Novel Learning Algorithms for Efficient Mobile Sink Data Collection Using Reinforcement Learning in Wireless Sensor Network
作者: Santosh SoniManish ShrivastavaDajana Cassioli
作者单位: 1Department of Information Technology, School of Studies (Engineering & Technology), Guru Ghasidas Vishwavidyalaya, Bilaspur, Chhattisgarh 495009, India
2Department of Computer Science and Engineering (Supervisor), School of Studies (Engineering & Technology), Guru Ghasidas Vishwavidyalaya, Bilaspur, Chhattisgarh 495009, India
刊名: Wireless Communications and Mobile Computing, 2018, Vol.2018
来源数据库: Hindawi Journal
DOI: 10.1155/2018/7560167
原始语种摘要: Generally, wireless sensor network is a group of sensor nodes which is used to continuously monitor and record the various physical, environmental, and critical real time application data. Data traffic received by sink in WSN decreases the energy of nearby sensor nodes as compared to other sensor nodes. This problem is known as hot spot problem in wireless sensor network. In this research study, two novel algorithms are proposed based upon reinforcement learning to solve hot spot problem in wireless sensor network. The first proposed algorithm RLBCA, created cluster heads to reduce the energy consumption and save about 40% of battery power. In the second proposed algorithm ODMST, mobile sink is used to collect the data from cluster heads as per the demand/request generated from cluster...
全文获取路径: Hindawi  (合作)
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关键词翻译
关键词翻译
  • wireless 无线的
  • mobile 可动的
  • Data 数据
  • request 需求
  • overhead 塔顶馏出物
  • sensor 感受器
  • routing 定线
  • cluster 
  • Network 网络
  • received 接收