A Human Location Prediction-Based Routing Protocol in Mobile Crowdsensing-Based Urban Sensor Networks

Abstract

Mobile crowdsensing (MCS) has recently emerged as an urban-sensing paradigm that takes advantage of smartphone sensing capabilities and user mobility. A major challenge in mobile crowdsensing-based urban sensor networks is how to efficiently transfer data from sensors to the sink (e.g., the server center). Therefore, this study proposes a human location prediction-based routing protocol (HLPRP) in such networks. Specifically, a human location prediction (HLP) model is designed to estimate the location of mobile nodes. The proposed HLP model is based on a recurrent neural network with long short-term memory cells. The movement history of each person is used in the HLP model to predict their future locations. Experimental results on real traces are used to validate the proposed HLP model. Then, using predicted location information from the HLP model, packet delivery predictability is obtained. Packet delivery predictability represents the possibility that a node will deliver a packet to its destination and is used to select optimal relay nodes to maximize the packet delivery ratio, minimize the packet delivery cost, and reduce delivery latency. In addition, the proposed routing protocol considers social strength for relay selection. To evaluate the HLPRP, we conduct simulations and compare results with other routing protocols, showing that the HLPRP can outperform existing protocols.