This paper introduces an unmanned aerial vehicle (UAV)-enabled network slicing problem to provide content delivery, sensing data gathering, and mobile edge computing (MEC) services. Three tenants provide services to their clients by sharing a common infrastructure of a set of UAVs. The content delivery tenant needs to guarantee that each of its clients (users) receives the required content, the sensing tenant aims to gather an adequate amount of uncorrelated data, and the MEC tenant provides computing service to its clients. An energy consumption minimization framework is considered to meet the tenants’ requirements by optimizing the number of deployed UAVs, the deployment location of each UAV, the transmit power of each deployed UAV, the user-UAV association, and the transmission power as well as the computing resources of each UAV. Taking into account the spatial correlation among the sensing users, a subset of these users is activated to gather the required sensing information. A solution approach technique inherited from graph theory is presented, in which the Lagrange approach derives the transmission power and computing resource allocation expressions. Simulation results illustrate that the proposed framework significantly reduces the total energy consumption.

Integrated sensing and communication (ISAC) is an emerging technique to enable radar and communication systems deployment on a shared hardware, channel characteristics, signal processing methods, etc. This integration improves the deployment efficiency and requires more sophisticated resource allocation and optimization techniques. The ISAC signal is designed to sense targets and to carry private information which could be at risk of being eavesdropped. This paper considers an ISAC framework in which a set of aerial eavesdroppers poses the threat of intercepting the downlink communication from a base station to a set of users. The eavesdroppers are moving, and their unknown locations are estimated based on the echo signal. A maximum likelihood-based scheme is developed to estimate the eavesdroppers’ channels, including coarse estimation with refines to estimate each eavesdropper’s complex channel gain, elevation and azimuth angles. The corresponding Cramér-Rao lower bounds of the estimated parameters are also provided. Given that the eavesdroppers are moving, a long short-term memory (LSTM) deep network is employed to predict their channels and also to enable a less frequent estimation process. Meta-learner LSTM is also presented to provide few-shot learning and provide generalization capability to any trajectory with a few fine-tuning steps. Based on the predicted eavesdroppers’ channels, two secure precoding algorithms are developed based on successive convex approximation and zero forcing techniques to improve the sum secrecy rate for the users. Simulation results illustrate that the developed framework provides substantial improvement in communication secrecy when compared with other benchmark approaches.

This paper considers an integrated sensing and communication (ISAC) system framework, in which an aerial eavesdropper poses the threat to intercept the downlink communication from a base station to a set of users. The eavesdropper is moving and its unknown location is estimated based on the echo signal. A maximum likelihood-based scheme is developed to estimate the eavesdropper channel, which performs a coarse estimation and further refines the estimated parameters. A long short-term memory deep network is employed to predict the eavesdropper channel and also to enable a less frequent estimation process. Based on the predicted eavesdropper’s channel, a precoding optimization algorithm is developed to improve the sum secrecy rate for the users. Simulation results illustrate that the developed framework provides substantial improvement in the communication secrecy when compared with other benchmark approaches.

This paper considers minimizing the age-of-information (AoI) and transmit power consumption in a vehicular network, where a roadside unit (RSU) provides timely updates about a set of physical processes to vehicles. We consider non-orthogonal multi-modal information dissemination, which is based on superposed message transmission from RSU and successive interference cancellation (SIC) at vehicles. The formulated problem is a multi-objective mixed-integer nonlinear programming problem; thus, a Pareto-optimal front is very challenging to obtain. First, we leverage the weighted-sum approach to decompose the multi-objective problem into a set of multiple single-objective sub-problems corresponding to each predefined objective preference weight. Then, we develop a hybrid deep Q-network (DQN)-deep deterministic policy gradient (DDPG) model to solve each optimization sub-problem respective to predefined objective-preference weight. The DQN optimizes the decoding order, while the DDPG solves the continuous power allocation. The model needs to be retrained for each sub-problem. We then present a two-stage meta-multi-objective reinforcement learning solution to estimate the Pareto front with a few fine-tuning update steps without retraining the model for each sub-problem. Simulation results illustrate the efficacy of the proposed solutions compared to the existing benchmarks and that the meta-multi-objective reinforcement learning model estimates a high-quality Pareto frontier with reduced training time.

In this paper, we consider a simultaneous transmitting and reflecting (STAR)-reconfigurable intelligent surface (RIS)-assisted multi mobile-edge-computing (MEC) system, where servers can be placed on both sides of the STAR-RIS and each device offloads a part of its computational tasks to the MEC servers. Specifically, we formulate a weighted-sum computing and communication latency minimization problem to jointly optimize the offloading data volume, edge computing resource of servers, multi-user detection (MUD) matrices, as well as energy splitting coefficients and phase-shifts of the STAR-RIS in the presence of coupling between transmission and reflection phase shifts. Using block coordinate descent (BCD), we decompose the computing and communication problems and solve them in an iterative manner through alternating optimization. We show that the optimal offloading volume can be given by establishing the equivalence of the local computing latency and edge computing latency of servers. Also, we proved that the edge resource allocation problem is jointly convex in both the transmit and reflect MEC resources. Therefore, the optimal MEC resources can be found using KKT conditions and the bisection search method. Numerical results demonstrate the effectiveness of the proposed STAR-RIS-enabled multi-MEC system in terms of obtained latency and convergence compared to the conventional benchmarks.

