Security and forensics represent two key components for network management, especially to guarantee the trusted operation of massive access networks such as the Internet of Things (IoT). As a core technology to provide low latency and high communication for IoT, Mobile Edge Computing (MEC) pulls computing resources from remote cloud centers to devices. The process of MEC service involves three types of entities: devices, data generated by devices and digital evidence generated after the data interaction. These entities are fully distributed and difficult to protect through traditional, highly centralized security and authentication mechanisms. As a decentralized shared ledger and database, the emerging blockchain is considered to provide cooperative trust and collaborative action among multiple subjects while ensuring the integrity and confidentiality of data. Because of its anonymity, non-tampering and traceability, the blockchain arouses research on the combination of blockchain and edge computing for device security, data security and forensics in IoT. This survey analyzes the application of blockchain in MEC-IoT systems and mainly focuses on approaches and technologies to manage the security and forensics issues for IoT. Finally, we present open issues and prospects for future work and research directions.

With the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

Face regions are visual focuses in conversational video communications, thus better reconstruction quality of the regions of interest (ROI) is highly desired or necessary in the bandwidth-constrained conversational video coding. In this paper, we introduce an efficient motion based face detection method to identify face blocks in the first step, which can reduce computational complexity substantially without any loss in face detection results. Then an active contour model is applied to find face contours for more refined and compact face regions. Based on the well-located and compact face regions, facial feature priority based bit allocation is proposed for face ROI based conversational video coding. Experimental results demonstrate that the proposed face region based coding can considerably improve the coding results in the face regions, compared with two other relevant video coding schemes, in terms of objective rate-distortion performance as well as subjective visual quality.