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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.

The rapid development of 5G communication networks has ushered in a new era of high-speed, low-latency wireless connectivity, as well as the enabling of transformative technologies. However, a crucial aspect of ensuring reliable communication is the accurate modeling of path loss, as it directly impacts signal coverage, interference, and overall network efficiency. This review paper critically assesses the performance of path loss models in mid-band and high-band frequencies and examines their effectiveness in addressing the challenges of 5G deployment. In this paper, we first present the summary of the background, highlighting the increasing demand for high-quality wireless connectivity and the unique characteristics of mid-band (1–6 GHz) and high-band (>6 GHz) frequencies in the 5G spectrum. The methodology comprehensively reviews some of the existing path loss models, considering both empirical and machine learning approaches. We analyze the strengths and weaknesses of these models, considering factors such as urban and suburban environments and indoor scenarios. The results highlight the significant advancements in path loss modeling for mid-band and high-band 5G channels. In terms of prediction accuracy and computing effectiveness, machine learning models performed better than empirical models in both mid-band and high-band frequency spectra. As a result, they might be suggested as an alternative yet promising approach to predicting path loss in these bands. We consider the results of this review to be promising, as they provide network operators and researchers with valuable insights into the state-of-the-art path loss models for mid-band and high-band 5G channels. Future work suggests tuning an ensemble machine learning model to enhance a stable empirical model with multiple parameters to develop a hybrid path loss model for the mid-band frequency spectrum.