Comprehensive Energy Efficient Algorithm for WSN

Wireless sensor networks has been widely used. Energy problem is one of the important problems influencing the complete application. Sensor nodes use batteries as power source and have quite limit lifetime. So, efficiency of energy management becomes a key requirement in wireless sensor network design. Based on particle swarm optimization and ant colony optimization, a comprehensive algorithm with weight analysis has been proposed in the paper. In the algorithm, optimization method would be firstly used to determine the nodes number; then, particle  swarm optimization would be used to divide the networks into some clusters; finally, ant colony optimization is used to require the best transmission path and select the cluster head. The simulation results show that the new algorithm has higher energy efficiency and balanced energy consumption. It can extend the network lifetime

WSN Routing Algorithm Based on Routing Strategy with Ant Colony Optimization

To reduce the energy consumptions of wireless sensor network nodes (WSN), avoiding communication conflicts caused by different nodes simultaneously using shared channel, this paper proposes a dual-channel routing algorithm for sensor networks (CORA) based on Ant Colony optimization strategy. The algorithm can reduce the nodes mutual inhibition competition in channel contention by using dual channel communication model; compress the path finding ranger for ant colony through the maximum infection ball to reduce the energy consumption for network; propose a two-tier network with combined dispatching to effectively abate network congestion by means of layered graph. The simulation results show that compared with an energy-efficient routing algorithm based on Ant strategy and an energy-efficient routing algorithm based on Ant Colony optimization strategy, the CORA algorithm can lower down the network congestion to 17 % and reduce message conflicts between data packets and control packets, effectively reducing the energy consumption for average communicating time of packets and network

Combining Harmony search algorithm and Ant Colony Optimization algorithm to increase the lifetime of Wireless Sensor Networks

Wireless Sensor Networks are the new generation of networks that typically are formed great numbers of nodes and the communications of these nodes are done as Wireless. The main goal of these networks is collecting data from neighboring environment of network sensors. Since the sensor nodes are battery operated and there is no possibility of charging or replacing the batteries, the lifetime of the networks is dependent on the energy of sensors. The objective of this research, is to combine the Harmony Search Algorithm and Ant Colony Optimization Algorithm, as successful meta heuristic algorithm to routing at wireless sensor to increase lifetime at this type of networks. To this purpose, algorithm called HS-ACO is suggested. In this algorithm, two criterion of reduction consumption of energy and appropriate distribution of consumption energy between nodes of sensor leads to increase lifetime of network is considered. Results of simulations, show the capability of the proposed algorithm in finding the Proper path and establishment appropriate balance in the energy consumed by the nodes. Propose algorithm is better than Harmony Search algorithm and Ant Colony Optimization algorithm and Genetic Ant algorithm

Enhanced ant colony system for reducing packet loss in wireless sensor network

Routing packets from source nodes to destination nodes in Wireless Sensor Network (WSN) is complicated due to the heterogeneous nature and distribution of sensor nodes. Packet loss problem in WSN may occur when the sensor node carrying more packets than its capacity. This can affect throughput, energy consumption and success rate of the WSN system. This paper proposes an improved Ant Colony Optimization (ACO) algorithm for packet routing to solve packet loss problem in wireless sensor network. The proposed algorithm is inspired from a variant of ACO which is Ant Colony System (ACS) that consists of local and global pheromone updates to enhance routing path exploration and exploitation. Experimental results showed that the proposed algorithm improved the performance of the proposed ACS algorithm in terms of reducing packet loss and increasing the energy efficiency of sensor nodes

KFOA: K-mean clustering, Firefly based data rate Optimization and ACO routing for Congestion Control in WSN

Wireless sensor network (WSN) is assortment of sensor nodes proficient in environmental information sensing, refining it and transmitting it to base station in sovereign manner. The minute sensors communicate themselves to sense and monitor the environment. The main challenges are limited power, short communication range, low bandwidth and limited processing. The power source of these sensor nodes are the main hurdle in design of energy efficient network. The main objective of the proposed clustering and data transmission algorithm is to augment network performance by using swarm intelligence approach. This technique is based on K-mean based clustering, data rate optimization using firefly optimization algorithm and Ant colony optimization based data forwarding. The KFOA is divided in three parts: (1) Clustering of sensor nodes using K-mean technique and (2) data rate optimization for controlling congestion and (3) using shortest path for data transmission based on Ant colony optimization (ACO) technique. The performance is analyzed based on two scenarios as with rate optimization and without rate optimization. The first scenario consists of two operations as k- mean clustering and ACO based routing. The second scenario consists of three operations as mentioned in KFOA. The performance is evaluated in terms of throughput, packet delivery ratio, energy dissipation and residual energy analysis. The simulation results show improvement in performance by using with rate optimization technique

