Energy transparency is a concept that makes a program’s energy consumption visible, from hardware up to software, through the different system layers. Such transparency can enable energy optimizations at each layer and between layers, as well as help both programmers and operating systems make energy-aware decisions. In this article, we focus on deeply embedded devices, typically used for Internet of Things (IoT) applications, and demonstrate how to enable energy transparency through existing static resource analysis (SRA) techniques and a new target-agnostic profiling technique, without hardware energy measurements. Our novel mapping technique enables software energy consumption estimations at a higher level than the Instruction Set Architecture (ISA), namely the LLVM intermediate representation (IR) level, and therefore introduces energy transparency directly to the LLVM optimizer. We apply our energy estimation techniques to a comprehensive set of benchmarks, including single- and multithreaded embedded programs from two commonly used concurrency patterns: task farms and pipelines. Using SRA, our LLVM IR results demonstrate a high accuracy with a deviation in the range of 1% from the ISA SRA. Our profiling technique captures the actual energy consumption at the LLVM IR level with an average error of 3%.

Internet of Things (IoT) and blockchains are enabling technologies for modern healthcare applications, offering the improved monitoring of patient health and higher data integrity guarantees. However, in rural settings, communication reliability can pose a challenge that constrains real-time data usage. Additionally, the limited computation and communication resources of IoT sensors also means that they may not participate directly in blockchain transactions, reducing trust. This paper proposes a solution to these challenges, enabling the use of blockchain-based IoT healthcare devices in low-bandwidth rural areas. This integrated system, named hybrid channel healthcare chain (HC${}^2$), uses two communication channels: short-range communication for device authorisation and bulk data transfer, and long-range the radio for light-weight monitoring and event notifications. Both channels leverage the same cryptographic identity information, and through the use of a cloud-based digital twin, the IoT device is able to sign its own transactions, without disclosing the key to said twin. Patient data are encrypted end to end between the IoT device and data store, with the blockchain providing a reliable record of the data lifecycle. We contribute a model, analytic evaluation and proof of concept for the HC${}^2$ system that demonstrates its suitability for the stated scenarios by reducing the number of long-range radio packets needed by 87× compared to a conventional approach.

There is growing adoption of smart devices such as digital locks with remote control and sophisticated authentication mechanisms. However, a lack of attention to device security and user-awareness beyond the primary function of these IoT devices may be exposing users to invisible risks. This paper extends upon prior work that defined the "droplock", an attack whereby a smart lock is turned into a wireless fingerprint harvester. We perform a more in-depth analysis of a broader range of vulnerabilities and exploits that make a droplock attack easier to perform and harder to detect. Analysis is extended to a range of other smart lock models, and a threat model is used as the basis to recommend stronger security controls that may mitigate the risks of such as attack.

Increasingly, machine learning inference is implemented on relatively low-powered edge devices, where battery life is a key performance criterion. In this work, we demonstrate how C++20 coroutines can be used to reorganise the execution order of an iterative inference task on an edge device. A Prognostic and Health Management (PHM) application receives streams of vibration data as envelope spectra from a wireless sensor network and processes them locally through an array of Support Vector Machines. In our experiments on ARM Cortex A72 & A53 64-bit SoCs, this method can reduce energy consumption for the task by up to 18%, reduce overall energy use by up to 20% and cut execution time by up to 20.5%. Furthermore, peak power levels are reduced by up to 4.5%, which can increase battery lifespan by reducing wear. We demonstrate that the necessary changes to the C++ code are simple, repeatable and generally applicable to iterative inference tasks.

Trust mechanisms for Internet of Things (IoT) devices are commonly used by manufacturers and other ecosystem participants. However, end users face a challenge in establishing trust in devices, particularly as device encounters become more frequent thanks to the proliferation of new and unique products. Communication or even physical interaction with a device can expose a user to various threats, such as biometric theft or exploit of their own device. To address this, we propose a mechanism for verifying the integrity and trustworthiness of an IoT device before physical interaction or any significant communication has taken place.

IoT data demands are growing, with Distributed Ledger Technologies (DLTs) offering secure data management, provided they can meet scaling and efficiency requirements that are more restrictive than in conventional application environments. This article comprehensively surveys 27 DLTs of varying paradigms and implementation methods, proposes a scoring method for determining DLT-IoT integration suitability, and then applies that method to the surveyed DLTs. Six DLTs were shortlisted as the most promising, which were then subjected to in-depth analysis around three IoT use cases: health-IoT, e-commerce and automotive manufacturing. We discuss the viability of lightweight DLTs and identify crucial future research directions.