TY - JOUR
T1 - Sensor search techniques for sensing as a service architecture for the internet of things
AU - Perera, Charith
AU - Zaslavsky, Arkady
AU - Liu, Chi Harold
AU - Compton, Michael
AU - Christen, Peter
AU - Georgakopoulos, Dimitrios
PY - 2014/2
Y1 - 2014/2
N2 - The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating "things" or Internet Connected Objects (ICOs) that will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection, and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM considers user preferences and a broad range of sensor characteristics such as reliability, accuracy, location, battery life, and many more. This paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This paper also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.
AB - The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating "things" or Internet Connected Objects (ICOs) that will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection, and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM considers user preferences and a broad range of sensor characteristics such as reliability, accuracy, location, battery life, and many more. This paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This paper also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.
KW - Indexing and ranking
KW - Multidimensional data fusion
KW - Quantitative reasoning
KW - Search and selection
KW - Semantic querying
KW - Sensors
UR - http://www.scopus.com/inward/record.url?scp=84896956480&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2013.2282292
DO - 10.1109/JSEN.2013.2282292
M3 - Article
SN - 1530-437X
VL - 14
SP - 406
EP - 420
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 2
M1 - 6605518
ER -