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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 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. 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 micro-controller 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 RFIDImpulse 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 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 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.

Publication informationPresence-Teleoperators and Virtual Environments, 12 (5): 523-537 Publisher MIT PressItem record/more information

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In modern energy aware buildings, lighting control systems are put in place so to maximise the energy-efficiency of the lighting system without effecting the comfort of the occupant. In many cases this involves utilising a set of presence sensors, with actuators, to determine when to turn on/off or dim lighting, when it is deemed necessary. Such systems are installed using standard tuning values statically fixed by the system installer. This can cause inefficiencies and energy wastage as the control system is never optimised to its surrounding environment. In this paper, we investigate a Wireless Sensor Network (WSN) as a viable tool that can help in analysing and evaluating the energy-efficiency of an existing lighting control system in a low-cost and portable solution. We introduce LightWiSe (LIGHTting evaluation through WIreless SEnsors), a wireless tool which aims to evaluate lighting control systems in existing office buildings. LightWiSe determines points in the control system that exhibit energy wastage and to highlight areas that can be optimised to gain a greater efficiency in the system. It will also evaluate the effective energy saving to be obtained by replacing the control system with a more judicious energy saving solution. During a test performed in an office space, with a number of different lighting control systems we could highlight a number of areas to reduce waste and save energy. Our findings show that each system tested can be optimised to achieve greater efficiency. LightWiSe can highlight savings in the region of 50% to 70% that are achievable through optimising the current control system or installing an alternative.

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Activity recognition is fundamental to many of the services envisaged in pervasive computing and ambient intelligence scenarios. However, delivering sufficiently robust activity recognition systems that could be deployed with confidence in an arbitrary real-world setting remains an outstanding challenge. Developments in wireless, inexpensive and unobtrusive sensing devices have enabled the capture of large data volumes, upon which a variety of machine learning techniques have been applied in order to facilitate interpretation of and inference upon such data. Much of the documented research in this area has in the main focused on recognition across pre-segmented sensor data. Such approaches are insufficient for near real-time analysis as is required for many services, such as those envisaged by ambient assisted living. This paper presents a novel near real-time sensor segmentation approach that incorporates the notions of both sensor and time correlation.