2 research outputs found
Performance Analysis of ML-based MTC Traffic Pattern Predictors
Prolonging the lifetime of massive machine-type communication (MTC) networks
is key to realizing a sustainable digitized society. Great energy savings can
be achieved by accurately predicting MTC traffic followed by properly designed
resource allocation mechanisms. However, selecting the proper MTC traffic
predictor is not straightforward and depends on accuracy/complexity trade-offs
and the specific MTC applications and network characteristics. Remarkably, the
related state-of-the-art literature still lacks such debates. Herein, we assess
the performance of several machine learning (ML) methods to predict Poisson and
quasi-periodic MTC traffic in terms of accuracy and computational cost. Results
show that the temporal convolutional network (TCN) outperforms the long-short
term memory (LSTM), the gated recurrent units (GRU), and the recurrent neural
network (RNN), in that order. For Poisson traffic, the accuracy gap between the
predictors is larger than under quasi-periodic traffic. Finally, we show that
running a TCN predictor is around three times more costly than other methods,
while the training/inference time is the greatest/least.Comment: IEEE Wireless Communications Letters Print ISSN: 2162-2337 Online
ISSN: 2162-234
Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions
Technology solutions must effectively balance economic growth, social equity,
and environmental integrity to achieve a sustainable society. Notably, although
the Internet of Things (IoT) paradigm constitutes a key sustainability enabler,
critical issues such as the increasing maintenance operations, energy
consumption, and manufacturing/disposal of IoT devices have long-term negative
economic, societal, and environmental impacts and must be efficiently
addressed. This calls for self-sustainable IoT ecosystems requiring minimal
external resources and intervention, effectively utilizing renewable energy
sources, and recycling materials whenever possible, thus encompassing energy
sustainability. In this work, we focus on energy-sustainable IoT during the
operation phase, although our discussions sometimes extend to other
sustainability aspects and IoT lifecycle phases. Specifically, we provide a
fresh look at energy-sustainable IoT and identify energy provision, transfer,
and energy efficiency as the three main energy-related processes whose
harmonious coexistence pushes toward realizing self-sustainable IoT systems.
Their main related technologies, recent advances, challenges, and research
directions are also discussed. Moreover, we overview relevant performance
metrics to assess the energy-sustainability potential of a certain technique,
technology, device, or network and list some target values for the next
generation of wireless systems. Overall, this paper offers insights that are
valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the
Communications Societ