There are several IoT platforms providing a variety of services for different applications. Finding the optimal fit between application and platform is challenging since it is hard to evaluate the effects of minor platform changes. Several websites offer reviews based on user ratings to guide potential users in their selection. Unfortunately, review data are subjective and sometimes conflictingindicating that they are not objective enough for a fair judgment. Scientific papers are known to be the reliable sources of authentic information based on evidence-based research. However, literature revealed that though a lot of work has been done on theoretical comparative analysis of IoT platforms based on their features, functions, architectures, security, communication protocols, analytics, scalability, etc., empirical studies based on measurable metrics such as response time, throughput, and technical efficiency, that objectively characterize user experience seem to be lacking. In an attempt to fill this gap, this study used web analytic tools to gather data on the performance of some selected IoT cloud platforms. Descriptive and inferential statistical models were used to analyze the gathered data to provide a technical ground for the performance evaluation of the selected IoT platforms. Results showed that the platforms performed differently in the key performance metrics (KPM) used. No platform emerged best in all the KPMs. Users' choice will therefore be based on metrics that are most relevant to their applications. It is believed that this work will provide companies and other users with quantitative evidence to corroborate social media data and thereby give a better insight into the performance of IoT platforms. It will also help vendors to improve on their quality of service (QoS).

The global drive towards a net-zero world and commitments from advanced countries towards a significant cut of greenhouse gases by 2030 are objectives that have compelled a new technological paradigm that must align with new global initiatives towards meeting agreed objectives towards the net-zero world. The Internet-of-Things (IoT) is a technology that has shown strong viability in assisting nations and corporations in meeting global commitments to the net-zero goal. To be effective, the operation of IoT devices must meet very tight constraints; consequently, the design of the electronics, and applications that run on IoT devices must be optimal especially in power utilization considerations. It is in this regard that this paper presents the design of a power prediction model for a Cortex M processor. The design was achieved through a unique methodology in which an instruction dissector played a central role in determining the exact instructions that were executed, rather than using a generalized set of instructions from an instruction grouping. Multivariate Adaptive Regression Splines (MARS) was used as the machine learning technique for deriving the prediction models. A key feature of using MARS is that the predictor variables having the greatest effect on the models were identified as the MVN and SBC instructions. A performance comparison of the design in this paper was made with similar designs where it was shown that the approach of the design in this work yielded more robust models with a reported error rate of 0.000629193 for logic power and 0.023096787 for data power.