Communication and Computation Resource Allocation and Offloading for Edge Intelligence Enabled Fault Detection System in Smart Grid

With the rapid development of national economy, our demand for power resources has also increased rapidly. The traditional power grid can not be achieved operational status of equipment for effective monitoring and control, we will not be able to ensure the quality and quality of electricity service. Cloudbased build smart grid information platform enables gathering the parameter of grid operation state, greatly increasing the economic efficiency of power enterprises. Firstly, cloud computing and smart grid briefly described, and then pointed out that cloud computing applications in the smart grid information platform, and specialized of cloud-based smart grid information platform, in order for the power grid enterprises to enhance their level of service and provide effective reference. KEYWORD: cloud computing; smart power grids; information platform; Application 4th International Conference on Mechanical Materials and Manufacturing Engineering (MMME 2016) © 2016. The authors Published by Atlantis Press 202 3 THE MAIN PROBLEM IS STILL THE SMART GRID INFORMATION PLATFORM FACING The transition to a cloud computing data center. Our data center because of the different regions and the degree of information presents different characteristics, some data centers already have a basic cloud computing capabilities, but most data centers still remain in the initial level, to ensure the normal operation of the existing data center power under the premise, gradual transition to cloud computing data center in order to meet future requirements of the development of smart grid. Restricting data transmission problem. State Grid Corporation between provincial branches and there are a lot of data needs to be transmitted, will bring massive data transmission efficiency decreases, taking into account the load balancing device between the cloud node, thus building a wide area network channels to resolve data transmission problem. Security of data transmission. Data Center for Cloud Computing power companies to build internal private cloud, capable of self-maintenance and management, system wide area network data transmission services, according to the third security zone defined security partition, and install proprietary equipment production area and external public network isolation . 4 CLOUD COMPUTING APPLICATIONS IN THE SMART GRID INFORMATION POLICY PLATFORM Integrate and optimize the management of heterogeneous resources. Among the smart grid systems, data resources, information systems between various platforms and mutually dispersed, severe heterogeneity, thus leading to the integration of the quality of information sharing to increase difficulty, unable to effectively improve network management efficiency for the power distribution system electrical work quality management and marketing operations system maintenance upgrade will not be able to effectively meet demand (Jie Yang, 2014). Cloud computing technology enables the power system to effectively integrate the various business data and computing resources on the basis of information for business collaboration platform built on effective, able to satisfy the huge demand for smart grid information resource sharing. Cloud computing network virtualization and application virtualization technology, the server virtualization technology and other resources to achieve each service abstraction, and then realize the difference between the system and the software effectively eliminated by the appropriate method to achieve the effective integration of information resources, and then to optimize the management, greatly improving the efficiency of heterogeneous resources management applications. Distributed storage and management of large volume of data. After application of smart grid information platform construction, customer service requests varied, huge data volume information brought it to the storage and management of data provides a great challenge. Cloud computing technology in general face amount of data each smart grid system and unit collected using forms distributed storage of data to achieve the effective storage, greatly to ensure the security and reliability of data storage. GFS effective use of technology with each node, the master server is responsible for the effective management of the whole file system, the file system and complete the work of the metadata stored in the data block is the main server for data storage block sliced, and by saving three or more redundant backup storage. At the same time, client access and complete access to the main block server information. Fast Power System Parallel Computation and Treatment. Smart grids and information platform for the large volume of data to provide stable operation of the power grid data base, but more critical is the analysis and design of each system operating data, planning and decision-making, and even failures and transient stability, state estimation and intelligent a variety of computing and content analysis and decision-making process, its huge computing needs, general computing system can not reach its goal (Wang, Mengxue, 2015). Cloud computing technology through parallel computing technology has greatly enhanced parallel processing capability of the power system, which provides parallel programming model makes more simple and efficient parallel algorithms. MapReduce as Google made an important parallel programming model, and its role in large data sets in parallel computing plays a very prominent. Parallel processing job results by the client to achieve effective user submitted, via decomposition automated master role, the corresponding task scheduling to working nodes and nodes work completed by the specific calculations and perform the appropriate task. 5 SMART GRID INFORMATION PLATFORM CLOUD COMPUTING ANALYSIS Cloud Computing Smart Grid Information Platform Architecture. Smart grid technology information platform architecture cloud computing include the following several levels and aspects (Yan Cui, 2015): First, that the platform infrastructure layer is the integration of the various hardware resources and related management capabilities of virtualized using virtual technology to achieve the corresponding hardware devices such as computers and network storage devices abstraction, and then complete the automation and optimization of resource data and load management; secondly, the main platform for the integration of the various layers of software

