Energy-Efficient Multi-UAV Data Collection for IoT Networks with Time Deadlines

Ghdiri, Oussama; Jaafar, Wael; Alfattani, Safwan; Abderrazak, Jihene Ben; Yanıkömeroğlu, Halim

doi:10.1109/globecom42002.2020.9322626

Cited by 32 publications

(16 citation statements)

References 11 publications

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“…They modelled their problem as a constrained Markov decision process (MDP) and provided a deep reinforcement learning solution. Meanwhile, Ghdiri et al provided a cluster-based approach for deadline aware task computation in UAVs [9]. Lastly, Yao et al prefered a generalized Nash equilibrium approach for the offloading problem in a UAV swarm [10].…”

Section: Related Workmentioning

confidence: 99%

“…4) ILP Solver: In addition to the heuristics we explained above, we use GUROBI Solver [11] to find the optimum solution for our multi-objective maximization problem (Eqs. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Despite the NP-hard property of our problem, that method could be used for only small solution space problems.…”

Section: A Q-learning Approachmentioning

confidence: 99%

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Reinforcement Learning-Based Deadline and Battery-Aware Offloading in Smart Farm IoT-UAV Networks

Catherine¹,

Pamuklu²,

Syed³

et al. 2022

Preprint

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Unmanned aerial vehicles (UAVs) with mounted base stations are a promising technology for monitoring smart farms. They can provide communication and computation services to extensive agricultural regions. With the assistance of a Multi-Access Edge Computing infrastructure, an aerial base station (ABS) network can provide an energy-efficient solution for smart farms that need to process deadline critical tasks fed by IoT devices deployed on the field. In this paper, we introduce a multiobjective maximization problem and a Q-Learning based method which aim to process these tasks before their deadline while considering the UAVs' hover time. We also present three heuristic baselines to evaluate the performance of our approaches. In addition, we introduce an integer linear programming (ILP) model to define the upper bound of our objective function. The results show that Q-Learning outperforms the baselines in terms of remaining energy levels and percentage of delay violations.

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Section: Related Workmentioning

confidence: 99%

Section: A Q-learning Approachmentioning

confidence: 99%

Reinforcement Learning-Based Deadline and Battery-Aware Offloading in Smart Farm IoT-UAV Networks

Catherine¹,

Pamuklu²,

Syed³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For the last two objectives, a model must be used to estimate the speed of the UAV and its energy consumption on each segment forming the path. Several papers define energy models for quadcopters based on the aerodynamic and electric properties of the vehicles such as [ 19 , 20 , 21 , 22 ]. The disadvantage of using such model is that it does not consider the autopilot and the UAV as a single system.…”

Section: Uav Modelmentioning

confidence: 99%

Parallel Algorithm on GPU for Wireless Sensor Data Acquisition Using a Team of Unmanned Aerial Vehicles

Roberge

Tarbouchi

2021

Sensors

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This paper proposes a framework for the wireless sensor data acquisition using a team of Unmanned Aerial Vehicles (UAVs). Scattered over a terrain, the sensors detect information about their surroundings and can transmit this information wirelessly over a short range. With no access to a terrestrial or satellite communication network to relay the information to, UAVs are used to visit the sensors and collect the data. The proposed framework uses an iterative k‑means algorithm to group the sensors into clusters and to identify Download Points (DPs) where the UAVs hover to download the data. A Single-Source–Shortest-Path algorithm (SSSP) is used to compute optimal paths between every pair of DPs with a constraint to reduce the number of turns. A genetic algorithm supplemented with a 2-opt local search heuristic is used to solve the multi-travelling salesperson problem and to find optimized tours for each UAVs. Finally, a collision avoidance strategy is implemented to guarantee collision-free trajectories. Concerned with the overall runtime of the framework, the SSSP algorithm is implemented in parallel on a graphics processing unit. The proposed framework is tested in simulation using three UAVs and realistic 3D maps with up to 100 sensors and runs in just 20.7 s, a 33.3× speed-up compared to a sequential execution on CPU. The results show that the proposed method is efficient at calculating optimized trajectories for the UAVs for data acquisition from wireless sensors. The results also show the significant advantage of the parallel implementation on GPU.

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“…For the special case where the takeoff and landing locations of the UAV are known, a distance minimization algorithm was proposed. To address the case where sensed data do not have uniform deadlines, O. Ghdiri et al [109] investigated using multiple UAVs to serve clusters of IoT devices. Specifically, the work in [109] focused on optimizing the number of required UAVs, UAV trajectories and cluster formation to minimize the total energy consumed for data collection.…”

Section: E Iot Relaying Based On Mobile Nodes: Uav Relay-assisted Iot Networkmentioning

confidence: 99%

“…To address the case where sensed data do not have uniform deadlines, O. Ghdiri et al [109] investigated using multiple UAVs to serve clusters of IoT devices. Specifically, the work in [109] focused on optimizing the number of required UAVs, UAV trajectories and cluster formation to minimize the total energy consumed for data collection. To cluster the IoT sensors, the authors proposed an improved K-means algorithm, and for cluster head positioning, the algorithm minimized the distance between the UAV dockstation and the cluster head.…”

Section: E Iot Relaying Based On Mobile Nodes: Uav Relay-assisted Iot Networkmentioning

confidence: 99%

Relaying in the Internet of Things (IoT): A Survey

Uyoata¹,

Mwangama

Adeogun

2021

IEEE Access

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The deployment of relays between Internet of Things (IoT) end devices and gateways can improve link quality. In cellular-based IoT, relays have the potential to reduce base station overload. The energy expended in single-hop long-range communication can be reduced if relays listen to transmissions of end devices and forward these observations to gateways. However, incorporating relays into IoT networks faces some challenges. IoT end devices are designed primarily for uplink communication of small-sized observations toward the network; hence, opportunistically using end devices as relays needs a redesign of both the medium access control (MAC) layer protocol of such end devices and possible addition of new communication interfaces. Additionally, the wake-up time of IoT end devices needs to be synchronized with that of the relays. For cellular-based IoT, the possibility of using infrastructure relays exists, and noncellular IoT networks can leverage the presence of mobile devices for relaying, for example, in remote healthcare. However, the latter presents problems of incentivizing relay participation and managing the mobility of relays. Furthermore, although relays can increase the lifetime of IoT networks, deploying relays implies the need for additional batteries to power them. This can erode the energy efficiency gain that relays offer. Therefore, designing relay-assisted IoT networks that provide acceptable trade-offs is key, and this goes beyond adding an extra transmit RF chain to a relay-enabled IoT end device. There has been increasing research interest in IoT relaying, as demonstrated in the available literature. Works that consider these issues are surveyed in this paper to provide insight into the state of the art, provide design insights for network designers and motivate future research directions.

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Energy-Efficient Multi-UAV Data Collection for IoT Networks with Time Deadlines

Cited by 32 publications

References 11 publications

Reinforcement Learning-Based Deadline and Battery-Aware Offloading in Smart Farm IoT-UAV Networks

Reinforcement Learning-Based Deadline and Battery-Aware Offloading in Smart Farm IoT-UAV Networks

Parallel Algorithm on GPU for Wireless Sensor Data Acquisition Using a Team of Unmanned Aerial Vehicles

Relaying in the Internet of Things (IoT): A Survey

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