Internet Of Things Edge Research Articles

A Reliable and Lightweight Trust Computing Mechanism for IoT Edge Devices Based on Multi-Source Feedback Information Fusion

The integration of Internet of Things (IoT) and edge computing is currently a new research hotspot. However, the lack of trust between IoT edge devices has hindered the universal acceptance of IoT edge computing as outsourced computing services. In order to increase the adoption of IoT edge computing applications, first, IoT edge computing architecture should establish efficient trust calculation mechanism to alleviate the concerns of numerous users. In this paper, a reliable and lightweight trust mechanism is originally proposed for IoT edge devices based on multi-source feedback information fusion. First, due to the multi-source feedback mechanism is used for global trust calculation, our trust calculation mechanism is more reliable against bad-mouthing attacks caused by malicious feedback providers. Then, we adopt lightweight trust evaluating mechanism for cooperations of IoT edge devices, which is suitable for large-scale IoT edge computing because it facilitates low-overhead trust computing algorithms. At the same time, we adopt a feedback information fusion algorithm based on objective information entropy theory, which can overcome the limitations of traditional trust schemes, whereby the trust factors are weighted manually or subjectively. And the experimental results show that the proposed trust calculation scheme significantly outperforms existing approaches in both computational efficiency and reliability.

Internet of Things-Based Consumer Electronics: Reviewing Existing Consumer Electronic Devices, Systems, and Platforms and Exploring New Research Paradigms

Consumer electronics (CE) based on the Internet of Things (IoT) are playing a key role in boosting the global economy by improving quality of life and creating new markets, products, and research paradigms. This special issue provides a publication medium for articles that either review existing IoT-based CE devices, systems, and platforms or explore novel research paradigms such as smart homes and smart cities. In this issue, you will find articles on smart home environments, commercial devices on the edge of IoT, consumer cognitive systems, and wearable privacy. The target audience includes educators, researchers, and students who are engaged in IoT-based research and education.

A Multi-agent Based Flexible IoT Edge Computing Architecture Harmonizing Its Control with Cloud Computing

Recently, in large-scale Internet of Things (IoT) systems based on cloud computing, problems such as increase in network load, delay in response, and invasion of privacy have become concerning. To solve these problems, edge computing has been introduced to the IoT systems. However, if the cloud function is excessively migrated to the edge, the collected data cannot be shared between IoT systems, thus reducing the system's usefulness. In this paper, we propose a multi-agent based flexible IoT edge computing architecture to balance global optimization by a cloud and local optimization by edges and to optimize the role of the cloud server and the edge servers dynamically. Further, as an application example, we introduce an energy management system based on the proposed edge computing system architecture to demonstrate the effectiveness of our proposal.

Low-VDD Operation of SRAM Synaptic Array for Implementing Ternary Neural Network

For Internet of Things (IoT) edge devices, it is very attractive to have the local sensemaking capability instead of sending all the data back to the cloud for information processing. For image pattern recognition, neuro-inspired machine learning algorithms have demonstrated enormous powerfulness. To effectively implement learning algorithms on-chip for IoT edge devices, on-chip synaptic memory architectures have been proposed to implement the key operations such as weighted-sum or matrix-vector multiplication. In this paper, we proposed a low-power design of static random access memory (SRAM) synaptic array for implementing a low-precision ternary neural network. We experimentally demonstrated that the supply voltage (VDD) of the SRAM array could be aggressively reduced to a level, where the SRAM cell is susceptible to bit failures. The testing results from 65-nm SRAM chips indicate that VDD could be reduced from the nominal 1–0.55 V (or 0.5 V) with a bit error rate ~0.23% (or ~1.56%), which only introduced ~0.08% (or ~1.68%) degradation in the classification accuracy. As a result, the power consumption could be reduced by more than $8\times $ (or $10\times $ ).

