A Secured Data Management Scheme for Smart Societies in Industrial Internet of Things Environment

Smart societies have an increasing demand for quality-oriented services and infrastructure in an Industrial Internet of Things (IIoT) paradigm. Smart urbanization faces numerous challenges. Among them, secured energy Demand Side Management (DSM) is of particular concern. The IIoT renders the industrial systems to malware, cyber attacks, and other security risks. The IIoT with the amalgamation of Big Data analytics can provide efficient solutions to such challenges. This paper proposes a secured and trusted multi-layered DSM engine for a smart social society using IIoT-based Big Data analytics. The proposed engine uses a centralized approach to achieve optimum DSM over a Home Area Network (HAN). To enhance the security of this engine, a payload-based authentication scheme is utilized that relies on a lightweight handshake mechanism. Our proposed method utilizes the lightweight features of Constrained Application Protocol (CoAP) to facilitate the clients in monitoring various resources residing over the server in an energy-efficient manner. In addition, data streams are processed using Big Data analytics with MapReduce parallel processing. The proposed authentication approach is evaluated using NetDuino Plus 2 boards that yield a lower connection overhead, memory consumption, response time and a robust defense against various malicious attacks. On the other hand, our data processing approach is tested on reliable datasets using Apache Hadoop with Apache Spark to verify the proposed DMS engine. The test results reveal that the proposed architecture offers valuable insights into the smart social societies in the context of IIo

A Secured Data Management Scheme for Smart Societies in Industrial Internet of Things Environment

Smart societies have an increasing demand for quality-oriented services and infrastructure in an Industrial Internet of Things (IIoT) paradigm. Smart urbanization faces numerous challenges. Among them, secured energy Demand Side Management (DSM) is of particular concern. The IIoT renders the industrial systems to malware, cyber attacks, and other security risks. The IIoT with the amalgamation of Big Data analytics can provide efficient solutions to such challenges. This paper proposes a secured and trusted multi-layered DSM engine for a smart social society using IIoT-based Big Data analytics. The proposed engine uses a centralized approach to achieve optimum DSM over a Home Area Network (HAN). To enhance the security of this engine, a payload-based authentication scheme is utilized that relies on a lightweight handshake mechanism. Our proposed method utilizes the lightweight features of Constrained Application Protocol (CoAP) to facilitate the clients in monitoring various resources residing over the server in an energy-efficient manner. In addition, data streams are processed using Big Data analytics with MapReduce parallel processing. The proposed authentication approach is evaluated using NetDuino Plus 2 boards that yield a lower connection overhead, memory consumption, response time and a robust defense against various malicious attacks. On the other hand, our data processing approach is tested on reliable datasets using Apache Hadoop with Apache Spark to verify the proposed DMS engine. The test results reveal that the proposed architecture offers valuable insights into the smart social societies in the context of IIo

Cloud-Edge Orchestration for the Internet-of-Things: Architecture and AI-Powered Data Processing

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordThe Internet-of-Things (IoT) has been deeply penetrated into a wide range of important and critical sectors, including smart city, water, transportation, manufacturing and smart factory. Massive data are being acquired from a fast growing number of IoT devices. Efficient data processing is a necessity to meet diversified and stringent requirements of many emerging IoT applications. Due to the constrained computation and storage resources, IoT devices have resorted to the powerful cloud computing to process their data. However, centralised and remote cloud computing may introduce unacceptable communication delay since its physical location is far away from IoT devices. Edge cloud has been introduced to overcome this issue by moving the cloud in closer proximity to IoT devices. The orchestration and cooperation between the cloud and the edge provides a crucial computing architecture for IoT applications. Artificial intelligence (AI) is a powerful tool to enable the intelligent orchestration in this architecture. This paper first introduces such a kind of computing architecture from the perspective of IoT applications. It then investigates the state-of-the-art proposals on AI-powered cloud-edge orchestration for the IoT. Finally, a list of potential research challenges and open issues is provided and discussed, which can provide useful resources for carrying out future research in this area.Engineering and Physical Sciences Research Council (EPSRC