Computation offloading services provide required computing resources for vehicles with computation-intensive tasks. Past computation offloading research mainly focused on mobile edge computing (MEC) or cloud computing, separately. This paper presents a collaborative approach based on MEC and cloud computing that offloads services to automobiles in vehicular networks. A cloud-MEC collaborative computation offloading problem is formulated through jointly optimizing computation offloading decision and computation resource allocation. Since the problem is non-convex and NP-hard, we propose a collaborative computation offloading and resource allocation optimization (CCORAO) scheme, and design a distributed computation offloading and resource allocation algorithm for CCORAO scheme that achieves the optimal solution. The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.

In today's society, the rapid development of the Internet makes People's Daily life become more intelligent and diversified. Today's society has entered a multifaceted era where everything is interconnected. Artificial intelligence technology is gradually replacing some traditional human services, such as intelligent robot customer service instead of traditional human customer service, intelligent face scanning security check in railway stations instead of traditional manual ticket checking, unmanned supermarket automatic checkout has liberated some social labor costs. All these changes are the result of the development of artificial intelligence technology in today's society. In recent years, unicorn startups focused on biometrics have sprung up all around us, such as BTU and its MEG VII (Face ++). Thanks to the development of Internet and artificial intelligence technology, in many application fields, the traditional access control and identity authentication technology based on password verification is gradually transforming to the scheme based on biometric identification verification. Secure identity authentication is very important to the application of Internet. Face recognition is the most popular technology among all biometric identification technologies. In the field of biometric identification technology, it has become the most widely used technology in the field of identity authentication because of its unique non-invasive, support for infrared and visible light, no need for user cooperation and many other advantages. In the field of education, examinee identification, pedestrian identification detection at the entrance of railway stations, face electronic payment, intelligent video surveillance system, intelligent attendance and access control system, intelligent unmanned supermarkets and customs clearance ports become the pioneer fields of face recognition applications. It can be seen that the era of “national face brushing” has arrived, and the application of face recognition technology will only be more and more widespread in the current era and in the future. However, due to the sensitivity of biometric data and the heterogeneity and openness of network environment, the privacy leakage of biometric data is difficult to avoid. At present, fog computing and edge computing have been paid more and more attention in many fields. In the case that cloud service providers are unable to provide sufficient security, edge computing shows its advantages. In this paper, mobile edge computing is introduced for the first time into the face privacy protection identity authentication system based on cloud server outsourcing computing. It can not only greatly reduce the interaction frequency between users and cloud server, improve the availability and fault tolerance of the system, but also contribute to the implementation of privacy protection scheme. A deep constitutional neural network for face feature extraction is trained using deep learning framework Cafe. Cosine similarity is used to complete face verification. A privacy protection scheme based on the secure nearest neighbor algorithm is proposed, which can not only protect the security of the face feature data at the edge computing node, but also allow the edge computing node to complete the face recognition operation against the encrypted face feature data. In addition, the encryption scheme does not require large computing resources, and the accuracy of face recognition in cipher text is exactly the same as that in explain. At present, most of the solutions either have high computational complexity or poor security performance. How to reduce the computational complexity and improve the real-time performance of the system while ensuring the high security of the private data has important research significance and value. Therefore, in the cloud server outsourcing computing environment, how to complete biometric identification on the premise of protecting the privacy of biological data has become a research hot spot.

With the rapid development of artificial intelligence (AI) technology, the field of education has ushered in new opportunities and challenges. Especially in college English teaching, the application of AI provides new possibilities for improving the teaching effect. This paper aims to explore how to effectively improve the quality of college English teaching in the context of AI. By analyzing the current situation and problems in college English teaching and combining them with the development of AI technology, this paper puts forward corresponding strategies and schemes, aiming to provide reference for college English teaching.

