Unnamed aerial vehicles (UAVs) or drones have attracted growing interest in the last few years for multiple applications; thanks to their advantages in terms of mobility, easy movement, and flexible positioning. In UAV-based communications, mobility and higher line-of-sight probability represent opportunities for the flying UAVs while the limited battery capacity remains its major challenge. Thus, they can be employed for specific applications where their permanent presence is not mandatory. Data gathering from wireless sensor networks is one of these applications. This paper proposes an energy-efficient solution minimizing the UAV and/or sensors energy consumption while accomplishing a tour to collect data from the spatially distributed wireless sensors. The objective is to determine the positions of the UAV “stops” from which it can collect data from a subset of sensors located in the same neighborhood and find the path that the UAV should follow to complete its data gathering tour in an energy-efficient manner. A non-convex optimization problem is first formulated then, an efficient and low-complex technique is proposed to iteratively achieve a sub-optimal solution. The initial problem is decomposed into three sub-problems: The first sub-problem optimizes the positioning of the stops using linearization. The second one determines the sensors assignment to stops using clustering. Finally, the path among these stops is optimized using the travel salesman problem. Selected numerical results show the behavior of the UAV versus various system parameters and that the achieved energy is considerably reduced compared to the one of existing approaches.

Micro unmanned aerial vehicles (MUAVs) have attracted much interest in multiple applications. Most of the MUAV-based applications require time-limited access to the spectrum to complete data transmission due to limited battery capacity of the flying units. These characteristics are the origin of two challenges in MUAV-based communications: 1) efficient-energy management and 2) opportunistic spectrum access. This paper proposes an energy-efficient solution to minimize the MUAV’s flying and communication energies while integrating cognitive radio technology. A non-convex optimization problem exploiting the mobility of MUAVs is developed for the underlay operating mode where the data rate threshold of the spectrum’s owner has to be respected. The objective is to determine a joint optimized 3-D location and transmit power control solution by which the secondary MUAV can complete its transmission. A deterministic algorithm based on the Weber formulation is proposed to solve the optimization problem. The performances of the deterministic approach are compared to those of a meta-heuristic algorithm, namely particle swarm optimization algorithm (PSO). Selected numerical results illustrate the behavior of the MUAV versus various system parameters. It is shown that the proposed solution achieves very close results to those of the PSO in spite of their different conceptional constructions.

Distributed machine learning (ML) algorithms are imperative for the next-generation Internet of Things (IoT) networks, thanks to preserving the privacy of users’ data and efficient usage of the communication resources. Federated learning (FL) is a promising distributed ML algorithm where the models are trained at the edge devices over the local data sets, and only the model parameters are shared with the cloud server (CS) to generate global model parameters. Nevertheless, due to the limited battery life of the edge devices, improving the energy-efficiency is a prime concern for FL. In this work, we investigate a resource allocation scheme to reduce the overall energy consumption of FL in the relay-assisted IoT networks. We aim at minimizing the overall energy consumption of IoT devices subject to the FL time constraint. FL time consists of model training computation time and wireless transmission latency. Toward this goal, a joint optimization problem, considering scheduling the IoT devices with the relays, transmit power allocation, and computation frequency allocation, is formulated. Due to the NP-hardness of the joint optimization problem, a global optimal solution is intractable. Therefore, leveraging graph theory, joint near-optimal, and low-complexity suboptimal solutions are proposed. Efficiency of our proposed solutions over several benchmark schemes is verified via extensive simulations. Simulation results show that the proposed near-optimal scheme achieves 6, 4, and 2 times lower energy consumption, respectively, compared to the considered fixed, computation adaptation, and power adaptation schemes. Such an appealing energy efficiency comes at the cost of slightly increased FL time compared to the fixed and computation only adaptation schemes.

Federated learning (FL) has emerged as a distributed machine learning (ML) technique to train models without sharing users’ private data. In this paper, we introduce a decentralized FL scheme that is called federated learning empowered overlapped clustering for decentralized aggregation (FL-EOCD). The introduced FL-EOCD leverages device-to-device (D2D) communications and overlapped clustering to enable decentralized aggregation, where a cluster is defined as a coverage zone of a typical device. The devices located on the overlapped clusters are called bridge devices (BDs). In the proposed FL-EOCD scheme, a clustering topology is envisioned where clusters are connected through BDs, so as the aggregated models of each cluster is disseminated to the other clusters in a decentralized manner without the need for a global aggregator or an additional hop of transmission. To evaluate our proposed FL-EOCD scheme as opposed to baseline FL schemes, we consider minimizing the overall energy-consumption of devices while maintaining the convergence rate of FL subject to its time constraint. To this end, a joint optimization problem, considering scheduling the local devices/BDs to the CHs and computation frequency allocation, is formulated, where an iterative solution to this joint problem is devised. Extensive simulations are conducted to verify the effectiveness of the proposed FL-EOCD algorithm over FL conventional schemes in terms of energy consumption, latency, and convergence rate.

Grant-free non-coherent index-modulation (NC-IM) has been recently considered as an efficient massive access scheme for enabling cost- and energy-limited Internet-of-Things (IoT) devices that transmit small data packets. This paper investigates the grant-free NC-IM scheme combined with orthogonal frequency division multiplexing for applicant to unmanned aerial vehicle (UAV)-based massive IoT access. Specifically, each device is assigned a unique non-orthogonal signature sequence codebook. Each active device transmits one of its signature sequences in the given time-frequency resources, by modulating the information in the index of the transmitted signature sequence. For small-scale multiple-input multiple-output (MIMO) deployed at the UAV-based aerial base station (BS), by jointly exploiting the space-time-frequency domain device activity, we propose a computationally efficient space-time-frequency joint activity and blind information detection (JABID) algorithm with significantly improved detection performance. Furthermore, for large-scale MIMO deployed at the aerial BS, by leveraging the sparsity of the virtual angular-domain channels, we propose an angular-domain based JABID algorithm for improving the system performance with reduced access latency. In addition, for the case of high mobility IoT devices and/or UAVs, we introduce a time-frequency spread transmission (TFST) strategy for the proposed JABID algorithms to combat doubly-selective fading channels. Finally, extensive simulation results are illustrated to verify the superiority of the proposed algorithms and the TFST strategy over known state-of-the-art algorithms.

Resource optimization is investigated for an unmanned aerial vehicle (UAV)-mounted relay assisted Internet-of-things (U-IoT) network. A comprehensive network structure is proposed by incorporating laser-driven adaptive wireless-power-transfer at the UAV relay, wirelessly powered backscatter communication in the radio-frequency access links, and modulating retro-reflector based free space optical backhaul link in an optimization framework. Our objective is to maximize the number of the connected IoT devices with the UAV relay for uplink data transmission while satisfying the heterogeneous quality-of-service requirements of the IoT devices. Towards this objective, a novel optimization problem is formulated by considering queueing-overflow probability constraints of the IoT devices with stochastic data arrival, backhaul capacity constraint, and energy causality constraint at the UAV relay. The considered resource optimization is NP-hard, and an iterative solution is proposed by exploiting structure of the optimization problem. Furthermore, a three-stage optimization is devised to solve an NP-complete fractional optimization problem at each iteration of the proposed solution. An algorithm of polynomial computational complexity is developed for joint connectivity maximization and resource allocation, and convergence of the developed algorithm is proved. Using extensive simulations, efficiency of the proposed algorithm is demonstrated for improving the supportable arrival rate per IoT device and the number of the connected IoT devices in uplink of a U-IoT network.