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

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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.

Abstract-Charging management for Electric Vehicles (EVs) on-the-move has become an increasingly important research problem in smart cities. Major technical challenges include the selection of Charging Stations (CSs) to guide charging plans, and the design of cost-efficient communication infrastructure between the power grid and EVs. In this article, we first present a brief review on state-of-the-art EV charging management schemes. Next, by incorporating battery switch technology to enable fast charging service, a Publish/Subscribe (P/S) communication framework is provisioned to support the EV charging service. Upon that, we develop a fully distributed charging management scheme with the consideration of urban travel uncertainties, e.g., traffic congestions and drivers' preferences. This would benefit from low privacy sensitivity, as EVs' status information will not be released through management. Results demonstrate a guidance for the provisioning of P/S communication framework to improve EV drivers' experience, e.g., charging waiting time and total trip duration. Also, the benefit of P/S communication framework is reflected in terms of the communication efficiency. Open research issues of this emerging area are also presented.

Abstract-RPL, an IPv6 routing protocol for Low power Lossy Networks (LLNs), is considered to be the de facto routing standard for the Internet of Things (IoT). However, more and more experimental results demonstrate that RPL performs poorly when it comes to throughput and adaptability to network dynamics. This significantly limits the application of RPL in many practical IoT scenarios, such as an LLN with high-speed sensor data streams and mobile sensing devices. To address this issue, we develop BRPL, an extension of RPL, providing a practical approach that allows users to smoothly combine any RPL Object Function (OF) with backpressure routing. BRPL uses two novel algorithms, QuickTheta and QuickBeta, to support time-varying data traffic loads and node mobility respectively. We implement BRPL on Contiki OS, an open-source operating system for the Internet of Things. We conduct an extensive evaluation using both real-world experiments based on the FIT IoT-LAB testbed and large-scale simulations using Cooja over 18 virtual servers on the Cloud. The evaluation results demonstrate that BRPL not only is fully backward compatible with RPL (i.e. devices running RPL and BRPL can work together seamlessly), but also significantly improves network throughput and adaptability to changes in network topologies and data traffic loads. The observed packet loss reduction in mobile networks is, at a minimum, 60% and up to 1000% can be seen in extreme cases.

Abstract-Wireless Sensor Networks with Mobile Sinks (WSN-MSs) are considered a viable alternative to the heavy cost of deployment of traditional wireless sensing infrastructures at scale. However, current state-of-the-art approaches perform poorly in practice due to their requirement of mobility prediction and specific assumptions on network topology. In this paper, we focus on lowdelay and high-throughput opportunistic data collection in WSN-MSs with general network topologies and arbitrary numbers of mobile sinks. We first propose a novel routing metric, Contact-Aware ETX (CA-ETX), to estimate the packet transmission delay caused by both packet retransmissions and intermittent connectivity. By implementing CA-ETX in the defacto TinyOS routing standard CTP and the IETF IPv6 routing protocol RPL, we demonstrate that CA-ETX can work seamlessly with ETX. This means that current ETXbased routing protocols for static WSNs can be easily extended to WSN-MSs with minimal modification by using CA-ETX. Further, by combing CA-ETX with the dynamic backpressure routing, we present a throughput-optimal scheme Opportunistic Backpressure Collection (OBC). Both CA-ETX and OBC are lightweight, easy to implement, and require no mobility prediction. Through test-bed experiments and extensive simulations, we show that the proposed schemes significantly outperform current approaches in terms of packet transmission delay, communication overhead, storage overheads, reliability, and scalability.