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The Internet of Things (IoT) refers to a pervasive presence of interconnected and uniquely identifiable physical devices. These devices' goal is to gather data and drive actions in order to improve productivity, and ultimately reduce or eliminate reliance on human intervention for data acquisition, interpretation and use. The proliferation of these connected low-power devices will result in a data explosion that will significantly increase data transmission costs with respect to energy consumption and latency. Edge computing reduces these costs by performing computations at the edge nodes, prior to data transmission, to interpret and/or utilize the data. While much research has focused on the IoT's connected nature and communication challenges, the challenges of IoT embedded computing with respect to device microprocessors has received much less attention. This article explores IoT applications' execution characteristics from a microarchitectural perspective and the microarchitectural characteristics that will enable efficient and effective edge computing. To tractably represent a wide variety of next-generation IoT applications, we present a broad IoT application classification methodology based on application functions, to enable quicker workload characterizations for IoT microprocessors. We then survey and discuss potential microarchitectural optimizations and computing paradigms that will enable the design of right-provisioned microprocessors that are efficient, configurable, extensible, and scalable. Our work provides a foundation for the analysis and design of a diverse set of microprocessor architectures for next-generation IoT devices.

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The emerging compressed sensing (CS) theory can significantly reduce the number of sampling points that directly corresponds to the volume of data collected, which means that part of the redundant data is never acquired. It makes it possible to create stand-alone and net-centric applications with fewer resources required in Internet of things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinear reconstruction algorithm and random sampling on a sparse basis that provides a promising approach to compress signal and data in information systems. This paper investigates how CS can provide new insights into data sampling and acquisition in wireless sensor networks and IoT. At first, we briefly introduce the CS theory in respect of the sampling and transmission coordination during the network lifetime through providing a compressed sampling process with low computation costs. Then, a compressed sensing-based framework is proposed for IoT, in which the end nodes measure, transmit, and store the sampled data in the framework. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Performance is evaluated with respect to network size using datasets acquired by a real-life deployment.

Purpose – The purpose of this paper is to provide an in-depth overview of the security requirements and challenges for Internet of Things (IoT) and discuss security solutions for various enabling technologies and implications to various applications. Design/methodology/approach – Security requirements and solutions are analysed based on a four-layer framework of IoT on sensing layer, network layer, service layer, and application layer. The cross-layer threats are analysed followed by the security discussion for the enabling technologies including identification and tracking technologies, WSN and RFID, communication, networks, and service management. Findings – IoT calls for new security infrastructure based on the new technical standards. As a consequence, new security design for IoT shall pay attention to these new standards. Security at both the physical devices and service-applications is critical to the operation of IoT, which is indispensable for the success of IoT. Open problems remain in a number of areas, such as security and privacy protection, network protocols, standardization, identity management, trusted architecture, etc. Practical implications – The implications to various applications including supervisory control and data acquisition, enterprise systems, social IoT are discussed. The paper will serve as a starting point for future IoT security design and management. The security strategies for IoT should be carefully designed by managing the tradeoffs among security, privacy, and utility to provide security in multi-layer architecture of IoT. Originality/value – The paper synthesizes the current security requirements for IoT and provides a clear framework of security infrastructure based on four layers. Accordingly, the security requirements and potential threats in the four-layer architecture are provided in terms of general devices security, communication security, network security, and application security.