Multi-access edge computing (MEC) combines the elements of telecommunications networking and information technology to provide cloud computing services at the edge of the network. Different from the traditional cloud computing system, MEC is usually implemented at cellular base stations or other edge nodes. This position of MEC servers allows the execution of applications within close proximity of end-users, which substantially reduces end-to-end delay, congestion, and burden on the backhaul. However, the efficiency of the MEC system is greatly affected by the task offloading policy (since there can be multiple computation nodes, MEC servers, and users, and tasks can have diverse characteristics) and computation resource allocation decisions (computation resources are limited, and a large number of users affect the task execution delay). Therefore, we formulate the problems as optimization problems and design algorithms using techniques such as heuristics and machine learning (Deep Reinforcement Learning) to find an effective task offloading and resource allocation policy that meets different requirements of tasks, users, and platforms (e.g., energy consumption, and resource budget).

Being able to accurately predict human mobility can inspire a lot of potential and promising applications, including location-based service networks, city planning, and infectious disease control. Therefore, the purpose of our work is to design low-cost prediction models that estimate the next locations and future encounters of the individuals with high accuracy. In order to achieve our goal, we analyze spatio-temporal characteristics along with social relationships and use them to construct prediction models based on machine learning techniques (such as support vector machines, feed-forward neural networks, and recurrent neural networks). 

We study mobile sensor network (MSN) architectures and distributed algorithms to monitor the moving phenomena in the disaster area in an open and unknown environment using a group of autonomous mobile sensor (MS) nodes. Monitoring a moving phenomenon is challenging due to the limited communication/sensing ranges of MS nodes, the phenomenon's unpredictable changes in distribution and position, and the lack of information on the sensing area. We propose distributed and robust algorithms in order to address these challenges and maximize the weighted sensing coverage.

Opportunistic networks are a type of delay-tolerant network in which mobile users connect with each other by exploiting pairwise contacts using wireless technologies such as WiFi and Bluetooth without infrastructure. This kind of network can achieve quick and low-cost deployment, which is suitable for emergency situations such as natural disasters and military conflicts. Therefore, we have studied routing protocols for opportunistic networks and designed novel human mobility models to reproduce the movements of mobile users in real life. Specifically, first people's social characteristics, such as daily routines, interests, and social relationships, are analyzed, and then the forwarding algorithm and the human movement model are proposed under the consideration of these obtained characteristics.