A grid-based coverage approach for target tracking in hybrid sensor networks

Selection and navigation of mobile sensor nodes using a sensor network

Hybrid sensor network (HSN) is composed of a set of static and mobile sensor nodes. Besides the capabilities of sensing, computing and communicating like static nodes, mobile nodes have locomotion capability particularly. Area coverage is one of the most fundamental metrics for hybrid sensor network because it is crucial to the quality of monitoring. In order to improve the network coverage, we employ the mobility of mobile nodes to compensate for the uneven deployment of static sensors. In this paper we propose a 3-phase algorithm based on Hamilton Circuit. Conducted simulation shows that the algorithm can improve coverage performance with less energy conservation. Experimental results verify the feasibility and validity of the algorithm proposed.

Recently, there has been increasing interest in Mobile Wireless Sensor Networks MWSNs that are constructed by mobile sensor nodes held by ordinary people, and it has led to a new concept called urban sensing. In such MWSNs, mobile sensor nodes densely exist, and thus, there are basically many sensor nodes that can sense a geographical point in the entire sensing area. To reduce the communication cost for gathering sensor data, it is desirable to gather the sensor data from the minimum number of mobile sensor nodes which are necessary to guarantee the sensing coverage or the quality of services. In this paper, to achieve this, we propose a data gathering method using mobile agents in dense MWSNs. The proposed method guarantees the sensing coverage of the entire area using mobile agents that autonomously perform sensing operations, transmit sensor data, and move between sensor nodes. By gathering only sensor data generated by sensor nodes where mobile agents are running, our proposed method can achieve efficient gathering of sensor data.

When detecting a target or monitoring a physical phenomenon in a region, the deployment problem is fundamental in these applications. Traditionally, stationary sensor networks are deployed to carry out the sensing operations. It is well known that the mobility of sensor nodes can improve the coverage and the probability of the detecting, so we deal with the problem of detecting a target using hybrid sensor networks which contain both stationary sensors and mobile sensors. In this paper, to begin with, we prove that the node deployment problem is NP-complete. Then, one polynomial algorithm for node deployment in hybrid wireless sensor networks is proposed, which aims at minimizing the number of all sensors to reduce the cost. The simulations verify the efficiency of our algorithm.

Objectives: Randomly moving target search in an unknown location is a complex problem which does not have a linear solution. This paper proposes Energy aware - Bio-inspired Hybrid technique for target finding Area surveillance application. Methods/Statistical Analysis: In recent years, many researchers tried to find a generic solution which requires minimum energy for target searching problem. For the proposed work four algorithms are proposed which are MSN’s Movement Prediction Algorithm (MMPA), Leader Selection Algorithm (LSA), and Leader’s Movement Predication Algorithm (LMPA), and Follower’s Algorithm. Particle Swarm Optimization (PSO) technique is used to move the Mobile Sensor Nodes (MSN) in searching area. Findings: There is a need to study the hybrid or heterogeneous Sensor network (combination of the Static sensor network and Mobile sensor network) as it is less studied in research literature. The major hurdle is communication between heterogeneous sensor nodes. Due to the mobility of MSN, topology changes frequently and it affects the communication in the network. In area surveillance domain, finding the randomly moving target using sensor node is a complex and non-linear problem. There is no straight forward solution. Nature based techniques may be more effective for such problem. Bio-inspired Hybrid WSN technique is to be studied for its suitability for target finding and target tracking application in area surveillance domain. To perform the work, two scenarios are considered and calculated their energy to prove that ifmore number of SSNs is planted, the target can be found in less time and ultimately overall energy of network reduces. Through the simulation, it is shown that E-BHAS reduces the energy when more number of SSNs is involved in the network. Application/ Improvements: Borderline Surveillance, Disaster Management, under water Surveillance, Traffic Monitoring etc. Keywords: Area Surveillance, Energy, Hybrid Wireless Sensor Network, Particle Swarm Optimization

In hybrid wireless sensor networks composed of both static and mobile sensor nodes, the random deployment of stationary nodes may cause coverage holes in the sensing field. Hence, mobile sensor nodes are added after the initial deployment to overcome the coverage holes problem. To achieve optimal coverage, an efficient algorithm should be employed to find the best positions of the additional mobile nodes. This paper presents a genetic algorithm that searches for an optimal or near optimal solution to the coverage holes problem. The proposed algorithm determines the minimum number and the best locations of the mobile nodes that need to be added after the initial deployment of the stationary nodes. The performance of the genetic algorithm was evaluated using several metrics, and the simulation results demonstrated that the proposed algorithm can optimize the network coverage in terms of the overall coverage ratio and the number of additional mobile nodes.