The development of an infrastructure for multiple autonomous agents, with an application to urban traffic signal control, is described. The agent-based infrastructure, Cybele, allows for distributed computing, interagent communication, agent migration, and computational resource allocation. The agents that are used to solve the traffic signal control problem are known collectively as DAARTS (Decentralized Adaptive Agents for contRol of Traffic Signals). DAARTS adopts a hierarchical multiagent-based architecture in which the lowest level (intersection agents) involves individual intersection-traffic dynamics and phase selection based on “local” information, while higher levels take into account the supervisory (network-level) dynamics. The controller design is based on a receding-horizon model predictive control approach. Coordination between intersections is achieved in a decentralized manner at the lowest level. The agents are integrated into a simulation test bed with the microsimulator CORSIM, using the DAARTS simulation tool kit. This kit enables communications between the CORSIM real-time extension, the communications management functions of Cybele, and the traffic control agents. The control process is truly distributed, and each of these components can reside on a different computer. Descriptions of all of the software components are given, and the control algorithm is discussed in detail. Some encouraging results from the simulation of a small network are included. Ongoing and future research activities are discussed.

This paper proposes an offloading strategy for communication resources and computing resources. Based on the communication background of NOMA, this strategy combines D2D communication and cellular mobile communication to consider the use of resources in the communication process. According to the performance of communication resources, the power of cellular network users and D2D users is controlled to make the task unloading decision between users and small MEC server base stations. The purpose is to find the minimum sum of the total cost of the user group within the coverage of the entire base station. The cost is obtained from the task completion time and the base station usage payment. The experimental results show that this method can find the optimal solution of the user group cost, and it also has a good performance in user fairness. In reality, taking D2D communication into consideration for coordinated distribution in a cellular network can greatly reduce the cost of the two.

Service caching refers to caching the necessary programs or/and the related databases for performing computational tasks at edge servers, which has been considered to save both computation and communication resources in mobile edge computing (MEC) systems. In this paper, we investigate computation service caching in a multi-unmanned aerial vehicle (UAV) enabled MEC system, where each UAV equipped with an edge server acts as an aerial computing platform to serve the ground devices. Furthermore, the UAVs can serve the devices cooperatively through the provided computing and caching resources. Aiming at minimizing the maximum task completion latency among all devices, we formulate a joint service caching, task offloading, communication and computation resource allocation, as well as UAV placement optimization problem, while guaranteeing the energy budget of all devices and UAVs. The problem is a mixed integer non-linear programming problem, and we decompose it into four sub-problems, and then propose an iterative algorithm based on block coordinate descent (BCD) and successive convex approximation (SCA) techniques to obtain near-optimal solution. Numerical results show that our proposed algorithm can achieve lower task completion latency than other baselines while guaranteeing better fairness among all devices.

The development of Artificial Intelligence technology can be used to speed up the process of resolving disputes in Islamic law, especially its use in resolving inheritance disputes. Artificial Intelligence technology has emerged as a potential tool for accelerating and increasing the efficiency of resolving disputes over Islamic inheritance. This article aims to analyze the use of Artificial Intelligence technology in accelerating the settlement of inheritance disputes in Islamic family law, policy formulations for the use of Artificial Intelligence technology in accelerating the settlement of inheritance disputes, and Opportunities and challenges of using Artificial Intelligence technology in accelerating the settlement of inheritance disputes in Islamic law in the era of the ASEAN community 5.0. This research method is juridical-normative through a conceptual approach. The data used is secondary data collected through library research. The results of this study indicate that the use of Artificial Intelligence in resolving Islamic inheritance disputes can assist the process of resolving inheritance disputes by automating several tasks, accelerating decision-making, and reducing costs. Artificial Intelligence technology can provide many benefits if it is implemented carefully and by prevailing Islamic principles, and the use of AI technology in the settlement of inheritance disputes in Islamic law in the era of the ASEAN Community 5.0 offers great opportunities to increase efficiency and accuracy. However, the use of Artificial Intelligence technology in resolving inheritance disputes also has several challenges.