KFOA: K-mean clustering, Firefly based data rate Optimization and ACO routing for Congestion Control in WSN

Wireless sensor network (WSN) is assortment of sensor nodes proficient in environmental information sensing, refining it and transmitting it to base station in sovereign manner. The minute sensors communicate themselves to sense and monitor the environment. The main challenges are limited power, short communication range, low bandwidth and limited processing. The power source of these sensor nodes are the main hurdle in design of energy efficient network. The main objective of the proposed clustering and data transmission algorithm is to augment network performance by using swarm intelligence approach. This technique is based on K-mean based clustering, data rate optimization using firefly optimization algorithm and Ant colony optimization based data forwarding. The KFOA is divided in three parts: (1) Clustering of sensor nodes using K-mean technique and (2) data rate optimization for controlling congestion and (3) using shortest path for data transmission based on Ant colony optimization (ACO) technique. The performance is analyzed based on two scenarios as with rate optimization and without rate optimization. The first scenario consists of two operations as k- mean clustering and ACO based routing. The second scenario consists of three operations as mentioned in KFOA. The performance is evaluated in terms of throughput, packet delivery ratio, energy dissipation and residual energy analysis. The simulation results show improvement in performance by using with rate optimization technique

A Hybrid Cluster and Chain-based Routing Protocol for Lifetime Improvement

International audienceThe main challenge in the field of Wireless Sensor Networks (WSNs) is the energy conservation as long as possible. Clustering paradigm has proven its ability to prolong the network lifetime. The present paper proposes two algorithms using an approach that combines fuzzy c-means and ant colony optimization to form the clusters and manage the transmission of data in the network. First, fuzzy c-means is used to construct a predefined number of clusters. Second, we apply Ant Colony Optimization (ACO) algorithm to form a local shortest chain in each cluster. A leader node is randomly chosen at the beginning since all cluster nodes have the same amount of energy. In the next transmission, a remaining energy parameter is employed to select leader node. In the first algorithm, leader nodes transmit data in single hop to the distant base station (BS) while in the second the ACO algorithm is applied again to form a global chain between leader nodes and the BS. Simulation results show that the second proposed algorithm consumes less energy and effectively prolongs the network lifetime compared respectively with the first proposed and the LEACH algorithms

Energy efficient data collection with multiple mobile sink using artificial bee colony algorithm in large-scale WSN

In most wireless sensor networks (WSN), multi-hop routing algorithm is used to transmit the data collected by sensors to user. Multi-hop forwarding leads to energy hole problem and high transmission overhead in large scale WSN. In order to address these problems, this paper proposes multiple mobile sink based data collection algorithm, which introduces energy balanced clustering and Artificial Bee Colony based data collection. The cluster head election is based on the residual energy of the node. In this study, we focused on a large-scale and intensive WSN which allows a certain amount of data latency by investigating mobile Sink balance from three aspects: data collection maximization, mobile path length minimization, and network reliability optimization. Simulation results show that, in comparison with other algorithms such Random walk and Ant Colony Optimization, the proposed algorithm can effectively reduce data transmission, save energy, improve network data collection efficiency and reliability, and extend the network lifetime

A Trust Based Congestion Aware Hybrid Ant Colony Optimization Algorithm for Energy Efficient Routing in Wireless Sensor Networks (TC-ACO)

Congestion is a problem of paramount importance in resource constrained Wireless Sensor Networks, especially for large networks, where the traffic loads exceed the available capacity of the resources. Sensor nodes are prone to failure and the misbehavior of these faulty nodes creates further congestion. The resulting effect is a degradation in network performance, additional computation and increased energy consumption, which in turn decreases network lifetime. Hence, the data packet routing algorithm should consider congestion as one of the parameters, in addition to the role of the faulty nodes and not merely energy efficient protocols. Unfortunately most of the researchers have tried to make the routing schemes energy efficient without considering congestion factor and the effect of the faulty nodes. In this paper we have proposed a congestion aware, energy efficient, routing approach that utilizes Ant Colony Optimization algorithm, in which faulty nodes are isolated by means of the concept of trust. The merits of the proposed scheme are verified through simulations where they are compared with other protocols.Comment: 6 pages, 5 figures and 2 tables (Conference Paper