Connectivity in Mobile Sensor Networks

Coverage of interest points and network connectivity are two main challenging and practically important issues of Wireless Sensor Networks (WSNs). Although many studies have exploited the mobility of sensors to improve the quality of coverage andconnectivity, little attention has been paid to the minimization of sensors’ movement, which often consumes the majority of the limited energy of sensors and thus shortens the network lifetime significantly. To fill in this gap, this paper addresses the challenges of the Mobile Sensor Deployment (MSD) problem and investigates how to deploy mobile sensors with minimum movement to form a WSN that provides both target coverage and network connectivity. To this end, the MSD problem is decomposed into two sub-problems: the Target COVerage (TCOV) problem and the Network CONnectivity (NCON) problem. We then solve TCOV and NCON one by one and combine their solutions to address the MSD problem. The NP-hardness of TCOV is proved. For a special case of TCOV where targets disperse from each other farther than double of the coverage radius, an exact algorithm based on the Hungarian method is proposed to find the optimal solution. For general cases of TCOV, two heuristic algorithms, i.e., the Basic algorithm based on clique partition and the TV-Greedy algorithm based on Voronoi partition of the deployment region, are proposed to reduce the total movement distance ofsensors. For NCON, an efficient solution based on the Steiner minimum tree with constrained edge length is proposed. Thecombination of the solutions to TCOV and NCON, as demonstrated by extensive simulation experiments, offers a promising solutionto the original MSD problem that balances the load of different sensors and prolongs the network lifetime consequently.

Deploying R&D sensors to monitor heterogeneous objects and accomplish temporal coverage

A rotatable and directional (R&D) sensor is a wireless device that has sector-shaped sensing coverage and rotatable ability. Such sensors can periodically rotate to monitor nearby objects in order to achieve temporal coverage. Specifically, an object is said to be δi-time covered if it can be monitored by R&D sensors for at least δi portion in each period, where 0<δi≤1. Given a set of heterogeneous objects with different δi values, the paper formulates a generalized R&D sensor deployment (GRSD) problem. It determines how to use the minimum number of R&D sensors to accomplish temporal coverage by letting each object be δi-time covered. The GRSD problem is proven to be NP-hard, and an efficient heuristic is developed based on the locations and coverage demands of objects. Experimental results demonstrate that the proposed GRSD heuristic significantly reduces the number of deployed sensors compared with existing methods, under various simulation scenarios.

3D Self-Deployment Algorithm in Mobile Wireless Sensor Networks

The sensor deployment problem of wireless sensor networks (WSNs) is a key issue in the researches and the applications of WSNs. Fewer works focus on the 3D autonomous deployment. Aimed at the problem of sensor deployment in three-dimensional spaces, the 3D self-deployment (3DSD) algorithm in mobile sensor networks is proposed. A 3D virtual force model is utilized in the 3DSD method. A negotiation tactic is introduced to ensure network connectivity, and a density control strategy is used to balance the node distribution. The proposed algorithm can fulfill the nodes autonomous deployment in 3D space with obstacles. Simulation results indicate that the deployment process of 3DSD is relatively rapid and the nodes are well distributed. Furthermore, the coverage ratio of 3DSD approximates the theoretical maximum value.

Process of 3D wireless decentralized sensor deployment using parsing crossover scheme

A Wireless Sensor Networks (WSN) usually consists of numerous wireless devices deployed in a region of interest, each able to collect and process environmental information and communicate with neighboring devices. It can thus be regarded as a Multi-Agent System for territorial security, where individual agents cooperate with each other to avoid duplication of effort and to exploit other agent’s capacities. The problem of sensor deployment becomes non-trivial when we consider environmental factors, such as terrain elevations. Due to the fact that all sensors are homogeneous, the chromosomes that encode sensor positions are actually interchangeable, and conventional crossover schemes such as uniform crossover would cause some redundancy as well as over-concentration in certain specific geographical area. We propose a Parsing Crossover Scheme that intends to reduce redundancy and ease geographical concentration pattern in an effort to facilitate the search. The proposed parsing crossover method demonstrates better performances than those of uniform crossover under different terrain irregularities.

