Optimization Problem In Wireless Sensor Networks Research Articles

Wireless sensor network coverage optimization based on novel multi‐strategy gold rush optimizer

AbstractIn Wireless Sensor Networks (WSN), there are three conflicting important issues, including extensive coverage, low node redundancy, and energy consumption for secondary deployment. To balance network coverage and longevity, a WSN coverage model with three objectives is developed. A novel multi‐strategy gold rush optimizer is proposed for the coverage optimization problem in WSN. First, the fusion search method becomes extensive and flexible by combining the migration strategy with the Levy flight mechanism and joining a follower group. Then, the dynamic opposite learning strategy is used to increase population diversity in the panning stage. In addition, a multi‐player collaborative strategy is utilized to improve search ability of the algorithm in the collaboration stage. Finally, the random differential mutation method is used to try to assist the algorithm in escaping local optima. The simulation results show that this algorithm compared with other algorithms performs better on all three objectives considered.

A Review Study of the Optimized Cluster Head Placement of Transmit-Only Nodes in Wireless Sensor Networks

Wireless sensor networks (WSNs) with transmit-only (TO) nodes are gaining increased popularity, in particular, in applications requiring heavy deployment of sensor nodes in harsh and inaccessible environments. The data collected by the members of the WSN is relayed to a nearby base station (BS) via a cluster-head (CH). Since the nodes are usually battery-limited, it is important to optimize the location of the CH in the sensing environment to prolong the lifetime of the entire network. In this paper, we describe this problem using a general energy model and review the main articles that discuss the energy optimization problem in WSNs and summarize their important results, in addition to the basic working principles of the different widely used energy-optimization algorithms.

Clustering based Dynamic Routing Algorithm for Efficient Energy Utilization in Wireless Sensor Networks (WSNs)

The significance of proper and efficient energy utilization is quite high in Wireless Sensor Networks (WSNs). As a result, network lifetime can be massively enhanced. Therefore, Graph enabled Energy Optimized Routing (GEOR) algorithm is adopted to extend the lifetime of the sensor network and minimize energy consumption in WSNs. An efficient solution for lifetime enhancement of sensor network and energy minimization are key objectives of the proposed GEOR algorithm. The proposed GEOR algorithm provides improved performance than the traditional Low Energy Adaptive Clustering Hierarchy (LEACH) approach. Here, the proposed GEOR algorithm enhances cluster size and the CH selection process is performed to elect CHs for multiple clusters present in the sensor network. A detailed solution for the optimization problem in WSNs is also presented. The mobility aspect of the sink node has a massive impact on energy consumption in WSNs. Experimental results are evaluated in terms of sensor network lifetime enhancement (number of alive nodes and number of dead nodes). Cluster formation and CH selection are performed based on the mobility cost function. Throughput and network lifetime results are obtained in terms of residual energy using the proposed GEOR algorithm. The result is compared against the traditional LEACH approach and the proposed GEOR algorithm shows superior performance.

Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization

In Wireless Sensor Networks (WSN), maintaining a high coverage and extending the network lifetime are two conflicting crucial issues considered by real world service providers. In this paper, we consider the coverage optimization problem in WSN with three objectives to strike the balance between network lifetime and coverage. These include minimizing the energy consumption, maximizing the coverage rate and maximizing the equilibrium of energy consumption. Two improved hybrid multi-objective evolutionary algorithms, namely Hybrid-MOEA/D-I and Hybrid-MOEA/D-II, have been proposed. Based on the well-known multi-objective evolutionary algorithm based on decomposition (MOEA/D), Hybrid-MOEA/D-I hybrids a genetic algorithm and a differential evolutionary algorithm to effectively optimize sub-problems of the multi-objective optimization problem in WSN. By integrating a discrete particle swarm algorithm, we further enhance solutions generated by Hybrid-MOEA/D-I in a new Hybrid-MOEA/D-II algorithm. Simulation results show that the proposed Hybrid-MOEA/D-I and Hybrid-MOEA/D-II algorithms have a significant better performance compared with existing algorithms in the literature in terms of all the objectives concerned.

