3D Placement of Drone-Mounted Remote Radio Head for Minimum Transmission Power Under Connectivity Constraints

Sarigiannidis

IEEE Trans. on Green Commun. Netw.

et al. 2022

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The Internet of Vehicles (IoV) is an emerging paradigm, which is expected to be an integral component of beyond-fifth-generation and sixth-generation mobile networks. However, the processing requirements and strict delay constraints of IoV applications pose a challenge to vehicle processing units. To this end, multi-access edge computing (MEC) can leverage the availability of computing resources at the edge of the network to meet the intensive computation demands. Nevertheless, the optimal allocation of computing resources is challenging due to the various parameters, such as the number of vehicles, the available resources, and the particular requirements of each task. In this work, we consider a network consisting of multiple vehicles connected to MEC-enabled roadside units (RSUs) and propose an approach that minimizes the total energy consumption of the system by jointly optimizing the task offloading decision, the allocation of power and bandwidth, and the assignment of tasks to MEC-enabled RSUs. Due to the original problem complexity, we decouple it into subproblems and we leverage the block coordinate descent method to iteratively optimize them. Finally, the numerical results demonstrate that the proposed solution can effectively minimize total energy consumption for various numbers of vehicles and MEC nodes while maintaining a low outage probability.

Section: Discussionmentioning

confidence: 99%

Joint Wireless Resource and Computation Offloading Optimization for Energy Efficient Internet of Vehicles

Sarigiannidis

IEEE Trans. on Green Commun. Netw.

et al. 2022

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“…In this direction, UAVs equipped with FSO transceivers can form wireless local area networks and act as relays between the IoT devices and the BS [19]. Due to the limited energy reserves, the position and transmission power of the UAVs have to be carefully specified [20], [21]. Furthermore, schemes that facilitate the accommodation of massives numbers of IoT devices can be leveraged [22], [23].…”

Section: Discussionmentioning

confidence: 99%

A Hybrid RF-FSO Offloading Scheme for Autonomous Industrial Internet of Things

IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

Argyriou

et al. 2022

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The ever increasing demand for bandwidth triggered by data-intensive applications is imposing a considerable burden on the radio-frequency (RF) spectrum. A promising solution to address the spectrum congestion problem is the adoption of free-space optical (FSO) communications. In this work, we consider a hybrid RF-FSO system that enables the task offloading process from Industrial Internet-of-Things devices to a multi-access edge computing (MEC)-enabled base station (BS). We propose a solution that minimizes the total energy consumption of the system by deciding whether the RF or FSO link will be used for the task offloading and optimally allocating the device transmission power while taking into account the task requirements in terms of delay. The proposed solution is based on a decomposition-driven algorithm that employs integer linear programming (ILP) and Lagrange dual decomposition. Finally, we carry out system-level Monte Carlo simulations to evaluate the performance of the solution. The simulation results show that the proposed solution can minimize the total energy consumption within a few iterations, while also considering the respective latency requirements.

“…Additionally, the horizontal positioning problem was formulated as a circle packing problem for maximum packing density and solved by utilizing the multilevel regular polygonbased placement algorithm. The authors in [17] focused on the energy-efficient placement optimization problem. To address its complexity, they decoupled it into two separate problems, namely the horizontal placement and the vertical placement problems.…”

Section: Introductionmentioning

confidence: 99%

Drone-Base-Station for Next-Generation Internet-of-Things: A Comparison of Swarm Intelligence Approaches

Goudos

IEEE Open J. Antennas Propag.

et al. 2022

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The emergence of next-generation internet-of-things (NG-IoT) applications introduces several challenges for the sixth-generation (6G) mobile networks, such as massive connectivity, increased network capacity, and extremely low-latency. To countermeasure the aforementioned challenges, ultra-dense networking has been widely identified as a possible solution. However, the dense deployment of base stations (BSs) is not always possible or cost-efficient. Drone-base-stations (DBSs) can facilitate network expansion and efficiently address the requirements of NG-IoT. In addition, due to their flexibility, they can provide on-demand connectivity in emergency scenarios or address temporary increases in network traffic. Nevertheless, the optimal placement of a DBS is not a straightforward task due to the limited energy reserves and the increased signal quality degradation in air-to-ground links. To this end, swarm intelligence approaches can be attractive solutions for determining the optimal position of the DBS in the three-dimensional (3D) space. In this work, we explore well-known swarm intelligence approaches, namely the Cuckoo Search (CS), Elephant Herd Optimization (EHO), Grey Wolf Optimization (GWO), Monarch Butterfly Optimization (MBO), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) and investigate their performance and efficiency in solving the aforementioned problem. In particular, we investigate the performance of three scenarios in the presence of different swarm intelligence approaches. Additionally, we carry out non-parametric statistical tests, namely the Friedman and Wilcoxon tests, in order to compare the different approaches.