scholarly journals A Cognitive Knowledged Energy-Efficient Path Selection Using Centroid and Ant-Colony Optimized Hybrid Protocol for WSN-Assisted IoT

2022 ◽  
Author(s):  
Nalluri Prophess Raj Kumar ◽  
G. Josemin Bala
Keyword(s):  
Energy Efficient ◽  
Path Selection ◽  
Ant Colony

scholarly journals A Cognitive knowledged Energy-Efficient path selection using Centroid and Ant-Colony Optimized Hybrid protocol for WSN-Assisted IoT

2021 ◽  
Author(s):  
Prophess Raj Kumar Nalluri ◽  
Josemin Bala Gnanadhas
Keyword(s):  
Ant Colony Optimization ◽  
Energy Efficient ◽  
Cluster Formation ◽  
Cluster Head ◽  
Base Station ◽  
Path Selection ◽  
Ant Colony ◽  
Long Distance ◽  
Coverage Area ◽  
Cluster Heads

Abstract In WSN-assisted IoT environment, the sensors are resource constrained. The energy, computing and storage resources of deployed sensors in the sensing area are limited. Clustering is the key method for saving energy in wireless sensor networks. A hybrid protocol named as an Energy Efficient Centroid-based Ant colony Optimization (EECAO) protocol is proposed in this paper to improve the performance of the sensor network in WSN-assisted IoT environments. The protocol uses the concept of centroid based clustering to gather the information of local clusters and ant colony optimization to relay that information to the base station. proposed hybrid protocol includes multiple clustering factors such as energy cost, channel consistency and cognitive sensor throughput to select cluster heads and a new distributed cluster formation for self-organizing deployed sensors. Selection of the super cluster head among the cluster heads is based on the energy centroid position for a defined coverage area. In EECAO protocol, the energy level of cognitive sensors is the key parameter for defining the position of centroid. To reduce the long-distance communication, path optimization between the super cluster heads and the base station is carried out using an ant routing model. Our simulation results indicate that EECAO protocol performs better when benchmarked against existing ETSP and EECRP protocols. The proposed hybrid protocol EECAO is well-suited for networks that requires long lifetime when the base station is placed at either center, border or outside the network.

An Optimal Energy-Efficient Path Selection Algorithm Based on Ant Colony-Genetic Optimization Scheme

2013 ◽  
Vol 347-350 ◽  
pp. 3153-3157
Author(s):  
Yun Jian Tan ◽  
Cai Hong Li ◽  
Rong Zhao ◽  
Jin Ze Du ◽  
Ya Li Yuan ◽  
...  
Keyword(s):  
Energy Efficient ◽  
High Speed ◽  
Optimization Design ◽  
Ant Colony Algorithm ◽  
Feedback Mechanism ◽  
Path Selection ◽  
Ant Colony ◽  
Optimal Energy ◽  
Routing Optimization ◽  
Efficient Scheme

In this paper, inspired to the high-speed global search ability for genetic algorithm and the positive feedback mechanism for ant colony algorithm, our energy-efficient scheme, called ACGR, was proposed for routing optimization design, in which the communication messages, treated as ants with limited lifetime, are sent by nodes for searching the optimal routing path. Through the proposed scheme, multiple candidate routing paths could be obtained firstly. Then each candidate path is considered as a gene sequence and through the selection, crossover and mutation operations on them, the optimal energy routing path is determined. Simulation results have shown that the proposed algorithm provides promising solution because it takes into account the energy of each node, and extends the lifetime of the wireless sensor network.

scholarly journals Research on the Uncertainty Decision Model of the Regional Collaborative Innovation System Based on an Improved Ant Colony Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Xiaona Zhang ◽  
Fayin Wang
Keyword(s):  
Decision Model ◽  
Ant Colony Algorithm ◽  
Innovation System ◽  
Path Selection ◽  
Collaborative Optimization ◽  
Ant Colony ◽  
Evaluation Index System ◽  
Regional Innovation ◽  
Collaborative Innovation ◽  
Improved Ant Colony Algorithm

The regional collaborative innovation system is a nonlinear complex system, which has obvious uncertainty characteristics in the aspects of member selection and evolution. Ant colony algorithm, which can do the uncertainty collaborative optimization decision-making, is an effective tool to solve the uncertainty decision path selection problem. It can improve the cooperation efficiency of each subsystem and achieve the goal of effective cooperation. By analysing the collaborative evolution mechanisms of the regional innovation system, an evaluation index system for the regional collaborative innovation system is established considering the uncertainty of collaborative systems. The collaborative uncertainty decision model is constructed to determine the regional innovation system’s collaborative innovation effectiveness. The improved ant colony algorithm with the pheromone evaporation model is applied to traversal optimization to identify the optimal solution of the regional collaborative innovation system. The collaboration capabilities of the ant colony include pheromone diffusion so that local updates are more flexible and the result is more rational. Finally, the method is applied to the regional collaborative innovation system.

scholarly journals An Energy Efficient Path Selection Using Swarm Intelligence in IoT SN

2021 ◽  
Vol 1916 (1) ◽  
pp. 012102
Author(s):  
D Arulanantham ◽  
C Palanisamy ◽  
G Pradeepkumar ◽  
S Kavitha
Keyword(s):  
Swarm Intelligence ◽  
Energy Efficient ◽  
Path Selection

scholarly journals Research on path selection system based on green transportation

2021 ◽  
Vol 275 ◽  
pp. 02043
Author(s):  
Linyi Qian
Keyword(s):  
Travel Time ◽  
Energy Efficient ◽  
Time Estimation ◽  
Clean Energy ◽  
Path Selection ◽  
Travel Time Estimation ◽  
Optimal Route ◽  
Actual Problem ◽  
Transportation Sector ◽  
Energy Transportation

The transportation sector already accounts for 14% of global greenhouse gas emissions. Therefore, controlling carbon emissions in the transportation sector has become a top priority for China and other countries around the world. In addition to the technological development of clean energy transportation, the most critical aspect of the globalization of new energy transportation industry and market is the optimization of access routes. This paper will be based on road travel time estimation and optimal path selection of energy efficient transportation research hotspots. Firstly, the accuracy of road travel time estimation is improved according to the basic law of traffic flow. At the same time, this paper defines the optimal route into two cases of shortest path and shortest time for solving. Finally, the actual solution process is given according to the actual problem, as well as the optimal route, aiming to promote the strong development in the field of intelligent navigation system and energy-efficient transportation.

Travelling and Steering Path Adjustment of Blueberry Harvesters

2011 ◽  
Vol 486 ◽  
pp. 25-28
Author(s):  
Zhi Peng Li ◽  
Dong Sheng Li
Keyword(s):  
Control System ◽  
Path Planning ◽  
Real Time ◽  
System Development ◽  
Path Selection ◽  
Ant Colony ◽  
New Knowledge ◽  
Knowledge Support ◽  
Virtual Modeling ◽  
Adjustment System

A picking and steering adjustment system for blueberry harvesters has been developed. In this paper, the main hardware and working principles of the system is introduced first, then the application of an ant colony simplification algorithm in the system development is presented. Information of virtual modeling the blueberry plant images and fruit distributions is obtained through the control system which is used as input for the ant colony simplification algorithm calculation. Then results are translated into real-time travelling path planning instructions for the blueberry harvester. The research provided technological and new knowledge support for future investigations into intelligent travelling path selection, thus playing an important role in mechanization and intelligent harvesting processes for blueberry harvesters.

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