Intelligent Mobile Service Computing

With the continuous expansion of application scopes of Internet of Vehicles, attacks also increase correspondingly. There have been too many attack accidents in IoV. Now the intelligent vehicles have become important targets of a hacker. Based on it, we look forward to constructing a cross-domain security model of IoV. Firstly we introduce the security domain and analyze its divisions. Secondly we study the structure of cross-domain of IoV. Finally we divide the security domain into three layers which are cross-domain application layer, cross-domain network layer, and cross-domain perception layer. Also we study the trust relationship in IoV, and make use of the method of trust evaluation to ensure transactions security and construct a trustworthy environment for cross-domain. Our scheme can partly ensure security of communications and vehicles’ privacy. Next step we will build a test platform and provide test basis for the implementation of secure technical scheme.

Due to the high real-time requirement and limited resources, the computing performance and power consumption are critical to embedded intelligent service systems. The thread group scheduling strategy, as a common method to optimize multi-core processor’s performance, performs poorly in power optimization. However, with data volume continues proliferating, the power consumption is still surging. Correspondingly, many power optimization methods, if not scheduled well, will swap between cache states frequently, thus degrading device performance. In this paper, we propose a framework that combines the thread scheduling strategies with dynamic power mode control strategies together to make better trade-off between system performance and power consumption. Experimental results show that compared to using only scheduling policies, the systems combining Dynamic Power Management (DPM) and Bank Usage Table (BUT) policies have 14.8% and 9.5% performance growth as well as 9.9% and 13.2% power consumption reduction, respectively. The energy delay product (EDP) is decreased by 20.1% and 22.5%, respectively.

From self-driving cars to AlphaGo, artificial intelligence (AI) is progressing rapidly. Artificial intelligence makes our lives more convenient, but it also may bring us dangers. Just like Russia’s president Vladimir Putin said: “Artificial intelligence is the future, not only for Russia, but for all humankind. It comes with enormous opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world.” So we should have a very convincing argument for its safety before applying an advanced intelligent system. How can we realize that argument is rigorously correct? Dijkstra said: “The only effective way to raise the confidence level of a program significantly is to give a convincing proof of its correctness” [8]. The answer is a mathematical proof. This is the reason why we need formal methods in AI. Formal methods are used to describe and analyze systems with a set of symbols and operations; depend on some mathematical methods and theories, such as algebra, logical, graph theory, or automata; and enhance the quality and safety of systems, so we call it formal.

5G, as well as, the future wireless broadband networks and services should collect data in a reliable way, in order to provide valuable data to the cloud computing data centers, so they can perform analysis of big data. Therefore, advanced mechanisms for machine learning, advanced machine-to-machine communications, and intelligent mobile edge computing with artificial intelligence for efficient analysis and processing of data in order to secure a prompt response and guaranteed QoS to the end users should be included at the network’s edge. This chapter is about 5G mobile and wireless networks and their cloud computing and QoS mechanisms. Furthermore, a novel advanced QoS concept for 5G mobile services based on Intelligent Multi-access Edge Computing together with radio network aggregation capability and cloud computing orchestration mechanisms are presented. In addition, network slicing in 5G is also elaborated. Finally, 5G features about vertical multi-homing and multi-streaming for smart end user terminal devices combined with the capability of radio network aggregation are also elaborated. The novelty in the presented concepts and platforms for Intelligent Multi-access Edge Computing and QoS mechanisms is that they provide the highest level of user access probability ratio, the greatest user throughput, and the greatest number of satisfied smart device users, with minimum service cost and optimized utilization of network assets due to the sharing of the traffic load. The performed analysis in this chapter demonstrates that performance gain with the Intelligent Multi-access Edge Computing module in 5G mobile terminal is higher if there are more available radio access points in comparison with the scenarios with a lower number of radio access points.

Due to the popularity of smart electric meter, electricity data is fast generated and abundantly transmitted through hierarchical servers in smart power grid domain. The missing record during transmission will influence subsequent analyses. However, it is not trivial to improve the quality of such continuous sensory data, because both interpolate accuracy and processing latency are hard to guarantee in practice through traditional means. We propose a data interpolation method for electricity data of smart meters in hierarchical edge environment. The missing records would be interpolated by predictive values through support vector regression in edge environment. In extensive experiments on real data, accuracy of data interpolation is guaranteed above 90% with executive time less than 20 milliseconds.

Distributed storage systems run transactions across machines to ensure serializability in single-service architecture. Traditional protocols for distributed transactions are based on two-phase commit (2PC) or multi-version concurrency control (MVCC). 2PC serializes transactions as soon as they conflict and MVCC resorts to aborts, leaving many opportunities for con-currency on the distributed storage system. In the micro-service architecture, service nodes are deployed in heterogeneous distributed systems. 2PC and MVCC are struggling to break through the performance limitations of their existence. This paper presents 2PC+, a novel concurrency control protocol for distributed transactions that outperforms 2PC allowing more concurrency in micro-service architecture. 2PC+ is based on the traditional 2PC and MVCC, combined with transaction thread synchronization blocking optimization algorithm SAOLA. Then, we verify the algorithm SAOLA using the temporal logic of action TLA+ language. Finally, we compare 2PC+ to 2PC applying a case of Ctrip MSECP. When concurrent threads of the service call reach a certain threshold, 2PC+’s RT performance is improved by 2.87 times to 3.77 times compared with 2PC, and 2PC+’s TPS performance is 323.7% to 514.4% higher than 2PC.