Microsystem based Energy Harvesting (EH-MEMS): Powering pervasivity of the Internet of Things (IoT) – A review with focus on mechanical vibrations

The paradigm of the Internet of Things (IoT) appears to be the common denominator of all distributed sensing applications, providing connectivity, interoperability and communication of smart entities (e.g. environments, objects) within a pervasive network. The IoT demands for smart, integrated, miniaturised and low-energy wireless nodes, typically powered by non-renewable energy storage units (batteries). The latter aspect poses constraints as batteries have a limited lifetime and often their replacement is impracticable. Availability of zero-power energy-autonomous technologies, able to harvest (i.e. convert) and store part of the energy available in the surrounding environment (vibrations, thermal gradients, electromagnetic waves) into electricity to supply wireless nodes functionality, would fill a significant part of the technology gap limiting the wide diffusion of efficient and cost effective IoT applications. Given the just depicted scenario, the realisation of miniaturised Energy Harvesters (EHs) leveraging on MEMS technology (MicroElectroMechanical-Systems), i.e. EH-MEMS, seems to be a key-enabling solution able to conjugate both main driving requirements of IoT applications, namely, energy-autonomy and miniaturisation/integration.This short review outlines the current state of the art in the field of EH-MEMS, with a specific focus on vibration EHs, i.e. converters capable to convert the mechanical energy scattered in environmental vibrations, into electric power. In particular, the issues in terms of conversion performance arising from EHs scaling down, along with the challenge to extend their operability on a frequency range of vibrations as wider as possible, are going to be discussed in the following. Keywords: Energy Harvesting (EH), MEMS, Internet of Things (IoE), Ultra-Low Power (ULP), Zero-power electronic

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs