The integration of the Internet of Things (IoT) with quantum communication networks opens the door to transformative applications, including massive machine communication, virtual reality (VR), the Metaverse, and a wide range of artificial intelligence (AI)-driven technologies. The Quantum IoT (QIoT) meets these demands by enabling ultrasecure communication, advanced data processing, and enhanced sensing capabilities. By leveraging quantum principles, such as entanglement and teleportation, QIoT achieves superior efficiency and security compared to traditional IoT systems. As a pioneering quantum computing and communication convergence, QIoT is expected to experience significant growth over the next decade, driven by rapid advancements in quantum technologies. However, QIoT networks face critical challenges, such as energy efficiency (EE) issues, memory limitations caused by qubit decoherence, and substantial losses during quantum communication, including the exponential decay of entanglement success over quantum optical channels. To address these challenges, we propose an optimal decoherence-aware entanglement generation framework to maximize EE in QIoT networks. Using Dinkelbach’s method and Lagrangian analysis, the framework reformulates the resource allocation (RA) problem as a nonlinear fractional programming model, which is solved iteratively through linear programming techniques. This approach ensures energy-efficient entanglement generation rates (EGRs) while maintaining reliable quantum communication. Simulation results validate the effectiveness of the proposed framework, highlighting its advantages and providing valuable insights for the development of QIoT systems.

Molecular communication (MC), which utilizes molecules to transmit data via diffusion channels, is a prominent system in nanonetworks. In particular, Data-collection (DC) scenarios are a challenging area of research that remains open for further investigation. In this letter, we focus on optimizing the energy efficiency (EE) of a molecular DC nanonetwork comprising a mobile nanorobot (NR) and energy-constrained nanosensors (NSs), taking into account the constraints on the molecular concentration, data rate, and available molecular resources. The defined optimization problem is a nonlinear fractional program that is difficult to solve. To determine the optimal solution, we use Dinkelbach's approach and Lagrangian analysis. The simulation results demonstrate the promising performance of the proposed framework.

Unmanned aerial vehicles (UAVs) are expected to play an important role in future wireless networks, serving as communication relays, computing servers, and flying infrastructure for ground users when ground-based infrastructure is congested or inaccessible. However, typical UAVs are powered by on-board batteries, which results in limited battery lifetime and poses a major restriction for UAV applications in communications. To overcome this, we propose a consistent and cost-aware energy procurement framework for a UAV powered concurrently by laser beams, emitted from locally deployed laser beam directors, and local renewable energy (RE) sources. The UAV intends to lower its overall energy cost for a certain operation cycle by optimizing the quantities of energy obtained from its battery as well as laser beams at each time period. Given the optimization results, we also propose a cost-aware UAV placement strategy with the ultimate goal of ensuring quality communication-energy links for the UAV, ground devices (GDs) and users. In addition, we assess the amount of additional procured RE that can be transferred via wireless power transfer to charge a set of distributed GDs. The simulations provide interesting insights into the efficiency of the proposed framework.

Forests have an important role in environmental preservation and maintenance. The primary threat is forest fires, which have disastrous repercussions. As a result, it is critical to identify and extinguish a fire before it spreads and destroys resources. To that end, we propose a forest fire detection and fighting mechanism using wireless sensor and actor networks (WSANs). Temperature sensors are utilized to detect fires, and actors (robots) are employed to extinguish them. Sensors and robots are distributed at random throughout the forest, forming clusters. Clustering, sleep/active scheduling for the sensors, and energy harvesting (EH)/moving modes for the robots, are used to extend and maximize the sensors/robots lifetime in the WSAN. In such a network, robots should move to the fire site as quickly as possible. To do this, we further propose a robot routing mechanism that focuses on determining the shortest path for each firefighting robot. In particular, each firefighting robot equipped with on-board processing uses a fuzzy$Q$-learning (FQL)-based trajectory mechanism to learn the shortest path to the fire zone in the least amount of time. Simulations are conducted to demonstrate the benefits of employing the proposed framework for rapid and effective fire response. When compared to the traditional$Q$-learning, the total approaching rate (a measure of how quickly the firefighting robots can reach the fire) to the fire spot is greater when utilizing the proposed FQL-based strategy.

This article discusses a cellular network assisted by an energy- and spectral-efficient unmanned aerial vehicle (UAV), in which the UAV is deployed to serve mobile users in the cellular network and enable mobile data offloading from a ground base station (GBS) by taking a circular flight route. We explore a visible light communication (VLC)-enabled UAV, in which a light-emitting diode (LED) is mounted on a rotary-wing UAV to offer communications to the users. Our aim is to simultaneously optimize both energy efficiency (EE) and spectral efficiency (SE) of the VLC-enabled UAV by jointly optimizing the common throughput of all users as well as the UAV’s trajectory and flying speed. We employ a unified metric, called resource efficiency (RE), and explore the RE optimization to obtain an adaptive EE–SE tradeoff. The problem posed is seen in a complex and nonconvex shape, making it hard to solve. Motivated by the enormous achievement of deep reinforcement learning (DRL) in solving complex control problems, we propose a DRL-based approach to handle this nonconvex and complicated optimization. The findings of the simulation reveal that the developed framework achieves a substantial performance in terms of the solution convergence as well as the promising quality of the solutions.

Increased environmental awareness coupled with the rising cost of energy have sparked a keen interest in the deployment of energy-efficient communication technologies over the infrastructure of cellular networks. Base stations (BSs) are responsible for the largest portion of power consumption and energy usage in cellular networks. Thus, sleep/wake-up scheduling strategies for BSs can significantly improve energy-efficiency (EE) of cellular networks. In this paper, we propose a Fuzzy Q-Learning (FQL) based energy-efficient sleep/wake-up mechanism for BSs in a heterogeneous network (HetNet). The goal is to save energy, without compromising the offered Quality of Service (QoS), by switching off the redundant BSs according to the local traffic profile and depending on the required area coverage and cell EE. The introduction of sleep mode for BSs may lead to a large-scale coverage loss, unless a specific remedial solution is exploited at the same time. To this end, we also propose to use device-to-device (D2D) communications to extend network coverage to the service areas of the switched-off BSs. Simulation results validate that the proposed framework provides significant improvements in power consumption and the EE.

This study investigates the spectrum shaping observed in ultra-wideband (UWB) communications. An auxiliary independent signal (AIS) to limit undesired spectral lines because of periodicity or unbalanced data sources (i.e. non-uniformly distributed) and or non-symmetric data modulation schemes are introduced. Each transmitted symbol is represented by a preamble AIS pulse followed by a set of transmitted data pulses that are weighted, delayed and summed in accordance with a predefined modulation. The use of the fifth derivative Gaussian pulse ensures that the UWB spectrum mask established by the Federal Communications Commission (FCC) is met, and a preamble signal is designed to eliminate spectral lines at specific frequencies that appear mainly by existing balanced or unbalanced binary data sources. A unified spectral analysis is adapted for general uncorrelated and correlated quaternary bi-orthogonal modulations. An optimisation problem is also introduced in the case of unbalanced data sources in order to minimise undesired spectral lines subject to comply with the FCC spectrum mask and maximise spectral utilisation.