Abstract In a variety of emerging networked computing system domains over the years, there have been bursts of activity on new medium access control (MAC) protocols, as new communication transceiver technologies with greater data‐movement performance or lower power dissipation have been introduced. To enable implementations flexible to evolving standards and improving application‐domain insight, such MAC protocols are typically initially implemented in software, and interface between applications or system software, typically executing on an embedded processor or microcontroller, and the evolving radio transceiver hardware. Many challenges exist in implementing MAC protocols across evolving or competing transceiver hardware implementations and processor architectures. Some of these challenges are peculiar to the requirements of MAC protocols, and others are a result of the plethora of system and processor architectures in the embedded systems domain. This article studies the challenges facing software implementations of MAC protocols running on embedded microcontrollers, and interfacing with radio transceiver hardware. Experience with an implementation of the IEEE 802.15.4 MAC across three hardware platforms with different processor, system, and systems software architectures is presented, focusing on implementation approach and interfaces. Pitfalls are pointed out, and guidelines are provided for ensuring that new MAC implementations are easily portable across processor architectures and transceiver hardware. Copyright © 2010 John Wiley & Sons, Ltd.

Appliance load monitoring systems aim to achieve per appliance energy decomposition. Such systems however lack automated set-up, which precludes widespread roll-outs. This work illustrates how human supervision can be reduced to a strict minimum; appliance activity states are captured with cheap wireless sensors, which enables accurate automated energy data annotation. This paper demonstrates the accuracy of this technique via an intuitive graphical user interface.

Sensing, monitoring and actuating systems are expected to play a key role in reducing buildings overall energy consumption. Leveraging sensor systems to support energy efficiency in buildings poses novel research challenges in monitoring space usage, controlling devices, interfacing with smart energy meters and communicating with the energy grid. In the attempt of reducing electricity consumption in buildings, identifying individual sources of energy consumption is key to generate energy awareness and improve efficiency of available energy resources usage. Previous work studied several non-intrusive load monitoring techniques to classify appliances; however, the literature lacks of an comprehensive system that can be easily installed in existing buildings to empower users profiling, benchmarking and recognizing loads in real-time. This has been a major reason holding back the practice adoption of load monitoring techniques. In this paper we present RECAP: RECognition of electrical Appliances and Profiling in real-time. RECAP uses a single wireless energy monitoring sensor easily clipped to the main electrical unit. The energy monitoring unit transmits energy data wirelessly to a local machine for data processing and storage. The RECAP system consists of three parts: (1) Guiding the user for profiling electrical appliances within premises and generating a database of unique appliance signatures; (2) Using those signatures to train an artificial neural network that is then employed to recognize appliance activities (3) Providing a Load descriptor to allow peer appliance benchmarking. RECAP addresses the need of an integrated and intuitive tool to empower building owners with energy awareness. Enabling real-time appliance recognition is a stepping-stone towards reducing energy consumption and allowing a number of major applications including load-shifting techniques, energy expenditure breakdown per appliance, detection of power hungry and faulty appliances, and recognition of occupant activity. This paper describes the system design and performance evaluation in domestic environment.

Recent advances in low-power wireless networking have enabled remote and nonintrusive access to households' electric meter readings, allowing direct real-time feedback on electricity consumption to home owners and energy providers. Fine-grained electricity billing based on appliance power load monitoring has been investigated for more than two decades, but has not yet witnessed wide commercial acceptance. In this paper, we argue that the required human supervision for profiling and calibrating appliance load monitoring systems is a key reason preventing large-scale commercial roll-outs. We propose ANNOT, a system to automate electricity data annotation leveraging cheap wireless sensor nodes. Characteristic sensory stimuli captured by sensor nodes placed next to appliances are translated into appliance operating state and correlated to the electricity data, autonomously generating the annotation of electricity data with appliance activity. The system is able to facilitate the acquisition of appliance signatures, training data and validate the monitoring output. We validate the concept by integrating the automated annotation system to the RECAP appliance load monitoring system.

Appliance load monitoring systems are designed to disaggregate the power load of a building in order to estimate the nature of individual loads, providing a real-time fine-grained recognition of active appliances. Monitoring nonintrusively appliances' contributions to a given load enables a wide range of applications, ranging from electricity bill decomposition to accurate electricity user profiling. This work demonstrates a real implementation of such appliance load monitoring system. An intuitive graphical user interface is proposed to drive the system setup for profiling appliances' signatures and for visualising the monitoring output.

This paper outlines an efficient variation of the Flooding Time Synchronization Protocol (FTSP) on real hardware with an IEEE 802.15.4 MAC layer. The paper compares the performance of two clock drift estimation techniques: Least Squares Linear Regression and the Kalman filter. In assessing the viability of time synchronization in an industrial setting, our reliance on real hardware gives confidence for the deployment in future products. The concrete result is a high time synchronization accuracy, with an achievable average error of 0.4 μs, and a repetition interval of a few seconds, while using a star network topology.