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Cellular-Internet-of-Things Enablers:

Time: Fri 2019-09-27 13.00

Location: Sal-C, Electrum 229, SE-164 40 Kista, Sweden, Stockholm (English)

Subject area: Telecommunication Information and Communication Technology

Doctoral student: Mohammad Istiak Hossain , Radio Systems Laboratory (RS Lab), Techno Economic

Opponent: Professor Heikki Hämmäinen, Aalto University

Supervisor: Associate Professor Jan Markendahl, Radio Systems Laboratory (RS Lab); Jens Zander, Radio Systems Laboratory (RS Lab)

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Internet of Things (IoT) services are gradually attaining the expected service growth rate estimated by market actors. New connectivity paradigms, like the low-power wide-area network (LPWAN), have emerged to address the immediate challenges of IoT connectivity service. In addition, a plethora of new connectivity and application platforms have been developed to support IoT services. Until now, the majority of IoT services have been small scale deployments or trials. The overall cost-effectiveness and scalability aspects of IoT service provisioning are still not well understood. Hence, the growth of IoT services requires attention from a multidisciplinary perspective to address the cost-efficient scalability of IoT communication and service platforms.The technical part of this thesis focuses on the impact of multiplicity on the physical random access channel (PRACH) performance. We investigate the performance limitations of the initial access resource allocation, considering the multiplicity effect on physical layer signature detection to ensure uniform accessibility of devices. The performance evaluation reveals that MAC-protocol designed for PRACH needs to consider the realistic impact of multiplicity on signature detection. Then, we propose an efficient algorithm to detect multiplicity with a higher confidence factor. We also investigate the trade-off of random-access collision and resource allocation utilisation to meet the IoT resource utilisation requirements. We propose a pool-based resource allocation procedure that uses supervised learning to optimise the performance of early data transmission (EDT). Our analysis suggests that with this approach, EDT can handle delay constraint IoT services efficiently.The economic part of the thesis addresses the cost-structure and scalability aspects of both connectivity and platform solutions. The overall research question is: "What factors are driving the costs of IoT connectivity and platform services and why?" We have developed a framework for cost structure analysis of IoT services. We present cost structure breakdown analysis for both IoT connectivity and IoT platform services. The evaluation results discuss conditions when a platform service provider should choose a platform as a service (PaaS), and when on-premises platform deployment is viable.The technical study contributes to shaping the assessment metrics of the random-access algorithm selection. This study proposes solutions to support heterogeneous IoT solutions in cellular-IoT systems. Furthermore, the study demonstrates the potential of supervised learning to optimise resource allocation. The proposed algorithm assures service scalability in terms of user density for massive-IoT, and delay constraint IoT use cases.The economic study is helpful for telecom managers and IoT service providers to understand the cost breakdown of IoT connectivity and platform solutions under a different scenario. The cost driver of different IoT communication technologies like LPWAN, LPLAN, and C-IoT can be estimated at a high level. The framework provides a comparison base which is helpful for the actors in the IoT domain to analyse and compare different service provisioning options.