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1. Sensor-Chain: A Lightweight Scalable Blockchain Framework for Internet of Things

   https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00195  

   Shahid, Abdur R.; Pissinou, Niki; Staier, Corey; Kwan, Rain (July 2019, IEEE International Conference on Internet of Things (iThings-2019))

   The Internet of Things (IoT), forming the foundation of Cyber Physical Systems (CPS), connects a huge number of ubiquitous sensing and mobile computing devices. The mobile IoT systems generate an enormous volume of a variety of dynamic context data and typically count on centralized architectures to process them. However, their inability to ensure security and decline in communication efficiency and response time with the increase in the size of IoT network are some of the many concerning weaknesses that are holding back the fast-paced growth of IoT. Realizing the limitations of centralized systems, recently blockchain-based decentralized architecture is being considered as the key to redesigning the IoT systems in a way that is designed to be secure, transparent, highly resistant to outages, auditable, and efficient. However, before realizing the new promise of blockchain for IoT, there are significant challenges to address. One fundamental challenge is the scale issue around data collection, storage, and analytic as IoT sensor devices possess limited computational power and storage capabilities. In particular, since the chain is always growing, IoT devices require more and more resources. Thus, an oversized chain poses storage and scalability problems. With this in mind, the overall goal of our research is to design a lightweight scalable blockchain framework for IoT of mobile devices. This framework, coined as "Sensor-Chain", promises a new generation of lightweight blockchain management with a superior reduction in resource consumption, and at the same time capable of retaining critical information about the IoT systems of mobile devices. 

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2. Enabling Deep Learning on IoT Edge: Approaches and Evaluation

   https://doi.org/10.1109/SEC.2018.00047  

   Qi, Xuan; Liu, Chen (October 2018, 1st Workshop on Computing Architecture for Edge Computing (ArchEdge))

   As we enter the Internet of Things (IoT) era, the size of mobile computing devices is largely reduced while their computing capability is dramatically improved. Meanwhile, machine learning technologies have been well developed and shown cutting edge performance in various tasks, leading to their wide adoption. As a result, moving machine learning, especially deep learning capability to the edge of the IoT is a trend happening today. But directly moving machine learning algorithms which originally run on PC platform is not feasible for IoT devices due to their relatively limited computing power. In this paper, we first reviewed several representative approaches for enabling deep learning on mobile/IoT devices. Then we evaluated the performance and impact of these methods on IoT platform equipped with integrated GPU and ARM processor. Our results show that we can enable the deep learning capability on the edge of the IoT if we apply these approaches in an efficient manner. 

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3. Stochastic Computing-based Baseband Processing for Resource Constraint IoT Devices

   https://doi.org/10.1007/978-3-031-14627-5_3  

   Kazi Ahmed, Yang Kim (January 2022, INCoS-2022)

   With the recent deployment of 5G network, the ever increasing IoT has got a tremendous boost in its expansion and already has penetrated well into the government, commercial and private sectors. With the countless IoT devices and myriad of applications, many of them are resource constrained and have limited energy budget. These IoT devices demand low-energy technique for their computing and communication tasks to stay active for longer period. The two main baseband processes that dissipate bulk of CPU power from the IoT device are synchronization and Finite Impulse Response (FIR) filtering. In this circumstance, hardware-based baseband processing can take these tasks off of the CPU and may significantly reduce energy consumption. While conventional Binary Radix Computing (BC)-based hardware modules can improve power dissipation, Stochastic Computing (SC)-based hardware will certainly cut down much more both the power as well as silicon space in comparison. With this motivation, we propose novel SC-based hardware designs in regards to synchronization and Finite Impulse Response (FIR) filter for resource constraint IoT devices. Comparative analysis shows that our proposed SC-based design can reduce significantly more power and silicon area compared to the BC as well as other proposed SC designs. 

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4. A Survey of Privacy Concerns in Wearable Devices

   https://doi.org/10.1109/BigData.2018.8622110  

   Datta, Prerit; Namin, Akbar Siami; Chatterjee, Moitrayee (December 2018, 2018 IEEE International Conference on Big Data (Big Data))

   With the continued improvement and innovation, technology has become an integral part of our daily lives. The rapid adoption of technology and its affordability has given rise to the Internet-of-Things (IoT). IoT is an interconnected network of devices that are able to communicate and share information seamlessly. IoT encompasses a gamut of heterogeneous devices ranging from a small sensor to large industrial machines. One such domain of IoT that has seen a significant growth in the recent few years is that of the wearable devices. While the privacy issues for medical devices has been well-researched and documented in the literature, the threats to privacy arising from the use of consumer wearable devices have received very little attention from the research community. This paper presents a survey of the literature to understand the various privacy challenges, mitigation strategies, and future research directions as a result of the widespread adoption of wearable devices. 

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5. Network Traffic Characteristics of IoT Devices in Smart Homes

   https://doi.org/10.1109/ICCCN52240.2021.9522168  

   Mainuddin, Md; Duan, Zhenhai; Dong, Yingfei (July 2021, 2021 International Conference on Computer Communications and Networks (ICCCN))

   Understanding network traffic characteristics of IoT devices plays a critical role in improving both the performance and security of IoT devices, including IoT device identification, classification, and anomaly detection. Although a number of existing research efforts have developed machine-learning based algorithms to help address the challenges in improving the security of IoT devices, none of them have provided detailed studies on the network traffic characteristics of IoT devices. In this paper we collect and analyze the network traffic generated in a typical smart homes environment consisting of a set of common IoT (and non-IoT) devices. We analyze the network traffic characteristics of IoT devices from three complementary aspects: remote network servers and port numbers that IoT devices connect to, flow-level traffic characteristics such as flow duration, and packet-level traffic characteristics such as packet inter-arrival time. Our study provides critical insights into the operational and behavioral characteristics of IoT devices, which can help develop more effective security and performance algorithms for IoT devices. 

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