Immune clonal selection algorithm for target coverage of wireless sensor networks

2011 ◽  
Vol 12 (1/2) ◽  
pp. 119 ◽  
Author(s):  
Xingjia Lu ◽  
Yongsheng Ding ◽  
Kuangrong Hao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Clonal Selection ◽  
Wireless Sensor ◽  
Target Coverage ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm

scholarly journals Replica Node Detection Using Enhanced Single Hop Detection with Clonal Selection Algorithm in Mobile Wireless Sensor Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
L. S. Sindhuja ◽  
G. Padmavathi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Clonal Selection ◽  
Wireless Sensor ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Mobile Wireless ◽  
Mobile Wireless Sensor Networks ◽  
Detection Ratio ◽  
Mobile Wireless Sensor

Security of Mobile Wireless Sensor Networks is a vital challenge as the sensor nodes are deployed in unattended environment and they are prone to various attacks. One among them is the node replication attack. In this, the physically insecure nodes are acquired by the adversary to clone them by having the same identity of the captured node, and the adversary deploys an unpredictable number of replicas throughout the network. Hence replica node detection is an important challenge in Mobile Wireless Sensor Networks. Various replica node detection techniques have been proposed to detect these replica nodes. These methods incur control overheads and the detection accuracy is low when the replica is selected as a witness node. This paper proposes to solve these issues by enhancing the Single Hop Detection (SHD) method using the Clonal Selection algorithm to detect the clones by selecting the appropriate witness nodes. The advantages of the proposed method include (i) increase in the detection ratio, (ii) decrease in the control overhead, and (iii) increase in throughput. The performance of the proposed work is measured using detection ratio, false detection ratio, packet delivery ratio, average delay, control overheads, and throughput. The implementation is done using ns-2 to exhibit the actuality of the proposed work.

Efficient Power Control and Clustering Method Based on Hybrid Clonal Genetic Algorithm in Wireless Sensor Networks

2012 ◽  
Vol 263-266 ◽  
pp. 889-897
Author(s):  
Xiang Xian Zhu ◽  
Su Feng Lu
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Clonal Selection ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Clonal Selection Algorithm ◽  
Clustering Methods

Wireless sensor networks (WSNs) lifetime for large-scale surveillance systems is defined as the time span that all targets can be covered. How to manage the combination of the sensor nodes efficiently to prolong the whole network’s lifetime while insuring the network reliability, it is one of the most important problems to research in WSNs. An effective optimization framework is then proposed, where genetic algorithm and clonal selection algorithm are hybridized to enhance the searching ability. Our goal can be described as minimizing the number of active nodes and the scheduling cost, thus reducing the overall energy consumption to prolong the whole network’s lifetime with certain coverage rate insured. We compare the proposed algorithm with different clustering methods used in the WSNs. The simulation results show that the proposed algorithm has higher efficiency and can achieve better network lifetime and data delivery at the base station.

scholarly journals Proficient QoS-Based Target Coverage Problem in Wireless Sensor Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 74315-74325 ◽  
Author(s):  
Manju ◽  
Samayveer Singh ◽  
Sandeep Kumar ◽  
Anand Nayyar ◽  
Fadi Al-Turjman ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Target Coverage ◽  
Coverage Problem ◽  
Target Coverage Problem

scholarly journals Maximum Lifetime Target Coverage in Wireless Sensor Networks

2019 ◽  
Vol 111 (3) ◽  
pp. 1525-1543 ◽  
Author(s):  
Nora Saadi ◽  
Ahcène Bounceur ◽  
Reinhardt Euler ◽  
Massinissa Lounis ◽  
Madani Bezoui ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Target Coverage ◽  
Maximum Lifetime

scholarly journals Intrusion Detection in Wireless Sensor Networks with an Improved NSA Based on Space Division

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Ruirui Zhang ◽  
Xin Xiao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Negative Selection ◽  
Wireless Sensor ◽  
Selection Algorithm ◽  
The Real ◽  
Negative Selection Algorithm ◽  
Detection Process ◽  
Spatial Partition

Inspired by the biological immune system, many researchers apply artificial immune principles to intrusion detection in wireless sensor networks, such as negative selection algorithms, danger theory, and dendritic cell algorithms. When applying the negative selection algorithm to wireless sensor networks, the characteristics of wireless sensor networks, such as frequent changes in network topology and limited resources, are not considered too much, which makes the detection effect to need improvement. In this paper, a negative selection algorithm based on spatial partition is proposed and applied to hierarchical wireless sensor networks. The algorithm first analyzes the distribution of self-set in the real-valued space then divides the real-valued space, and several subspaces are obtained. Selves are filled into different subspaces. We implement the negative selection algorithm in the subspace. The randomly generated candidate detector only needs to be tolerated with selves in the subspace where the detector is located, not all the selves. This operation reduces the time cost of distance calculation. In the detection process of detectors, the antigen which is to be detected only needs to match the mature detectors in the subspace where the antigen is located, rather than all the detectors. This operation speeds up the antigen detection process. Theoretical analysis and experimental results show that the algorithm has better time efficiency and quality of detectors, saves sensor node resources and reduces the energy consumption, and is an effective algorithm for wireless sensor network intrusion detection.

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