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.

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 Clone Chaotic Parallel Evolutionary Algorithm for Low-Energy Clustering in High-Density Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rui Yang ◽  
Mengying Xu ◽  
Jie Zhou
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Convergence Speed ◽  
High Density ◽  
Low Energy ◽  
Wireless Sensor ◽  
Initial Population ◽  
Clustering Model ◽  
Chaotic Operator

Because the sensors are constrained in energy capabilities, low-energy clustering has become a challenging problem in high-density wireless sensor networks (HDWSNs). Usually, sensor nodes tend to be tiny devices along with constrained clustering abilities. To have a low communication energy consumption, a low-energy clustering scheme should be designed properly. In this work, a new cloned chaotic parallel evolution algorithm (CCPEA) is proposed, and a low-energy clustering model is established to lower the communication energy consumption of HDWSNs. By introducing CCPEA into the low-energy clustering, an objective function is designed for evaluating the communication energy consumption. For this problem, we define a clone operator to minimize the communication energy consumption of HDWSNs, use the chaotic operator to randomly generate the initial population to expand the search range to avoid local optimization, and find the parallel operator to speed up the convergence speed. In the experiment, the effect of CCPEA is compared to heuristic approaches of particle swarm optimization (PSO) and simulated annealing (SA) for the HDWSNs with different numbers of sensors. Simulation experiments demonstrate that the presented CCPEA method achieves a lower communication energy consumption and faster convergence speed than PSO and SA.

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.

scholarly journals An Adaptive Immune Ant Colony Optimization for Reducing Energy Consumption of Automatic Inspection Path Planning in Industrial Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chaoqun Li ◽  
Jing Xiao ◽  
Yang Liu ◽  
Guohong Qi ◽  
Hu Qin ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Path Planning ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Industrial Wireless Sensor Networks ◽  
Efficient Operation ◽  
Adaptive Immune

Industrial wireless sensor networks (IWSNs) are usually fixedly deployed in industrial environments, and various sensor nodes cooperate with each other to complete industrial production tasks. The efficient work of each sensor node of IWSNs will improve the efficiency of the entire network. Automated robots need to perform timely inspection and maintenance of IWSNs in an industrial environment. Excessive inspection distance will increase inspection costs and increase energy consumption. Therefore, shortening the inspection distance can reduce production energy consumption, which is very important for the efficient operation of the entire system. However, the optimal detection path planning of IWSNs is an N-P problem, which can usually only be solved by heuristic mathematical methods. This paper proposes a new adaptive immune ant colony optimization (AIACO) for optimizing automated inspection path planning. Moreover, novel adaptive operator and immune operator are designed to prevent the algorithm from falling into the local optimum and increase the optimization ability. In order to verify the performance of the algorithm, the algorithm is compared with genetic algorithm (GA) and immune clone algorithm (ICA). The simulation results show that the inspection distance of IWSNs using AIACO is lower than that of GA and ICA. In addition, the convergence speed of AIACO is faster than that of GA and ICA. Therefore, the AIACO proposed in this paper can effectively reduce the inspection energy consumption of the entire IWSN system.

scholarly journals A Clustering Routing Algorithm Based on Improved Ant Colony Optimization Algorithms for Underwater Wireless Sensor Networks

Algorithms ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 250
Author(s):  
Xingxing Xiao ◽  
Haining Huang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Routing Algorithm ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Sink Node ◽  
Underwater Wireless Sensor Networks ◽  
Head Node

Because of the complicated underwater environment, the efficiency of data transmission from underwater sensor nodes to a sink node (SN) is faced with great challenges. Aiming at the problem of energy consumption in underwater wireless sensor networks (UWSNs), this paper proposes an energy-efficient clustering routing algorithm based on an improved ant colony optimization (ACO) algorithm. In clustering routing algorithms, the network is divided into many clusters, and each cluster consists of one cluster head node (CHN) and several cluster member nodes (CMNs). This paper optimizes the CHN selection based on the residual energy of nodes and the distance factor. The selected CHN gathers data sent by the CMNs and transmits them to the sink node by multiple hops. Optimal multi-hop paths from the CHNs to the SN are found by an improved ACO algorithm. This paper presents the ACO algorithm through the improvement of the heuristic information, the evaporation parameter for the pheromone update mechanism, and the ant searching scope. Simulation results indicate the high effectiveness and efficiency of the proposed algorithm in reducing the energy consumption, prolonging the network lifetime, and decreasing the packet loss ratio.

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.

Balanced Energy Consumption Approach Based on Ant Colony in Wireless Sensor Networks

2016 ◽  
pp. 267-293
Author(s):  
Sahabul Alam ◽  
Debashis De
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Balanced Energy Consumption

Now a days Wireless Sensor Networks (WSNs) have grown rapidly due to advancement of information technology. Sensor nodes are deployed over the field for collecting useful information. Sensor nodes have limited battery power and bandwidth. As a result it is critical for planning energy efficient protocols in WSNs. It is necessary to transfer and gather information in optimized way to reduce the energy dissipation. Ant Colony Optimization (ACO) is already proved to be better technique to optimize the network routing protocols in WSNs. Ant based routing can have significant role to extend the network life time and balance energy consumption in WSNs. In this chapter wireless sensor network architecture, routing factors of wireless sensor networks, computational intelligence technique, ant colony algorithm and ant colony based balanced energy consumption approaches in wireless sensor network have been discussed.

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