scholarly journals An Optimized Path Planning Model for Anchor-Free Localization in Wireless Sensor Networks

2021 ◽  
pp. 370-377
Author(s):  
Sitanshu Kumar ◽  
Dr. Sunil Rathod
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Path Planning ◽  
Network Topology ◽  
Wireless Sensor ◽  
Planning Model ◽  
Coverage Area ◽  
Proposed Model ◽  
Beacon Node

In Wireless Sensor Network (WSN), localization process is considered as a major challenge which is intended to maximize with minimized traveling distance of the beacon node. Further, the important issue is to improve coverage area of the anchor-based node and accuracy in calculation of the location of nodes. This paper mainly focuses on an enhanced path planning model using beacon node based upon their location. The proposed model focuses to improve coverage of the network topology by moving in zig-zag path fashion so that it will enhance the reachability of message in almost every possible corner of the deployed area. The proposed model is simulated extensively in a self-simulator with different scenarios and compared with SCAN and anchor-based model. The tested performance of the model is presented along with its analytical model. The simulation result shows that the proposed model gives the better performance as compared to all others existing model in terms of percentage of nodes settled and energy consumption.

Construction of Efficient Wireless Sensor Networks for Energy Minimization using a Modified ACO Algorithm.

2021 ◽  
Vol 11 ◽  
Author(s):  
Monojit Dey ◽  
Arnab Das ◽  
Avishek Banerjee ◽  
Ujjwal Kumar Kamila ◽  
Samiran Chattopadhyay
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Heuristic Algorithm ◽  
Energy Minimization ◽  
Wireless Sensor ◽  
Research Article ◽  
Prime Concern

In this paper, we have proposed different deployment strategies and have applied area-wise clustering along with modified Ant Colony Optimization to minimize energy consumption. Background: Previously some deployment strategies were used to enhance the lifetime of WSN. In our research, we have applied some novel deployment strategies like random, spiral, and S-pattern along with a novel area-wise clustering process to get better results than the existing literature as shown in Table 4. Objective: The main objective of the research article is to enhance the lifetime of Wireless Sensor Network with the help of different deployment strategies like random, spiral, and S-pattern). A novel clustering process (i.e., area-wise clustering), and a Meta-heuristic algorithm (modified ACO) are applied. Method: We have applied different methods for deployment strategies (random, spiral, and S-pattern). A novel clustering process (i.e., area-wise clustering), and a Meta-heuristic algorithm (modified ACO) are applied to get the desired results. Results: Random Deployment: 11.15 days to 15.09 days. Spiral Deployment: 11.25 days to 15.23 days. S-Pattern Deployment: 11.33 days to 15.33 days. Conclusion: In this paper, efficient Wireless Sensor Networks have been configured considering energy minimization as the prime concern. To minimize the energy consumption a modified ACO algorithm has been proposed. In our work, the minimization of energy consumption leads to an increment of the lifetime of WSN to a significant margin theoretically. The obtained result has been compared with the existing literature and it has been found that the proposed algorithm produced a better result than the existing literature.

Energy-Efficient Technique for Monitoring of Agricultural Areas with Terrestrial Wireless Sensor Networks

2019 ◽  
Vol 29 (09) ◽  
pp. 2050141 ◽  
Author(s):  
Muhammed Enes Bayrakdar
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Fading Channels ◽  
Energy Efficient ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Efficient Manner

In this paper, a monitoring technique based on the wireless sensor network is investigated. The sensor nodes used for monitoring are developed in a simulation environment. Accordingly, the structure and workflow of wireless sensor network nodes are designed. Time-division multiple access (TDMA) protocol has been chosen as the medium access technique to ensure that the designed technique operates in an energy-efficient manner and packet collisions are not experienced. Fading channels, i.e., no interference, Ricean and Rayleigh, are taken into consideration. Energy consumption is decreased with the help of ad-hoc communication of sensor nodes. Throughput performance for different wireless fading channels and energy consumption are evaluated. The simulation results show that the sensor network can quickly collect medium information and transmit data to the processing center in real time. Besides, the proposed technique suggests the usefulness of wireless sensor networks in the terrestrial areas.

scholarly journals Energy-efficient chain-based data gathering applied to communicating concrete

2020 ◽  
Vol 16 (8) ◽  
pp. 155014772093902
Author(s):  
Hang Wan ◽  
Michael David ◽  
William Derigent
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Hot Spot ◽  
Data Gathering ◽  
Information Modeling ◽  
Wireless Sensor ◽  
Energy Models

