Nonlinear Online Incentive Mechanism Design: Case Study of Edge Computing with Energy Budget

In this chapter, we use task offloading in Edge Computing (EC) systems as a case to study how we could design. The considered system model consists of IoT devices, a central controller (such as the BS), and multiple mobile users. At the beginning of each time slot, the BS firstly collects requests of offloading tasks from IoT devices and then broadcasts them to mobile users, who will then submit their valuations and available energy to the BS. After that, the BS determines the best mobile user for each task, the transmission power and bandwidth for both upload and download transmissions, the allocated computation resource, and the corresponding payment to mobile users. This process recurs along the time, and decision making in each time slot is correlated because of the energy constraint on each mobile user. With the objective of maximizing social welfare, we first formulate an offline optimization problem and design a nonlinear online truthful mechanism based on the rule of Maximal-in-Distributional Range (MIDR). Finally, we reconsider energy constraints to design a new nonlinear online incentive mechanism by rationally combining the previously derived one-shot ones. Theoretical analyses show that our designed nonlinear online incentive mechanism can guarantee individual rationality, truthfulness, a sound competitive ratio, and computational efficiency.