Security and privacy of the users have become significant concerns due to the involvement of the Internet of things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace making the existing security and privacy measur
es inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on efforts made in recent years addressing both security and privacy issues using ML algorithms and BC techniques. In this paper, we provide a summary of research efforts made in the past few years, starting from 2008 to 2019, addressing security and privacy issues using ML algorithms and BCtechniques in the IoT domain. First, we discuss and categorize various security and privacy threats reported in the past twelve years in the IoT domain. Then, we classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. Finally, we identify and illuminate several challenges and future research directions in using ML algorithms and BC techniques to address security and privacy issues in the IoT domain.
Wireless edge is about distributing intelligence to the wireless devices wherein the distribution of accurate time reference is essential for time-critical machine-type communication (cMTC). In 5G-based cMTC, enabling time synchronization in the wire
less edge means moving beyond the current synchronization needs and solutions in 5G radio access. In this article, we analyze the device-level synchronization needs of potential cMTC applications: industrial automation, power distribution, vehicular communication, and live audio/video production. We present an over-the-air (OTA) synchronization scheme comprised of 5G air interface parameters, and discuss their associated timing errors. We evaluate the estimation error in device-to-base station propagation delay from timing advance (TA) under random errors and show how to reduce the estimation error. In the end, we identify the random errors specific to dense multipath fading environments and discuss countermeasures.
