Load-conscious maximization of base-station location privacy in wireless sensor networks

2017 ◽  
Vol 124 ◽  
pp. 126-139 ◽  
Author(s):  
Nikolaos Baroutis ◽  
Mohamed Younis
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Location Privacy ◽  
Base Station ◽  
Wireless Sensor ◽  
Base Station Location ◽  
Station Location

Bidirection Greedy Walk Based Mobile-Base-Station-Location Privacy in Wireless Sensor Networks

2013 ◽  
Vol 427-429 ◽  
pp. 2497-2501
Author(s):  
Yong Fu Zhao ◽  
Jian Bo Yao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Location Privacy ◽  
Base Station ◽  
Wireless Sensor ◽  
Random Data ◽  
Base Station Location ◽  
Mobile Base Station ◽  
Station Location ◽  
Mobile Base

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.

Mobile-Base-Station Location Privacy Based on Greedy Random Walk in WSN

2013 ◽  
Vol 427-429 ◽  
pp. 2502-2507
Author(s):  
Nan Nan Zhao ◽  
Jian Bo Yao
Keyword(s):  
Wireless Sensor Networks ◽  
Random Walk ◽  
Location Privacy ◽  
Base Station ◽  
Wireless Sensor ◽  
Random Data ◽  
Base Station Location ◽  
Mobile Base Station ◽  
Station Location ◽  
Mobile Base

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-station-locations become a crux issue. If base-station 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, GROW (Greedy Random Walk) is proposed to preserve mobile-base-station-location privacy. In GROW, data are forwarded and stored at pass nodes in the network, the base-station move in greedy random-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, GROW has smaller message latencies, while providing satisfactory mobile-base-station-location privacy.

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
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Dissipation ◽  
Cluster Head ◽  
Base Station ◽  
Wireless Sensor ◽  
Substantial Impact ◽  
Base Station Location ◽  
Station Location

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.

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