scholarly journals Novel Bacteria Foraging Optimization for Energy-efficient Communication in Wireless Sensor Network

2018 ◽  
Vol 8 (6) ◽  
pp. 4755
Author(s):  
Hemavathi P ◽  
Nandakumar A. N.
Keyword(s):  
Wireless Sensor Network ◽  
Swarm Intelligence ◽  
Sensor Network ◽  
Energy Efficient ◽  
Residual Energy ◽  
Optimization Techniques ◽  
Wireless Sensor ◽  
Efficient Communication ◽  
Communication Demands ◽  
Standard Energy

Optimization techniques based on Swarm-intelligence has been reported to have significant benefits towards addressing communication issues in Wireless Sensor Network (WSN). We reviewed the most dominant swarm intelligence technique called as Bacteria Foraging Optimization (BFO) to find that there are very less significant model towards addressing the problems in WSN. Therefore, the proposed paper introduced a novel BFO algorithm which maintains a very good balance between the computational and communication demands of a sensor node unlike the conventional BFO algorithms. The significant contribution of the proposed study is to minimize the iterative steps and inclusion of minimization of both receiving / transmittance power in entire data aggregation process. The study outcome when compared with standard energy-efficient algorithm was found to offer superior network lifetime in terms of higher residual energy as well as data transmission performance.

scholarly journals Cooperative Game Theory Approach for Energy-Efficient Node Clustering in Wireless Sensor Network

2020 ◽  
Vol 20 (2) ◽  
pp. 76
Author(s):  
Chaeriah Bin Ali Wael ◽  
Nasrullah Armi ◽  
Arumjeni Mitayani ◽  
Suyoto Suyoto ◽  
Salita Ulitia Prini ◽  
...  
Keyword(s):  
Game Theory ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Cooperative Game ◽  
Energy Efficient ◽  
Cooperative Game Theory ◽  
Residual Energy ◽  
Wireless Sensor ◽  
Theory Approach ◽  
Energy Efficient Clustering

Energy consumption is one of the critical challenges in designing wireless sensor network (WSN) since it is typically composed of resource-constrained devices. Many studies have been proposed clustering to deal with energy conservation in WSN. Due to its predominance in coordinating the behaviors of many players, game theory has been considered for improving energy efficiency in WSN. In this paper, we evaluate the performance of cooperative game theoretic clustering (CGC) algorithm which employs cooperative game theory in a form of 3-agent cost sharing game for energy-efficient clustering in WSN. Furthermore, we compared its performance to a well-known traditional clustering method, low-energy adaptive clustering hierarchy (LEACH), in terms of network lifetime and stability, and total residual energy. The simulation results show that CGC has better performance compared to LEACH due to the cooperation among cluster heads in coalition. CGC has higher alive nodes with stability improvement of first node dies (FND) by 65%, and the improvement by 52.4% for half node dies (HND). However, with the increasing of the number of nodes, the performance of LEACH is getting better compared to CGC.

Energy-Efficient Routing for WSNs

2020 ◽  
pp. 128-157
Author(s):  
Padmapriya N. ◽  
N. Kumaratharan ◽  
Aswini R.
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Sensor Network ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
Energy Efficient Routing ◽  
Wireless System

A wireless sensor network (WSN) is a gathering of sensor hubs that powerfully self-sort themselves into a wireless system without the use of any previous framework. One of the serious issues in WSNs is the energy consumption, whereby the system lifetime is subject to this factor. Energy-efficient routing is viewed as the most testing errand. Sensor organizes for the most part work in perplexing and dynamic situations and directing winds up repetitive assignment to keep up as the system measure increments. This chapter portrays the structure of wireless sensor network the analysis and study of different research works identified with energy-efficient routing in wireless sensor networks. Along these lines, to beat all the routing issues, the pattern has moved to biological-based algorithms like swarm intelligence-based strategies. Ant colony optimization-based routing protocols have shown outstanding outcomes as far as execution when connected to WSN routing.

