Energy consumption is one of the important design aspect for data collection in wireless sensor networks (WSNs). This paper studies data collection from a set of sensor nodes (SNs) in WSNs enabled by ...multiple unmanned aerial vehicles (UAVs). We aim to minimize the maximum mission completion time among all UAVs by jointly optimizing the UAV trajectory, as well as the wake-up scheduling and association for SNs, while ensuring that each SN can successfully upload the targeting amount of data with a given energy budget. The formulated problem is a non-convex problem which is difficult to be solved directly. To tackle this problem, we first propose a simple scheme that each UAV only collects data while hovering, termed as hovering mode (Hmode). For this mode, in order to find the optimized hovering locations for each SN and the serving order among all locations, we propose an efficient algorithm by leveraging the min-max multiple Traveling Salesman Problem (min-max m-TSP) and convex optimization techniques. Furthermore, we propose the more general scheme that enables continuous data collection even while flying, termed as flying mode (Fmode). By leveraging bisection method and time discretization technique, the original problem is transformed into a discretized equivalent with a finite number of optimization variables, based on which a Karush-Kuhn-Tucker (KKT) solution is obtained by applying the successive convex approximation (SCA) technique. The simulation results show that the proposed multi-UAV enabled data collection with joint trajectory and communication design achieves significant performance gains over the benchmark schemes.
Unmanned aerial vehicle (UAV)-enabled communication has emerged as an appealing technology for efficient data collection in wireless sensor networks (WSNs). This paper considers a scenario where ...multiple UAVs collect data from a group of sensor nodes (SNs) on the ground. We study the fundamental tradeoff between the aerial cost , which is defined by the propulsion energy consumption and operation costs of all UAVs, and the ground cost , which is defined as the energy consumption of all SNs. To characterize such a tradeoff, an optimization problem is formulated to minimize the weighted sum of the above two costs, by optimizing the UAV trajectory jointly with wake-up time allocation, as well as the transmit power of all SNs. As the formulated problem is non-convex, it is difficult to be optimally solved in general. To tackle this issue, we decouple it into two sub-problems: UAV trajectory and wake-up time allocation optimization, as well as SN transmit power optimization. We propose an iterative algorithm to solve the two sub-problems by leveraging successive convex approximation and alternating optimization techniques. In addition, a new approach is proposed to design the UAV initial trajectory with multiple travelling salesman problem (MTSP) technique. Simulations are conducted to corroborate our study and show the flexible tradeoff achieved by the proposed design for cost balance between UAVs and SNs.
Integrating unmanned aerial vehicles (UAVs) into cellular networks offers a promising solution to support their efficient operations and achieve high-quality communication with the ground. In this ...paper, we consider a cellular-connected UAV communication system, in which one energy-constrained UAV flies from a given initial location to a final location while uploading data to the ground base stations (GBSs) along its flight. We study the joint design of UAV operation time, communication scheduling, as well as UAV trajectory and transmit power to maximize the data uploading throughput, subject to the communication quality of service (QoS) requirement and UAV energy budget constraints. We first consider an offline design approach by utilizing only the channel distribution information (CDI) that is available prior to the UAV's flight, which is formulated as a non-convex optimization problem and challenging to solve. By using path disretization and successive convex approximation (SCA) techniques, an efficient alternating optimization algorithm is proposed, which can converge to a solution that satisfies the Karush-Kuhn-Tucker (KKT) conditions. Then, we further study the online design approach by utilizing the instantaneous channel state information (CSI) that is available to the UAV in real time along its flight. As the online design problem has similar structure as that of the offline design, an adaptive online optimization algorithm is proposed. To further reduce the computational complexity, a low-complexity online algorithm based on receding horizon optimization (RHO) is developed by utilizing a combined offline and online design approach. Simulations are conducted to corroborate our study and the results demonstrate the performance gain of proposed designs as compared to various baseline schemes. Furthermore, our results unveil the tradeoff between system throughput and UAV endurance in the considered system.
Cellular-connected unmanned aerial vehicles (UAVs) are expected to become integral components of future cellular networks. To this end, one of the important problems to address is how to support ...energy-efficient UAV operation while maintaining reliable connectivity between those aerial users and cellular networks. In this paper, we aim to minimize the energy consumption of cellular-connected UAV via jointly designing the mission completion time and UAV trajectory, as well as communication base station (BS) associations, while ensuring a satisfactory communication connectivity with the ground cellular network during the UAV flight. An optimization problem is formulated by taking into account the UAV's flight energy consumption and various practical aspects of the air-ground communication models, including BS antenna pattern, interference from non-associated BSs and local environment. The formulated problem is difficult to tackle due to the lack of closed-form expressions and non-convexity nature. To this end, we first assume that the channel knowledge map (CKM) or radio map for the considered area is available, which contains rich information about the relatively stable (large-scale) channel parameters. By utilizing path discretization technique, we obtain a discretized equivalent problem and develop an efficient solution based on graph theory by employing convex optimization technique and a dynamic-weight shortest path algorithm over graph. Next, we study the more practical case that the CKM is unavailable initially. By transforming the optimization problem to a Markov decision process (MDP), we develop a deep reinforcement learning (DRL) algorithm based on multi-step learning and double Q-learning over a dueling Deep Q-Network (DQN) architecture, where the UAV acts as an agent to explore and learn its moving policy according to its local observations of the measured signal samples. Extensive simulations are carried out and the results show that our proposed designs significantly outperform baseline schemes. Furthermore, our results reveal new insights of energy-efficient UAV flight with connectivity requirements and unveil the tradeoff between UAV energy consumption and time duration along line segments.
