Virtual Force Algorithm Research Articles

Node Placement Strategy in Wireless Sensor Network

The performance and quality of services in wireless sensor networks (WSNs) depend on coverage and connectivity. Node placement is a fundamental issue closely related to the coverage and connectivity in sensor networks. Node placement influences the target position, coverage area, and connectivity in sensor networks. In random deployment, sensor nodes are deployed randomly in a non-invasive way. The deployment process may cause issues like coverage holes, overlapping, and connectivity failure. Enhancing coverage and connectivity are important for sensor networks to provide a reliable communication within sensing. Placing many sensor nodes in a WSN application region area is not the best solution due to cost and it results in multiple sensors used. Mobile sensor node is used as an alternative to overcome the random deployment problem. The virtual force based self node deployment is used in the mobility sensor to improve the coverage and connectivity area. Virtual Force Algorithm (VFA) approach using virtual repulsive and attractive forces is used to find the optimal node placement to minimize the problems. Simulation results proofed that a uniform deployment achieved using VFA approach with an optimal sensing range to cover the region of interest.

An Extended Virtual Force-Based Approach to Distributed Self-Deployment in Mobile Sensor Networks

Virtual physics based approach is one of the major solutions for self-deployment in mobile sensor networks with stochastic distribution of nodes. To overcome the connectivity maintenance and nodes stacking problems in the traditional virtual force algorithm (VFA), an extended virtual force-based approach is investigated to achieve the ideal deployment. In low-[Formula: see text] VFA, the orientation force is proposed to guarantee the continuous connectivity. While in high-[Formula: see text] VFA, a judgment of distance force between node and its faraway nodes is considered for preventing node stacking from nonplanar connectivity. Simulation results show that self-deployment by the proposed extended virtual force approach can effectively reach the ideal deployment in the mobile sensor networks with different ratio of communication range to sensing range. Furthermore, it gets better performance in coverage rate, distance uniformity, and connectivity uniformity than prior VFA.

Exploiting Jamming-Caused Neighbor Changes for Jammer Localization

Jamming attacks are especially harmful when ensuring the dependability of wireless communication. Finding the position of a jammer will enable the network to actively exploit a wide range of defense strategies. In this paper, we focus on developing mechanisms to localize a jammer by exploiting neighbor changes. We first conduct jamming effect analysis to examine how the communication range alters with the jammer's location and transmission power using free-space model. Then, we show that a node's affected communication range can be estimated purely by examining its neighbor changes caused by jamming attacks and thus, we can perform the jammer location estimation by solving a least-squares (LSQ) problem that exploits the changes of communication range. Compared with our previous iterative-search-based virtual force algorithm, our LSQ-based algorithm exhibits lower computational cost (i.e., one step instead of iterative searches) and higher localization accuracy. Furthermore, we analyze the localization challenges in real systems by building the log-normal shadowing model empirically and devising an adaptive LSQ-based algorithm to address those challenges. The extensive evaluation shows that the adaptive LSQ-based algorithm can effectively estimate the location of the jammer even in a highly complex propagation environment.

Two-Stage Dynamic Sensor Deployment Strategy Based on Virtual Force and Genetic Algorithm in Wireless Sensor Networks

Two-Stage Dynamic Sensor Deployment Strategy Based on Virtual Force and Genetic Algorithm in Wireless Sensor Networks

Mobile Wireless Sensor Networks K-Covering Algorithm

In mobile wireless sensor networks, sensor nodes may due to their own moves, energy loss, physical damage, errors and failures or other factors, will result the case of coverage holes, and then lead to inefficient network coverage. This paper designed a new algorithm based on clustering approach for mobile wireless sensor network, can monitor the network in real-time and repairing coverage holes quickly. Simulation results show that, the algorithm used in mobile wireless sensor network not only can achieve the desired uniformity of coverage, but also increase the coverage area, and better performance than the Virtual force algorithm(VFA) [1].

A pragmatic approach to area coverage in hybrid wireless sensor networks

AbstractSuccess of Wireless Sensor Networks (WSN) largely depends on whether the deployed network can provide desired area coverage with acceptable network lifetime. This paper seeks to address the problem of determining the current coverage achieved by the non‐deterministic deployment of static sensor nodes and subsequently enhancing the coverage using mobile sensors. We identify three key elements that are critical for ensuring effective area coverage in Hybrid WSN: (i) determining the boundary of the target region and evaluating the area coverage (ii) locating coverage holes and maneuvering mobile nodes to fill these voids, and (iii) maintaining the desired coverage over the entire operational lifetime of the network. We propose a comprehensive solution that addresses all of the aforementioned aspects of the area coverage, called MAPC (mobility assisted probabilistic coverage). MAPC is a distributed protocol that operates in three distinct phases. The first phase identifies the boundary nodes using the geometric right‐hand rule. Next, the static nodes calculate the area coverage and identify coverage holes using a novel probabilistic coverage algorithm (PCA). PCA incorporates realistic sensing coverage model for range‐based sensors. The second phase of MAPC is responsible for navigating the mobile nodes to plug the coverage holes. We propose a set of coverage and energy‐aware variants of the basic virtual force algorithm (VFA). Finally, the third phase addresses the problem of coverage loss due to faulty and energy depleted nodes. We formulate this problem as an Integer Linear Program (ILP) and propose practical heuristic solutions that achieve similar performance as that of the optimal ILP solution. A guiding principle in our design process has been to ensure that the MAPC can be readily implemented in real‐world applications. We implemented the boundary detection and PCA algorithm (i.e., Phase I) of the MAPC protocol on off‐the‐shelf sensor nodes and results show that the MAPC can successfully identify boundary nodes and accurately determine the area coverage in the presence of real radio irregularities observed during the experiments. Extensive simulations were carried out to evaluate the complete MAPC protocol and the results demonstrate that MAPC can enhance and maintain the area coverage, while reducing the total energy consumption by up to 70% as compared with the basic VFA. Copyright © 2010 John Wiley & Sons, Ltd.

