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Coflow is a prominent network abstraction for modeling communication patterns in data centers. Since coflow scheduling in large-scale data centers is $\mathcal{NP}$-hard, this paper investigates this problem within heterogeneous parallel…

The nonlinear programming (NLP) problem to solve distribution-level optimal power flow (D-OPF) poses convergence issues and does not scale well for unbalanced distribution systems. The existing scalable D-OPF algorithms either use…

This paper proposes a point-to-point passive optical network (P2P-PON) architecture as an energy-efficient and low-latency backhaul solution for visible light communication (VLC)-enabled indoor fog computing systems. The proposed…

Low-power and long-range communication tech- nologies such as LoRa are becoming popular in IoT applications due to their ability to cover kilometers range with milliwatt of power consumption. One of the major drawbacks of LoRa is the data…

Network interfaces in most LAN computing devices are usually severely under-utilized, wasting energy while waiting for new packets to arrive. In this paper, we present two algorithms for opportunistically powering down unused network…

Power Delivery Networks (PDNs) are critical for maintaining voltage integrity in modern multiprocessor systems. Conventional early-stage PDN planning relies on static or worst-case power assumptions, often leading to over-provisioned…

Machine-type communications (MTC) devices in 5G will use the Non-orthogonal multiple access (NOMA) technology for massive connections. These devices switch between the transmission mode and the sleep mode for battery saving; and their…

In this paper, a neural-network (NN)-based online optimal control method (NN-OPT) is proposed for ultra-capacitors (UCs) energy storage system (ESS) in hybrid AC/DC microgrids involving multiple distributed generations (e.g., Photovoltaic…

Optical neural network (ONN) is emerging as an attractive proposal for machine-learning applications, enabling high-speed computation with low-energy consumption. However, there are several challenges in applying ONN for industrial…

The recent rapid deployment of datacenter infrastructures for performing large language models (LLMs) and related artificial intelligence (AI) applications in the clouds is predicted to incur an exponentially growing energy consumption in…

Wireless powered communication networks (WPCNs), where multiple energy-limited devices first harvest energy in the downlink and then transmit information in the uplink, have been envisioned as a promising solution for the future…

Autonomous mobile agents require low-power/energy-efficient machine learning (ML) algorithms to complete their ML-based tasks while adapting to diverse environments, as mobile agents are usually powered by batteries. These requirements can…

We consider the \textsf{Unit Interval Selection} problem in the one-pass random order streaming model. Here, an algorithm is presented a sequence of $n$ unit-length intervals on the line that arrive in uniform random order, and the…

Ensuring both feasibility and efficiency in optimal power flow (OPF) operations has become increasingly important in modern power systems with high penetrations of renewable energy and energy storage. While deep neural networks (DNNs) have…

A passive optical network (PON) based on non-orthogonal multiple access (NOMA) meets low latency and high capacity. In the NOMA-PON, the asynchronous clocks between the strong and weak optical network units (ONUs) cause the timing error and…

Coherent Optical Orthogonal Frequency Division Multiplexing (CO-OFDM) based Elastic Optical Network (EON) is one of the emerging technologies being considered for next generation high data rate optical network systems. Routing and Spectrum…

In order to overcome the inherent latency in multi-user unmanned aerial vehicle (UAV) networks with orthogonal multiple access (OMA). In this paper, we investigate the UAV enabled uplink non-orthogonal multiple access (NOMA) network, where…

With the rapid growth in the telecommunications industry moving towards 5G and beyond (5GB) and the emergence of data-hungry and time-sensitive applications, Mobile Network Operators (MNOs) are faced with a considerable challenge to keep up…

Recurrent neural networks (RNN) have been successfully applied to various sequential decision-making tasks, natural language processing applications, and time-series predictions. Such networks are usually trained through back-propagation…

Recently, there has been a surge of interest in adopting deep neural networks (DNNs) for solving the optimal power flow (OPF) problem in power systems. Computing optimal generation dispatch decisions using a trained DNN takes significantly…