Heuristic Algorithms for Scheduling Resources in Time-Constrained Wireless Sensor Networks

2015 ◽  
Author(s):  
Y.G. Kim ◽  
Yu Wang ◽  
B.S. Park ◽  
H.H. Choi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Heuristic Algorithms ◽  
Wireless Sensor

scholarly journals Heuristic Algorithms for One-Slot Link Scheduling in Wireless Sensor Networks under SINR

2015 ◽  
Vol 11 (3) ◽  
pp. 806520 ◽  
Author(s):  
Hui Deng ◽  
Jiguo Yu ◽  
Dongxiao Yu ◽  
Guangshun Li ◽  
Baogui Huang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Heuristic Algorithms ◽  
Wireless Sensor ◽  
Link Scheduling

Improved Data Throughput Collection in Energy Harvesting Wireless Sensor Networks Using a Mobile Data Mule

2019 ◽  
Vol 13 ◽  
Author(s):  
Naween Kumar ◽  
Dinesh Dash
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Heuristic Algorithms ◽  
Data Gathering ◽  
The Other ◽  
Wireless Sensor ◽  
Data Throughput ◽  
Data Mule ◽  
Better Than

Background: In energy harvesting wireless sensor networks (EH-WSNs), sensors are harvesting energy from the renewable environment to make their operations endless and uninterrupted. However, in such a network, the time-varying nature of harvesting imposes a challenging issue in obtaining improved data-throughput. The use of a static-sink in EH-WSNs to improve data-throughput is less reliable because there is no assurance of the network connectivity. To alleviate such shortcomings, a data mule (MDM) has been introduced in EH-WSN for collecting sensors’ data. In this article, the MDM-based distance constrained tour finding problem is formulated such that the data-throughput can be improved within a given delay constraint. Method: To solve the problem, we devise two different heuristic algorithms based on two different metrics. Result: The obtained experimental results demonstrate that the devised algorithms are more effective than the existing algorithms in terms of data-throughput. Conclusion: The data-throughput values of the first proposed algorithm are about 6.14% and 3.56% better than the other for two different data gathering time durations of 100 sec and 800 sec. The data-throughput values of the second proposed algorithm are about 5.03% and 5.25% better than the other for two different data gathering time durations of 100 sec and 800 sec.

scholarly journals ECS-NL: An Enhanced Cuckoo Search Algorithm for Node Localisation in Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3576
Author(s):  
Vaibhav Kotiyal ◽  
Abhilash Singh ◽  
Sandeep Sharma ◽  
Jaiprakash Nagar ◽  
Cheng-Chi Lee
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Information Source ◽  
Critical Role ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Wireless Sensor ◽  
Search Process

Node localisation plays a critical role in setting up Wireless Sensor Networks (WSNs). A sensor in WSNs senses, processes and transmits the sensed information simultaneously. Along with the sensed information, it is crucial to have the positional information associated with the information source. A promising method to localise these randomly deployed sensors is to use bio-inspired meta-heuristic algorithms. In this way, a node localisation problem is converted to an optimisation problem. Afterwards, the optimisation problem is solved for an optimal solution by minimising the errors. Various bio-inspired algorithms, including the conventional Cuckoo Search (CS) and modified CS algorithm, have already been explored. However, these algorithms demand a predetermined number of iterations to reach the optimal solution, even when not required. In this way, they unnecessarily exploit the limited resources of the sensors resulting in a slow search process. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to minimise the Average Localisation Error (ALE) and the time taken to localise an unknown node. In this algorithm, we have implemented an Early Stopping (ES) mechanism, which improves the search process significantly by exiting the search loop whenever the optimal solution is reached. Further, we have evaluated the ECS algorithm and compared it with the modified CS algorithm. While doing so, note that the proposed algorithm localised all the localisable nodes in the network with an ALE of 0.5–0.8 m. In addition, the proposed algorithm also shows an 80% decrease in the average time taken to localise all the localisable nodes. Consequently, the performance of the proposed ECS algorithm makes it desirable to implement in practical scenarios for node localisation.

Design of Optimal Localization Model for Wireless Sensor Network Nodes Using Improved Krill Herd Algorithm

2021 ◽  
Author(s):  
Jafarsadegh Kamfar ◽  
Hessam Zandhessami ◽  
Mahmood Alborzi
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Heuristic Algorithms ◽  
Smart Cities ◽  
Wireless Sensor ◽  
Localization Error ◽  
Research Issues ◽  
Krill Herd Algorithm ◽  
Krill Herd

Abstract Nowadays, Wireless Sensor Networks (WSNs) are significantly applied in engineering and scientific research. WSNs consist of a group of distributed space sensors that track the environment's physical conditions and control the collected data at one central location. Examples of these sensors' applications are smart cities, transport, volcano surveillance and environmental activity, earthquake monitoring, medicine, post-disaster response, and military control. Wireless sensor networks have a lot of research issues like access to the media, implementation, time synchronization, network security and localization of the nodes. One of the most critical problems in this network research is the optimum position of the sensors to have access to maximum coverage and increase network life span to decrease maintenance costs, develop and manage the network. One of the main causes of the failure in these networks is running out of sensor battery and replacing them which impose high costs to maintenance and managing of the network. In order to solve the issues related to optimization and localization, researchers have focused on the algorithms like Swarm Intelligence (SI), because they enable us to solve complicated issues of optimization and NP-Hard issues to solve optimization. However, most of these algorithms are specialized for a purpose or a special program, and the majority of the solutions are not compatible with most of the wireless network sensors. The DV-Hop is one of the most popular node algorithms. But the main problem of the DV-Hop is the possibility of error in calculating the assessed distance between the unknown node and the nodes of anchor. Therefore, minimizing this error is the key to improve this algorithm. To reduce the problem of high localization error, two meta-heuristic algorithms have been proposed based on a combination. In this paper, a new optimization method based on a combination of Krill Herd Algorithm (KHA) and Particle Swarm Optimization (PSO) called KHAPSO is suggested to improve DV-Hop. Simulation results in MATLAB 2016 show that the KHAPSO model has a lower mean error compared to the DV-Hop, DV-Hop-KHA and DV-Hop-PSO models. Also, energy consumption in the KHAPSO model is less in comparison to the other models. The KHAPSO model with 400 unknown nodes and 30 anchor nodes was able to reduce energy consumption by about 35% and at the same time 27% reduction in Average Localization Error (ALE) compared to DV-Hop.

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