Abstract
The trend toward technology ubiquity in human life is constantly increasing and the same tendency is clear in all technologies aimed at human monitoring. In this framework, several smart home system architectures have been presented in literature, realized by combining sensors, home servers, and online platforms. In this paper, a new system architecture suitable for human monitoring based on Wi-Fi connectivity is introduced. The proposed solution lowers costs and implementation burden by using the Internet connection that leans on standard home modem-routers, already present normally in the homes, and reducing the need for range extenders thanks to the long range of the Wi-Fi signal. Since the main drawback of the Wi-Fi implementation is the high energy drain, low power design strategies have been considered to provide each battery-powered sensor with a lifetime suitable for a consumer application. Moreover, in order to consider the higher consumption arising in the case of the Wi-Fi/Internet connectivity loss, dedicated operating cycles have been introduced obtaining an energy savings of up to 91%. Performance was evaluated: in order to validate the use of the system as a hardware platform for behavioral services, an activity profile of a user for two months in a real context has been extracted.
Highlights
The concept of a Smart Home has evolved remarkably over the past few decades
We present a new home monitoring system entirely based on Wi-Fi connectivity, in a fashion strictly compliant with the Internet of Thing (IoT) paradigm
A new Smart Home system architecture conceived for behavioral analysis and based on Wi-Fi connectivity has been presented
Summary
The evolution of ICT (Information and Communication Technologies) has allowed for the addition of many advanced features to smart homes over time, extending the possible applications. The monitoring of human activity in the home environment has particular importance. Modern systems exploit human monitoring mainly to improve the energy management of the building [2,3,4,5,6,7]. Data related to the users and how they live in their own home environment can find straightforward applications in health management, allowing for early detection of behavioral trends and anomalies possibly relevant to one’s wellbeing [8,9,10,11,12,13,14]
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