A comparison of heuristics applied to the sensor deployment problem in two dimensions

This paper presents some interesting results that compare three heuristics applied to the sensor deployment problem. These heuristics are the genetic algorithm (GA), differential evolution (DE), and vertex swap algorithm (VSA). In both of two test problems, solved independently hundreds of times, the VSA routinely demonstrates better performance in terms of the quality of the solution and in lower execution times. The VSA is also combined with both the GA and DE to produce hybrid metaheuristics. However, repeated tests demonstrated that this provides little to no improvement in the quality of the final solution. Thus, a simple local search may be all that is needed for certain types of problems.

Particle swarm inspired underwater sensor self-deployment.

Underwater sensor networks (UWSNs) can be applied in sea resource reconnaissance, pollution monitoring and assistant navigation, etc., and have become a hot research field in wireless sensor networks. In open and complicated underwater environments, targets (events) tend to be highly dynamic and uncertain. It is important to deploy sensors to cover potential events in an optimal manner. In this paper, the underwater sensor deployment problem and its performance evaluation metrics are introduced. Furthermore, a particle swarm inspired sensor self-deployment algorithm is presented. By simulating the flying behavior of particles and introducing crowd control, the proposed algorithm can drive sensors to cover almost all the events, and make the distribution of sensors match that of events. Through extensive simulations, we demonstrate that it can solve the underwater sensor deployment problem effectively, with fast convergence rate, and amiable to distributed implementation.

Dynamic generalized controllability and observability functions with applications to model reduction and sensor deployment

In this paper we introduce the notion of Dynamic Generalized Controllability and Observability functions for nonlinear systems. These functions are called dynamic and generalized since they make use of additional states (dynamic extension) and are such that partial differential inequalities are solved in place of equations. The presence of the dynamic extension permits the construction of classes of canonical controllability and observability functions without relying on the solution of any partial differential equation or inequality. The effectiveness of the proposed concept is validated by means of two applications: the model reduction problem via balancing and the sensor deployment problem in a continuous stirred tank reactor (CSTR).

Sensor Deployment and Scheduling for Target Coverage Problem in Wireless Sensor Networks

Network lifetime plays an integral role in setting up an efficient wireless sensor network. The objective of this paper is twofold. The first one is to deploy sensor nodes at optimal locations such that the theoretically computed network lifetime is maximum. The second is to schedule these sensor nodes such that the network attains the maximum lifetime. Thus, the overall objective of this paper is to identify optimal deployment locations of the given sensor nodes with a pre-specified sensing range, and to schedule them such that the network lifetime is maximum with the required coverage level. Since the upper bound of the network lifetime for a given network can be computed mathematically, we use this knowledge to compute locations of deployment such that the network lifetime is maximum. Further, the nodes are scheduled to achieve this upper bound. In this paper, we use artificial bee colony algorithm and particle swarm optimization for sensor deployment problem followed by a heuristic for scheduling. A comparative study shows that artificial bee colony algorithm performs better for sensor deployment problem. The proposed heuristic was able to achieve the theoretical upper bound in all the experimented cases.

Optimal Deployment and Scheduling with Directional Sensors for Energy-Efficient Barrier Coverage

In recent years, barrier coverage problem in directional sensor networks has been an interesting research issue. Most of the existing solutions to this problem aim to find as many barrier sets as possible to enhance coverage for the target area, which did not consider the power conservation. In this paper, we address the efficient sensor deployment (ESD) problem and energy-efficient barrier coverage (EEBC) problem for directional sensor networks. First, we describe a deployment model for the distribution of sensor locations to analyze whether a target area can be barrier covered. By this model, we examine the relationship between the probability of barrier coverage and network deployment parameters. Moreover, we model the EEBC as an optimization problem. An efficient scheduling algorithm is proposed to prolong the network lifetime when the target area is barrier covered. Simulation results are presented to demonstrate the performance of this algorithm.