Optimal Node Clustering and Scheduling in Wireless Sensor Networks

Selection and rotation of cluster head (CH) is a well known optimization problem in hierarchical Wireless sensor networks (WSNs), which affects its overall network performance. Population-based metaheuristic particularly Artificial bee colony (ABC) has shown to be competitive over other metaheuristics for solving optimization problems in WSNs. However, its search equation contributes to its insufficiency due to poor exploitation phase and low convergence rate. This paper, presents an improved artificial bee colony (iABC) metaheuristic with an improved search equation, which will be able to search an optimal solution to improve its exploitation capabilities moreover, in order to increase the global convergence of the proposed metaheuristic, an improved approach for population sampling is introduced through Student’s-t distribution. The proposed metaheuristic maintain a balance between exploration and exploitation search abilities with least memory requirements, with the use of first of its kind compact Student’s-t distribution, which is particularly suitable for WSNs limited hardware environment. Further utilising the capabilities of the proposed metaheuristic, an improved artificial bee colony based clustering and scheduling (iABC-CS) scheme is introduced, to obtain optimal cluster heads (CHs) along with optimal CH scheduling in WSNs. Simulation results manifest that iABC-CS outperform over other well known clustering algorithms on the basis of packet delivery ratio, energy consumption, network lifetime and end to end delay.

Improved metaheuristic-based energy-efficient clustering protocol with optimal base station location in wireless sensor networks

Efficient clustering is a well-documented NP-hard optimization problem in wireless sensor networks (WSNs). Variety of computational intelligence techniques including evolutionary algorithms, reinforcement learning, artificial immune systems and recently, artificial bee colony (ABC) metaheuristic have been applied for efficient clustering in WSNs. Due to ease of use and adaptive nature, ABC arose much interest over other population-based metaheuristics for solving optimization problems in WSNs. However, its search equation contributes to its insufficiency due to comparably poor exploitation cycle and requirement of certain control parameters. Thus, we propose an improved artificial bee colony (iABC) metaheuristic with an improved solution search equation to improve exploitation capabilities of existing metaheuristic. Further, to enhance the global convergence of the proposed metaheuristic, an improved population sampling technique is introduced through Student’s t-distribution, which require only one control parameter to compute and store and therefore increase efficiency of proposed metaheuristic. The proposed metaheuristic maintain a good balance between exploration and exploitation search abilities with least memory requirements; moreover, the use of first-of-its-kind compact Student’s t-distribution makes it suitable for limited hardware requirements of WSNs. Additionally, an energy-efficient clustering protocol based on iABC metaheuristic is presented, which inherits the capabilities of the proposed metaheuristic to obtain optimal cluster heads along with an optimal base station location to improve energy efficiency in WSNs. Simulation results show that the proposed clustering protocol outperforms other well-known protocols on the basis of packet delivery, throughput, energy consumption, network lifetime and latency as performance metric.

Improved artificial bee colony metaheuristic for energy-efficient clustering in wireless sensor networks

Energy-efficient clustering is a well known NP-hard optimization problem for complex and dynamic Wireless sensor networks (WSNs) environment. Swarm intelligence (SI) based metaheuristic like Ant colony optimization, Particle swarm optimization and more recently Artificial bee colony (ABC) has shown desirable properties of being adaptive to solve optimization problem of energy efficient clustering in WSNs. ABC arose much interest over other population-based metaheuristics for solving optimization problems in WSNs due to ease of implementation however, its search equation contributes to its insufficiency due to poor exploitation phase and storage of certain control parameters. Thus, we propose an improved Artificial bee colony (iABC) metaheuristic with an improved search equation to enhance its exploitation capabilities and in order to increase the global convergence of the proposed metaheuristic, an improved population sampling technique is introduced through Student’s-t distribution, which require only one control parameter to compute and store, hence increase efficiency of proposed metaheuristic. The proposed metaheuristic maintain a good balance between exploration and exploitation search abilities with least memory requirements, moreover the use of first of its kind compact Student’s-t distribution, make it suitable for limited hardware requirements of WSNs. Further, an energy efficient bee clustering protocol based on iABC metaheuristic is introduced, which inherit the capabilities of the proposed metaheuristic to obtain optimal cluster heads and improve energy efficiency in WSNs. Simulation results show that the proposed clustering protocol outperforms other well known SI based protocols on the basis of packet delivery, throughput, energy consumption and extend network lifetime.

