Paper Info
Title
A Socioecological Model for Advanced Service Discovery in Machine-to-Machine Communication Networks.
Abstract
The new development of embedded systems has the potential to revolutionize our lives and will have a significant impact on future Internet of Thing (IoT) systems if required services can be automatically discovered and accessed at runtime in Machine-to-Machine (M2M) communication networks. It is a crucial task for devices to perform timely service discovery in a dynamic environment of IoTs. In this article, we propose a Socioecological Service Discovery (SESD) model for advanced service discovery in M2M communication networks. In the SESD network, each device can perform advanced service search to dynamically resolve complex enquires and autonomously support and co-operate with each other to quickly discover and self-configure any services available in M2M communication networks to deliver a real-time capability. The proposed model has been systematically evaluated and simulated in a dynamic M2M environment. The experiment results show that SESD can self-adapt and self-organize themselves in real time to generate higher flexibility and adaptability and achieve a better performance than the existing methods in terms of the number of discovered service and a better efficiency in terms of the number of discovered services per message.
Year
DOI
Venue
2016
10.1145/2811264
ACM Trans. Embedded Comput. Syst.
Keywords
Field
DocType
Design,Algorithms,Performance,Machine-to-machine communication networks,social-ecological model,service discovery
Adaptability,Machine to machine,World Wide Web,Social ecological model,Telecommunications network,Computer science,Internet of Things,Real-time computing,Service discovery,Distributed computing
Journal
Volume
Issue
ISSN
15
2
1539-9087
Citations 
PageRank 
References 
5
0.43
13
Authors
5
Name
Order
Citations
PageRank
Lu Liu11501170.70
Nick Antonopoulos253148.72
Minghui Zheng351.78
Yongzhao Zhan434451.09
Zhijun Ding534630.28