Zigbee Based Home Automation and Agricultural Monitoring System A mesh networking approach for autonomous and manual system control

Today’s generation of electronic devices are more enhanced and capable than the previous ones with exciting changes in technology has seen to control a variety of home devices with the help of a home automation system. These devices can include lights, fans, doors, surveillance systems and consumer electronics. However along with the smartness and intuitiveness we want a system which is economic as well as low power consuming. ZigBee technology collects and monitors different types of measurements that reflect energy consumption and environment parameters. This paper details the designing of a protocol to monitor various environmental conditions in a home. We are using advanced technology of Micaz motes (which have their own routing capabilities), NESC language programming and Moteworks (used as a data acquisition platform)

Human identification via unsupervised feature learning from UWB radar data

This paper presents an automated approach to automatically distinguish the identity of multiple residents in smart homes. Without using any intrusive video surveillance devices or wearable tags, we achieve the goal of human identification through properly processing and analyzing the received signals from the ultra-wideband (UWB) radar installed in indoor environments. Because the UWB signals are very noisy and unstable, we employ unsupervised feature learning techniques to automatically learn local, discriminative features that can incorporate intra-class variations of the same identity, and yet reflect differences in distinguishing different human identities. The learned features are then used to train an SVM classifier and recognize the identity of residents. We validate our proposed solution via extensive experiments using real data collected in real-life situations. Our findings show that feature learning based on K-means clustering, coupled with whitening and pooling, achieves the highest accuracy, when only limited training data is available. This shows that the proposed feature learning and classification framework combined with the UWB radar technology provides an effective solution to human identification in multi-residential smart homes

Multi-Residential Activity Labelling in Smart Homes with Wearable Tags Using BLE Technology

Smart home platforms show promising outcomes to provide a better quality of life for residents in their homes. One of the main challenges that exists with these platforms in multi-residential houses is activity labeling. As most of the activity sensors do not provide any information regarding the identity of the person who triggers them, it is difficult to label the sensor events in multi-residential smart homes. To deal with this challenge, individual localization in different areas can be a promising solution. The localization information can be used to automatically label the activity sensor data to individuals. Bluetooth low energy (BLE) is a promising technology for this application due to how easy it is to implement and its low energy footprint. In this approach, individuals wear a tag that broadcasts its unique identity (ID) in certain time intervals, while fixed scanners listen to the broadcasting packet to localize the tag and the individual. However, the localization accuracy of this method depends greatly on different settings of broadcasting signal strength, and the time interval of BLE tags. To achieve the best localization accuracy, this paper studies the impacts of different advertising time intervals and power levels, and proposes an efficient and applicable algorithm to select optimal value settings of BLE sensors. Moreover, it proposes an automatic activity labeling method, through integrating BLE localization information and ambient sensor data. The applicability and effectiveness of the proposed structure is also demonstrated in a real multi-resident smart home scenario