Energy Efficient Service Selection from IoT Based on QoS Using HMM with KNN and XGBoost

The data gathering and composite the services through the IoT devices are the significant need in current scenario. There are many existing systems which gather the data from IoT devices and provide as an analysis based on the service linked with them. The objective of this paper is to build a middl...

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Bibliographic Details
Published in:Wireless personal communications 2022, Vol.124 (4), p.3591-3602
Main Authors: SaiRamesh, L., Sabena, S., Selvakumar, K.
Format: Article
Language:English
Subjects:
Algorithms
Availability
Communications Engineering
Computer Communication Networks
Electronic devices
Engineering
Markov chains
Middleware
Networks
Prediction models
Ranking
Response time
Signal,Image and Speech Processing
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Summary:The data gathering and composite the services through the IoT devices are the significant need in current scenario. There are many existing systems which gather the data from IoT devices and provide as an analysis based on the service linked with them. The objective of this paper is to build a middleware for IOT tech stack, which can recognize different services and features and categorize them via a ranking solution similar to the page ranking algorithm. The service ranking algorithm (SRA) are linked with HMM to fixates on domain specific requirements and controls services based on said fixated domain. Hence, this algorithm has to be dynamic in nature and should be able to accommodate different domain schemas as possible, where the parameters of distinction for each domain is to be pre specified and the algorithm is to be tuned accordingly. Before ranking begins, selecting the relevant service based on the availability of services, the Service Provider has to decide the kind of services to be offered for the clients based on the weight’s reliability, completeness and energy availability. For implementing this, many intelligent systems are suggested to choose the low cost and high reliable services. In this chapter, a fuzzy rule-based K Nearest Neighbour Classifier is used for categorize the IoT service based on user request. In addition, XGBoost (Extreme Gradient Boosting) is used for dynamic service selection from the available categorized services based on response time and cost. Hidden Markov Model (HMM) is the prediction model used in this proposed work to solve the energy prediction problem.
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-022-09527-y