This article focuses on minimizing the energy consumption of a fleet of unmanned aerial vehicles (UAVs) disseminating information to a set of Internet of Things devices. In the considered scenario, each device wants to download a subset of files from a library of files. Considering the storage capacity of the UAVs, a framework is provided that minimizes energy consumption by optimally selecting the contributing UAVs, placing files, and planning the trajectory of each contributing UAV. In this framework, a combinatorial optimization problem is formulated, which is hard to solve directly for a practical number of devices, files, and/or UAVs. In order to tackle this challenge, we develop three solution approaches, namely, a multichromosome genetic algorithm (GA), a hybrid genetic-ant colony algorithm, and a GA with heuristic file placement. Results show that the proposed solution approaches minimize the total energy consumption and provide near-optimal solutions. Results also illustrate that the proposed framework optimizes the number of UAVs participating in the information delivery mission.

This paper addresses the problem of minimizing a network-wide energy consumption in Internet of underwater things (IoUT) devices which are given a mission to survey an underwater area of interest by letting each device in the IoUT act as a sensor, an aggregator, a relay, or an inactivate device. A framework is provided, in which a role is assigned to each device in the IoUT. In this framework, we formulate an optimization problem to minimize the total energy consumption with constraints over binary role assignment decision variables. A genetic algorithm (GA) is devised to solve the formulated optimization problem. Simulation results show that the proposed framework can significantly save energy compared to a baseline approach, where there is no data aggregation. Results also illustrate that the proposed GA provides performance close to the optimal solution, which is obtained through exhaustive search.

This paper addresses the problem of minimizing the energy consumption of data gathering from a set of Internet-of-things (IoT) devices using an unmanned aerial vehicle (UAV). The spatial correlation among the data of the IoT devices is considered. A framework is provided, in which a subset of devices are selected to contribute, and the optimal path that the UAV should follow, along with the aggregation points at which the UAV stops and aggregates the data in an energy-efficient fashion is also considered. In this framework, an optimization problem is formulated to minimize the energy expenditure of the IoT devices and UAV while the latter tours to aggregate the required information from the former. A solution based on a greedy algorithm is provided, in which the optimization problem is decomposed into two complementary sub-problems. The first sub-problem selects the contributing devices using a genetic algorithm. The second sub-problem optimizes the locations of the data aggregation points and assigns the active devices to each aggregation point. Simulation results show that the proposed framework can save significant energy.

In this paper, we propose a modified time-switching relaying (TSR) protocol for dual-hop relay networks with simultaneous wireless information and power transfer (SWIPT) technique. We study the outage performance of the proposed TSR protocol and compare it with the conventional TSR and power-splitting relaying (PSR) protocols. A unified analytical expression is derived for the outage probability, in addition to studying the performance at high signal-to-noise ratio (SNR) values where a unified approximate expression for the outage probability is provided and analyzed in terms of diversity order and coding gain. The results show that the proposed TSR protocol outperforms the conventional TSR protocol existing in literature. Also, findings illustrate that applying the SWIPT technique in multi-destination relay networks results in a unity diversity order.

We study the problem of reducing the download time of multiple files requested by multiple clients from multiple cloud/fog storage servers. Given possible previous file downloads by the clients, network coding can be efficiently exploited to expedite the download process. Since each client can tune to only one server at a time, the sets of clients served by the different servers must be disjoint in order to guarantee a maximum reduction in download time. To accomplish disjoint download mechanisms, a dual conflict network coding graph is proposed. Given the intractability of the long-term optimal solution, we propose an online algorithm using the designed dual conflict graph. For the case of one file request per client, both asymptotic lower and upper bounds of the performance of the proposed conflict-free algorithm are derived. Simulation results show that this proposed algorithm exhibits near optimum performance compared to the optimum solution, and a significant reduction in download time as compared to the per-server network coding scheme. Furthermore, imperfect feedback environment scenarios are investigated. A maximum likelihood approach is employed at the server to estimate the network state, which is then incorporated in our proposed algorithm to reduce the download time in such scenarios.

In this paper, we study the performance of simultaneous wireless information and power transfer (SWIPT) technique in a multi-destination dual-hop underlay cognitive relay network with multiple primary receivers. Information transmission from the secondary source to destinations is performed entirely via a decode-and-forward (DF) relay. The relay is assumed to have no embedded power source and to harvest energy from the source signal using a power splitting (PS) protocol and employing opportunistic scheduling to forward the information to the selected destination. We derive analytical expressions for the outage probability assuming Rayleigh fading channels and considering the energy harvesting efficiency at relay, the source maximum transmit power and primary receivers interference constraints. The system performance is also studied at high signal-to-noise ratio (SNR) values where approximate expressions for the outage probability are provided and analyzed in terms of diversity order and coding gain. Monte-Carlo simulations and some numerical examples are provided to validate the derived expressions and to illustrate the effect of various system parameters on the system performance. In contrast to their conventional counterparts where a multi-destination diversity is usually achieved, the results show that the multi-destination cognitive radio relay networks with the SWIPT technique achieve a constant diversity order of one.

In this paper, we design a conflict free instantly decodable network coding (IDNC) solution for file download from distributed storage servers. Considering previously downloaded files at the clients from these servers as side information, IDNC can speed up the current download process. However, transmission conflicts can occur since multiple servers can simultaneously send IDNC combinations of files to the same client, which can tune to only one of them at a time. To avoid such conflicts and design more efficient coded download patterns, we propose a dual conflict IDNC graph model, which extends the conventional IDNC graph model in order to guarantee conflict free server transmissions to each of the clients. We then formulate the download time minimization problem as a stochastic shortest path problem whose action space is defined by the independent sets of this new graph. Given the intractability of the solution, we design a channel-aware heuristic algorithm and show that it achieves a considerable reduction in the file download time, compared to applying the conventional IDNC approach separately at each of the servers.