An energy cooperation method of wireless sensor networks based on partially observable Markov decision processes

被引:7
作者
Zhang, Qin [1 ]
Liu, Yutang [2 ]
机构
[1] Xinxiang Univ, Sch Math & Stat, Xinxiang 453003, Henan, Peoples R China
[2] Henan Inst Technol, Sch Sci, Xinxiang 453003, Henan, Peoples R China
关键词
POMDP; WSN; Energy cooperation; Target following; MANAGEMENT;
D O I
10.1016/j.seta.2022.102997
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The performance of routing protocols in wireless sensor networks, such as energy conservation, energy and load balancing. These indicators have a very important impact on the network lifetime. Energy cooperation technology refers to the purposeful transfer of energy from nodes with sufficient energy to nodes with insufficient energy. Firstly, according to POMDP (Partially Observable Markov Decision Process) model, related theories and solving algorithms, this paper proposes an energy cooperation method based on POMDP, establishes an interference decision model based on POMDP, and solves the model. This paper proposes an energy balance routing algorithm based on multi-attribute decision making (EBR_MDM). This algorithm is used to solve the application problem of wireless sensor networks with stable data flow. Including the uneven distribution of network energy, hot spots and energy holes, packet loss caused by node overload and retransmission energy consumption. According to the relative entropy, the attribute weight is calculated, and the optimal forward data is selected to the neighboring nodes. The simulation results show that the network running time of EBR_MDM is increased by 22.1 %, 48.7 % and 101.3 % respectively compared with other comparison protocols. Finally, because the sensor nodes are randomly arranged in the predetermined monitoring area, and the distribution of each node is chaotic, this paper establishes TMA (Trilateral Measurement Algorithm) algorithm to locate the target. The simulation results show that the positioning error of TMA may be about 1.2 m due to trilateration, but it is still within an acceptable range.
引用
收藏
页数:7
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共 28 条
[1]   RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method [J].
Ahmadianfar, Iman ;
Heidari, Ali Asghar ;
Gandomi, Amir H. ;
Chu, Xuefeng ;
Chen, Huiling .
EXPERT SYSTEMS WITH APPLICATIONS, 2021, 181
[2]   Energy-efficiency-based CMAC protocol with hybrid time-power splitting relaying for wireless ad-hoc networks [J].
Akande, Damilare Oluwole ;
Salleh, Mohd Fadzli Mohd .
IET COMMUNICATIONS, 2019, 13 (17) :2778-2785
[3]  
Diddigi RB, 2017, IEEE WIREL COMMUN LE, VPP, P1
[4]   Monarch butterfly optimization: A comprehensive review [J].
Feng, Yanhong ;
Deb, Suash ;
Wang, Gai-Ge ;
Alavi, Amir H. .
EXPERT SYSTEMS WITH APPLICATIONS, 2021, 168
[5]   A randomized point-based value iteration POMDP enhanced with a counting process technique for optimal management of multi-state multi-element systems [J].
Fereshtehnejad, Ehsan ;
Shafieezadeh, Abdollah .
STRUCTURAL SAFETY, 2017, 65 :113-125
[6]   Harris hawks optimization: Algorithm and applications [J].
Heidari, Ali Asghar ;
Mirjalili, Seyedali ;
Faris, Hossam ;
Aljarah, Ibrahim ;
Mafarja, Majdi ;
Chen, Huiling .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2019, 97 :849-872
[7]   Fault Tolerance in Data Gathering Wireless Sensor Networks [J].
Huang, Guangyan ;
Zhang, Yanchun ;
He, Jing ;
Cao, Jinli .
COMPUTER JOURNAL, 2011, 54 (06) :976-987
[8]   The evolution of cooperation in multi-games with aspiration-driven updating rule [J].
Huang, Yi Jie ;
Deng, Zheng Hong ;
Song, Qun ;
Wu, Tao ;
Deng, Zhi Long ;
Gao, Ming Yu .
CHAOS SOLITONS & FRACTALS, 2019, 128 :313-317
[9]   Development of Deer Hunting linked Earthworm Optimization Algorithm for solving large scale Traveling Salesman Problem [J].
Kanna, S. K. Rajesh ;
Sivakumar, K. ;
Lingaraj, N. .
KNOWLEDGE-BASED SYSTEMS, 2021, 227
[10]   Semi-myopic algorithm for resource allocation in wireless body area networks [J].
Karimzadeh-Farshbafan, Mohammad ;
Ashtiani, Farid .
IET WIRELESS SENSOR SYSTEMS, 2018, 8 (01) :26-35