With the numerous IoT devices, the cloud-centric data processing fails to meet the requirement of all IoT applications. The limited computation and communication capacity of the cloud necessitate the Edge Computing, i.e., starting the IoT data processing at the edge and transforming the connected devices to intelligent devices. Machine learning, the key means for information inference, should extend to the cloud-to-things continuum too. This article reviews the role of machine learning in IoT from the cloud down to embedded devices. Different usages of machine learning for application data processing and management tasks are studied. The state-of-the-art usages of machine learning in IoT are categorized according to their application domain, input data type, exploited machine learning techniques, and where they belong in the cloud-to-things continuum. The challenges and research trends toward efficient machine learning on the IoT edge are discussed. Moreover, the publications on the ‘machine learning in IoT’ are retrieved and analyzed systematically using machine learning classification techniques. Then, the growing topics and application domains are identified.

With the widespread use of IoT applications and the increasing trend in the number of connected smart devices, the concept of routing has become very challenging. In this regard, the IPv6 Routing Protocol for Low-power and Lossy Networks (PRL) was standardized to be adopted in IoT networks. Nevertheless, while mobile IoT domains have gained significant popularity in recent years, since RPL was fundamentally designed for stationary IoT applications, it could not well adjust with the dynamic fluctuations in mobile applications. While there have been a number of studies on tuning RPL for mobile IoT applications, but still there is a high demand for more efforts to reach a standard version of this protocol for such applications. Accordingly, in this survey, we try to conduct a precise and comprehensive experimental study on the impact of various mobility models on the performance of a mobility-aware RPL to help this process. In this regard, a complete and scrutinized survey of the mobility models has been presented to be able to fairly justify and compare the outcome results. A significant set of evaluations has been conducted via precise IoT simulation tools to monitor and compare the performance of the network and its IoT devices in mobile RPL-based IoT applications under the presence of different mobility models from different perspectives including power consumption, reliability, latency, and control packet overhead. This will pave the way for researchers in both academia and industry to be able to compare the impact of various mobility models on the functionality of RPL, and consequently to design and implement application-specific and even a standard version of this protocol, which is capable of being employed in mobile IoT applications.

The massive number of Internet of Things (IoT) devices generates a hard-to-manage volume of data. Cloud/centric processing approaches for IoT data suffer from high and unpredictable network latency, which causes poor experience in real-time IoT applications such as healthcare. To address this issue, in edge computing, the data inference starts from the data source (i.e. the IoT devices). However, the constrained computational capabilities of the IoT device and the power-hungry data transmission demand a trade-off between onboard processing and computation offloading. Hence, the IoT information inference requires efficient and lightweight techniques that are tailored for this trade-off and respect the constrained resources on IoT devices such as wearables. This article presents a hierarchical classification approach that decomposes the problem into three classifiers in two hierarchy layers: In the first layer, a lightweight classifier executes directly on the IoT device and decides whether to offload the computation to the gateway or to perform it onboard. The second layer comprises a lightweight classifier on the IoT device (can only distinguish a subset of classes) and a complex classifier on the gateway (to distinguish the remaining classes). The experimental results (using a real-world dataset for human activity recognition and implemented on a wearable IoT device) show higher accuracy (92% on average) than a non-hierarchical classifier (87% on average). The execution time and power measurements on the IoT device show 3× energy/saving for the classification.

Mobile portable embedded devices are becoming an integral part of our daily activities in the vision of Internet of Things (IoT). Nevertheless, due to lack of mobility support in the IPv6 routing protocol for low-power and lossy networks (RPLs), which is standardized for multihop IoT infrastructures, providing reliable communications in terms of packet delivery ratio (PDR) in mobile IoT applications has become significantly challenging. While several studies tried to enhance the adaptability of RPL to network dynamics, their utilized routing metrics have prevented them from establishing long-lasting reliable paths. Furthermore, the stochastic parent replacement policy in the standard version of RPL has intensified this challenge. Aside from this, due to the existing tradeoff between reliability and power efficiency, most of the existing approaches have only concentrated on one of these concerns without paying attention to the other one. To address these issues, this article introduces ARMOR, a routing mechanism built upon RPL, which employs a novel mobility-aware routing metric, i.e., time to reside (TTR), and a corresponding parent replacement policy. According to the motion characteristics of the mobile objects, TTR provides an estimation of how long the nodes will be in the transmission range of each other. This enables ARMOR to select nodes, which provide longer connection period and consequently higher reliability. In comparison with the state of the art, while keeping the power consumption constant, ARMOR significantly improves the amount of PDR in the network by up to $2.5\times $ , while it enhances the reliability against the original version of this protocol by up to $4.2\times $ .

FPGAs are being offered in the cloud as accelerator resources that can be shared among multiple users (i.e. tenants). Recently, various approaches have shown that fault attacks launched from one tenant region to another are possible, leading to timing faults or crashes of the FPGA. It is, therefore, important that malicious tenants are limited in their ability to cause such security problems. So far, the existing countermeasures against such attacks check the configuration bitstreams before they are reconfigured. Such offline approaches have various practical limitations, e.g. they may force the tenants to unveil their design secrets. In this paper, we present LoopBreaker, a novel runtime solution that can disable the entire activity of a malicious tenant region, in order to rapidly stop a potential attack before it results in a crash (i.e. Denial-of-Service). We implemented and tested multiple attack types and found that realistic attacks demand at least 12–26 µs to be successful. A partial reconfiguration to overwrite the malicious tenant region demands 200 µs in our realworld implementation, which is too slow to prevent the attack from leading to a crash. Instead, our proposed LoopBreaker method only needs 1.5 µs to stop a malicious tenant, which makes it the first online approach that can successfully stop challenging voltage drop-based attacks from causing a crash.

With the rise of the Internet of Things (IoT), resource-constrained and power-constrained devices attract more attention. The need for lightweight solutions as alternatives to resource-intensive applications became more urgent. Moreover, as the number of connected devices grew, authenticating them became more challenging. Traditionally, this would be performed by using hash functions and secure memory to store a key, which both come at a high cost. physical unclonable functions (PUFs) emerged as a suitable lightweight alternative to hash functions to authenticate the devices. Using the inherent minute differences between integrated circuits (ICs), they can generate IC-specific responses for input challenges coming from a so-called verifier. Through the years, machine learning (ML) has been used to attack PUFs by modeling them and accurately predicting their response to a given challenge. This stimulated research on ML-resilient PUFs. This resilience came with the significant area and challenge-to-response delay overheads. In this work, we introduce the novel cascaded PUF (CaPUF) and show that it is resilient against state-of-the-art ML-based attacks, i.e., logistic regression (LR) and support vector machines (SVMs). These attacks could not achieve accuracy better than 52% against our CaPUF, which is only as good as flipping a coin. Additionally, our CaPUF requires 89% less area compared to state-of-the-art ML-resilient PUFs.