Optimizing Iot Embedded Cooling Systems in Data Centers for Energy Efficiency in Telecommunication Firms in Kenya: a Case for a Leading Telecommunications Firm in Kenya

Abstract

While the adoption of IoT embedded cooling systems represents a significant stride towards improving energy efficiency, it remains scantily explored regarding the extent to which IoT embedded cooling systems result in the intended energy efficiency. In the context of energy efficiency, extant studies have largely focused on digitalization, and in industries other than telecommunications. Against this backdrop, the present study sought to bridge this gap in the Kenyan body of knowledge by assessing the effect of optimizing IoT embedded cooling systems in data centers on energy efficiency in telecommunication firms in Kenya with reference to a leading telecommunications firm in Kenya. More specifically, the study sets out to establish the extent of implementation of IoT embedded cooling systems in data centers by a leading telecommunications firm in Kenya. It also sought to determine the effect of advanced sensor deployment; dynamic cooling management; load distribution in data centers; and real-time data analytics in data center cooling systems on energy efficiency in telecommunication firms in Kenya. This study employed a case study design with a focus on a leading telecommunications firm in that has eleven major data centers. Data was collected using semi-structured interviews and direct observations and analyzed through both thematic analysis and cross-referencing with a systematic literature review. The study findings indicate that the firm in Kenya has strategically implemented IoT embedded cooling systems in a phased approach across its data centers, with 8 out of 11 facilities operational and the remaining 3 in pilot phases. It was also found that the integration of advanced sensor technologies has significantly improved cooling performance in terms of energy consumption and operational resilience by enabling precise, adaptable cooling strategies based on real-time data feedback. The study further reveals that the firm in implementation of dynamic cooling management systems effectively optimizes energy consumption by responding to real-time environmental data, particularly in high-density areas, thereby enhancing overall data center performance and reliability. The study further highlights the benefits of advanced load distribution strategies in reducing energy costs, minimizing carbon emissions, and supporting operational resilience by balancing workloads and optimizing resource allocation based on real-time analytics. The findings point to the leading firm’s leadership in leveraging IOT embedded cooling systems to enhance efficiency and resilience across its data centers. It is recommended that telecommunication firms in Kenya strategically integrate advanced IoT embedded cooling systems, advanced sensor technologies, DCM systems, and real-time data analytics across all data centers to optimize operational efficiency, enhance energy savings, and maintain a competitive edge in sustainable data center management.