Optimization of Sensor Nodes Deployment Based on An Improved Differential Evolution Algorithm for Coverage Area Maximization

This paper focuses on enhancement of lifetime in an event driven wireless sensor network (WSN). The network lifetime is enhanced by optimal deployment of sensor nodes in a given region as premature shortening of life takes place leading to uneven consumption of energy. In the proposed scheme, the senor nodes are linearly deployed over the sub-regions and the inter-node distance between the sensor nodes depends on the statistics of events sensed by the sensor nodes in multi hop mode of communication. The performance is measured with respect to the residual energy at each sensor node and total number of days spent by sensor nodes. The lifetime of the sensor network is defined as the survival of some fraction of nodes. For an approximate Poisson distribution for occurrence of events in different sub-regions, the variation of energy consumption in sensor nodes is negligibly small. Further if some nodes are having finite residual energy even after failure of some nodes, then those nodes can continue their communication in single hop mode and hence the lifetime of the network is further extended.

In Wireless Sensor Networks (WSNs), the coverage means how well a sensor network will monitor the total area of the region. Node deployment is one of the important issues that need to be solved. The number of sensor nodes has direct impact on the cost of the Wireless Sensor Networks. So, keeping this in mind, the proper deployment of nodes can reduce the complexity of problems, energy consumption can be reduced and thus extends the lifetime of the Wireless Sensor Networks. In this paper, we are considering the tactical environment, so the deployment of sensor node becomes random. We are forming the tessellation of the deployment as Rhombus other than Triangle, Square and Hexagon that are taken ideally for deploying the sensor nodes and after applying the proposed method the life time of the given network increases.

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.

Wormhole attack is a harmful attack that disrupts the normal functioning of the network by manipulating routing protocols and exhausting network resources. The paper presents a technique that detects the presence of wormhole attack in wireless sensor network (WSN) using artificial neural network (ANN). The proposed technique uses connectivity information between any two sensor nodes as the detection feature. The proposed technique has been implemented considering the deployment of sensor nodes in the wireless sensor network area under uniform, Poisson, Gaussian, exponential, gamma & beta probability distributions. The proposed technique does not require any additional hardware resources and gives a comparatively high percentage of detection accuracy. KeywordsWireless sensor networkProbability distributionWormhole attackANN

Radio propagation modeling and real test of ZigBee based indoor wireless sensor networks

One of the most important constraints in wireless sensor networks is the energy efficiency problem. To maximize the wireless sensor networks lifetime after the sensor nodes deployment, dynamic power management (DPM) should be carefully taken into account in wireless sensor networks. The goal of DPM is to reduce power dissipation by putting the sensor node into different states. In this paper, a new method of DPM based on wavelet neural networks is proposed to conserve energy. There are two salient aspects to this approach. First, the next event's time which is a non-stationary series is predicted as accurate as possible by wavelet neural networks. Nodes in deeper sleep states consume lower energy while asleep, but incur a longer delay and higher energy cost to awaken. Second, the nodes state in which lie wireless sensor network should lie is decided through the predictable time associated with the threshold time, residual power. The simulation results show that the energy consumption is significantly reduced and the whole lifetime of the wireless sensor networks is greatly prolonged through the proposed method.

Coverage and connectivity are the two main functionalities of wireless sensor network. Stochastic node deployment or random deployment almost always cause hole in sensing coverage and cause redundant nodes in area. In the other hand precise deployment of nodes in large area is very time consuming and even impossible in hazardous environment. One of solution for this problem is using mobile robots with concern on exploration algorithm for mobile robot. In this work an autonomous deployment method for wireless sensor nodes is proposed via multi-robot system which robots are considered as node carrier. Developing an exploration algorithm based on spanning tree is the main contribution and this exploration algorithm is performing fast localization of sensor nodes in energy efficient manner. Employing multi-robot system and path planning with spanning tree algorithm is a strategy for speeding up sensor nodes deployment. A novel improvement of this technique in deployment of nodes is having obstacle avoidance mechanism without concern on shape and size of obstacle. The results show using spanning tree exploration along with multi-robot system helps to have fast deployment behind efficiency in energy.

Connectivity and coverage constrained wireless sensor nodes deployment using steepest descent and genetic algorithms

Wireless Sensor Network (WSN) has been utilized in environmental monitoring where large number of connected sensor nodes collaboratively sense and report conditions of the monitoring area. Indoor event sensing and tracking is one of WSN applications that has a significant effect on saving cost economically and humanitarianly. Efficient sensor node deployment can ensure coverage, connectivity, and stability of the network. This paper presents the mathematical modeling of a node deployment for indoor wireless sensor networks where the optimal number of nodes required to satisfy the coverage of the indoor area and network connectivity requirements is determined. The proposed solution contributes to the reduction in the cost and maintenance of the network as less number of sensors is needed, compared to the traditional grid node deployment.

