Unmanned Aerial Vehicles (UAVs) have been considered as a promising approach for providing additional computation capability and extensive coverage to ground mobile devices, especially in the scenario where communication infrastructure is unavailable. This paper investigates a UAV-assisted multi-access edge computing system, where a number of ground mobile devices are served by a flying UAV and a ground base station, both of them are equipped with computation resources. The system aims to minimize the weighted cost of time latency and energy consumption, subject to the constraints on offloading decisions and resource competition. Since this problem is NP-hard, and the mobile devices are autonomous, we propose a game theory based scheme to find the optimal solution, and prove the existence of Nash equilibrium. Meanwhile, we propose another two schemes as the benchmark to evaluate the efficiency and effectiveness of the game-theoretic solution. Simulation results show that the game-theoretic scheme is able to achieve a near optimal performance, and the convergence time is also stable when the number of mobile devices increases.

Multiaccess edge computing (MEC) is a new paradigm to meet the demand of resource-hungry and latency-sensitive services by enabling the placement of services and execution of computing tasks at the edge of radio access networks much closer to resource-constrained devices. However, how to serve more requests while reducing service latency by exploiting limited resources (storage capacities, CPU cycles, communication bandwidth) is still a critical issue in the multidevice MEC-assisted IoT networks, since the time-varying computing demands of devices and unavailability of future information make it difficult to determine where to handle computation tasks and which services to cache. In this article, we propose a twin-timescale framework to jointly optimize adaptive request scheduling (RS) and cooperative service caching (SC) in the multidevices and MEC-assisted networks, in order to explore request dynamic, MECs heterogeneity, service difference. To accommodate the unavailability of future information and unknown system dynamics, we, respectively, formulate RS and SC as partially observable Markov decision process (POMDP) problems. Then, we propose a deep reinforcement learning (DRL)-based online algorithm to improve the service latency reduction ratio and hit rate, which do not require a priori knowledge such as service popularity. Moreover, we give the optimal CPU cycles and communication bandwidth allocations in order to further minimize the average service latency. Extensive and trace-driven simulation results demonstrate the efficacy of the proposed approach.

The explosive growth of multimedia contents, especially videos, is pushing forward the paradigm of cloud-based media hosting today. However, the wide attacking surface of the public cloud and the growing security awareness from the society are both calling for data encryption before outsourcing to cloud. Under the circumstance of encrypted videos, how to still preserve all the service benefits of cloud media center remains to be fully explored. In this paper, we present a secure system architecture design as our initial effort toward this direction, which bridges together the advancements of video coding techniques and secure deduplication. Our design enables the cloud with the crucial deduplication functionality to completely eliminate the extra storage and bandwidth cost, which would have been incurred by hosting encrypted videos from different entities. The design is also carefully tailored to the scalable video coding (SVC) techniques to support heterogeneous networks and devices for high-quality adaptive video dissemination. We show fully functional system implementations with structure-aware encryption design and structure-aware deduplication strategies that are both completely compliant with the video format in SVC. Extensive security analysis and experiments via our prototype deployed on Azure cloud platform show the practicality of the design. Our work can also be easily extended to support other media applications that employ media files with scalable structures.

With the rapid development of embedded smart devices, a new data collection paradigm, mobile crowd-sensing (MCS), has been proposed. MCS allows individuals from the crowd to act as sensors and contribute their observation data. However, existing MCS systems are mostly based on third-party platforms, and there is no guarantee that a center is completely credible. In addition, security and privacy issues should not be ignored. During MCS’ execution, the participants’ various information and truth value are usually exposed, and the computation related to data privacy cannot be verified. In this paper, we integrate the blockchain into the MCS scenario to design a blockchain based privacy-preserving quality control mechanism, which prevents data from being tampered with, and denied, ensuring that the reward is distributed fairly. In the new system, we propose a privacy preserving participant selection scheme and the result can be verified (i.e., security against malicious node) without any third-party arbiter. Finally, considering the issues with sensing data privacy and efficiency in the truth discovery process, we propose a new privacy-aware crowdsensing design with iterative truth discovery based on rational secure multi-party computation. The experimental results show that compared to the prior result, the proposed solutions are highly practical and facilitate quality control without violating the participant’s privacy.

By allocating tasks to participants, crowdsensing has shown large potential in addressing large-scale data sensing problems. Considering the problem of unfair payment, negative work of participants, and cooperative cheating, how to assess data quality of tasks reliably is an important problem in crowdsensing. Therefore, a lightweight blockchain-based model for data quality assessment is proposed in this article. First, there are two data quality assessment processes in the model. One is implemented in the selection of participants and the other is implemented in data quality assessment. Second, consensus mechanism and smart contracts are redesigned to be suitable for crowdsensing. The lightweight consensus mechanism delegated proof of reputation (DPoR) is proposed in the blockchain-based model instead of proof of work (PoW). Furthermore, three smart contracts, verifiers selection contract (VSC), participants employment contract (PEC), and data verify contract (DVC), are generated to constrain the behaviors of the involved parties. Finally, expectation-maximization (EM) algorithm with multiverifiers is proposed to evaluate the performance of task participants. Experiments on the open data sets Wine Quality show that our new method outperforms the existing methods in improving the quality of sensing task.