scholarly journals Elite Immune Ant Colony Optimization-Based Task Allocation for Maximizing Task Execution Efficiency in Agricultural Wireless Sensor Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Mengying Xu ◽  
Jie Zhou
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Task Allocation ◽  
Convergence Speed ◽  
Ant Colony ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Task Execution ◽  
Temperature And Humidity

The research of agricultural wireless sensor networks (AWSNs) plays an important role in the field of facility agricultural technology. The temperature and humidity nodes in AWSNs are so tiny that they are limited on computation, network management, information collection, and storage size. Under this condition, task allocation plays a key role in improving the performance of AWSNs to reduce energy consumption and computational constraints. However, the optimization of task allocation is a nonlinearly constrained optimization problem whose complexity increases when constraints such as limited computing capabilities and power are undertaken. In this paper, an elite immune ant colony optimization (EIACO) is proposed to deal with the problem of task allocation optimization, which is motivated by immune theory and elite optimization theory. The EIACO uses ant colony optimization (ACO) to combine the clone operator and elite operator together for the optimization of task allocation. The performances of EIACO with different numbers of temperature and humidity sensor nodes and tasks have been compared by both genetic algorithm (GA) and simulated annealing (SA) algorithm. Simulation results show that the proposed EIACO has a better task execution efficiency and higher convergence speed than GA and SA. Furthermore, the convergence speed of EIACO is faster than GA and SA. Therefore, the whole system efficiency can be improved by the proposed algorithm.

scholarly journals Minimization of Energy Consumption for Routing in High-Density Wireless Sensor Networks Based on Adaptive Elite Ant Colony Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jing Xiao ◽  
Chaoqun Li ◽  
Jie Zhou
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Convergence Speed ◽  
High Density ◽  
Ant Colony ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Elite Ant Colony

High-density wireless sensor networks (HDWSNs) are usually deployed randomly, and each node of the network collects data from complex environments. Because the energy of sensor nodes is powered by batteries, it is basically impossible to replace batteries or charge in the complex surroundings. In this paper, a QoS routing energy consumption model is designed, and an improved adaptive elite ant colony optimization (AEACO) is proposed to reduce HDWSN routing energy consumption. This algorithm uses the adaptive operator and the elite operator to accelerate the convergence speed. So, as to validate the efficiency of AEACO, the AEACO is contrast with particle swarm optimization (PSO) and genetic algorithm (GA). The simulation outcomes show that the convergence speed of AEACO is sooner than PSO and GA. Moreover, the energy consumption of HDWSNs using AEACO is reduced by 30.7% compared with GA and 22.5% compared with PSO. Therefore, AEACO can successfully decrease energy consumption of the whole HDWSNs.

scholarly journals Low-Energy Secure Routing Protocol for WSNs Based on Multiobjective Ant Colony Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xueli Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Ant Colony Optimization Algorithm

As one of the three pillars of information technology, wireless sensor networks (WSNs) have been widely used in environmental detection, healthcare, military surveillance, industrial data sampling, and many other fields due to their unparalleled advantages in deployment cost, network power consumption, and versatility. The advent of the 5G standard and the era of Industry 4.0 have brought new opportunities for the development of wireless sensor networks. However, due to the limited power capacity of the sensor nodes themselves, the harsh deployment environment will bring a great difficulty to the energy replenishment of the sensor nodes, so the energy limitation problem has become a major factor limiting its further development; how to improve the energy utilization efficiency of WSNs has become an urgent problem in the scientific and industrial communities. Based on this, this paper researches the routing technology of wireless sensor networks, from the perspective of improving network security, and reducing network energy consumption, based on the study of ant colony optimization algorithm, further studies the node trust evaluation mechanism, and carries out the following research work: (1) study the energy consumption model of wireless sensor networks; (2) basic ant colony algorithm improvement; (3) multiobjective ant colony algorithm based on wireless sensor routing algorithm optimization. In this study, the NS2 network simulator is used as a simulation tool to verify the performance of the research algorithm. Compared with existing routing algorithms, the simulation results show that the multiobjective ant colony optimization algorithm has better performance in evaluation indexes such as life cycle, node energy consumption, node survival time, and stability compared with the traditional algorithm and the dual cluster head ant colony optimization algorithm.

An Ant Colony Optimization Approach to Power Allocation in Wireless Sensor Networks

2012 ◽  
Vol 263-266 ◽  
pp. 954-958
Author(s):  
Xiang Yang Liu ◽  
Da Wang ◽  
Jin Pan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Power Allocation ◽  
Detection Performance ◽  
Ant Colony ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Ant Colony System ◽  
Optimization Approach

The ant colony optimization algorithm is good at solving multidimensional optimization problem. The allocation of power resource of a node in wireless sensor networks should make the detection performance of the whole network maximum, which is complex due to the detection probability of the whole system cannot be expressed explicitly. Therefore, continuous ant colony system (CACS) is adopted to optimize the allocation of each node’s power between sensing and communications. The results show that it can lead to a good power allocation. At the same time, the scheme that all sensor nodes have identical power assignment can achieve nearly the same detection performance as compared that achieved by the best scheme searched by CACS. As a result, particu-larly for a large number of sensors, an identical power allocation scheme for each node can be employed to achieve nearly the best detection performance.

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.

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

2016 ◽  
pp. 277-299
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.

Ant Colony Algorithms for Steiner Trees

2011 ◽  
pp. 1551-1575 ◽  
Author(s):  
Gurdip Singh ◽  
Sanjoy Das ◽  
Shekhar V. Gosavi ◽  
Sandeep Pujar
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Steiner Trees ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Graph Theoretic ◽  
Ant Colony Algorithms ◽  
Complex Optimization

This chapter introduces ant colony optimization as a method for computing minimum Steiner trees in graphs. Tree computation is achieved when multiple ants, starting out from different nodes in the graph, move towards one another and ultimately merge into a single entity. A distributed version of the proposed algorithm is also described, which is applied to the specific problem of data-centric routing in wireless sensor networks. This research illustrates how tree based graph theoretic computations can be accomplished by means of purely local ant interaction. The authors hope that this work will demonstrate how innovative ways to carry out ant interactions can be used to design effective ant colony algorithms for complex optimization problems.

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