Efficient response speed and information processing speed are among the characteristics of mobile edge computing (MEC). However, MEC easily causes information leakage and loss problems because it requires frequent data exchange. This work proposes an anonymous privacy data protection and access control scheme based on elliptic curve cryptography (ECC) and bilinear pairing to protect the communication security of the MEC. In the proposed scheme, the information sender encrypts private information through the ECC algorithm, and the information receiver uses its own key information and bilinear pairing to extract and verify the identity of the information sender. During each round of communication, the proposed scheme uses timestamps and random numbers to ensure the freshness of each round of conversation. Experimental results show that the proposed scheme has good security performance and can provide data privacy protection, integrity verification, and traceability for the communication process of MEC. The proposed scheme has a lower cost than other related schemes. The communication and computational cost of the proposed scheme are reduced by 31.08% and 22.31% on average compared with those of the other related schemes.

Remaining time prediction of business processes plays an important role in resource scheduling and plan making. The structural features of single process instance and the concurrent running of multiple process instances are the main factors that affect the accuracy of the remaining time prediction. Existing prediction methods does not take full advantage of these two aspects into consideration. To address this issue, a new prediction method based on trace representation is proposed. More specifically, we first associate the prefix set generated by the event log to different states of the transition system, and encode the structural features of the prefixes in the state. Then, an annotation containing the feature representation for the prefix and the corresponding remaining time are added to each state to obtain an extended transition system. Next, states in the extended transition system are partitioned by the different lengths of the states, which considers concurrency among multiple process instances. Finally, the long short-term memory (LSTM) deep recurrent neural networks are applied to each partition for predicting the remaining time of new running instances. By extensive experimental evaluation using synthetic event logs and real-life event logs, we show that the proposed method outperforms existing baseline methods.

Although content caching and recommendation are two complementary approaches to improve the user experience, it is still challenging to provide an integrated paradigm to fully explore their potential, due to the high complexity and complicated tradeoff relationship. To provide an efficient management framework, the joint design of content delivery and recommendation in wireless content caching networks is studied in this paper. First, a joint transmission scheme of content objects and recommendation lists is designed with edge caching, and an optimization problem is formulated to balance the utility and cost of content caching and recommendation, which is an mixed integer nonlinear programming problem. Second, a reinforcement learning based algorithm is proposed to implement real time management of content caching, recommendation and delivery, which can approach the optimal solution without iterations during each decision epoch. Finally, the simulation results are provided to evaluate the performance of our proposed scheme, which show that it can achieve lower cost than the existing content caching and recommendation schemes.

Mobile edge computing (MEC) is an emerging technolohgy that extends cloud computing to the edge of a network. MEC has been applied to a variety of services. Specially, MEC can help to reduce network delay and improve the service quality of recommendation systems. In a MEC-based recommendation system, users' rating data are collected and analyzed by the edge servers. If the servers behave dishonestly or break down, users' privacy may be disclosed. To solve this issue, we design a recommendation framework that applies local differential privacy (LDP) to collaborative filtering. In the proposed framework, users' rating data are perturbed to satisfy LDP and then released to the edge servers. The edge servers perform partial computing task by using the perturbed data. The cloud computing center computes the similarity between items by using the computing results generated by edge servers. We propose a data perturbation method to protect user's original rating values, where the Harmony mechanism is modified so as to preserve the accuracy of similarity computation. And to enhance the protection of privacy, we propose two methods to protect both users' rating values and rating behaviors. Experimental results on real-world data demonstrate that the proposed methods perform better than existing differentially private recommendation methods.

Multi-access Edge Computing (MEC) is one of the key technologies of the future 5G network. By deploying edge computing centers at the edge of wireless access network, the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios. Meanwhile, with the development of IOV (Internet of Vehicles) technology, various delay-sensitive and compute-intensive in-vehicle applications continue to appear. Compared with traditional Internet business, these computation tasks have higher processing priority and lower delay requirements. In this paper, we design a 5G-based vehicle-aware Multi-access Edge Computing network (VAMECN) and propose a joint optimization problem of minimizing total system cost. In view of the problem, a deep reinforcement learning-based joint computation offloading and task migration optimization (JCOTM) algorithm is proposed, considering the influences of multiple factors such as concurrent multiple computation tasks, system computing resources distribution, and network communication bandwidth. And, the mixed integer nonlinear programming problem is described as a Markov Decision Process. Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption, optimize computing offloading and resource allocation schemes, and improve system resource utilization, compared with other computing offloading policies.

With the development of the Internet of Things (IoT), spatio-temporal crowdsourcing (mobile crowdsourcing) has become an emerging paradigm for addressing location-based sensing tasks. However, the delay caused by network transmission has led to low data processing efficiency. Fortunately, edge computing can solve this problem, effectively reduce the delay of data transmission, and improve data processing capacity, so that the crowdsourcing platform can make better decisions faster. Therefore, this paper combines spatio-temporal crowdsourcing and edge computing to study the Multi-Objective Optimization Task Assignment (MOO-TA) problem in the edge computing environment. The proposed online incentive mechanism considers the task difficulty attribute to motivate crowd workers to perform sensing tasks in the unpopular area. In this paper, the Weighted and Multi-Objective Particle Swarm Combination (WAMOPSC) algorithm is proposed to maximize both platform's and crowd workers' utility, so as to maximize social welfare. The algorithm combines the traditional Linear Weighted Summation (LWS) algorithm and Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to find pareto optimal solutions of multi-objective optimization task assignment problem as much as possible for crowdsourcing platform to choose. Through comparison experiments on real data sets, the effectiveness and feasibility of the proposed method are evaluated.

Mobile edge computing is trending nowadays for its computation efficiency and privacy. The rapid development of e-commerce show great interest in mobile edge computing due to numerous rise of small and middle-sized enterprises(SMEs) in the internet. This paper predicts the overall sales volume of the enterprise through the classic ARIMA model, and notes that the behavior and arrival differences between the new and old customer groups will affect the accuracy of our forecasts, so we then use Pareto/NBD to explore the repeated purchases of customers at the individual level of the old customer and the SVR model to predict the arrival of new customers, thus helping the enterprise to make layered and accurate marketing of new and old customers through machine learning . In general, machine learning relies on powerful computation and storage resources, while mobile edge computing typically provides limited computation resources locally. Therefore, it is essential to combine machine learning with mobile edge computing to further promote the proliferation of data analysis among SMEs.