scholarly journals A p-norm Flow Optimization Problem in Dense Wireless Sensor Networks

Author(s):  
M. Kalantari ◽  
M. Haghpanahi ◽  
M. Shayman
Author(s):  
Ajay Kaushik ◽  
S. Indu ◽  
Daya Gupta

Wireless sensor networks (WSNs) are becoming increasingly popular due to their applications in a wide variety of areas. Sensor nodes in a WSN are battery operated which outlines the need of some novel protocols that allows the limited sensor node battery to be used in an efficient way. The authors propose the use of nature-inspired algorithms to achieve energy efficient and long-lasting WSN. Multiple nature-inspired techniques like BBO, EBBO, and PSO are proposed in this chapter to minimize the energy consumption in a WSN. A large amount of data is generated from WSNs in the form of sensed information which encourage the use of big data tools in WSN domain. WSN and big data are closely connected since the large amount of data emerging from sensors can only be handled using big data tools. The authors describe how the big data can be framed as an optimization problem and the optimization problem can be effectively solved using nature-inspired algorithms.


2018 ◽  
Vol 200 ◽  
pp. 00005
Author(s):  
Halima Lakhbab

Wireless sensor networks are used for monitoring the environment and controlling the physical environment. Information gathered by the sensors is only useful if the positions of the sensors are known. One of the solutions for this problem is Global Positioning System (GPS). However, this approach is prohibitively costly; both in terms of hardware and power requirements. Localization is defined as finding the physical coordinates of a group of nodes. Localization is classified as an unconstrained optimization problem. In this work, we propose a new algorithm to tackle the problem of localization; the algorithm is based on a hybridization of Particle Swarm Optimization (PSO) and Simulated Annealing (SA). Simulation results are given to illustrate the robustness and efficiency of the presented algorithm.


2013 ◽  
Vol 427-429 ◽  
pp. 2540-2544 ◽  
Author(s):  
Jia Liang Lv ◽  
Ying Long Wang ◽  
Huan Qing Cui ◽  
Nuo Wei

Localization is one of the key technologies of wireless sensor networks, and the problem of localization is always formulated as an optimization problem. Particle swarm optimization (PSO) is easy to implement and requires moderate computing resources, which is feasible for localization of sensor networks. To improve the efficiency and precision of PSO-based localization methods, this paper proposes a novel three-dimensional PSO method based on weight selection (WSPSO). Simulation results show that the proposed method outperforms standard PSO and existing localization algorithms.


2012 ◽  
Vol 476-478 ◽  
pp. 2091-2095
Author(s):  
Mao Song Ge ◽  
Chun Yan Fu ◽  
Xue Li Tai ◽  
Ji Ping Guo

Recently some existing works have developed computing Join query technology over wireless sensor networks. But these techniques are static, and are only used in special application designated connection rate. In order to construct the data query method that suits environment, this paper proposes an optimization join query method that is applicable to limited bandwidth context. The dynamic join optimization problem in multi-hop wireless sensor network is resolved and a cost-based model is designed. This algorithm can predict the selectivity in advance, and can optimize the pairs of data streams. Experimental verification on standard data sets verified the effectiveness of the proposed method.


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