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( Zeyu Sun ),( Yongsheng Zhang ),( Xiaofei Xing ),( Houbing Song ),( Huihui Wang ),( Yangjie Cao ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.8
In the process of k-coverage of the target node, there will be a lot of data redundancy forcing the phenomenon of congestion which reduces network communication capability and coverage, and accelerates network energy consumption. Therefore, this paper proposes a novel energy balanced k-coverage control algorithm based on probability model (EBKCCA). The algorithm constructs the coverage network model by using the positional relationship between the nodes. By analyzing the network model, the coverage expected value of nodes and the minimum number of nodes in the monitoring area are given. In terms of energy consumption, this paper gives the proportion of energy conversion functions between working nodes and neighboring nodes. By using the function proportional to schedule low energy nodes, we achieve the energy balance of the whole network and optimizing network resources. The last simulation experiments indicate that this algorithm can not only improve the quality of network coverage, but also completely inhibit the rapid energy consumption of node, and extend the network lifetime.
Shared Spatio-temporal Attention Convolution Optimization Network for Traffic Prediction
Pengcheng Li,Changjiu Ke,Hongyu Tu,Houbing Zhang,Xu Zhang 한국정보처리학회 2023 Journal of information processing systems Vol.19 No.1
The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methodsare weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structuresand insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flowprediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that isdesigned to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graphoptimization module is used to model the dynamic road network structure. The experimental evaluationconducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all timeintervals.