Technologies to support the Internet of Things are becoming more important as the need to better understand our environments and make them smart increases. As a result it is predicted that intelligent devices and networks, such as WSNs, will not be isolated, but connected and integrated, composing computer networks. So far, the IP-based Internet is the largest network in the world; therefore, there are great strides to connect WSNs with the Internet. To this end, the IETF has developed a suite of protocols and open standards for accessing applications and services for wireless resource constrained networks. However, many open challenges remain, mostly due to the complex deployment characteristics of such systems and the stringent requirements imposed by various services wishing to make use of such complex systems. Thus, it becomes critically important to study how the current approaches to standardization in this area can be improved, and at the same time better understand the opportunities for the research community to contribute to the IoT field. To this end, this article presents an overview of current standards and research activities in both industry and academia
Abstract-Reliable real-time sensing plays a vital role in ensuring the reliability and safety of industrial Cyber-Physical Systems (CPSs) such as wireless sensor and actuator networks. For many reasons, such as harsh industrial environments, faultprone sensors, or malicious attacks, sensor readings may be abnormal or faulty. This could lead to serious system performance degradation or even catastrophic failure. Current anomaly detection approaches are either centralized and complicated, or restricted due to strict assumptions, which are not suitable for practical large-scale Networked Industrial Sensing Systems (NISSs) where sensing devices are connected via digital communications, such as wireless sensor networks or smart grid systems. In this paper, we introduce a fully distributed general-anomalydetection (GAD) scheme, which uses graph theory and exploits spatiotemporal correlations of physical processes to carry out real-time anomaly detection for general large-scale NISSs. We formally prove the scalability of our GAD approach and evaluate the performance of GAD for two industrial applications: building structure monitoring and smart grids. Extensive trace-driven simulations validate our theoretical analysis, and demonstrate that our approach can significantly outperform state-of-the-art approaches in terms of detection accuracy and efficiency.
The emerging Fog paradigm has been attracting increasing interests from both academia and industry, due to the low-latency, resilient, and cost-effective services it can provide.Many Fog applications such as video mining and event monitoring, rely on data stream processing and analytics, which are very popular in the Cloud, but have not been comprehensively investigated in the context of Fog architecture. In this article, we present the general models and architecture of Fog data streaming, by analyzing the common properties of several typical applications. We also analyze the design space of Fog streaming with the consideration of four essential dimensions (system, data, human, and optimization), where both new design challenges and the issues arise from leveraging existing techniques are investigated, such as Cloud stream processing, computer networks, and mobile computing.
It is predicted that billions of intelligent devices and networks, such as wireless sensor networks (WSN), will not be isolated but connected and integrated with computer networks in future Internet-of-Things (IoT). In order to well maintain those sensor devices, it is often necessary to evolve devices to function correctly by allowing device management entities to remotely monitor and control devices without consuming significant resources. In this paper, we propose a lightweight RESTful web service approach to enable device management of wireless sensor devices. Specifically, motivated by the recent development of IPv6 based open standards for accessing wireless resource constrained networks, we consider to implement 6LoWPAN/RPL/CoAP protocols on sensor devices and propose a CoAP based device management solution to allow easy access and management of IPv6 sensor devices. By developing a prototype cloud system, we successfully demonstrate the proposed solution in efficient and effective management of wireless sensor devices.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.