The quality of service (QoS) and lifetime of wireless sensor networks (WSNs) are severely degraded by coverage holes generated by random deployment or battery exhaustion of sensors. This work firstly introduces a novel confident information coverage CIC) model to dramatically reduce the density of sensor nodes and accurately detect confident information coverage holes (CICHs). Then, the problem of repairing confident information coverage holes (RCICHs) for big data collection in a large-scale heterogeneous WSN (LS-HWSN) which widely spreads over a geographic area with thousands of stationary sensor nodes and mobile sensor nodes is formulated, called as RCICH problem, which is to effectively repair CICHs considering that the transmitted data velocity of sensor nodes is different. Furthermore, we prove it to be NP-completeness. The target of the problem is to find a subset of mobile sensor nodes from all mobile sensor nodes while minimizing the amount of lost throughputs LTs) of all dispatched mobile sensor nodes or maximizing the amount of repairing transmission times (RTTs) for all dispatched mobile sensor nodes, with different objectives. Finally, based on the CIC model and the data-centric perspective, two heuristic schemes including a centralized dispatch scheme and a distributed dispatch scheme are proposed to effectively solve the RCICH problem. Simulation results show that the proposed schemes effectively repair CICHs while increasing the QoS and lifetime of the LS-HWSN with the topology control of a fan-shaped clustering (FSC) protocol.

Aiming at the problems of low coverage of all fixed sensor nodes in wireless sensor networks and the high cost of deployment of mobile sensor nodes, a hybrid sensor network deployment strategy based on virtual force is proposed, and then the cluster node can find out the hot point (center point of the grid) which is not covered by the sensor node. Then the task is assigned to the mobile sensor nodes by the Hungarian algorithm. Each mobile node based on virtual force algorithm autonomously moves to the uncovered grid center or near the grid center. Simulation results show that the deployment strategy can effectively improve the coverage of the network, and it is superior to the virtual force algorithm deployment strategy based on all mobile sensor nodes in terms of coverage rate and moving distance.

Mobile sensor nodes are featured with node mobility along with standard sensor functions. They can move around after being deployed. In many situations placement of static sensor nodes might not be possible and human intervention is not feasible. Mobile sensor nodes are very useful in such hazardous and disastrous situations. When a group of mobile sensor nodes are deployed to cover an area for search operations, coordination among the deployed nodes is very important. A mobile sensor node (MSN) depletes more energy during the traversal. Whenever there arises a failure, in one of the deployed mobile sensor nodes, the remaining fault-free mobile sensor nodes should travel to cover the remaining uncovered area. In this work we propose a Mobile Traversal Algorithm (MTA), for mobile sensor nodes to cover a rectangular region of interest (ROI). It makes MSNs to travel shorter distances to extend effective operational duration of the network and also to provide fault tolerance mechanism.

In the past decade, interest in fixed wireless sensor networks shifted to reflect current developments in mobile sensor networks with prominent mobility. However, due to the issues caused by joining or leaving the sensor network, a mobile sensor node is vulnerable to lose its connectivity. To regain its connectivity, a mobile sensor node must spend more time and energy to perform the reassociation procedure, resulting in an efficiency decay of the sensor network. To overcome this problem, we design a kernel moving average scheme based on the quality indicator prediction in the media access layer. The proposed scheme can reduce the nonconnectivity time of mobile sensor node through early performance of the reassociation procedure.

This paper focuses on Wireless Sensor Network (WSN) redeployment. A critical problem in WSN is handling the coverage hole because these nodes are normally spread randomly into a terrain area. The objective of this paper is to repair the coverage hole with a less movement from mobile sensor node that it can save energy. Initially, sensor nodes are deployed randomly. Static sensor nodes will be added to enhance the coverage of the network. This paper compares the performance of two WSN mobile node redeployment methods known as Hybrid Particle Swarm Algorithm (IHPSA) and Energy Balanced Redeployment Algorithm (EBRA). IHPSA choose the nodes from redundant to move to the holes location while EBRA use repulsive and attractive forces in order to maximize the coverage. Consequently it will maximize the coverage and all nodes can communicate with each other as well as sending all the data to the base station. The movements of the mobile sensor nodes are minimized to reduce the energy consumption due to mobility. Based on the simulation done, it is found that IHPSA outperforms EBRA in terms of coverage and connectivity. IHPSA algorithm not only solve the coverage hole problem but also solve the energy depletion that always occurs in sensor nodes movement and full connectivity can be guaranteed.