Improved Dynamic K-Coverage Algorithms in Mobile Sensor Networks

In this paper, four PSO based distributed algorithms are presented to attain k-coverage in the target filed. In the first algorithm named K-Coverage Particle Swarm Optimization (KPSO), each static sensor which discovers an event in its sensing range, implements Particle Swarm Optimization (PSO) algorithm in a distributed manner on its mobile sensors. The calculation time is considered as a big bottleneck in PSO, so a second algorithm named K-Coverage Virtual Force directed Particle Swarm Optimization (KVFPSO) is presented, comprised of Virtual Force and KPSO algorithms. In the first and second proposed algorithms, the best experiences of the particles were used to determine their speed. It is possible that these responses might not be the final result and cause extra movements. Another algorithm named KVFPSO-Learning Automata (KVFPSO-LA) is introduced based on which the speed of particles is corrected by using the existing knowledge and the feedback from the actual implementation of the algorithm. To improve performance of the algorithm, Improved KVFPSO-LA is introduced, in which static sensors are equipped with learning automata. Simulation results show that the proposed protocols perform well with respect to balanced energy consumption among nodes, thus maximizing network life-time.

Optimal Deployment with Self-Healing Movement Algo-rithm for Particular Region in Wireless Sensor Network

Optimizing deployment of sensors with self-healing ability is an efficient way to solve the problems of cov-erage, connectivity and the dead nodes in WSNs. This work discusses the particular relationship between the monitoring range and the communication range, and proposes an optimal deployment with self-healing movement algorithm for closed or semi-closed area with irregular shape, which can not only satisfy both coverage and connectivity by using as few nodes as possible, but also compensate the failure of nodes by mobility in WSNs. We compute the maximum efficient range of several neighbor sensors based on the dif-ferent relationships between monitoring range and communication range with consideration of the complex boundary or obstacles in the region, and combine it with the Euclidean Minimum Spanning Tree (EMST) algorithm to ensure the coverage and communication of Region of Interest (ROI). Besides, we calculate the location of dead nodes by Geometry Algorithm, and move the higher priority nodes to replace them by an-other Improved Virtual Force Algorithm (IVFA). Eventually, simulation results based-on MATLAB are presented, which do show that this optimal deployment with self-healing movement algorithm can ensure the coverage and communication of an entire region by requiring the least number of nodes and effectively compensate the loss of the networks.

Novel Deployment Schemes for Mobile Sensor Networks.

Virtual Force Algorithm (VFA) is becoming a main solution to area coverage forhomogeneous wireless sensor networks with random distribution of mobile sensor nodes.Consider the factors of the convergence, the boundary in Region Of Interest (ROI), effec-tive distance of acting force and useless moving, etc, VFA is improved to overcome the aboveproblems. Furthermore, an expression of exponential function for the relationship of vir-tual force is proposed to converge rapidly. Extensive simulation results indicate that theseimproved VFA get better performance in coverage rate, moving energy consumption, conver-gence etc. than original VFA.

충돌 벡터를 이용한 이동로봇의 동적 장애물 회피

An efficient obstacle avoidance algorithm is proposed in this paper to avoid dynamic obstacles using a collision vector while a tele-operated mobile robot is moving. For the verification of the algorithm, an operator watches through a monitor and controls the mobile robot with a force-reflection joystick. The force-reflection joystick transmits a virtual force to the operator through the Inter-net, which is generated by an adaptive impedance algorithm. To keep the mobile robot safe from collisions in an uncertain environment, the adaptive impedance algorithm generates the virtual force which changes the command of the operator by pushing the operator's hand to a direction to avoid the obstacle. In the conventional virtual force algorithm, the avoidance of moving obstacles was not solved since the operator cannot recognize the environment realistically by the limited communication bandwidth and the narrow view-angle of the camera. To achieve the dynamic obstacle avoidance, the adaptive virtual force algorithm is proposed based on the collision vector that is a normal vector from the obstacle to the mobile robot. To verify the effectiveness of the proposed algorithm, mobile robot navigation experiments with multiple moving obstacles have been performed, and the results are demonstrated.

An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed co-evolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.

Sensor deployment and target localization in distributed sensor networks

The effectiveness of cluster-based distributed sensor networks depends to a large extent on the coverage provided by the sensor deployment. We propose a virtual force algorithm (VFA) as a sensor deployment strategy to enhance the coverage after an initial random placement of sensors. For a given number of sensors, the VFA algorithm attempts to maximize the sensor field coverage. A judicious combination of attractive and repulsive forces is used to determine the new sensor locations that improve the coverage. Once the effective sensor positions are identified, a one-time movement with energy consideration incorporated is carried out, that is, the sensors are redeployed, to these positions. We also propose a novel probabilistic target localization algorithm that is executed by the cluster head. The localization results are used by the cluster head to query only a few sensors (out of those that report the presence of a target) for more detailed information. Simulation results are presented to demonstrate the effectiveness of the proposed approach.