Advances in micro-electronics and miniaturized mechanical systems are redefining the scope and extent of the energy constraints found in battery-operated wireless sensor networks (WSNs). On one hand, ambient energy harvesting may prolong the systems’ lifetime or possibly enable perpetual operation. On the other hand, wireless energy transfer allows systems to decouple the energy sources from the sensing locations, enabling deployments previously unfeasible. As a result of applying these technologies to WSNs, the assumption of a finite energy budget is replaced with that of potentially infinite , yet intermittent , energy supply, profoundly impacting the design, implementation, and operation of WSNs. This article discusses these aspects by surveying paradigmatic examples of existing solutions in both fields and by reporting on real-world experiences found in the literature. The discussion is instrumental in providing a foundation for selecting the most appropriate energy harvesting or wireless transfer technology based on the application at hand. We conclude by outlining research directions originating from the fundamental change of perspective that energy harvesting and wireless transfer bring about.

We present code instrumentation strategies to allow transiently-powered embedded sensing devices efficiently checkpoint the system's state before energy is exhausted. Our solution, called HarvOS, operates at compile-time with limited developer intervention based on the control-flow graph of a program, while adapting to varying levels of remaining energy and possible program executions at run-time. In addition, the underlying design rationale allows the system to spare the energy-intensive probing of the energy buffer whenever possible. Compared to existing approaches, our evaluation indicates that HarvOS allows transiently-powered devices to complete a given workload with 68% fewer checkpoints, on average. Moreover, our performance in the number of required checkpoints rests only 19% far from that of an \"oracle\" that represents an ideal solution, yet unfeasible in practice, that knows exactly the last point in time when to checkpoint.

Fault diagnosis plays a major role in railway condition monitoring, as early diagnosis of the emerging faults can save valuable time, reduce maintenance costs and, most significantly, help save people's lives. However, the conventional data-driven methods used to diagnose track faults, especially in underdeveloped countries, use push trolley/train-based track recording vehicles (TRV) that rely heavily on manual extraction of track data. It is a very demanding process and significantly affects the final results due to its reliance on human judgment in assessing track conditions and its suboptimal performance. In contrast, with the advent of IoT-based smart inertial measurement units, the data-driven fault diagnosis became a core component in the smart industrial automation safety system. We proposed, Muhafiz, a prototype that is an automated and portable TRV with a novel design based on axle-based acceleration methodology for rail track fault diagnosis. Our contribution concluded, based on site-specific experimentation, that Muhafiz is 87% more efficient than the traditional push trolley-based TRV mechanism.