Mobile sensor nodes are an ideal solution for efficiently collecting measurements for a variety of applications. Mobile sensor nodes offer a particular advantage when measurements must be made in hazardous and/or adversarial environments. When mobile sensor nodes must operate in hostile environments, it would be advantageous for them to be able to avoid undesired interactions with hostile elements. It is also of interest for the mobile sensor node to maintain low-observability in order to avoid detection by hostile elements. Conventional path-planning strategies typically attempt to plan a path by optimizing some performance metric. The problem with this approach in an adversarial environment is that it may be relatively simple for a hostile element to anticipate the mobile sensor node's actions (i.e. optimal paths are also often predictable paths). Such information could then be leveraged to exploit the mobile sensor node. Furthermore, dynamic adversarial environments are typically characterized by high-uncertainty and highcomplexity that can make synthesizing paths featuring adequate performance very difficult. The goal of this work is to develop a path-planner anchored in info-gap decision theory, capable of generating non-deterministic paths that satisfy predetermined performance requirements in the face of uncertainty surrounding the actions of the hostile element(s) and/or the environment. This type of path-planner will inherently make use of the time-tested security technique of varying paths and changing routines while taking into account the current state estimate of the environment and the uncertainties associated with it.

Unknown target search, in an unknown environment, is a complex problem in Wireless Sensor Network (WSN). It does not have a linear solution when target’s location and searching space is unknown. For the past few years, many researchers have invented novel techniques for finding a target using either Static Sensor Node (SSN) or Mobile Sensor Node (MSN) in WSN i.e. Hybrid WSN. But there is a lack of research to find a solution using hybrid WSN. In the current research, the problem has been addressed mostly using non-biological techniques. Due to its complexity and having a non-linear solution, Bio-inspired techniques are most suited to solve the problem. This paper proposes a solution for searching of randomly moving target in unknown area using only Mobile sensor nodes and combination of both Static and Mobile sensor nodes. In proposed technique coverage area is determined and compared. To perform the work, novel algorithms like MSNs Movement Prediction Algorithm (MMPA), Leader Selection Algorithm (LSA), Leader’s Movement Prediction Algorithm (LMPA) and follower algorithm are implemented. Simulation results validate the effectiveness of proposed work. Through the result, it is shown that proposed hybrid WSN approach with less number of sensor nodes (combination of Static and Mobile sensor nodes) finds target faster than only MSN approach.

Sufficient coverage of the sensing field is critical for many applications in wireless sensor networks. However, the network can experience coverage holes when one or more sensor nodes stop functioning due to energy depletion or physical damage. This impairs network connectivity and impedes performance. Therefore, it is imperative to automatically repair the coverage hole as it occurs. In this paper, we propose a novel distributed algorithm called neighbor intervention by farthest point that repairs coverage holes using mobile sensor nodes in the immediate vicinity. Participating nodes collaborate to approximate the area of the hole, then consider their coverage redundancy, residual energy, and moving distance to select a suitable replacement to cover it. The effectiveness of the proposed algorithm is evaluated by simulation. We compared the performance of the proposed algorithm with several baseline approaches using extensive simulations. The results show that the proposed algorithm substantially outperforms the baseline algorithms.

Sensor nodes are randomly placed in the region of interest. There are hole areas where no sensor nodes exist in some regions. Coverage holes may occur because some nodes run out of energy or physical destruction of sensor nodes. It may reduce the data reliability of the sensor network, and affect the performance of the entire sensor network, such as changing network topology, disconnecting data links, and worsening transmission loads. By discovering the coverage hole in the sensor network and additionally placing new sensor nodes in the detected coverage hole, the coverage hole problem can be solved. Recovery of the coverage hole needs to be designed so that the coverage area is maximized with minimal consumption of sensor resources. In this paper, we propose a method for adding a mobile sensor node at the optimal location by exchanging information between one-hop neighbor border nodes. The proposed method is very intuitive and simple compared to other methods of identifying and restoring coverage holes by structuring nodes in the form of Bornoy polygons in the sensor network.