Wireless communication is one of the fastest growing industry segments today. Many types of wireless networks are now being used for applications such as personal communication, entertainment, rural and urban healthcare, smart home building, inventory control, and surveillance. This book introduces the basic concepts of wireless networks and mobile computing to give engineering students at the undergraduate/graduate level a solid background in the field. It also looks at the latest research and challenging problems in the field to serve as a reference for advanced-level researchers. Wireless Networks and Mobile Computing begins with an introduction to the different types of wireless networks, including Wi-Fi, ZigBee, cellular mobile, ad hoc, cognitive radio, wireless mesh, and wireless sensor. Subsequent chapters address more advanced topics such as: Mobility, bandwidth, and node location management issues in mobile networks Message communication techniques and protocols in ad hoc networks Recent research and future direction of wireless local area networks (WLANs) Deployment of sensor nodes in wireless sensor networks (WSNs) Energy-efficient communication in wireless networks Security aspects of wireless communication The book includes exercises at the end of every chapter to help give students a better insight into the topics presented. It includes a number of advanced-level exercises, which are research problems that may be taken up by researchers in the respective areas. This book provides a valuable reference for classroom study/teaching as well as for technology development and research in the relevant areas.

This paper conducts a study on the application of sensor technology in intelligent substations. The network topology of the sensor network, the deployment of sensor nodes, and the security mechanism of the sensor network are analyzed. The final application scheme is proposed following the analysis above. Different network topologies are adopted for wired and wireless sensor networks. At the same time, the improved self-organizing map (SOM) algorithm is used for node deployment. The advanced encryption standard (AES) encryption improvement algorithm is adopted for data encryption and decryption to attain timely and efficient multifaceted sensing of information in intelligent substations under the guarantee of data security.

Sensor network becomes most emerging technology in these days and it plays a important role in every field. WSN also becomes important topic in nowadays because this technology is used in very sensitive areas like Education, Military Services, Medical field, Surveillance, Agriculture monitoring, Industrial Monitoring, etc. Sensor networks is interconnected distributed network consist of relatively simple, low-cost, low-power sensor nodes. Energy conservation is biggest challenge in wireless sensor network because after deployment of these tiny sensor nodes it is not possible to recharge them. There is lots of energy saving schemes that are used to decrease the power consumed and to increase the life of network life. Clustering is most prevalent technology which is able to make sensor networks energy-efficient. The scheme aims to minimize the communication distance of sensors. In this article, a survey on different energy based Clustering routing protocols has been done.

Energy consumption and lifetime of networks are the two most important constraints for wireless sensor networks (WSNs). Moreover, for perceiving events with respect to time, WSNs need to maintain coverage quality. This paper focuses on coverage problem with a number of sensors selected from deployed sensor nodes, which develop chain oriented communication. To mitigate the constraints of sensor nodes researchers employ a variety of logical topologies, among which chain oriented topology is one of the significant contender. Moreover, choosing only a selected number of sensors, instead of choosing all of them saves more energy and lengthens lifetime of the network. The main criterion to select a sensor node we propose in this paper is the use of shared sensing region of a sensor node with its neighbours. Simulation results show that our protocol saves a large number of sensor nodes while maintaining significant amount of coverage ratio.

Network lifetime play a vital role in setting up an energy efficient wireless sensor network. The lifetime of the network can be improved by efficient deployment and scheduling of the sensor nodes inside the network. In this paper, on the basis of mathematically calculated upper bound lifetime of the network, the sensor nodes are efficiently deployed by using Simulated Annealing and Particle Swarm Optimization with pre-specified sensing range of the sensor nodes and on the second fold the sensor nodes are efficiently scheduled by applying the Simulated Annealing and Dempster Shafer Theory. The overall objective of this paper is to find the optimized location and schedule for sensor nodes. The comparative study shows that the simulated annealing and particle swarm optimization algorithms performs better for sensors deployment. Simulated annealing and Dempster Shafer theory achieves the goal to provide the different schedules with high efficiency.

Spatiotemporal monitoring of large fields for water quality motivates this paper. The primary goal of this paper is to find a suitable deployment strategy for mobile sensor nodes in aquatic fields, which receives the least estimation error with minimal sensor nodes. Typically, the resources of mobile sensor nodes are limited in the case of large field monitoring, so they cannot be deployed arbitrarily. Hence, an optimal sensor node deployment strategy with minimally required sensor nodes becomes a primary focus of this paper. To address this problem, an optimal sensor node deployment strategy termed Rapid Random exploring tree with Linear Reduction (RRLR) is developed in this paper. It relocates sensor nodes to their best sensing locations while reducing the required number of sensor nodes without losing information. The approach is based on an environmental model and the linear dependence of sensor readings. In particular, the spatiotemporal correlation of the sensor node deployment in a large geographic area of interest is minimized. It is also proved that the optimization objective function, which uses the prior estimation error, is submodular and results in a near-optimal solution. Simulation results show that RRLR requires much fewer sensor nodes to achieve the same or lower estimation error when compared to benchmark algorithms.