Privacy-Aware Sensor Data Upload Management for Securely Receiving Smart Home Services

2020 ◽  
Author(s):  
Sopicha Stirapongsasuti ◽  
Yugo Nakamura ◽  
Keiichi Yasumoto
Keyword(s):  
Smart Home ◽  
Sensor Data ◽  
Data Upload

Anomaly Detection Using Temporal Data Mining in a Smart Home Environment

2008 ◽  
Vol 47 (01) ◽  
pp. 70-75 ◽  
Author(s):  
V. Jakkula ◽  
D. J. Cook
Keyword(s):  
Machine Learning ◽  
Smart Home ◽  
Learning Algorithms ◽  
Synthetic Data ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Temporal Data Mining ◽  
Activity Data ◽  
Real Activity ◽  
Smart Home Environment

Summary Objectives: To many people, home is a sanctuary. With the maturing of smart home technologies, many people with cognitive and physical disabilities can lead independent lives in their own homes for extended periods of time. In this paper, we investigate the design of machine learning algorithms that support this goal. We hypothesize that machine learning algorithms can be designed to automatically learn models of resident behavior in a smart home, and that the results can be used to perform automated health monitoring and to detect anomalies. Methods: Specifically, our algorithms draw upon the temporal nature of sensor data collected in a smart home to build a model of expected activities and to detect unexpected, and possibly health-critical, events in the home. Results: We validate our algorithms using synthetic data and real activity data collected from volunteers in an automated smart environment. Conclusions: The results from our experiments support our hypothesis that a model can be learned from observed smart home data and used to report anomalies, as they occur, in a smart home.

scholarly journals Understanding Smart Home Sensor Data for Ageing in Place Through Everyday Household Routines: A Mixed Method Case Study

2017 ◽  
Vol 5 (6) ◽  
pp. e52 ◽  
Author(s):  
Yasmin van Kasteren ◽  
Dana Bradford ◽  
Qing Zhang ◽  
Mohan Karunanithi ◽  
Hang Ding
Keyword(s):  
Mixed Method ◽  
Smart Home ◽  
Sensor Data ◽  
Ageing In Place

scholarly journals Mining Productive-associated Periodic Frequent Pattern in Body Sensor Data for Smart Home Care

2017 ◽  
Author(s):  
Wala Ismail ◽  
Mohammad Mehedi Hassan
Keyword(s):  
Vital Sign ◽  
Smart Home ◽  
Healthcare Providers ◽  
The Elderly ◽  
Frequent Pattern ◽  
Sensor Data ◽  
Body Sensor Networks ◽  
Periodic Patterns ◽  
Frequent Items

The understanding of various health-oriented vital sign data generated from body sensor networks (BSN) and discovery of the association between the generated parameters is an important task that may assist and promote important decision making in healthcare. For example, in a smart home scenario where the occupants’ health status is continuously monitored remotely, it is essential to provide required assistance when an unusual or critical situation is detected in their vital sign data. In this paper, we present an efficient approach to mine the incomplete (partial) periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce the productive-associated partial periodic frequent patterns as the set of correlated partial periodical frequent items. The combination of these measures has the advantage of empowering healthcare providers and patients for quality of diagnosis, and also for better treatment and smart care, especially for the elderly people at smart home. We developed an efficient algorithm named PPFP-Growth (Productive Periodic Frequent Pattern growth) to discover all productive associated partial periodic patterns using these measures. PPFP-Growth is efficient, and the productiveness measure removes uncorrelated periodic items. An experimental evaluation on synthetic and real datasets shows the efficiency of the proposed PPFP-Growth algorithm, and can filter a huge number of partial periodic patterns to reveal only the correlated ones.

scholarly journals Managing sensor data streams in a smart home application

2020 ◽  
Vol 32 (4) ◽  
pp. 247 ◽  
Author(s):  
Johan Jansson ◽  
Ismo Hakala
Keyword(s):  
Data Streams ◽  
Smart Home ◽  
Sensor Data

Design of Embedded Smart Home System Based on Internet of Things

2015 ◽  
Vol 738-739 ◽  
pp. 247-250
Author(s):  
Peng Ju Zhang ◽  
Gai Zhi Guo ◽  
Zong Zuo Yu
Keyword(s):  
Wireless Sensor Network ◽  
Internet Of Things ◽  
Sensor Network ◽  
Smart Home ◽  
Electrical Equipment ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Real Time Control ◽  
System Solution ◽  
Automation Control

This paper presents an Embedded Smart Home system solution using the Internet of things. The Smart Home system can be sectioned into four parts: wireless sensor network, embedded smart gateway, Server and Client. The major technical of the wireless sensor network is ZigBee. The wireless sensor network is developed based on the Z-Stack protocol stack and the wireless chip CC2530. Its main work is to sensor the environmental parameter of the house and the automation control of the electrical equipment in the house. It can also support the RFID access control and camera monitor. ARM microprocessor is the core of the Embedded Smart Home control system. It adopts the embedded Linux technology, the development platform is Qt. The gateway communicates with the wireless sensor network by the serial port. It communicates with Server by the TCP socket and transmits to each client, or communicates with the client by using the wireless communication module directly. It also provides the user interface for sensor data check and real time control equipment. The server plays a role of bridge. It is mainly responsible for the communication and big data processing between the client and the control center. Partial hardware electric diagram and software flowchart were provided. Field using indicates that this system is economical and flexible.

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