Energy efficient clustering protocol based on improved metaheuristic in wireless sensor networks

Energy efficient clustering is a well accepted NP-hard optimization problem in Wireless sensor networks (WSNs). Diverse paradigm of Computational intelligence (CI) including Evolutionary algorithms (EAs), Reinforcement learning (RL), Artificial immune systems (AIS), and more recently, Artificial bee colony (ABC) metaheuristic have been used for energy efficient clustering in WSNs. Due to ease of use and adaptive nature, ABC arose much interest over other population-based metaheuristics for solving optimization problems in WSNs. However, its search equation, which is comparably poor at exploitation and require storage of certain control parameters, contributes to its insufficiency. Thus, we present an improved Artificial bee colony (iABC) metaheuristic with an improved solution search equation to improve its exploitation capabilities. Additionally, in order to increase the global convergence of the proposed metaheuristic, an improved population sampling technique is introduced through Student′s−t distribution, which require only one control parameter to compute and store, hence increase efficiency of proposed metaheuristic. The proposed metaheuristic maintain a good balance between exploration and exploitation search abilities with least memory requirements, moreover the use of first of its kind compact Student′s−t distribution, make it suitable for limited hardware requirements of WSNs. Further, an energy efficient clustering protocol based on iABC metaheuristic is introduced, which inherit the capabilities of the proposed metaheuristic to obtain optimal cluster heads (CHs) and improve energy efficiency in WSNs. Simulation results shows that the proposed clustering protocol outperforms other well known protocols on the basis of packet delivery, throughput, energy consumption, network lifetime and latency as performance metric.

Improved metaheuristic based energy-efficient clustering protocol for wireless sensor networks

Energy-efficient clustering protocols are much sought specially for low-power, multi-functional Wireless Sensor Networks (WSNs). With the application of Computational Intelligence (CI) based approaches, various metaheuristics have been developed for energy-efficient clustering in WSNs. Artificial Bee Colony (ABC) is one such metaheuristic which arose much interest over other population-based metaheuristics for solving optimization problems in WSNs due to its ease of implementation and adaptive nature. However, its solution search equation, which is poor at exploitation process, contributes to its insufficiency. Thus, we present an improved Artificial Bee Colony (iABC) metaheuristic with an improved solution search equation to improve its exploitation capabilities. Additionally, in order to increase the global convergence of the proposed metaheuristic, an improved population sampling technique is introduced through Student's-t distribution. The proposed metaheuristic maintains a good balance between exploration and exploitation search abilities with least memory requirements, moreover the use of first of its kind compact Student's-t distribution makes it suitable for limited hardware requirements of WSNs. Further, an energy efficient clustering protocol BeeCluster based on iABC metaheuristic is introduced, which inherits the capabilities of the proposed metaheuristic to obtain optimal cluster heads (CHs) and improves energy-efficiency in WSNs. Simulation results show that the proposed clustering protocol outperforms other well known protocols on the basis of packet delivery, throughput, energy consumption, network lifetime and latency as performance metric.

A Novel Enhanced Coverage Optimization Algorithm for Effectively Solving Energy Optimization Problem in WSN

In Wireless Sensor Networks (WSN), Efficient-Energy Coverage (EEC) is one of the important issues for considering the (WSNs) implementation. In this study, we have developed the new algorithm ECO (Enhanced Coverage Optimization) for solving the EEC problem effectively. The proposed algorithm uses three types of major work for effectively solving the problem. One of the three pheromones is the local pheromone, which helps an ant organize its coverage set with fewer sensors. The other two pheromones are global pheromones, one of which is used to optimize the number of required active sensors per Point of Interest (PoI) and the other is used to form a sensor set that has as many senses as an ant has selected the number of active sensors by using the former pheromone. This study also introduces one technique that leads to a more realistic approach to solving the EEC problem that is to utilize the probabilistic sensor detection model. The main goal of ECO is Efficient Coverage on target area with minimum energy consumption and increased network's lifetime.