Energy-Aware Memory Mapping for Hybrid FRAM-SRAM MCUs in Intermittently-Powered IoT Devices

Forecasts project that by 2020, there will be around 50 billion devices connected to the Internet of Things (IoT), most of which will operate untethered and unplugged. While environmental energy harvesting is a promising solution to power these IoT edge devices, it introduces new complexities due to the unreliable nature of ambient energy sources. In the presence of an unreliable power supply, frequent checkpointing of the system state becomes imperative, and recent research has proposed the concept of in-situ checkpointing by using ferroelectric RAM (FRAM), an emerging non-volatile memory technology, as unified memory in these systems. Even though an entirely FRAM-based solution provides reliability, it is energy inefficient compared to SRAM due to the higher access latency of FRAM. On the other hand, an entirely SRAM-based solution is highly energy efficient but is unreliable in the face of power loss. This paper advocates an intermediate approach in hybrid FRAM-SRAM microcontrollers that involves judicious memory mapping of program sections to retain the reliability benefits provided by FRAM while performing almost as efficiently as an SRAM-based system. We propose an energy-aware memory mapping technique that maps different program sections to the hybrid FRAM-SRAM microcontroller such that energy consumption is minimized without sacrificing reliability. Our technique consists of eM-map , which performs a one-time characterization to find the optimal memory map for the functions that constitute a program and energy-align , a novel hardware-software technique that aligns the system’s powered-on time intervals to function execution boundaries, which results in further improvements in energy efficiency and performance. Experimental results obtained using the MSP430FR5739 microcontroller demonstrate a significant performance improvement of up to 2x and energy reduction of up to 20% over a state-of-the-art FRAM-based solution. Finally, we present a case study that shows the implementation of our techniques in the context of a real IoT application.

Secure Data Sharing and Searching at the Edge of Cloud-Assisted Internet of Things

Over the last few years, smart devices are able to communicate with each other and with Internet/cloud from short to long range. As a consequence, a new paradigm is introduced called Internet of Things (IoT). However, by utilizing cloud computing, resource limited IoT smart devices can get various benefits like offload data storage and processing burden at cloud. To support latency sensitive, real-time data processing, mobility and high data rate IoT applications, working at the edge of the network offers more benefits than cloud. In this paper, we propose an efficient data sharing scheme that allows smart devices to securely share data with others at the edge of cloud-assisted IoT. In addition, we also propose a secure searching scheme to search desired data within own/shared data on storage. Finally, we analyze the performance based on processing time of our proposed scheme. The results demonstrate that our scheme has potential to be effectively used in IoT applications.

IIoTEED: An Enhanced, Trusted Execution Environment for Industrial IoT Edge Devices

With the advent of the Internet of Things (IoT), security has emerged as a major design goal for smart connected devices. This explosion in connectivity created a larger attack surface area. Software-based approaches have been applied for security purposes; however, these methods must be extended with security-oriented technologies that promote hardware as the root of trust. The ARM TrustZone can enable trusted execution environments (TEEs), but existing solutions disregard real-time needs. Here, the authors demonstrate why TrustZone is becoming a reference technology for securing IoT edge devices, and how enhanced TEEs can help meet industrial IoT applications real-time requirements.

A Distributed Consensus Algorithm for Decision Making in Service-Oriented Internet of Things

In a service-oriented Internet of things (IoT) deployment, it is difficult to make consensus decisions for services at different IoT edge nodes where available information might be insufficient or overloaded. Existing statistical methods attempt to resolve the inconsistency, which requires adequate information to make decisions. Distributed consensus decision making (CDM) methods can provide an efficient and reliable means of synthesizing information by using a wider range of information than existing statistical methods. In this paper, we first discuss service composition for the IoT by minimizing the multi-parameter dependent matching value. Subsequently, a cluster-based distributed algorithm is proposed, whereby consensuses are first calculated locally and subsequently combined in an iterative fashion to reach global consensus. The distributed consensus method improves the robustness and trustiness of the decision process.