The Grid 2: Blueprint for a New Computing Infrastructure

A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1315&context=electricalengineeringfacpub

A Survey on Smart Grid CommunicationInfrastructures: Motivations, Requirements andChallenges

The Internet Protocol.

Network Simulator NS2

This paper conducts a comprehensive study on the application of big data and machine learning in the electrical power grid introduced through the emergence of the next-generation power system-the smart grid (SG). Connectivity lies at the core of this new grid infrastructure, which is provided by the Internet of Things (IoT). This connectivity, and constant communication required in this system, also introduced a massive data volume that demands techniques far superior to conventional methods for proper analysis and decision-making. The IoT-integrated SG system can provide efficient load forecasting and data acquisition technique along with cost-effectiveness. Big data analysis and machine learning techniques are essential to reaping these benefits. In the complex connected system of SG, cyber security becomes a critical issue; IoT devices and their data turning into major targets of attacks. Such security concerns and their solutions are also included in this paper. Key information obtained through literature review is tabulated in the corresponding sections to provide a clear synopsis; and the findings of this rigorous review are listed to give a concise picture of this area of study and promising future fields of academic and industrial research, with current limitations with viable solutions along with their effectiveness.

/pdf/application-of-big-data-and-machine-learning-in-smart-grid-1taystle2u.pdf

Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review

Wireless sensor networks (WSNs) have gained wide attention from researchers in the last few years because it has a vital role in countless applications. The main function of WSN is to process extracted data and to transmit it to remote locations. A large number of sensor nodes are deployed in the monitoring area. Therefore, deploying the minimum number of nodes that maintain full coverage and connectivity is of immense importance for research. Hence, coverage and connectivity issues, besides maximizing the network lifetime, represented the main concern to be considered in this paper. The key point of this paper is to classify different coverage techniques in WSNs into three main parts: coverage based on classical deployment techniques, coverage based on meta-heuristic techniques, and coverage based on self-scheduling techniques. Moreover, multiple comparisons among these techniques are provided considering their advantages and disadvantages. Additionally, performance metrics that must be considered in WSNs and comparison among different WSNs simulators are provided. Finally, open research issues, as well as recommendations for researchers, are discussed.

/pdf/deployment-techniques-in-wireless-sensor-networks-coverage-33sqyeldy1.pdf

Deployment Techniques in Wireless Sensor Networks, Coverage and Connectivity: A Survey

In this paper, we seek to highlight the concept of Internet of Things (IoT) in general, as well as reviewing the main challenges of the IoT environment by focusing on the recent research directions in this topic. Recently, IoT has emerged as a new technology that is used to express a modern wireless telecommunication network, and it can be defined as an intelligent and interoperability node interconnected in a dynamic global infrastructure network, also it seeks to implement the connectivity concept of anything from anywhere at anytime. Indeed, the IoT environment possesses a large spectrum of challenges has a broad impact on their performance, which can be divided into two categories, namely, i) General challenges: such as communication, heterogeneity, virtualization and security; and ii) Unique challenges: such as wireless sensor network (WSN), Radio Frequency Identification (RFID), and finally Quality of service (QoS) that is considered as a common factor between both general and special challenges. In addition, this paper highlights the main applications of the IoT.

/pdf/internet-of-things-iot-definitions-challenges-and-recent-3v1l0ug7ct.pdf

Internet of Things (IoT): Definitions, Challenges and Recent Research Directions

Optimization of live virtual machine migration in cloud computing: A survey and future directions

Fog computing (FC) can be considered as a computing paradigm which performs Internet of Things (IoT) applications at the edge of the network. Recently, there is a great growth of data requests and FC which lead to enhance data accessibility and adaptability. However, FC has been exposed to many challenges as load balancing (LB) and adaptation to failure. Many LB strategies have been proposed in cloud computing, but they are still not applied effectively in fog. LB is an important issue to achieve high resource utilization, avoid bottlenecks, avoid overload and low load, and reduce response time. In this paper, a LB and optimization strategy (LBOS) using dynamic resource allocation method based on Reinforcement learning and genetic algorithm is proposed. LBOS monitors the traffic in the network continuously, collects the information about each server load, handles the incoming requests, and distributes them between the available servers equally using dynamic resource allocation method. Hence, it enhances the performance even when it’s the peak time. Accordingly, LBOS is simple and efficient in real-time systems in fog computing such as in the case of healthcare system. LBOS is concerned with designing an IoT-Fog based healthcare system. The proposed IoT-Fog system consists of three layers, namely: (1) IoT layer, (2) fog layer, and (3) cloud layer. Finally, the experiments are carried out and the results show that the proposed solution improves the quality-of-service in the cloud/fog computing environment in terms of the allocation cost and reduce the response time. Comparing the LBOS with the state-of-the-art algorithms, it achieved the best load balancing Level (85.71%). Hence, LBOS is an efficient way to establish the resource utilization and ensure the continuous service.

Hesham A. Ali

Papers

Deployment Techniques in Wireless Sensor Networks, Coverage and Connectivity: A Survey

Internet of Things (IoT): Definitions, Challenges and Recent Research Directions

Optimization of live virtual machine migration in cloud computing: A survey and future directions

A load balancing and optimization strategy (LBOS) using reinforcement learning in fog computing environment

Image compression algorithms in wireless multimedia sensor networks: A survey