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Wireless sensor networks consist of sensor nodes and sink nodes. Due to the variety of applications, the interconnection between wireless sensor networks and Internet is needed. In order to have the access to the Internet, a sensor node must select a sink node as the gateway to the Internet. The selection of sink has great influence on the delivery latency and network efficiency. In this paper, we present a novel sink selection scheme which take transmission latency and sink forwarding latency into consideration. The optimal sink for the sensor node is the closest sink that has the least wait send packets. Simulation results demonstrate that this scheme has a low sink discovery overhead, a low end-to-end packet delivery latency, and a good packet delivery ratio.

Human activity recognition (HAR) is a prominent subfield of pervasive computing and also provides context of many applications such as healthcare, education, and entertainment. Most wearable HAR studies assume that sensing device placement and orientation are fixed and never change. However, this condition is actually not always guaranteed in the real scenario and recognition result is influenced by the distortion as consequence. To handle this, our work proposes a new model based on convolutional neural network to extract robust features which are invariant of device placement and orientation, to train machine learning classifiers. We first carry out experiments to show negative effects of this problem. Then, we apply the convolutional neural network-based hybrid structure on the HAR. Results show that our method provides 15% to 40% accuracy promotion on public data set and 10% to 20% promotion on our own data set, both with distortion.Trans Emerging Tel Tech. 2020;31:e3823.wileyonlinelibrary.com/journal/ett

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Researches on efficient wireless routing protocols have attracted a lot of attention recently. The classic wireless routing protocols, such as AODV and DSR, are distance-driven and table-driven, which are rarely addressing the energy consumption issue. Therefore, it is necessary to develop a new network protocol that can provide higher energy efficiency and achieve longer network lifetime. Considering the limited power supply of sensor nodes, we proposed a Location-based Energy Distribution Optimization (LEDO) routing protocol that is based on the node location information and the balanced energy consumption strategy. The performance evaluation of LEDO and the performance comparison with AODV and DSR has been done basing on NS-2 simulator platform. The simulation results show that the LEDO effectively balances the energy consumption of nodes and extends the network lifetime.