Mobile cloud computing (MCC) is a new technology that has been developed to overcome the restrictions of smart mobile devices (e.g. battery, processing power, storage capacity, etc.) to send a part of the program (with complex computing) to the cloud server (CS). In this paper, we study a multi-cell with multi-input and multi-output (MIMO) system in which the cell-interior users request service for their processing from a common CS. Also, the problem of the optimum offloading is considered as an optimization problem with optimization parameters including communication resources (such as bandwidth, transmit power and backhaul link capacity) and computational resources (such as the capacity of cloud server) in the downlink network. The main goal is to minimize the total energy consumption by mobile users (MUs) for processing with the delay limitation for each use. This issue leads to a non-convex problem and to solve the problem, we use successive convex approximation (SCA) method. We finally show that the joint optimization of these parameters leads to reducing the energy consumption of the network with simulation examples.

Smart Grid is one of the critical technologies that provide essential services to sustain social and economic developments. There are various cyber attacks on the Smart Grid system in recent years, which resulted in various negative repercussions. Therefore, understanding the characteristics and evaluating the consequences of an attack on the Smart Grid system is essential. The combination of Graphical Security Model (GrSM), including Attack Tree (AT) and Attack Graph (AG), and the Common Vulnerability Score System (CVSS) is a potential technology to analyze attack on Smart Grid system. However, there are a few research works about Smart Grid attack analysis using GrSM and CVSS. In this research, we first conduct a comprehensive study of the existing research on attack analysis using GrSM and CVSS, ranging from (1) Traditional Networks, (2) Emerging Technologies, to (3) Smart Grid. We indicate that the framework for automating security analysis of the Internet of Things is a promising direction for Smart Grid attack analysis using GrSM and CVSS. The framework has been applied to assess security of the Smart Grid system. A case study using the PNNL Taxonomy Feeders R4-12.47-2 and Smart Grid network model with gateways was conducted to validate the utilized framework. Our research is enriched by capturing all potential attack paths and calculating values of selected security metrics during the vulnerability analysis process. Furthermore, AG can be generated automatically. The research can potentially be utilized in Smart Grid cybersecurity training.KeywordsSmart GridGraphical Security Model (GrSM)Common Vulnerability Score System (CVSS)Attack analysisAttack treeAttack graph

Importance. The widespread use of artificial intelligence technologies affects the language education system. In the few years since the advent of artificial intelligence technology, it has transformed from a theoretical concept into a dynamic force that changes ideas about classical language education. The transition to digital didactics defines new types of relationships between participants in the educational process: the teacher, the student, the educational environment and new technologies. Accordingly, the role of the teacher and the content of didactic and methodological principles of teaching a foreign language using artificial intelligence technologies are changing. The integration of artificial intelligence technologies into the process of teaching a foreign language, on the one hand, indicates the intensification of the educational process and automation of a number of pedagogical tasks, and, on the other hand, the imperfection of new technologies and potential risks and challenges when using them in practice. The main purpose of the research is to define and describe the principles of teaching a foreign language based on artificial intelligence technologies, acting as a full-fledged individual assistant for each student.Research Methods. To conduct research in the field of linguodidactics, an integrated approach is used, including theoretical and empirical methods. At the first stage of the research, theoretical methods are used, including the study of scientific literature on the research topic, analysis, generalization and classification of the information received. At the second stage of the study, empirical methods are applied, including observation and description.Definition of Concepts. Two key concepts of “artificial intelligence technologies” and “neural networks” are considered, and neural networks are classified by type of architecture and scope of application.Results and Discussion. The use of hybrid forms of learning makes it possible to use artificial intelligence technologies as an effective tool for teaching a foreign language right now. Artificial intelligence technologies play a leading role in digital didactics, allowing you to personalize the learning process and enhance the importance of students’ learning autonomy. Digital didactics, based on traditional didactic and methodological principles, involves the implementation of the following principles of learning: the principle of expediency, the principle of contextual learning, the principle of interactivity, the principle of accessibility, the principle of personalization of learning, the principle of multimodality, the principle of gamification, the principle of inclusive assessment.Conclusion. A system of principles of teaching a foreign language based on artificial intelligence technologies is presented, taking into account the peculiarities of the organization of the educational process in relation to digital didactics. It is worth noting that this system of principles is open and can be supplemented with new learning principles, as well as the content of these principles may change with the development of artificial intelligence technologies. The results obtained can be used in further research on the integration of artificial intelligence technologies into language education, as well as in the methodology of teaching foreign languages based on modern technologies.

