Software Defined Networks based Smart Grid Communication: A Comprehensive Survey

The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid a.k.a., smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SG communication (SGC) system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. This article serves as a comprehensive survey on SDN-based SGC. In this article, we first discuss taxonomy of advantages of SDNbased SGC.We then discuss SDN-based SGC architectures, along with case studies. Our article provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC.Comment: Accepte

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Enhancing Security and Energy Efficiency in Wireless Sensor Network Routing with IOT Challenges: A Thorough Review

Wireless sensor networks (WSNs) have emerged as a crucial component in the field of networking due to their cost-effectiveness, efficiency, and compact size, making them invaluable for various applications. However, as the reliance on WSN-dependent applications continues to grow, these networks grapple with inherent limitations such as memory and computational constraints. Therefore, effective solutions require immediate attention, especially in the age of the Internet of Things (IoT), which largely relies on the effectiveness of WSNs. This study undertakes a comprehensive review of research conducted between 2018 and 2020, categorizing it into six main domains: 1) Providing an overview of WSN applications, management, and security considerations. 2) Focusing on routing and energy-saving techniques. 3) Reviewing the development of methods for information gathering, emphasizing data integrity and privacy. 4) Emphasizing connectivity and positioning techniques. 5) Examining studies that explore the integration of IoT technology into WSNs with an eye on secure data transmission. 6) Highlighting research efforts aimed at energy efficiency. The study addresses the motivation behind employing WSN applications in IoT technologies, as well as the challenges, obstructions, and solutions related to their application and development. It underscores that energy consumption remains a paramount issue in WSNs, with untapped potential for improving energy efficiency while ensuring robust security. Furthermore, it identifies existing approaches' weaknesses, rendering them inadequate for achieving energy-efficient routing in secure WSNs. This review sheds light on the critical challenges and opportunities in the field, contributing to a deeper understanding of WSNs and their role in secure IoT applications

Network-based business process management: embedding business logic in communications networks

Advanced Business Process Management (BPM) tools enable the decomposition of previously integrated and often ill-defined processes into re-usable process modules. These process modules can subsequently be distributed on the Internet over a variety of many different actors, each with their own specialization and economies-of-scale. The economic benefits of process specialization can be huge. However, how should such actors in a business network find, select, and control, the best partner for what part of the business process, in such a way that the best result is achieved? This particular management challenge requires more advanced techniques and tools in the enabling communications networks. An approach has been developed to embed business logic into the communications networks in order to optimize the allocation of business resources from a network point of view. Initial experimental results have been encouraging while at the same time demonstrating the need for more robust techniques in a future of massively distributed business processes.active networks;business process management;business protocols;embedded business logic;genetic algorithms;internet distributed process management;payment systems;programmable networks;resource optimization

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Optimization of Energy-Efficient Cluster Head Selection Algorithm for Internet of Things in Wireless Sensor Networks

The Internet of Things (IoT) now uses the Wireless Sensor Network (WSN) as a platform to sense and communicate data. The increase in the number of embedded and interconnected devices on the Internet has resulted in a need for software solutions to manage them proficiently in an elegant and scalable manner. Also, these devices can generate massive amounts of data, resulting in a classic Big Data problem that must be stored and processed. Large volumes of information have to be produced by using IoT applications, thus raising two major issues in big data analytics. To ensure an efficient form of mining of both spatial and temporal data, a sensed sample has to be collected. So for this work, a new strategy to remove redundancy has been proposed. This classifies all forms of collected data to be either relevant or irrelevant in choosing suitable information even before they are forwarded to the base station or the cluster head. A Low-Energy Adaptive Clustering Hierarchy (LEACH) is a cluster-based routing protocol that uses cluster formation. The LEACH chooses one head from the network sensor nodes, such as the Cluster Head (CH), to rotate the role to a new distributed energy load. The CHs were chosen randomly with the possibility of all CHs being concentrated in one locality. The primary idea behind such dynamic clustering was them resulted in more overheads due to changes in the CH and advertisements. Therefore, the LEACH was not suitable for large networks. Here, Particle Swarm Optimization (PSO) and River Formation Dynamics are used to optimize the CH selection (RFD). The results proved that the proposed method to have performed better compared to other methods

Prospectiva de seguridad de las redes de sensores inalámbricos

En las Redes de Sensores Inalámbricos (WSN), los nodos son vulnerables a los ataques de seguridad porque están instalados en un entorno difícil, con energía y memoria limitadas, baja capacidad de procesamiento y transmisión de difusión media; por lo tanto, identificar las amenazas, los retos y las soluciones de seguridad y privacidad es un tema candente hoy en día. En este artículo se analizan los trabajos de investigación que se han realizado sobre los mecanismos de seguridad para la protección de las WSN frente a amenazas y ataques, así como las tendencias que surgen en otros países junto con futuras líneas de investigación. Desde el punto de vista metodológico, este análisis se muestra a través de la visualización y estudio de trabajos indexados en bases de datos como IEEE, ACM, Scopus y Springer, con un rango de 7 años como ventana de observación, desde 2013 hasta 2019. Se obtuvieron un total de 4.728 publicaciones, con un alto índice de colaboración entre China e India. La investigación planteó desarrollos, como avances en los principios de seguridad y mecanismos de defensa, que han llevado al diseño de contramedidas en la detección de intrusiones. Por último, los resultados muestran el interés de la comunidad científica y empresarial por el uso de la inteligencia artificial y el aprendizaje automático (ML) para optimizar las medidas de rendimiento.In Wireless Sensor Networks (WSN), nodes are vulnerable to security attacks because they are installed in a harsh environment with limited power and memory, low processing power, and medium broadcast transmission. Therefore, identifying threats, challenges, and solutions of security and privacy is a talking topic today. This article analyzes the research work that has been carried out on the security mechanisms for the protection of WSN against threats and attacks, as well as the trends that emerge in other countries combined with future research lines. From the methodological point of view, this analysis is shown through the visualization and study of works indexed in databases such as IEEE, ACM, Scopus, and Springer, with a range of 7 years as an observation window, from 2013 to 2019. A total of 4,728 publications were obtained, with a high rate of collaboration between China and India. The research raised developments, such as advances in security principles and defense mechanisms, which have led to the design of countermeasures in intrusion detection. Finally, the results show the interest of the scientific and business community in the use of artificial intelligence and machine learning (ML) to optimize performance measurements