Reducing energy consumption within buildings has been an active area of research in the past decade; more recently, there has been an increased influx of activity, motivated by a variety of issues including legislative, tax-related, as well as an increased awareness of energy-related issues. Energy usage both in commercial and residential buildings represents a significant portion of overall energy consumption; however, much of this may be categorized as waste, that is, energy usage that does not fulfil a definite purpose. In the past decade, the viability of Wireless Sensor Network (WSN) technologies has been demonstrated, leading to increased possibilities for novel services for building energy management. This development has resulted in numerous approaches being proposed for harnessing WSNs for energy management and conservation. This article surveys the state-of-the-art in building energy management systems. A generic architecture is proposed after which a detailed taxonomy of existing documented systems is presented. Gaps in the literature are highlighted and directions for future research identified.

This paper describes and validates an extension to the widely used Flooding Time Synchronisation Protocol (FTSP) namely Dynamic Flooding Time Synchronisation Protocol for Wireless Sensor Networks. The extension enhances FTSP by dynamically optimising communication exchange with the dual aim of conserving energy whilst improving accuracy. This optimisation is realised by altering the transmission rate of motes based on real-time monitoring and the WSN application accuracy requirements. The proposed mechanism, thus, eliminates the need with FTSP to determine and pre-configure a suitable transmission interval for a particular environment. Our results show that the proposed protocol extension can perform as well as FTSP in a stable temperature environment and can achieve similar accuracies with a much reduced (up to 75 percent) number of transmissions in an environment whereby motes are exposed to temperature fluctuations.

Abstract Sensor network monitoring and control are currently addressed separately through specialized tools. However, the high degree of coupling of network state to the physical environment in which the network is deployed demands that users can monitor the network and respond to network state changes continuously. This paper presents the open‐source Octopus visualization and control tool. Octopus is a protocol‐independent tool that provides live information about the network topology and sensor data in order to enable live debugging of deployed sensor networks. It enables operators to reconfigure the network behavior, such as switching between time‐driven, event‐driven, and query‐driven modes or between awake and sleep modes of one, many, or all nodes through its graphical interface. Octopus also supports changing duty cycles of nodes, data reporting period, or sensing thresholds in event‐driven networks. Reconfiguration of nodes is achieved through short request messages that support typical reconfiguration options without the overhead of epidemically sending new program images over the air. Our empirical tests showcase Octopus's capacity to debug application behavior and to characterize heterogeneous network performance under multiple settings, as a step toward establishing a rules database that relates data delivery to network‐level parameters, and toward enabling autonomous network reconfiguration. Copyright © 2009 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.

Energy efficiency is a central challenge in sensor networks, and the radio is a major contributor to overall energy node consumption. Current energy-efficient MAC protocols for sensor networks use a fixed low-power radio mode for putting the radio to sleep. Fixed low-power modes involve an inherent trade-off: deep sleep modes have low current draw and high energy cost and latency for switching the radio to active mode, while light sleep modes have quick and inexpensive switching to active mode with a higher current draw. This paper proposes adaptive radio low-power sleep modes based on current traffic conditions in the network. It first introduces a comprehensive node energy model, which includes energy components for radio switching, transmission, reception, listening, and sleeping, as well as the often disregarded microcontroller energy component for determining the optimal sleep mode and MAC protocol to use for given traffic scenarios. The model is then used for evaluating the energy-related performance of our recently proposed RFID impulse protocol enhanced with adaptive low-power modes, and comparing it against BMAC and IEEE 802.15.4, for both MicaZ and TelosB platforms under varying data rates. The comparative analysis confirms that RFID impulse with adaptive low-power modes provides up to 20 times lower energy consumption than IEEE 802.15.4 in low traffic scenario. The evaluation also yields the optimal settings of low-power modes on the basis of data rates for each node platform, and provides guidelines and a simple algorithm for the selection of appropriate MAC protocol, low-power mode, and node platform for a given set of traffic requirements of a sensor network application.

The efficient management of scarce network resources, including energy and bandwidth, represents a central challenge for wireless sensor networks. The current trend in resource management relies on the introduction of control mechanisms, such as control message exchanges, node-specific addressing, and storage of partial network state information. These mechanisms typically incur communication and processing overhead that does not scale well for larger or denser networks. Instead of introducing control mechanisms for network resource management, this article proposes and evaluates a Directed Broadcast with Overhearing (DBO) approach for sensor networks that combines directed broadcast at the network layer with CSMA and packet overhearing at the MAC layer. Through avoidance of control messaging and exchange of network state information, DBO trades off limited packet duplication overhead for control messaging overhead. This article introduces an analytical model that provides the basis for DBO evaluation and for analysis of the approach's transient packet retransmissions, route convergence, and energy consumption in the average and worst cases. We also present the model implementation details and the simulation experiments that explore the suitability of DBO for networks of different sizes with three different radio models that vary the width of grey regions, and we compare DBO's energy consumption against conventional unicast beacon-based and snooping-based routing protocols. The results indicate that that DBO's route convergence requires an average of five hops for ideal radio reception, seven hops for narrow grey regions, and twelve hops for wide grey regions. These results confirm that DBO shifts energy consumption from critical nodes near the base station to nodes near the source. The overall energy consumption of limited packet duplication overhead with DBO compared to unicast routing shrinks for medium- to large-size networks, rendering it more favorable than conventional communication approaches for large and dense sensor networks.