An efficient dependable smart power grid relies on the secure real-time data collection and transmission service provided by a monitoring system. In such a system, the measuring units, such as phasor measurement units (PMUs) and smart meters (SMs), are critical. These measuring equipments function as sensors in the smart grid. Data exchanges between these sensors and the central controller are protected by various security protocols. These protocols usually contain computationally intensive cryptographic algorithms that cause heavy energy overhead to the sensor nodes. Since PMUs and SMs are mostly energy-constrained, the problem of how to ensure the secure communication with minimum energy cost becomes a critical issue for the functionality of the whole smart grid. In this article, we focus on the low power secure communication of the PMUs and SMs. We take two wireless sensor platforms as examples to experimentally investigate the approaches and principles of reconciling the two conflicting system requirements-communication security and low energy consumptions. The proposed methods are general ones and applicable to other energy-constrained yet security sensitive systems.

The Multi-Access Edge Computing (MEC) constitutes computing over virtualized resources distributed at the edge of mobile network. For mobile users, an optimal allocation of communication and computing resources changes over time and space, and the resource allocation becomes a complex problem. Moreover, for delay constrained applications, the resource allocation to mobile users cannot be solved by approaches designed for static users, as a solution would not be obtained within a desired time. Thus, in this paper, we propose a low-complexity computing and communication resource allocation for offloading of real-time computing tasks generated with a high arrival rate by the mobile users. We exploit probabilistic modeling of the users’ movement to pre-allocate the computing resources at base stations and to select suitable communication paths between the users and the base station with the pre-allocated computing resources. The simulations show that the proposed algorithm keeps the offloading delay below 100 ms for the small tasks even with the arrival rate of five tasks per second per user, while the state-of-the-art algorithms can handle only up to 0.5 tasks per second per user. Thus, the proposal enables an exploitation of the MEC for various real-time applications even if the users are moving.

SUMMARY This paper discusses optimal energy management considering battery characteristics for smart grid and microgrid systems. Energy storage systems are very important for energy peak shifting and peak load cutting in microgrid and smart grid systems, but optimal battery formulation and energy management in the use of expensive energy storage system and methods of taking account of battery characteristics are still in the developmental stage. First, the optimal battery placement for a smart grid system with lithium-ion batteries is considered as a means of lifetime extension. The battery choice index for this optimal battery utilization problem of optimal energy management is discussed. Second, a smart grid system with a distributed battery system for grid frequency control with consideration of distributed control and battery characteristics is discussed. Optimal controllers for distributed battery systems to reduce the battery load and to expend the battery lifetime are designed. Simulations are used to demonstrate the effectiveness of the proposed method in comparison with conventional methods.

With the gradual maturity of artificial intelligence (AI) technology, it is urgent to change and innovate the traditional media communication paradigm. Current research and application areas include language processing, image recognition, AI systems, intelligent video technology, and text recognition. With the rapid development of artificial intelligence technology, it is used in all walks of life in society. All sectors of society have also set off the exploration and application of artificial intelligence. In the field of news communication, artificial intelligence technology brings opportunities and challenges to the news industry in publishing news content forms and organizations. Exploring the new development trends and countermeasures of the news industry in the era of artificial intelligence has become a key topic for the news communication industry and academia. With the support of the huge communication network, the information transmission of short video news is more abundant. While people obtain information, it can also enhance the interactivity of the news. In a fast-paced society, short video news is undoubtedly closer to life. However, due to some reasons, human factors lead to incomplete access to information. In this paper, an intelligent neural network is proposed to be applied to an intelligent robot or intelligent system so that it can better obtain information to edit short video news and disseminate it, and compare it with the existing such machines or systems on the market today. This paper analyzes the development of short video news dissemination based on artificial intelligence and proposes improvement strategies based on specific examples.