Context-Driven Service Deployment Using Likelihood-Based Approach for Internet of Things Scenarios

Nandan Banerji, Chayan Paul, Bikash Debnath (), Biplab Das, Gurpreet Singh Chhabra, Bhabendu Kumar Mohanta () and Ali Ismail Awad
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Nandan Banerji: Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar 737136, India
Chayan Paul: Department of Computer Science and Engineering, Swami Vivekananda University, Kolkata 700121, India
Bikash Debnath: Amity Institute of Information Technology, Amity University, Kolkata 700135, India
Biplab Das: Department of Commerce and Management, St. Xaviers University, Kolkata 700160, India
Gurpreet Singh Chhabra: Department of Computer Science and Engineering, Gandhi Institute of Technology and Management (Deemed to be University), Visakhapatnam 530045, India
Bhabendu Kumar Mohanta: College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
Ali Ismail Awad: College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates

Future Internet, 2024, vol. 16, issue 10, 1-17

Abstract: In a context-aware Internet of Things (IoT) environment, the functional contexts of devices and users will change over time depending on their service consumption. Each iteration of an IoT middleware algorithm will also encounter changes occurring in the contexts due to the joining/leaving of new/old members; this is the inherent nature of ad hoc IoT scenarios. Individual users will have notable preferences in their service consumption patterns; by leveraging these patterns, the approach presented in this article focuses on how these changes impact performance due to functional-context switching over time. This is based on the idea that consumption patterns will exhibit certain time-variant correlations. The maximum likelihood estimation (MLE) is used in the proposed approach to capture the impact of these correlations and study them in depth. The results of this study reveal how the correlation probabilities and the system performance change over time; this also aids with the construction of the boundaries of certain time-variant correlations in users’ consumption patterns. In the proposed approach, the information gleaned from the MLE is used in arranging the service information within a distributed service registry based on users’ service usage preferences. Practical simulations were conducted over small (100 nodes), medium (1000 nodes), and relatively larger (10,000 nodes) networks. It was found that the approach described helps to reduce service discovery time and can improve the performance in service-oriented IoT scenarios.

Keywords: internet of things; context awareness; distributed service registry; service discovery; maximum likelihood estimation (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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