scholarly journals Optimal Base Station Location for Network Lifetime Maximization in Wireless Sensor Network

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2760
Author(s):  
Sandrine Mukase ◽  
Kewen Xia ◽  
Abubakar Umar

Wireless sensor networks have attracted worldwide attention in recent years. The failure of the nodes is caused by unequal energy dissipation. The reasons that cause unequal energy dissipation are, first and foremost, the distance between the nodes and the base station, and secondly, the distance between the nodes themselves. In wireless sensor networks, the location of the base station has a substantial impact on the network’s lifetime effectiveness. An improved genetic algorithm based on the crossover elitist conservation genetic algorithm (CECGA) is proposed to optimize the base station location, while for clustering, the K-medoids clustering (KMC) algorithm is used to determine optimal medoids among sensor nodes for choosing the appropriate cluster head. The idea is to decrease the communication distance between nodes and the cluster heads as well as the distance among nodes. For data routing, a multi-hop technique is used to transmit data from the nodes to the cluster head. Implementing an evolutionary algorithm for this optimization problem simplifies the problem with improved computational efficiency. The simulation results prove that the proposed algorithm performed better than compared algorithms by reducing the energy use of the network, which results in increasing the lifetime of the nodes, thereby improving the whole network.

2013 ◽  
Vol 427-429 ◽  
pp. 2497-2501
Author(s):  
Yong Fu Zhao ◽  
Jian Bo Yao

As wide applications of wireless sensor networks, privacy concerns have emerged as the main obstacle to success. When wireless sensor networks are used to battlefield, the privacy about base-sation-locations become a crux issue. If base-sation location will be exposed to adversary, the consequence is inconceivable. Random data collection scheme has a problem that message latencies become larger higher for protecting mobile-base-station-location privacy .In this paper, BDGW (Bidirection Greedy Walk) is proposed to preserve mobile-base-station-location privacy. In BDGW, data are forwarded by greedy walk and stored at pass nodes in the network, the base-sation move in bidirection greedy walk to collect data from the local nodes occasionally, which prevents the attackers from predicting their locations and movements. Compared to random data collection scheme, BDGW has smaller message latencies, while providing satisfactory mobile-base-station-location privacy.


2020 ◽  
Vol 30.8 (147) ◽  
pp. 14-21
Author(s):  
Thanh Huong Nguyen ◽  
◽  
Dang Toan Dao ◽  

Energy efficiency is one of the important factors when exploiting Wireless Sensor Networks, especially for increasing lifespan and performance. In the network nowadays, the number of sensor nodes can reach hundreds or thousands and can be arranged in complex hierarchical architecture. Besides, the current sensor nodes have a small size, limited battery source but are operated in vast areas. The clustered-based method has been an effective and potentially extensible means of boosting the management and operation of such large-scale networks and minimizing the overall energy consumption. In this paper, the issue of arranging and routing the nodes in the sensor network in a hierarchical manner is investigated, in which each lowest level sensor nodes are grouped in a cluster with a common cluster head, then the cluster-head plays an intermediate role transmit the information back and forth between the sensor nodes and the base station. In this way, the route to exchange information can not only be optimized with respect to the distance but also for energy spent on the communication. In order to do so, this paper proposed a novel method based on a Genetic Algorithm to establish a routing protocol to achieve energy optimization. The results demonstrate that this approach can decrease the energy consumption according to the optimized routing through clustering and increase the performance superior to the other clustering schemes.


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