Dual Transmission Power and Ant Colony Optimization based Lifespan Maximization Protocol for Sensor Networks

2015 ◽  
Vol 11 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Rohini Sharma ◽  
D. K. Lobiyal
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Technique ◽  
Residual Energy ◽  
Base Station ◽  
Ant Colony ◽  
Sensor Nodes ◽  
Transmission Power ◽  
Route Length ◽  
Leach Protocol ◽  
Dual Transmission

A main characteristic of wireless sensor network (WSN) is its limited battery power. Non-uniform energy depletion in WSN, leads to formation of energy holes in certain areas of network. For a uniform consumption of energy among sensor nodes, some points should be considered like the residual energy of the nodes, energy consumed in the communication and route length. In this work, the authors has achieved the uniform consumption of energy by using dissimilar transmission power levels for communication between cluster heads and the sink node, and for intra- cluster communication. Further, they have used ant colony optimization technique for routing between the base station and sensors which are not the member of any cluster. They have proposed dual transmission power levels and ant colony optimization based (DTP-ACO) protocol to improve the lifespan of the network. Results demonstrate that DTP-ACO protocol outperforms LEACH protocol in provisions of the life span, residual energy, packets sent to the base station and throughput of the network.

Sizing of current conveyors by means of an Ant Colony Optimization technique

2011 ◽  
Author(s):  
Bachir Benhala ◽  
Ali Ahaitouf ◽  
Abdellah Mechaqrane ◽  
Brahim Benlahbib ◽  
Farid Abdi ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Technique ◽  
Ant Colony ◽  
Current Conveyors

scholarly journals QALPA- An Efficient QoS Aware Hybrid Localization Model Using Particle Swarm Optimization and Ant Colony Optimization for Cognitive Wireless Sensor Networks

2021 ◽  
Vol 14 (1) ◽  
pp. 270-280
Author(s):  
Abhijit Halkai ◽  
◽  
Sujatha Terdal ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Large Scale ◽  
Cluster Head ◽  
Particle Swarm ◽  
Base Station ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Localization Model

A sensor network operates wirelessly and transmits detected information to the base station. The sensor is a small sized device, it is battery-powered with some electrical components, and the protocols should operate efficiently in such least resource availability. Here, we propose a novel improved framework in large scale applications where the huge numbers of sensors are distributed over an area. The designed protocol will address the issues that arise during its communication and give a consistent seamless communication system. The process of reasoning and learning in cognitive sensors guarantees data delivery in the network. Localization in Scarce and dense sensor networks is achieved by efficient cluster head election and route selection which are indeed based on cognition, improved Particle Swarm Optimization, and improved Ant Colony Optimization algorithms. Factors such as mobility, use of sensor buffer, power management, and defects in channels have been identified and solutions are presented in this research to build an accurate path based on the network context. The achieved results in extensive simulation prove that the proposed scheme outperforms ESNA, NETCRP, and GAECH algorithms in terms of Delay, Network lifetime, Energy consumption.

Adapting Ant Colony for Topology Optimization of Compliant Mechanisms With Variable Material Density

2017 ◽  
Author(s):  
Nadim Diab
Keyword(s):  
Topology Optimization ◽  
Ant Colony Optimization ◽  
High Performance ◽  
Compliant Mechanisms ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Intensity Level ◽  
Material Density ◽  
Swarm Intelligence Optimization

Swarm intelligence optimization techniques are widely used in topology optimization of compliant mechanisms. The Ant Colony Optimization has been implemented in various forms to account for material density distribution inside a design domain. In this paper, the Ant Colony Optimization technique is applied in a unique manner to make it feasible to optimize for the beam elements’ cross-section and material density simultaneously. The optimum material distribution algorithm is governed by two various techniques. The first technique treats the material density as an independent design variable while the second technique correlates the material density with the pheromone intensity level. Both algorithms are tested for a micro displacement amplifier and the resulting optimized topologies are benchmarked against reported literature. The proposed techniques culminated in high performance and effective designs that surpass those presented in previous work.

ACO powered by Local Searches Algorithms for the Solution of TSP Problems

2020 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Nisreen L. Ahmed
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Technique ◽  
General Purpose ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
New Approach ◽  
Local Searches ◽  
Probabilistic Technique ◽  
The Social ◽  
Great Deluge Algorithm

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and other animals. Ants, in particular, have inspired a number of methods and techniques among which the most studied and successful is the general-purpose optimization technique, also known as ant colony optimization, In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.  Ant Colony Optimization (ACO) algorithm is used to arrive at the best solution for TSP. In this article, the researcher has introduced ways to use a great deluge algorithm with the ACO algorithm to increase the ability of the ACO in finding the best tour (optimal tour). Results are given for different TSP problems by using ACO with great deluge and other local search algorithms.

Source Location Privacy Using Ant Colony Optimization in Wireless Sensor Networks

2016 ◽  
pp. 227-245
Author(s):  
Jyoti Prakash Singh ◽  
Paramartha Dutta
Keyword(s):  
Ant Colony Optimization ◽  
Location Privacy ◽  
Source Location ◽  
Base Station ◽  
Ant Colony ◽  
Source Node ◽  
Wireless Sensor ◽  
Difficult Situation ◽  
Source Location Privacy ◽  
Pheromone Level

One of the important application domain of sensor network is monitoring a region and/or tracking a target. In such type of applications, the location of the source node tracking that target is very important. At the same time, if the location of the node currently tracking the target is captured by an adversary then that target may fall into a difficult situation. In this chapter, a solution to source location privacy with the help of Ant Colony Optimization is proposed. The idea of pheromone level is normally used to find out the shortest path between the source node and the base station that to minimize the energy consumption of the networks. The pheromone level of the ants is used here to guide the ants to follow different paths to hide the source location from adversaries who uses traffic analysis to capture the source node.

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