On Deployment of Wireless Sensors on 3-D Terrains to Maximize Sensing Coverage by Utilizing Cat Swarm Optimization With Wavelet Transform

In this paper, a deterministic sensor deployment method based on wavelet transform (WT) is proposed. It aims to maximize the quality of coverage of a wireless sensor network while deploying a minimum number of sensors on a 3-D surface. For this purpose, a probabilistic sensing model and Bresenham's line of sight algorithm are utilized. The WT is realized by an adaptive thresholding approach for the generation of the initial population. Another novel aspect of the paper is that the method followed utilizes a Cat Swarm Optimization (CSO) algorithm, which mimics the behavior of cats. We have modified the CSO algorithm so that it can be used for sensor deployment problems on 3-D terrains. The performance of the proposed algorithm is compared with the Delaunay Triangulation and Genetic Algorithm based methods. The results reveal that CSO based sensor deployment which utilizes the wavelet transform method is a powerful and successful method for sensor deployment on 3-D terrains.

Optimal deployment of mobile sensors for target tracking in 2D and 3D spaces

This paper proposes a control strategy to autonomously deploy optimal placements of range-only mobile sensors in 2D and 3D spaces. Based on artificial potential approaches, the control strategy is designed to minimize the intersensor and external potentials. The inter-sensor potential is the objective function for optimal sensor placements. A placement is optimal when the inter-sensor potential is minimized. The external potential is introduced to fulfill constraints on sensor trajectories. Since artificial potential approaches can handle various issues such as obstacle avoidance and collision avoidance among sensors, the proposed control strategy provides a flexible solution to practical autonomous optimal sensor deployment. The control strategy is applied to several optimal sensor deployment problems in 2D and 3D spaces. Simulation results illustrate how the proposed control strategy can improve target tracking performance.

A Bipopulation-Based Evolutionary Algorithm for Solving Full Area Coverage Problems

The full area coverage sensor deployment problem is a challenging issue in wireless sensor networks. We focus on sensor deployment strategies that aim to acquire a full-coverage state with a minimum number of sensors in a predetermined target region that includes non-penetrable obstacles. This paper presents an efficient bipopulation-based evolutionary full area coverage (BEFAC) algorithm that involves a bipopulation structure composed of a full- and partial-coverage populations. Fitness functions, stochastic unary recombination operators, and selection procedures between the two populations are well designed. Through applying the proposed BEFAC, a full-coverage state is acquired with a minimum number of deployed sensors in the target region, which has non-penetrable obstacles, and the algorithm avoids getting caught in local minima. The performance results reveal that BEFAC outperforms the conventional deployment methods in terms of the number of deployed sensors and the number of required fitness evaluations.

Uncertainty-Aware Sensor Deployment Strategy in Mixed Wireless Sensor Networks

Deployment is a fundamental issue in wireless sensor networks (WSNs), which affects the performance and lifetime of the networks. Usually the sensor locations are precomputed based on “perfect” sensor detection model, whereas sensors may not always provide reliable information, either due to operational tolerance levels or environmental factors. Therefore, it is very important to take into account this uncertainty in the deployment process. In this paper, we address the problem of sensor deployment in a mixed sensor network where the mobile and static nodes work collaboratively to perform deployment optimization task. We consider the Gaussian white noise in the environment and present a centralized algorithm (FABGM for short) which discoveres vacancies by using detection model based on false alarm and moves the mobile nodes according to the method based on bipartite graph matching in this study. In this algorithm, the management node of the WSNs collects the geographical information of all of the static and mobile sensors. Then, the management node executes the algorithm to get the best matches between mobile sensors and coverage holes. Simulation results are presented to demonstrate the effectiveness of the proposed approach.

Distributed k-Coverage Decision Scheme for System Deployment in Mobile Sensor Networks

K-coverage sensor deployment is an important issue in wireless sensor networks (WSNs) applications. This is because an efficient topology structure significantly affects the quality of service and lifetime of WSNs. In the present work, we study the sensor deployment problem and propose novel distributed decision schemes to guide sensor movement to achieve k-coverage deployment. In our schemes, the kth order Voronoi diagram is used to discover the regions that do not meet the k-coverage requirement. Two different movement strategies are designed to determine the optimal location of each mobile sensor through local topological information. Finally, simulation results validate the effectiveness and accuracy of the proposed schemes.

