## 1 Introduction
RNNs have emerged as a potential model for processing sequential data in a variety of applications, such as time-series prediction, video tagging, speed recognition, and generating image descriptions. However, RNNs suffer from both gradient vanishing and gradient explosion problems with long sequences of input. Therefore, we use LSTMs to overcome the problem. A RNN with LSTM cells contains states that enable them to process variable-length sequences of input. In other words, those states are capable of capturing historical information from an arbitrary length of the context window. In the proposed HLP model, the movement histories of mobile users in current and previous time slots (e.g., mobile users' identities, mobile users' positions, the time slot of the day, the day of the week) are used to predict their positions in the next several time slots with high accuracy. Note that edge nodes are deployed in specific locations in MCS-based urban sensor networks, and packet destinations are among the edge nodes. Packet delivery predictability is calculated based on the probability that a mobile node will visit an edge node's position. A mobile node with a high probability of meeting an edge node during a certain time slot has a high value for packet delivery predictability to that edge node. Packet delivery predictability represents the possibility that a node will deliver a packet to its destination. In addition, the social relationships between nodes are taken into account based on their movement histories. Two nodes have high social strength if they interact frequently over a long period of time. Finally, the human location prediction-based routing protocol (HLPRP) for mobile crowdsensing-based urban sensor networks is designed based on a HLP-based relay selection and social strengths between nodes. More specifically, the proposed routing algorithm has two phases. When a packet is generated, the proposed forwarding algorithm quickly spreads a limited number of copies of the packet throughout the network in the first phase. Then, optimal relay nodes are selected to forward the packet based on packet delivery predictability and social strength in the second phase. To evaluate the performance of the HLPRP, the proposed HLP model is first trained and tested using a University at Buffalo (UB) dataset [17]. Then, we compare the HLPRP against other routing protocols. According to the obtained results, the HLPRP can outperform existing protocols in terms of delivery cost (\(DC\)), packet delivery latency (\(PDL\)), and packet delivery ratio (\(PDR\)). In summary, this study has the following main contributions. * First, we design a RNN-based model for human location prediction. Using the predicted information, the packet delivery predictability is proposed and used for relay selection. * Second, we propose a forwarding algorithm based on the HLP and social strength. There are two phases in the proposed forwarding algorithm. In the first phase, a limited number of copies of the packet are quickly spread throughout the network. In the second phase, packet delivery predictability and social relationships are used to select optimal relay nodes, with the goal of maximizing the \(PDR\), minimizing the \(DC\), and reducing the \(PDL\). * Third, using the UB dataset [17], we conduct various experiments to validate the proposed routing protocol. The \(DC\), \(PDL\), and \(PDR\) are used to evaluate network performance. The simulation results demonstrate that the HLPRP can outperform existing routing protocols. The rest of this paper is organized as follows. First, Section 2 discusses related work. Then, the network model and problem definition are presented in Section 3. Section 4 describes the proposed routing protocol in more detail. In Section 5, simulation results from the proposed routing protocol are evaluated. Finally, the conclusion is presented in Section 6.