On the Development of Energy-Efficient Distributed Source Localization Algorithm in Wireless Sensor Networks Using Modified Swarm Intelligence

2020 ◽  
pp. 143-173
Author(s):  
Harikrushna Gantayat ◽  
Trilochan Panigrahi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Swarm Intelligence ◽  
Source Localization ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Distributed Source

Performance Analysis of TBC-ACO Routing Protocol with Existing Routing Protocols of Wireless Sensor Networks

2017 ◽  
Vol 7 (8) ◽  
pp. 169
Author(s):  
A. Radhika ◽  
D. Haritha
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Swarm Intelligence ◽  
Routing Protocol ◽  
Routing Protocols ◽  
Energy Efficient ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Congestion Aware

Wireless Sensor Networks, have witnessed significant amount of improvement in research across various areas like Routing, Security, Localization, Deployment and above all Energy Efficiency. Congestion is a problem of  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 misbehaviour 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 .Nowadays, the main central point of attraction is the concept of Swarm Intelligence based techniques integration in WSN.  Swarm Intelligence based Computational Swarm Intelligence Techniques have improvised WSN in terms of efficiency, Performance, robustness and scalability. The main objective of this research paper is to propose congestion aware , energy efficient, routing approach that utilizes Ant Colony Optimization, in which faulty nodes are isolated by means of the concept of trust further we compare the performance of various existing routing protocols like AODV, DSDV and DSR routing protocols, ACO Based Routing Protocol  with Trust Based Congestion aware ACO Based Routing in terms of End to End Delay, Packet Delivery Rate, Routing Overhead, Throughput and Energy Efficiency. Simulation based results and data analysis shows that overall TBC-ACO is 150% more efficient in terms of overall performance as compared to other existing routing protocols for Wireless Sensor Networks.

A Comprehensive Review of Ant Colony Optimization (ACO) based Energy-Efficient Routing Protocols for Wireless Sensor Networks

2014 ◽  
Vol 3 (3) ◽  
pp. 33-55 ◽  
Author(s):  
Anand Nayyar ◽  
Rajeshwar Singh
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Routing Protocols ◽  
Energy Efficient ◽  
Ant Colony ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficient Routing

Wireless Sensor Networks (WSNs) have always been a hot area of researchers for finding more solutions towards making WSN network more energy efficient and reliable. Energy efficient routing is always a key challenging task to enhance the network lifetime and balance energy among the sensor nodes. Various solutions have been proposed in terms of energy efficient routing via protocol development, various techniques have also been incorporated like Genetic Algorithm, Swarm Intelligence etc. The main aim of this research paper to study all the routing protocols which are energy efficient and are based on Ant Colony Optimization (ACO). This paper also highlights the pros and cons of each of routing protocol which has been developed on lines of Energy Efficiency and has also been compared among one another to find which protocol outwits one another. Further, we conclude that Swarm Intelligence being a novel and bio-inspired field has contributed as well as contributing much in the area of improving routing issues of sensor networks.

scholarly journals Design of an Energy Efficient Cluster based Localization Algorithm (EECBLA) for Underwater Wireless Sensor Networks

2020 ◽  
Vol 9 (7) ◽  
pp. 37-42
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Network Connectivity ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Node Mobility ◽  
Localization Error ◽  
Localization Algorithm ◽  
Underwater Wireless Sensor Networks

In underwater Wireless Sensor Networks (UWSN) the node mobility is higher as the nodes drift with the water so identifying the exact location of the nodes is a challenging task. Terrestrial WSN use Global Positioning System (GPS) to locate the nodes, but the same cannot be applied for UWSN as they do not propagate under water. Underwater uses acoustic channel for communication which suffers from low bandwidth, high propagation delay, high bit error rate, high node mobility and variable sound speed which makes localization a challenging task. Hence there is an evolving requirement for energy efficient localization algorithm. To overcome these problems we propose an Energy Efficient Cluster Based Localization Algorithm (EECBLA) to minimize the energy utilization and identify an accurate location of the sensor nodes. EECBLA is a cluster based localization protocol where the GPS enabled high power anchor nodes are utilized to estimate the location of the un localized sensor nodes. To improve the localization accuracy it is necessary to improve the network connectivity. Network connectivity can be improved by increasing the lifetime of the sensor nodes. EECBLA proposes an energy efficient localization scheme where the node operates in active and sleep mode to efficiently utilize the available energy. In this research work we take a sample example and demonstrate how the location is estimated through TOA and Euclidean distance. Accuracy of TOA is measured as 8.36, 4.76, 13.21, 14.85, 16.26 and 3.60. The average estimated localization error of EECBLA is around 4m to 6m. EECBLA localization error is compared with other localization algorithms like 3DUL whose average localization error is 5m to 10m, SDMA whose average localization error is around 24% and localization error of Hybrid RSS ranges from 4.69m to 15.85m. When compared with these methodologies there is a decrease in localization error by 3%, and the increase in accuracy by 5% in EECBLA protocol

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