Fish Swarm Inspired Underwater Sensor Deployment

Underwater sensor networks (UWSNs) can be applied to sea resources reconnaissance, pollution monitoring, assistant navigation, etc., and they have become a hot field in wireless sensor networks. In open and complicated underwater environments, the monitored target is dynamic and uncertain. It is a key topic for sensors to adjust their positions according to the environment and targets, so as to cover the targets in an optimal manner. This paper defines the underwater sensor deployment problem and its performance evaluation metrics, and then presents a fish swarm in- spired sensor deployment algorithm. Simulating the behavior of fish swarm and taking the crowd factor into account, the method can drive the sensors to cover almost all the events, and make the sensors density match the events density. Through extensive comparative simulations, we demonstrate that the proposed algorithm can solve the underwater sensor deployment problem effectively and it is be simple, fast in convergence, and realizable in distributed manner.

A Novel Swarm Intelligence Algorithm and Its Application in Solving Wireless Sensor Networks Coverage Problems

Wireless sensor networks (WSNs) have attracted a great deal of research due to their wide-range of potential applications. Sensor deployment and coverage problems are their important issues. This article briefly introduces the principle of swarm intelligence (SI). A novel SI algorithm based on information sharing of Particle Swarm Optimization (PSO) and diversity maintenance mechanism of Artificial Immune System (AIS) is designed and the model of coverage problems is given. I ts applications in solving different deterministic and random coverage problems are given. The algorithm used in obtaining maximum coverage probability with given number of sensor nodes and minimum number of sensor nodes with required coverage probability of WSNs deterministic coverage, and determining the selected sensor nodes with coverage probability and connectivity requirement of WSNs random coverage, are analyzed in detail. The simulation results show the algorithm is practical. The applications of SI on K-coverage and connectivity problems in the future are also projected in the article.

INCOME: Practical land monitoring in precision agriculture with sensor networks

Land monitoring is a critical task to ensure the quality of agricultural production. Traditional precision agriculture techniques require intensive computation and expensive hardware devices. This paper explores the techniques of deploying sensor networks in the field for practical land monitoring. As an example, we accurately measure the dark-area/light-area ratios in agriculture fields based on the Monte Carlo theory. We formulate the minimum sensor deployment problem, whose aim is to minimize the number of sensor nodes needed to achieve measurement precision requirements while satisfying size limitation requirements. Size limitation requirements are specified so that manual treatments can be carried on sub-regions that have an extraordinary dark/light ratio. We propose an incremental deployment solution – INCOME – to solve the problem, which does not require any prior knowledge of the dark/light distribution of the field. A split/merge algorithm is designed in INCOME to divide the monitored field into sub-regions satisfying both requirements. We formally prove that the sensor number needed in INCOME is less than that of regular division, and analyze the sensors needed in the ideal case and worst case. Comprehensive simulation studies demonstrate that the performance of INCOME is close to the optimal solution.

Using Rotatable and Directional (R&amp;D) Sensors to Achieve Temporal Coverage of Objects and Its Surveillance Application

Due to hardware design or cost consideration, sensors may possess sector-like sensing coverage. Furthermore, by stepper motors, sensors can rotate to cover the objects around them. This type of sensors are called rotatable and directional (R&D) sensors. Through rotation, R&D sensors provide temporal coverage to objects by periodically” detecting their existence. In the paper, we first develop an event-driven surveillance system by R&D sensors, where objects are monitored by the sensors equipped with infrared detectors and cameras. When an object is taken away, the sensor monitoring the object reports a warning message along with detailed snapshots from the surroundings. Then, motivated by the system, we formulate an R&D sensor deployment problem, which tries to deploy the minimum number of R&D sensors to cover a given set of objects such that each object is covered by 0 < \delta \le 1 ratio of time in every frame. We show this problem to be NP-hard and propose two efficient heuristics. The maximum covering deployment (MCD) heuristic iteratively deploys a sensor to cover more objects, and performs well when objects congregate together. The disk-overlapping deployment (DOD) heuristic deploys sensors to cover the joint sectors of overlapped disks, so it works better when objects are arbitrarily placed in the sensing field. The paper contributes in defining a new temporal coverage model by R&D sensors, developing a surveillance application for this model, and proposing efficient heuristics to reduce the deployment cost.