Wireless Sensor Networks are very convenient to monitor structures or even materials, as in McBIM project (Materials communicating with the Building Information Modeling). This project aims to develop the concept of “communicating concretes,” which are concrete elements embedding wireless sensor networks, for applications dedicated to Structure Health Monitoring in the construction industry. Due to applicative constraints, the topology of the wireless sensor network follows a chain-based structure. Node batteries cannot be replaced or easily recharged, it is crucial to evaluate the energy consumed by each node during the monitoring process. This area has been extensively studied leading to different energy models to evaluate energy consumption for chain-based structures. However, no simple, practical, and analytical network energy models have yet been proposed. Energy evaluation models of periodic data collection for chain-based structures are proposed. These models are compared and evaluated with an Arduino XBee–based platform. Experimental results show the mean prediction error of our models is 5%. Realizing aggregation at nodes significantly reduces energy consumption and avoids hot-spot problem with homogeneous consumptions along the chain. Models give an approximate lifetime of the wireless sensor network and communicating concretes services. They can also be used online by nodes for a self-assessment of their energy consumptions.

Presenting the Hybrid Algorithm of Honeybee - Harmony in Clustering and Routing of Wireless Sensor Networks

2019 ◽  
Vol 9 (3) ◽  
pp. 357-371
Author(s):  
Mohammad Sedighimanesh ◽  
Hesam Zandhesami ◽  
Ali Sedighimanesh
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Network Lifetime ◽  
Cluster Head ◽  
Rechargeable Batteries ◽  
Base Station ◽  
Wireless Sensor

Background: Wireless sensor networks are considered as one of the 21st century's most important technologies. Sensors in wireless sensor networks usually have limited and sometimes non-rechargeable batteries, which they are supposed to be preserved for months or even years. That's why the energy consumption in these networks is of a great importance. Objective: One way to improve energy consumption in a wireless sensor network is to use clustering. In clustered networks, one node is known as the cluster head and other nodes as normal members, which normal nodes send the collected data to the cluster head, and the cluster head sends the information to the base station either by a single step or by multiple steps. Method: Using clustering simplifies resource management and increases scalability, reliability, and the network lifetime. Although the cluster formation involves a time- overhead and how to choose the cluster head is another problem, but its advantages are more than its disadvantages. : The primary aim of this study is to offer a solution to reduce energy consumption in the sensor network. In this study, during the selection of cluster heads, Honeybee Algorithm is used and also for routing, Harmonic Search Algorithm is used. In this paper, the simulation is performed by using MATLAB software and the proposed method is compared with the Low Energy Adaptive Clustering Hierarchy (LEACH) and the multi-objective fuzzy clustering algorithm (MOFCA). Result and Conclusion: By simulations of this study, we conclude that this research has remarkably increased the network lifetime with respect to EECS, LEACH, and MOFCA algorithms. In view of the energy constraints of the wireless sensor network and the non-rechargeable batteries in most cases, providing such solutions and using metaheuristic algorithms can result in a significant reduction in energy consumption and, consequently, increase in the network lifetime.

STBCP: An Energy Efficient Sub-threshold Bee Colony-based Protocol for Wireless Sensor Networks

2019 ◽  
Vol 9 (4) ◽  
pp. 443-453
Author(s):  
Ghazaleh Kia ◽  
Alireza Hassanzadeh
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Energy Efficient ◽  
Average Energy ◽  
Wireless Sensor ◽  
Battery Lifetime ◽  
Bee Colony

Background & Objective: In this paper, a new energy efficient LEACH-based protocol for wireless sensor network is presented. One of the main issues in Wireless Sensor Networks (WSNs) is the battery consumption. In fact, changing batteries is a time consuming task and expensive. It is even impossible in many remote WSNs. Methods: The main goal of the presented protocol is to decrease the energy consumption of each node and increase the network lifetime. Lower power consumption results in longer battery lifetime. This protocol takes the advantage of sub-threshold technique and bee colony algorithm in order to optimize the energy consumption of a WSN. Simulation results show that the energy consumption of the wireless sensor network reduces by 25 percent using STBCP in comparison with recent LEACHbased protocols. It has been shown that the average energy of the network remains balanced and the distribution of residual energy in each round is equitable. Conclusion: In addition, the lifetime of a network using STBCP protocol has been increased by 23 percent regarding recently presented routing protocols.

