Customer-in-Loop Adaptive Supply Chain Migration Model to Enable IoT
N. Z. Azeemi1, G. Al-Utaibi2, O. Al-Basheer3
1Naeem Zafar Azeemi*, School of Engineering and Technology, Al Dar University College, Dubai, UAE.
2Ghassan Al Utaibi, School of Business Administration, Al Dar University College, Dubai, UAE.
3Omar Al Basheer, School of Engineering and Technology, Al Dar University College, Dubai, UAE.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 20, 2020. | Manuscript published on April 10, 2020. | PP: 1755-1762 | Volume-9 Issue-6, April 2020. | Retrieval Number: F4827049620/2020©BEIESP | DOI: 10.35940/ijitee.F4827.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Poised to smart citizenry engagement, an unprecedented deluge of high quality streaming services induce a major data traffic challenge in Fourth Generation (4G) bandwidth and coverage, the upcoming smart city expectations cannot be ignored, eventually. The bottlenecks in ever exploiting benefits such as e-Life, to name a few at Mobile Equipment Providers (MEP) tiers, are complemented at Device Dependent Device Independent (D3I) configurations inherent at various tiers of Mobile Service Providers (MSP). While enabling a Supply Chain Management (SCM) augments a unique system support involving the MSPs and MEPs for desired Customer Relationship Management (CRM), Ad hoc Resource Planning (ARP), which we found prevalent in migration scenario from 4G to 5G technology deployment. Despite its complexity both in term of one-to-many and many-to-one across diverse MSP and MEP options, SCM operational objectives sets forth a unique challenge, hence is the main objective for our work presented here. In this paper, we presented a framework to enhance the 4G legacy in mobile service provider capacity for smart city Machine-to-Machine (M2M) backbone. The migration process is assessed with proposed strategic, technical and operational indicators, which demonstrate its adaptability and flexibility while integrating in conventional 4G deployments, especially taking into account radio devices and applications. Web-enabled Software Define Radio devices and applications are used to index the migration cost and support SCM planning and execution. We identify, the decentralization of mobile service providers infrastructure plays a major role in reducing the embedded complexity which often appears as primary bottleneck. MSP as a key player in the elasticity of migration, we presented a platform to support large as well as low MEP-MSP co-deployments. Pareto multi-criteria optimization is used to find the strategic indicators which are primary Transformation Steering Factors (TSF), valid in both device dependent or device independent M2M migration. We expose our result for achieving TSF, while rolling interoperability and reconfiguration of device deployed in a typical volatile inter-MSP or intra-MSPs tiers. Pareto Migration Indicators (MI) are optimized successively progressing across the transformation schemes; relative to base-line MSP services, hence enabling a lucrative choice while elasticity of provider-centric cost depends adaptively on technology legacy and M2M access of User Equipment (UE).
Keywords: Supply Chain Management (SC M), IP Networks, IoT, Adaptive Optimization, Smart City.
Scope of the Article: Data Management