In this letter, we study unmanned aerial vehicle (UAV) enabled Internet-of-Things (IoT) system, where a rotary-wing UAV is employed to collect data from a set of IoT devices. By introducing the ...propulsion energy model for rotary-wing UAV, we aim to minimize the maximum energy consumption among all devices, while satisfying the UAV energy budget. The problem is formulated with a non-convex programming by jointly optimizing the communication scheduling and transmit power allocation for devices as well as the UAV trajectory. An efficient solution is proposed by applying the alternating optimization and successive convex approximation techniques. Simulation results show that the proposed solution can achieve significant gains over benchmark schemes for data collection over IoT system.
In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless sensor network, where a UAV is dispatched to collect the sensed data from distributed sensor nodes (SNs) for estimating an ...unknown parameter. It is revealed that in order to minimize the mean square error for the estimation, the UAV should collect the data from as many SNs as possible, based on which an optimization problem is formulated to design the UAV's trajectory subject to its practical mobility constraints. Although the problem is nonconvex and NP-hard, we show that the optimal UAV trajectory consists of connected line segments only. With this simplification, an efficient suboptimal solution is proposed by leveraging the classic traveling salesman problem and applying convex optimization techniques. Simulation results show that the proposed trajectory design achieves a significant performance gain in terms of the number of SNs whose data are successfully collected, as compared with other benchmark schemes.
In wireless sensor networks, utilizing the unmanned aerial vehicle (UAV) as a mobile data collector for the sensor nodes (SNs) is an energy-efficient technique to prolong the network lifetime. In ...this letter, considering a general fading channel model for the SN-UAV links, we jointly optimize the SNs' wake-up schedule and UAV's trajectory to minimize the maximum energy consumption of all SNs, while ensuring that the required amount of data is collected reliably from each SN. We formulate our design as a mixed-integer non-convex optimization problem. By applying the successive convex optimization technique, an efficient iterative algorithm is proposed to find a sub-optimal solution. Numerical results show that the proposed scheme achieves significant network energy saving as compared to benchmark schemes.
In this paper, we study an unmanned aerial vehicle (UAV) enabled video streaming system, where a rotary-wing UAV is dispatched as a mobile base station to serve multiple ground users (GUs) with ...dynamic adaptive streaming over HTTP (DASH). By introducing the Quality of Experience (QoE) utility model, we investigate the joint design of transmit power and bandwidth allocation as well as UAV trajectory for maximization of minimum utility among all GUs, subject to information-causality constraints for video playing and UAV total energy budget, which consists of both communication related energy and UAV propulsion energy. The formulated problem is non-convex and difficult to be solved directly. An efficient algorithm is proposed to obtain the suboptimal solution by applying the successive convex approximation and alternating optimization techniques. Simulation results show that the proposed solution can achieve significant gains in terms of QoE utility over benchmark schemes for energy-efficient video streaming.
Completion time and energy consumption of the unmanned aerial vehicle (UAV) are two important design aspects in UAV-enabled applications. In this article, we consider a UAV-enabled mobile-edge ...computing (MEC) system for Internet-of-Things (IoT) computation offloading with limited or no common cloud/edge infrastructure. We study the joint design of computation offloading and resource allocation, as well as UAV trajectory for minimization of energy consumption and completion time of the UAV, subject to the IoT devices' task and energy budget constraints. We first consider the UAV energy minimization problem without predetermined completion time, a discretized nonconvex equivalent problem is obtained by using the path discretization technique. An efficient alternating optimization algorithm for the discretized problem is proposed by decoupling it into two subproblems and addressing the two subproblems with successive convex approximation (SCA)-based algorithms iteratively. Subsequently, we focus on the completion time minimization problem, which is nonconvex and challenging to solve. By using the same path discretization approximation model to reformulate problem, a similar alternating optimization algorithm is proposed. Furthermore, we study the Pareto-optimal solution that balances the tradeoff between the UAV energy and completion time. The simulation results are provided to corroborate this article and show that the proposed designs outperform the baseline schemes. Our results unveil the tradeoff between completion time and energy consumption of the UAV for the MEC system, and the proposed solution can provide the performance close to the lower bound.
N6-methyladenosine (m6A) has emerged as an abundant modification throughout the transcriptome with widespread functions in protein-coding and noncoding RNAs. It affects the fates of modified RNAs, ...including their stability, splicing, and/or translation, and thus plays important roles in posttranscriptional regulation. To date, m6A methyltransferases have been reported to execute m6A deposition on distinct RNAs by their own or forming different complexes with additional partner proteins. In this review, we summarize the function of these m6A methyltransferases or complexes in regulating the key genes and pathways of cancer biology. We also highlight the progress in the use of m6A methyltransferases in mediating therapy resistance, including chemotherapy, targeted therapy, immunotherapy and radiotherapy. Finally, we discuss the current approaches and clinical potential of m6A methyltransferase-targeting strategies. Keywords: m6A, m6A methyltransferase, Cancer, Therapy resistance, Drug discovery
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