## 1 Introduction  
Mobile crowdsensing (MCS) [1, 2, 3] is an approach to data collection based on the widespread use of mobile devices with sensor capabilities (e.g., smartphones, smartwatches, and tablets). Mobile users can collect and share a lot of relevant data from their smart devices. MCS has been used in a variety of urban-sensing applications, such as environmental monitoring [4, 5], intelligent transportation systems [6, 7], and public safety [8]. In this work, MCS-based urban sensor networks are considered with the MCS as the main sensing paradigm for a multi-access edge computing environment and additional static sensors. This model includes a server center, edge nodes, sensors, and mobile users. Mobile users collect data using sensors in their smart devices. Sensors are deployed in certain places to capture data such as air pollution, radioactivity, noise level, and humidity. Edge nodes are placed in particular locations to gather and preprocess data from sensors and mobile users. Then, edge nodes send processed data to the server center, where it is used for a variety of applications. The server center communicates with edge nodes using an infrastructure-based wired or wireless network. For communication between other components (i.e., edge nodes, sensors, and mobile users), the wireless interfaces (e.g., Bluetooth 5.0) are used without the need for an infrastructure network. The primary benefit of this network is that it is cost-effective. However, owing to user mobility, the connections between nodes are intermittent, making data collection difficult. That requires an effective routing protocol to transfer data between edge nodes, sensors, and mobile users. A lot of routing protocols for transferring data between edge nodes, sensors, and mobile users have been studied [9; 10; 11; 12; 13; 14; 15]. For example, several routing methods use the flooding strategy [9; 10]. However, with the flooding strategy, messages are immediately spread when there are contacts between nodes. As a result, those routing protocols consume a lot of resources and have a high network overhead ratio. To minimize overhead, the number of replications was limited in [11]. Nevertheless, this routing protocol did not take into account optimal relay selection. Therefore, low mobility nodes can be selected to forward messages, resulting in a low packet delivery ratio and long delivery latency. In [12; 13; 14], nodes that recently encountered the destination are preferred for selection as relay nodes. However, in a real context, two individuals may often meet in the present moment, but their next meeting may occur in the distant future. For example, family members frequently interact in the morning but do not meet until the evening. As a result, the optimal relay nodes may not be chosen, and those routing protocols may not achieve high network performance. In [15], a day is divided into time slots, and the meeting probability between nodes is estimated for each time slot based on encounter histories. If two nodes frequently encountered each other in a certain time slot in the past, they will have a high probability of meeting in that time slot in the future. In other words, the estimated value of the meeting probability between two nodes in a time slot only depends on the statistics of their encounters in that time slot in the past. However, in the real context, meeting probability in a certain time slot can be affected by encountering information in other time slots during the day. Therefore, using statistics on encounters between nodes may not be the best method for predicting meeting probability. As a result, high network performance might not be obtained when using the meeting probability for relay selection. To address those issues, in this paper, we propose a human location prediction (HLP)-based relay selection model that uses a recurrent neural network (RNN) with long short-term memory (LSTM) cells [16].