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Design and Implementation of Practical Schedulers for M2M Uplink Networks
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Design and Implementation of Practical Schedulers for M2M Uplink Networks : Using MATLAB

Softcover reprint of the original 1st ed. 2018

Book Details

Format Paperback / Softback
ISBN-10 3030086135
ISBN-13 9783030086138
Edition Softcover reprint of the original 1st ed. 2018
Publisher Springer Nature Switzerland AG
Imprint Springer Nature Switzerland AG
Country of Manufacture GB
Country of Publication GB
Publication Date Dec 14th, 2018
Print length 214 Pages
Ksh 16,200.00
Publisher Out of Stock 0 in stock

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This book presents the design of delay-efficient packet schedulers for heterogeneous M2M uplink traffic classified into several classes, based on packet delay requirements, payload size, arrival process, etc.
This book presents the design of delay-efficient packet schedulers for heterogeneous M2M uplink traffic classified into several classes, based on packet delay requirements, payload size, arrival process, etc. Specifically, the authors use tools from queuing theory to determine the delay-optimal scheduling policy. The proposed packet schedulers are designed for a generic M2M architecture and thus equally applicable to any M2M application. Additionally, due to their low implementation complexity and excellent delay-performance, they authors show how they are also well-suited for practical M2M systems. The book pertains primarily to real-time process scheduler experts in industry/academia and graduate students whose research deals with designing Quality-of-Service-aware packet schedulers for M2M packet schedulers over existing and future cellular infrastructure. Presents queuing theoretic analysis and optimization techniques used to design proposed packet scheduling strategies;Provides utility functions to precisely model diverse delay requirements, which lends itself to formulation of utility-maximization problems for determining the delay- or utility-optimal packet scheduler;Includes detail on low implementation complexity of the proposed scheduler by using iterative and distributed optimization techniques.

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