An Algorithm with Efficiently Collecting and Aggregating Data for Wireless Sensor Networks

2021 ◽  
Author(s):  
Peng Xiong ◽  
Qinggang Su
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Cluster Head ◽  
Residual Energy ◽  
Base Station ◽  
Wireless Sensor ◽  
Cluster Heads ◽  
Battery Capacity

Due to the resource constraint, in wireless sensor network, the node processing ability, wireless bandwidth and battery capacity and other resources are scarcer. For improving the energy efficient and extend the lifetime of the network, this paper proposes a novel algorithm with the distributed and energy-efficient for collecting and aggregating data of wireless sensor network. In the proposed protocol, nodes can autonomously compete for the cluster head based on its own residual energy and the signal strength of its neighbouring nodes. To reduce the energy overhead of cluster head nodes, with a multi-hop way among cluster heads, the collected data from cluster heads is sent to a designated cluster head so as to further send these data to a base station. For improving the performance of the proposed protocol, a new cluster coverage method is proposed to fit the proposed protocol so that when the node density increases, network lifetime can be increased linearly as the number of nodes is increased. Simulations experiments show that network lifetime adopting the proposed protocol is sharply increased. And, the proposed protocol makes all the nodes die (network lifetime is defined as the death of last one node) in the last 40 rounds so that networks adopting the proposed protocol have higher reliability than networks adopting compared protocols.

scholarly journals HIWAEO: Hybrid Improved Whale Artificial Ecosystem Optimization Algorithm based Energy-Efficient Routing Protocol for Wireless Sensor Network

2021 ◽  
Author(s):  
R. Renuga Devi ◽  
T. Sethukarasi
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Network ◽  
Life Span ◽  
Sensor Network ◽  
Energy Efficient ◽  
Fitness Function ◽  
Residual Energy ◽  
The Other ◽  
Sensor Nodes ◽  
Wireless Sensor

Abstract Wireless Sensor Network (WSN) is a resource constraint network that utilizes more energy for transmitting and receiving the data. Hence energy efficiency is the vital issue faced by the WSN. Besides the packet routing process consumes more energy than the other processes. Moreover, the working of WSN is based on the battery life span of sensor nodes. Thus the constrained energy source affects the life span of the network battery. To tackle this issue, we proposed a novel method known as the Hybrid Improved Whale optimization-based Artificial Ecosystem optimization method (HIWAEO). This enhances the energy efficiency of the WSN and thereby improves the routing of the network. The energy-efficient WSN can be obtained by selecting optimal cluster head (CH) and forward nodes. To select the optimal CH the proposed method estimates the fitness function which includes node degree, space between the sensor nodes and space between the CH and base station (BS), residual energy, and node centrality. This estimated fitness function arranges the sensor nodes based on their increased energy and distance from the BS and the best node is chosen as the CH. Henceforth to obtain the routing efficiency the forward nodes are selected based on their residual energy and distance. The performance of the proposed method is analyzed with the other existing approaches for three conditions of BS alignment and concluded that our proposed method outperforms all the other approaches.

scholarly journals Survey on wireless sensor networks: energy efficient optimization routing algorithms

2020 ◽  
Vol 19 (2) ◽  
pp. 756
Author(s):  
V. Sivasankarareddy ◽  
G. Sundari
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Energy Efficient ◽  
Routing Algorithms ◽  
Optimization Techniques ◽  
Wireless Sensor ◽  
Wireless Sensing ◽  
Swarm Optimization ◽  
Data Extracting

Wireless Sensor Network is extensively utilized in numerous places, such as protection surveillance. In Wireless Sensor Network, sensing unit networks are particular arbitrarily in addition to likewise in-network relying upon the technique is used to extend the network. As sensing unit nodes make use of strength from batteries for noticing the facts in addition to forwarding data, it uses the capability for those answers. The sizable troubles in cordless networks include power optimization, protection, directing, and project type. In this paper, current procedures in escaping power utilization of Wireless Sensor Network in addition to distinctive protocols and also Methods are researched. Additionally, destiny research have a look at on strength efficiency in Wireless Sensor Network putting forward new terms as well as targets for in addition examination is mentioned. Depiction of optimizing strategies like particle swarm optimization set of rules as well as ant swarm optimization Formula is already possible for lowering the electricity loss and complements the life of sensor community but those strategies take in greater time. This paper gives surveying extraordinary different optimization techniques below the multi-objective facet that takes region in tradeoffs. Information extracting in sensing unit networks is the technique of obtaining software-enabled plans in addition to patterns with gratifying accuracy from a constant, speedy, in addition to probable non-ended flow of facts streams from sensor networks. Various boundaries in preceding optimization Algorithms and suggesting a great deal better treatment through applying Data extracting Strategies for Wireless Sensing unit Networks is carried out.

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