Energy-Efficient Routing for WSNs

2020 ◽  
pp. 128-157
Author(s):  
Padmapriya N. ◽  
N. Kumaratharan ◽  
Aswini R.
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Sensor Network ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
Energy Efficient Routing ◽  
Wireless System

A wireless sensor network (WSN) is a gathering of sensor hubs that powerfully self-sort themselves into a wireless system without the use of any previous framework. One of the serious issues in WSNs is the energy consumption, whereby the system lifetime is subject to this factor. Energy-efficient routing is viewed as the most testing errand. Sensor organizes for the most part work in perplexing and dynamic situations and directing winds up repetitive assignment to keep up as the system measure increments. This chapter portrays the structure of wireless sensor network the analysis and study of different research works identified with energy-efficient routing in wireless sensor networks. Along these lines, to beat all the routing issues, the pattern has moved to biological-based algorithms like swarm intelligence-based strategies. Ant colony optimization-based routing protocols have shown outstanding outcomes as far as execution when connected to WSN routing.

Improving Scalability and Balancing the Network Load Using Adaptive Multiparent Crossover Method in Wireless Sensor Networks

2020 ◽  
Vol 17 (5) ◽  
pp. 2415-2420
Author(s):  
Shibin David ◽  
J. Andrew ◽  
Basil Xavier ◽  
Isaac Joel Raj ◽  
R. Jennifer Eunice
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Load Balancing ◽  
Comparative Study ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Network Load ◽  
Crossover Method

Wireless sensor network comprises of scattered sensors to sense, monitor and aggregate the sensed information. The major issue in a wireless sensor network is to balance network load and to maintain less energy consumption where multi parent crossover method is considered. Multiparent cross over method will generate offspring from parent and aims at managing the load. In this paper a comparative study of different algorithms is done where the load balancing and energy consumption issue has been resolved.

scholarly journals Modified Echo State Network Enabled Dynamic Duty Cycle for Optimal Opportunistic Routing in EH-WSNs

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 98
Author(s):  
Rajkumar Singh Rathore ◽  
Suman Sangwan ◽  
Kabita Adhikari ◽  
Rupak Kharel
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Duty Cycle ◽  
Opportunistic Routing ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Echo State Network ◽  
Proposed Model

Minimizing energy consumption is one of the major challenges in wireless sensor networks (WSNs) due to the limited size of batteries and the resource constrained tiny sensor nodes. Energy harvesting in wireless sensor networks (EH-WSNs) is one of the promising solutions to minimize the energy consumption in wireless sensor networks for prolonging the overall network lifetime. However, static energy harvesting in individual sensor nodes is normally limited and unbalanced among the network nodes. In this context, this paper proposes a modified echo state network (MESN) based dynamic duty cycle with optimal opportunistic routing (OOR) for EH-WSNs. The proposed model is used to act as a predictor for finding the expected energy consumption of the next slot in dynamic duty cycle. The model has adapted a whale optimization algorithm (WOA) for optimally selecting the weights of the neurons in the reservoir layer of the echo state network towards minimizing energy consumption at each node as well as at the network level. The adapted WOA enabled energy harvesting model provides stable output from the MESN relying on optimal weight selection in the reservoir layer. The dynamic duty cycle is updated based on energy consumption and optimal threshold energy for transmission and reception at bit level. The proposed OOR scheme uses multiple energy centric parameters for selecting the relay set oriented forwarding paths for each neighbor nodes. The performance analysis of the proposed model in realistic environments attests the benefits in terms of energy centric metrics such as energy consumption, network lifetime, delay, packet delivery ratio and throughput as compared to the state-of-the-art-techniques.

Optimization of energy-efficient dynamic task assignment for wireless sensor networks based on particle swarm algorithm

2021 ◽  
pp. 1-11
Author(s):  
Shu Zhang ◽  
Jianhua Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Path Planning ◽  
Data Transmission ◽  
Energy Efficient ◽  
Cluster Head ◽  
Base Station ◽  
Wireless Sensor ◽  
Dynamic Task

This paper provides an in-depth analysis of the optimization of energy-efficient dynamic task allocation in wireless sensor networks through an improved particle swarm optimization algorithm, and introduces the idea of software-defined networking into wireless sensor network to propose a software-defined wireless sensor network non-uniform cluster routing protocol. The protocol decouples the data layer from the control layer, and the base station performs the cluster head election, network clustering, and routing control operations. The base station optimizes the cluster head election process by electing cluster head nodes using an improved particle cluster algorithm. Based on the elected cluster head nodes, the base station calculates their corresponding contention radius and plans the data transmission path. The results of the calculation are sent to the corresponding nodes for cluster creation and data transmission. The simulation results fully show that the use of this protocol can achieve the purpose of significantly extending the service life of the network. This paper comprehensively analyses the whole process of mobile charging of UAVs under improved conditions and proposes a path planning algorithm. The multi-level weighted charging path planning proposed in this paper considers both fairness and timeliness. Finally, the paper verifies the effectiveness of the algorithm.

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.

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