Energy efficiency is a central challenge in battery- operated sensor networks. Current energy-efficient mechanisms employ either duty cycling, which reduces idle listening but does not eliminate it, or low power wake-up radio, which adds complexity and cost to the sensor platform. In this paper, we propose a novel mechanism called RFIDImpulse that uses RFID technology as an out-of-band wake-up channel for sensor networks. RFIDImpulse is an on-demand mechanism that enables nodes to sleep until they have to send or receive packets. It relies on IEEE 802.15.4 radio to emulate an RFID reader at a sender node, and on an off-the-shelf RFID tag attached to the external interrupt pin of each sensor node. The sender can simply activate the receiver's tag before sending it data packets. This setup enables both radio and microcontroller to go into deep sleep mode until they need to be active. We develop an analytical model to evaluate the energy tradeoffs of RFIDImpulse, and then evaluate the mechanism against BMAC and IEEE 802.15.4 in high and low traffic scenarios. The results confirm that RFIDImpulse reduces the energy consumption relative to both protocols for low and medium traffic scenarios, and they reveal the thresholds for adaptive activation of RFIDImpulse based on traffic load.

Abstract—Energy-efficient performance is a central challenge in sensor network deployments, and the radio is a major contributor to overall energy node consumption. Current energy-efficient MAC protocols for sensor networks use a fixed low power radio mode for putting the radio to sleep. Fixed low power modes involve an inherent tradeoff: deep sleep modes have low current draw and high energy cost and latency for switching the radio to active mode, while light sleep modes have quick and inexpensive switching to active mode with a higher current draw. This paper proposes adaptive radio low power sleep modes based on current traffic conditions in the network, as an enhancement to our recent RFIDImpulse low power wake-up mechanism. The paper also introduces a comprehensive node energy model, that includes energy components for radio switching, transmission, reception, listening, and sleeping, as well as the often disregarded micro-controller energy component to evaluate energy performance for both MicaZ and TelosB platforms, which use different MCU’s. We then use the model for comparing the energy-related performance of RFIDImpulse enhanced with adaptive low power modes with BMAC and IEEE 802.15.4 for the two node platforms under varying data rates. The comparative analysis confirms that RFIDImpulse with adaptive low power modes provides up to 20 times lower energy consumption than IEEE 802.15.4 in low traffic scenario. The evaluation also yields the optimal settings of low power modes on the basis of data rates for each node platform, and it provides guidelines for the selection of appropriate MAC protocol, low power mode, and node platform for a given set of traffic requirements of a sensor network application. I.

Symbol synchronization in traditional hardware- driven communication systems has relied on the transmission of training sequences of symbols just before the beginning of the frame symbols. The use of training sequences is not suitable for software-driven communication systems, such as lightweight acoustic underwater sensor networks [4,5], in which the high symbol loss rate may cause the loss of training symbols, preventing accurate symbol synchronization. Software-driven communication networks require symbol synchronization that is resilient to a high loss environment, that does not represent large communication or processing overhead, and that is tunable to the noise profile of different environments. These requirements are emphasized for mote-based acoustic underwater sensor networks in which the bandwidth and processing capability are sparse. This paper proposes the use of a short signature synchronization symbol (S4) as both a preamble and post-amble to enable receiver synchronization in mote-based acoustic communication systems that rely on software modems. To synchronize to an incoming signal, the receiver performs cross-correlation of N reference signature symbols with the incoming signal to identify the beginning of the preamble and post-amble. The output of the cross-correlation yields 2N peak values, from which the receiver chooses the sharpest and most symmetric for synchronization to the beginning of the frame. Empirical experiments confirm a synchronization accuracy within 5 ms in air within a range of 10.5 m, and 11 ms in water within a range of 15 m.

The emerging technology of Wireless Sensor Networks offers the potential for a new and unparalleled level of surveillance for events occurring in an environment. The quality of surveillance will be directly related to the quantity and density of sensors that can be deployed, and that are actively participating in the monitoring at any given instant. In opposition to this is the requirement for judicious power management of the network in order to prolong its operational lifespan by minimizing node activity. In this paper we explore the effect on surveillance quality when using interpolation error as a mechanism for deciding node activations. More specifically we examine the effect of using this signal based hibernation technique on target localization error and node activity.

Notoriously, energy-efficient MAC protocols cause high latency of packets. Such delays may well increase when a routing protocol is applied. Therefore, quantifying the end-to-end delay and energy consumption when low duty cycle MAC and routing protocols are jointly used, is of particular interest. In this paper, we present a comprehensive evaluation of the MERLIN (MAC and efficient routing integrated with support for localization) protocol. MERLIN integrates MAC and routing features into a single architecture. In contrast to many sensor network protocols, it employs a multicast upstream and multicast downstream approach to relaying packets to and from the gateway. Simultaneous reception and transmission errors are notified by using asynchronous burst ACK and negative burst ACK. A division of the network into timezones, together with an appropriate scheduling policy, enables the routing of packets to the closest gateway. An evaluation of MERLIN has been conducted through simulation, against both the SMAC and the ESR routing protocols,which is an improved version of the DSR algorithm. The results illustrate how both SMAC and ESR, jointly used in low duty cycle scenarios, can cause an impractical and very high end-to-end delays. MERLIN, as an integrated approach, notably reduces the latency, resulting in nodes that can operate in a very low duty cycle. Consequently, an extension of the operative lifetime of the sensor network is achieved.