TY - CHAP
T1 - ConQeng
T2 - A Middleware for Quality of Context Aware Selection, Measurement and Validation
AU - Jagarlamudi, Kanaka Sai
AU - Zaslavsky, Arkady
AU - Loke, Seng W.
AU - Hassani, Alireza
AU - Medvedev, Alexey
PY - 2023/1
Y1 - 2023/1
N2 - A set of quality metrics (e.g., timeliness, completeness) together represent the Quality of Context (QoC); their values determine the usability of context to context consumers (IoT applications). Therefore, obtaining adequate ‘QoC from the context providers (context sources) represents a significant research challenge. This paper presents a framework called conQeng that addresses such a challenge through novel approaches in QoC-aware selection, QoC measurement and validation. ConQeng selects the potential context providers that deliver an adequate QoC during runtime, assesses their performance - for further selection, and transfers QoC-assured context to the context management platforms (CMPs). We have implemented conQeng in a simulated scenario involving autonomous cars, marketing service agencies as context consumers, and thermal and video cameras as context providers. The results demonstrate that it outperforms three heuristic approaches in reducing context acquisition cost and improving effectiveness and performance efficiency while obtaining adequate QoC.
AB - A set of quality metrics (e.g., timeliness, completeness) together represent the Quality of Context (QoC); their values determine the usability of context to context consumers (IoT applications). Therefore, obtaining adequate ‘QoC from the context providers (context sources) represents a significant research challenge. This paper presents a framework called conQeng that addresses such a challenge through novel approaches in QoC-aware selection, QoC measurement and validation. ConQeng selects the potential context providers that deliver an adequate QoC during runtime, assesses their performance - for further selection, and transfers QoC-assured context to the context management platforms (CMPs). We have implemented conQeng in a simulated scenario involving autonomous cars, marketing service agencies as context consumers, and thermal and video cameras as context providers. The results demonstrate that it outperforms three heuristic approaches in reducing context acquisition cost and improving effectiveness and performance efficiency while obtaining adequate QoC.
KW - Context management platforms
KW - QoC measurement
KW - QoC-aware selection
KW - Selection framework
UR - http://www.scopus.com/inward/record.url?scp=85147855393&partnerID=8YFLogxK
UR - https://go.openathens.net/redirector/westernsydney.edu.au?url=https://doi.org/10.1007/978-3-031-20936-9_17
U2 - 10.1007/978-3-031-20936-9_17
DO - 10.1007/978-3-031-20936-9_17
M3 - Chapter
SN - 9783031209352
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 211
EP - 225
BT - Internet of Things
A2 - González-Vidal, Aurora
A2 - Mohamed Abdelgawad, Ahmed
A2 - Sabir, Essaid
A2 - Ziegler, Sébastien
A2 - Ladid, Latif
PB - Springer
CY - Switzerland
ER -