Optimal Number Of Sensor Nodes Research Articles

Energy-Efficient Cluster in Wireless Sensor Network Using Life Time Delay Clustering Algorithms

Through Wireless Sensor Networks (WSN) formation, industrial and academic communities have seen remarkable development in recent decades. One of the most common techniques to derive the best out of wireless sensor networks is to upgrade the operating group. The most important problem is the arrangement of optimal number of sensor nodes as clusters to discuss clustering method. In this method, new client nodes and dynamic methods are used to determine the optimal number of clusters and cluster heads which are to be better organized and proposed to classify each round. Parameters of effective energy use and the ability to decide the best method of attachments are included. The Problem coverage find change ability network route due to which traffic and delays keep the performance to be very high. A newer version of Gravity Analysis Algorithm (GAA) is used to solve this problem. This proposed new approach GAA is introduced to improve network lifetime, increase system energy efficiency and end delay performance. Simulation results show that modified GAA performance is better than other networks and it has more advanced Life Time Delay Clustering Algorithms-LTDCA protocols. The proposed method provides a set of data collection and increased throughput in wireless sensor networks.

Optimal Sensor Placement for Underground Tunnel Monitoring via Wireless Sensor Networks

In replace of human labor, wireless sensor networks (WSNs) are increasingly being utilized to perform structural health monitoring of underground tunnel. Due to its complex environment, the deployment of sensor nodes poses a big challenge to related staff. How to use the optimal number of sensor nodes deployed in the underground tunnel to obtain a satisfactory monitoring is our main consideration. In this paper, we propose a deployment strategy based on the optimal index to provide guidelines for sensor node placement. The objective of the strategy is to put sensor nodes in a proper site to gain maximum sensing information, thus eliminating redundant sensor nodes as well as saving costs.

Barrier coverage of WSNs with the imperialist competitive algorithm

Barrier coverage in wireless sensor networks has been used in many applications such as intrusion detection and border surveillance. Barrier coverage is used to monitor the network borders to prevent intruders from penetrating the network. In these applications, it is critical to find optimal number of sensor nodes to prolong the network lifetime. Also, increasing the network lifetime is one of the important challenges in these networks. Various algorithms have been proposed to extend the network lifetime while guaranteeing barrier coverage requirements. In this paper, we use the imperialist competitive algorithm (ICA) for selecting sensor nodes to do barrier coverage monitoring operations called ICABC. The main objective of this work is to improve the network lifetime in a deployed network. To investigate the performance of ICABC, several simulations were conducted and the results of the experiments show that the ICABC significantly improves the performance than other state-of-art methods.

Maximizing Wireless Sensor Network Coverage With Minimum Cost Using Harmony Search Algorithm

Coverage, one of the most important performance metrics for wireless sensor networks, reflects on how well a sensor field is monitored. Coverage problem is a devoted study of a node placement optimization problem in the coverage configuration before network deployment, where the objective is to find the optimal locations to place sensor nodes, such that the number of nodes (or the network cost) can be minimized and the coverage requirements can be satisfied. In this paper, we propose a harmony search (HS)-based deployment algorithm that can locate the optimal number of sensor nodes as well as their optimal locations for maximizing the network coverage and minimizing the network cost. The ability of HS is modified to automatically evolve the appropriate number of sensor nodes as well as their optimal locations. This can be accomplished by integrating the concept of adaptable length encoding in each solution vector to represent a variable number of candidate sensor nodes. Network coverage ratio, number of sensor nodes, and minimum distance between sensor nodes are the chief elements of a new objective function that has been offered to confirm the choice of the optimal number of sensor nodes and their positions. Experimental results show the ability of the proposed algorithm to find the appropriate number of sensor nodes and their locations. Furthermore, a comparative study with a metaheuristic Genetic-based algorithm and a random deployment technique has also been conducted and its results confirm the superiority of the proposed algorithm.

Covering the Target Objects with Mobile Sensors by Using Genetic Algorithm in Wireless Sensor Networks

Wireless sensor networks have several applications, such as target detection and tracking, and monitoring battlefields. Coverage is one of the most important performance metrics for wireless sensor networks since it reflects how appropriate an event can be detected and monitored in the sensing field. To achieve optimal coverage, an efficient algorithm should be employed to find the best positions of sensor node deployment. In this paper, an efficient genetic algorithm is proposed to solve the coverage problem of the target objects. The optimal number of sensor nodes starts from fewer randomly deployed nodes and increases gradually in subsequence generations. The performance of the proposed genetic algorithm was evaluated, and the simulation results show that this approach can cover all the target objects as well as minimize the number of additional mobile sensor nodes.

Joint Sensor-Node Selection and Channel Allocation Scheme for Cognitive Radio Sensor Networks

The conventional wireless sensor networks (WSNs) operating on the license-free ISM band are now experiencing large interference from other ISM band based wireless devices. The cognitive radio sensor network (CRSN) can solve this issue of interference and it can provide better services within the domain of the WSNs. In this paper, we propose a novel joint sensor node selection and channel allocation scheme to improve the performance of the CRSNs. We consider the cluster oriented sensor network and form clusters using the K-means clustering algorithm. The node selection scheme is formulated using the knapsack problem where each cluster head selects the optimal number of sensor nodes (SNs). Later on, the Hungarian algorithm is employed to allocate the best channels among SNs for their reporting process. Simulation results show that our scheme outperforms the existing schemes in terms of prolonging the network life, selecting the more reliable SNs and allocating the optimal channels among SNs for their data transmission during the reporting process.

Maximizing the Lifetime of a Barrier of Wireless Sensors

To make a network last beyond the lifetime of an individual sensor node, redundant nodes must be deployed. What sleep-wake-up schedule can then be used for individual nodes so that the redundancy is appropriately exploited to maximize the network lifetime? We develop optimal solutions to both problems for the case when wireless sensor nodes are deployed to form an impenetrable barrier for detecting movements. In addition to being provably optimal, our algorithms work for nondisk sensing regions and heterogeneous sensing regions. Further, we provide an optimal solution for the more difficult case when the lifetimes of individual nodes are not equal. Developing optimal algorithms for both homogeneous and heterogeneous lifetimes allows us to obtain, by simulation, several interesting results. We show that even when an optimal number of sensor nodes has been deployed randomly, statistical redundancy can be exploited to extend the network lifetime by up to seven times. We also use simulation to show that the assumption of homogeneous lifetime can result in severe loss (two-thirds) of the network lifetime. Although these results are specifically for barrier coverage, they provide an indication of behavior for other coverage models.