D2D-Assisted Mobile Edge Computing: Optimal Scheduling under Uncertain Processing Cycles and Intermittent Communications

Mobile edge computing (MEC) has been regarded as a promising approach to deal with explosive computation requirements by enabling cloud computing capabilities at the edge of networks. Existing models of MEC impose some strong assumptions on the known processing cycles and unintermittent communications. However, practical MEC systems are constrained by various uncertainties and intermittent communications, rendering these assumptions impractical. In view of this, we investigate how to schedule task offloading in MEC systems with uncertainties. First, we derive a closed-form expression of the average offloading success probability in a device-to-device (D2D) assisted MEC system with uncertain computation processing cycles and intermittent communications. Then, we formulate a task offloading maximization problem (TOMP), and prove that the problem is NP-hard. For problem solving, if the problem instance exhibits a symmetric structure, we propose a task scheduling algorithm based on dynamic programming (TSDP). By solving this problem instance, we derive a bound to benchmark sub-optimal algorithm. For general scenarios, by reformulating the problem, we propose a repeated matching algorithm (RMA). Finally, in performance evaluations, we validate the accuracy of the closed-form expression of the average offloading success probability by Monte Carlo simulations, as well as the effectiveness of the proposed algorithms

TIMCC: On Data Freshness in Privacy-Preserving Incentive Mechanism Design for Continuous Crowdsensing Using Reverse Auction

© 2013 IEEE. As an emerging paradigm that leverages the wisdom and efforts of the crowd, mobile crowdsensing has shown its great potential to collect distributed data. The crowd may incur such costs and risks as energy consumption, memory consumption, and privacy leakage when performing various tasks, so they may not be willing to participate in crowdsensing tasks unless they are well-paid. Hence, a proper privacy-preserving incentive mechanism is of great significance to motivate users to join, which has attracted a lot of research efforts. Most of the existing works regard tasks as one-shot tasks, which may not work very well for the type of tasks that requires continuous monitoring, e.g., WIFI signal sensing, where the WiFi signal may vary over time, and users are required to contribute continuous efforts. The incentive mechanism for continuous crowdsensing has yet to be investigated, where the corresponding tasks need continuous efforts of users, and the freshness of the sensed data is very important. In this paper, we design TIMCC, a privacy-preserving incentive mechanism for continuous crowdsensing. In contrast to most existing studies that treat tasks as one-shot tasks, we consider the tasks that require users to contribute continuous efforts, where the freshness of data is a key factor impacting the value of data, which further determines the rewards. We introduce a metric named age of data that is defined as the amount of time elapsed since the generation of the data to capture the freshness of data. We adopt the reverse auction framework to model the connection between the platform and the users. We prove that the proposed mechanism satisfies individual rationality, computational efficiency, and truthfulness. Simulation results further validate our theoretical analysis and the effectiveness of the proposed mechanism

Efficient Three-stage Auction Schemes for Cloudlets Deployment in Wireless Access Network

Cloudlet deployment and resource allocation for mobile users (MUs) have been extensively studied in existing works for computation resource scarcity. However, most of them failed to jointly consider the two techniques together, and the selfishness of cloudlet and access point (AP) are ignored. Inspired by the group-buying mechanism, this paper proposes three-stage auction schemes by combining cloudlet placement and resource assignment, to improve the social welfare subject to the economic properties. We first divide all MUs into some small groups according to the associated APs. Then the MUs in same group can trade with cloudlets in a group-buying way through the APs. Finally, the MUs pay for the cloudlets if they are the winners in the auction scheme. We prove that our auction schemes can work in polynomial time. We also provide the proofs for economic properties in theory. For the purpose of performance comparison, we compare the proposed schemes with HAF, which is a centralized cloudlet placement scheme without auction. Numerical results confirm the correctness and efficiency of the proposed schemes.Comment: 22 pages,12 figures, Accepted by Wireless Network