Mobile edge computing system provides cloud computing capabilities at the edge of wireless mobile networks, ensuring low latency, highly efficient computing, and improved user experience. At the same time, computationally intensive components are offloaded from mobile devices to edge servers and distributed among the servers. Due to the special constraints (mobile devices’ battery capacities, limited computing resources of one single edge server, inevitable edge server failure, etc.), there emerges a following problem. 1) How to guarantee the reliability of the offloaded computing? This problem brings in the following two other problems. 2) How to find the appropriate offloading point in the mobile program such that the computing tasks offloaded to cloud can be maximized, while the transmission energy consumption is minimized? 3) What is the achievable minimum latency tasks allocation strategy among multiple users’ mobile devices and multiple edge servers? In this paper, we try to address the aforementioned problems. First, for the appropriate offloading point problem, we consider the offloading valuable basic constraint and propose a task merging strategy based on mobile program component call graphs to minimize the computational complexity of the program partition. Second, we formulate the second problem as a combinatorial optimization problem and transform it into an n-fold integer programming problem by mapping the remaining computing resources to a virtual component. Third, we design a reliable shadow component scheme between multilevel severs for the reliability problem. Finally, we develop a fast algorithm for the mix problem and analyze its performance and conduct experiments to prove the accuracy of our theoretical results.

As vehicles are becoming more and more intelligent, mobile data traffic in Vehicular Ad-Hoc Network (VANET) has been increasing dramatically. This makes the communication capacity of VANET systems and the computing resources of vehicles insufficient. In the meantime, location-aware large-scale distributed services with very low latency and high reliability are demanded by most of the novel functions such as accident alarming, and congestion warning, in the intelligent transportation system. To meet these claimed characteristics of VANET, we first present a novel architecture that integrates vehicular fog computing and vehicle-to-vehicle (V2V) communication technologies. Lower latency and higher quality services can be supplied to vehicles by nearby fog servers, which are virtualized from vehicles that locate close enough and communicate using the V2V link. However, like all collaborative systems, computing reliability is vital to collaborative VANET. In this paper, we design a novel energy efficient proactive replication mechanism. Follower vehicles calculate with a lazy rate act as backups of host vehicles to ensure the reliability of the system. Considering the time sensitivity of computing requirements in VANET, the upper bound on the total number of failures is proposed through theoretical analysis. Then, the lower bound on the lazy calculating rate of followers is derived by balancing the trade-offs between delay and energy. A fast algorithm for searching this lower bound based on the Discrete Newton Method is also proposed. Results of numerical experiments show that our new mechanism is effective in energy saving and reliability enhancing.

Using the increasingly wireless communication capacity of 5G/6G technology, edge intelligence (EI) enables modern vehicles to leverage the powerful computing resources of edge servers scattered aside the roads to implement intelligent transportation applications (ITA). The running of these intelligent applications is always accompanied by the calculating and transmitting of massive amounts of road situation data. For the protection of drivers and passengers, these complex processes should be handled in an ultra-reliable and low latent manner. Intelligent processing function offloading from terminals to edge servers is a promising approach to address these issues. However, the runtime environment specification of each intelligent processing function and the interdependence between two consecutive functions pose a challenge for the assignment of the offloaded functions among edge servers. In this paper, we propose a dependence-aware edge intelligent function offloading scheme for 6G-based Internet of Vehicle (IoV). All traditional ITAs are split into different chains of standard intelligent functions. Each edge server can provide some specific intelligent functional services. These services can receive data from cars and serve as different intelligent functions. Then, an intelligent application offloading scheme is changed into an embedding scheme of a service chain. An NP-hard objective function is constructed using a multi-winner committee selection model for this offloading service chain embedding problem. We design two algorithms to get the optimal assignment of intelligent functions using a greedy strategy and dynamic programming strategy separately. Finally, experiments show that when the proportion of vehicles meeting the constraint conditions is not in [9%,10%], our algorithms are fast.

In a situation where life becomes more stressful and challenging, people feel compelled to be more concerned about their mental situation. Different emotional statuses are external reactions to different mental states. Therefore, researchers always identify people’s mental situation by monitoring their real-time emotions. At the same time, due to the availability of built-in sensors in a smartphone, applications that can identify real-time emotions of mobile users are constantly emerging. However, compared to most emotion recognition algorithms, computing resources and battery life in mobile phones are always limited. This makes accuracy and latency of these applications are unsatisfactory. In this paper, we propose a micro-service platform for mobile emotion recognition application developers (MSPMERAD) which can supply high performance. First, a classifier fusion emotion recognition algorithm is proposed by using a dynamic adaptive fusion strategy. Second, this new algorithm is encapsulated into a micro-service. With other affiliated micro-services such as data uploading, preprocessing, etc., developers can ignore the implementation of the emotion recognition algorithm and just focus on how to collect sensor data and interact with users. The accuracy and latency of one application based on the MSPMERAD are compared with another application that is implemented using a locale emotion recognition algorithm. Experiments based on the daily behavior data of 50 student volunteers show that the application based on our platform has higher recognition accuracy with a more reasonable time.