Smart environments interconnect indoor building environments, indoor wireless sensor and actuator networks, smartphones, and human together to provide smart infrastructure management and intelligent user experiences. To enable the "smart" operations, a complete set of hardware and software components are required. In this work, we present Smart Syndesi, a system for creating indoor location-aware smart building environments using wireless sensor and actuator networks (WSANs). Smart Syndesi includes an indoor tracking system, a WSAN for indoor environmental monitoring and activation automation, and a gateway interconnecting WSAN, tracking system with mobile users.The indoor positioning system tracks the real-time location of occupants with high accuracy, which works as a basis for indoor location-based sensor actuation automation.To show how the multiple software hardware components are integrated, we implemented the system prototype and performed intensive experiments in indoor office environments to automate the indoor location-driven environmental sensor monitoring and activation process. The tracked indoor location of a user's smartphone triggers the retrieval of environmental measurements and activates the actuators automatically (i.e. turn on off lights, switch on off fans) based on the location and correlated environmental sensor information.
The first problem in smart environment application is to detect or identify the number and identification of the residents or occupants in home or office building environments. Such knowledge can be very useful to various home office automation applications, such as energy management, lighting control, and security. @cite_14 proposed to estimate the number of people in an indoor environment using information available in standard HVAC (heating, ventilating, and air conditioning) systems. Their approach avoids the installation of extra hard sensors in the environment and offers a new solution to this problem. @cite_27 offers a different solution to this problem by using the Google Glass, which combines data from both visual and inertial sensors. @cite_13 investigated the issue of building occupancy estimation by using a wireless @math sensor network.
@cite_27 @cite_14 @cite_13
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The paper addresses the problem of efficiently monitoring environmental fields in a smart building by the use of a network of wireless noisy sensors that take discretely-predefined measurements at their locations through time. It is proposed that the indoor environmental fields are statistically modeled by spatio-temporal non-parametric Gaussian processes. The proposed models are able to effectively predict and estimate the indoor climate parameters at any time and at any locations of interest, which can be utilized to create timely maps of indoor environments. More importantly, the monitoring results are practically crucial for building management systems to efficiently control energy consumption and maximally improve human comfort in the building. The proposed approach was implemented in a real tested space in a university building, where the obtained results are highly promising. A general approach is proposed to determine the common sensors that shall be used to estimate and classify the approximate number of people (within a range) in a room. The range is dynamic and depends on the maximum occupancy met in a training data set for instance. Means to estimate occupancy include motion detection, power consumption, CO2 concentration sensors, microphone or door window positions. The proposed approach is inspired by machine learning. It starts by determining the most useful measurements in calculating information gains. Then, estimation algorithms are proposed: they rely on decision tree learning algorithms because these yield decision rules readable by humans, which correspond to nested if-then-else rules, where thresholds can be adjusted depending on the living areas considered. In addition, the decision tree depth is limited in order to simplify the analysis of the tree rules. Finally, an economic analysis is carried out to evaluate the cost and the most relevant sensor sets, with cost and accuracy comparison for the estimation of occupancy. C45 and random forest algorithms have been applied to an office setting, with average estimation error of 0.19–0.18. Over-fitting issues and best sensor sets are discussed. Smart environment has been evolved as a hot research topic with the development of machine learning algorithms and wireless communication technologies. Existing smart home solutions usually need inhabitants to operate device controllers directly. However, for people with disabilities, it is inconvenient and difficult to perform such manual operations. Therefore, it is important to develop automatic and intelligent services to reduce operation inconvenience and improve comfort level. In this paper, an Improved Hidden Markov Model (IHMM) is presented to support personalized behavior prediction for people with disabilities. The model can learn behavior patterns of users and provide services to inhabitants automatically. Moreover, by breaking the time invariant hypothesis in Hidden Markov Model, we incorporate time information as positions of states, and develop a temporal state transition matrix to replace the fixed state transition matrix to demonstrate the probabilities of state transitions. As a result, different values of daily temperature sections are characterized and identified as hidden variables, which guide user activities. Evaluation of proposed work has shown that IHMM improves the prediction accuracy by at least 10 compared to the traditional HMM. During the last decades, location based services have become very popular and the developed indoor positioning systems have achieved an impressive accuracy. The problem though is that even if the only requirement is room-level localization, those systems are most of the times not cost-efficient and not easy to set-up, since they often require time-consuming calibration procedures. This paper presents a low-cost, threshold-based approach and introduces an algorithm that takes into account both the Received Signal Strength Indication (RSSI) of the Bluetooth Low Energy (BLE) beacons and the geometry of the rooms the beacons are placed in. Performance evaluation was done via measurements in an office environment composed of three rooms and in a house environment composed of six rooms. The experimental results show an improved accuracy in room detection when using the proposed algorithm, compared to when only considering the RSSI readings. This method was developed to provide context awareness to the international research project named SmartHeat. The projects aims to provide a system that efficiently heats a house, room by room, based on the habitants' habits and preferences.