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The high energy required by home appliances (like white goods, audio/video devices and communication equipments) and air conditioning systems (heating and cooling), makes our homes one of the most critical areas for the impact of energy consumption on natural environment. In this paper we present a work in progress within the European project AIM for the design of a system that can minimize energy waste in home environments efficiently managing devices operation modes. In our architecture we use a wireless sensor network to monitor physical parameters (like light and temperature) as well as the presence of users at home and in each of its rooms. With gathered data our system creates profiles of the behavior of house inhabitants and through a prediction algorithm is able to automatically set system parameters in order to optimize energy consumption and cost while guaranteeing the required comfort level. When users change their habits due to unpredictable events, the system is able to detect wrong predictions analyzing in real time information from sensors and to modify system behavior accordingly. By the automatic control of energy management system it is possible to avoid complex manual settings of system parameters that would prevent the introduction of home automation systems for energy saving into the mass market.

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Abstract-Wireless Multimedia Sensor Networks (WMSNs) are recently emerging as an extension to traditional scalar wireless sensor networks, with the distinctive feature of supporting the acquisition and delivery of multimedia content such as audio, images and video. In this paper, a complete framework is proposed and developed for streaming video flows in WMSNs. Such framework is designed in a cross-layer fashion with three main building blocks: (i) a hybrid DPCM/DCT encoder; (ii) a congestion control mechanism and (iii) a selective priority automatic request mechanism at the MAC layer. The system has been implemented on the IntelMote2 platform operated by TinyOS and thoroughly evaluated through testbed experiments on multi-hop WMSNs. The source code of the whole system is publicly available to enable reproducible research.

We propose an optimization-based framework to minimize the energy consumption in a sensor network when using an indoor localization system based on the combination of received signal strength (RSS) and pedestrian dead reckoning (PDR). The objective is to find the RSS localization frequency and the number of RSS measurements used at each localization round that jointly minimize the total consumed energy, while ensuring at the same time a desired accuracy in the localization result. The optimization approach leverages practical models to predict the localization error and the overall energy consumption for combined RSS-PDR localization systems. The performance of the proposed strategy is assessed through simulation, showing energy savings with respect to other approaches while guaranteeing a target accuracy