scholarly journals Competitive Game Theoretic Clustering-Based Multiple UAV-Assisted NB-IoT Systems

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 356
Author(s):  
Chunghyun Lee ◽  
Gunhee Jang ◽  
Nhu-Ngoc Dao ◽  
Demeke Shumeye Lakew ◽  
Cheol Lee ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Clustering Algorithm ◽  
Critical Role ◽  
System Capacity ◽  
Coverage Area ◽  
Competitive Game ◽  
Np Hard Problem ◽  
Aerial Vehicle ◽  
Game Theoretic ◽  
Iot Devices

Unmanned aerial vehicle (UAV) communication is regarded as a promising technology for lightweight Internet of Things (IoT) communications in narrowband-IoT (NB-IoT) systems deployed in rugged terrain. In such UAV-assisted NB-IoT systems, the optimal UAV placement and resource allocation play a critical role. Consequently, the joint optimization of the UAV placement and resource allocation is considered in this study to improve the system capacity. Because the considered optimization problem is an NP-hard problem and owing to its non-convex property, it is difficult to optimize both the UAV placement and resource allocation simultaneously. Therefore, a competitive clustering algorithm has been developed by exchanging strategies between the UAV and the adjacent IoT devices to optimize the UAV placement. With multiple iterations, the UAV and the IoT devices within the coverage area of the UAV, converge their clustering strategies, which are suboptimal, to satisfy both sides. The bordering IoT devices of the adjacent clusters are then migrated heuristically toward each other to obtain the optimal system capacity maximization. Finally, the transmission throughput is optimized using the Nash equilibrium. The simulation results demonstrate that the algorithms proposed in this study exhibit rapid convergence, within 10 iterations, even in a large environment. The performance evaluation demonstrates that the proposed scheme improves the system capacity of the existing schemes by approximately 28%.

scholarly journals Collaborative Offloading for UAV-enabled Time-Sensitive MEC Networks

2020 ◽  
Author(s):  
Wentao Li ◽  
Mingxiong Zhao ◽  
Yuhui Wu ◽  
Junjie Yu ◽  
Lingyan Bao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Trajectory Optimization ◽  
Policy Design ◽  
Descent Method ◽  
Total Energy Consumption ◽  
Aerial Vehicle ◽  
Iot Devices ◽  
Mutually Independent

Abstract Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV is rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization ($\mathbf{P_T}$), resource allocation at UAV ($\mathbf{P_R}$) and offloading decisions at IoT devices ($\mathbf{P_O}$), then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.

scholarly journals Collaborative offloading for UAV-enabled time-sensitive MEC networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wen-Tao Li ◽  
Mingxiong Zhao ◽  
Yu-Hui Wu ◽  
Jun-Jie Yu ◽  
Ling-Yan Bao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Trajectory Optimization ◽  
Policy Design ◽  
Descent Method ◽  
Total Energy Consumption ◽  
Aerial Vehicle ◽  
Iot Devices ◽  
Mutually Independent

AbstractRecently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV are rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization ($$\mathbf {P}_{\mathbf {T}}$$ P T ), resource allocation at UAV ($$\mathbf {P}_{\mathbf {R}}$$ P R ) and offloading decisions at IoT devices ($$\mathbf {P}_{\mathbf {O}}$$ P O ) and then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.

scholarly journals Collision Avoidance Resource Allocation for LoRaWAN

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1218
Author(s):  
Natalia Chinchilla-Romero ◽  
Jorge Navarro-Ortiz ◽  
Pablo Muñoz ◽  
Pablo Ameigeiras
Keyword(s):  
Resource Allocation ◽  
Low Power ◽  
Collision Avoidance ◽  
Propagation Model ◽  
System Capacity ◽  
Radio Link ◽  
New Paradigm ◽  
Correct Operation ◽  
Resource Allocation Algorithm ◽  
Iot Devices

The number of connected IoT devices is significantly increasing and it is expected to reach more than two dozens of billions of IoT connections in the coming years. Low Power Wide Area Networks (LPWAN) have become very relevant for this new paradigm due to features such as large coverage and low power consumption. One of the most appealing technologies among these networks is LoRaWAN. Although it may be considered as one of the most mature LPWAN platforms, there are still open gaps such as its capacity limitations. For this reason, this work proposes a collision avoidance resource allocation algorithm named the Collision Avoidance Resource Allocation (CARA) algorithm with the objective of significantly increase system capacity. CARA leverages the multichannel structure and the orthogonality of spreading factors in LoRaWAN networks to avoid collisions among devices. Simulation results show that, assuming ideal radio link conditions, our proposal outperforms in 95.2% the capacity of a standard LoRaWAN network and increases the capacity by almost 40% assuming a realistic propagation model. In addition, it has been verified that CARA devices can coexist with LoRaWAN traditional devices, thus allowing the simultaneous transmissions of both types of devices. Moreover, a proof-of-concept has been implemented using commercial equipment in order to check the feasibility and the correct operation of our solution.

scholarly journals Collaborative Offloading for UAV-enabled Time-Sensitive MEC Networks

2020 ◽  
Author(s):  
Wentao Li ◽  
Mingxiong Zhao ◽  
Yuhui Wu ◽  
Junjie Yu ◽  
Lingyan Bao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Trajectory Optimization ◽  
Policy Design ◽  
Descent Method ◽  
Total Energy Consumption ◽  
Aerial Vehicle ◽  
Iot Devices ◽  
Mutually Independent

Abstract Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV is rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization (PT), resource allocation at UAV (PR) and offloading decisions at IoT devices (PO), then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.

A Game-Theoretic Formulation of Multi-Agent Resource Allocation

2005 ◽  
Author(s):  
Jonathan Bredin ◽  
Rajiv T. Maheswaran ◽  
Cagri Imer ◽  
Tamer Basar ◽  
David Kotz ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Multi Agent ◽  
Game Theoretic

scholarly journals D2D Assisted Cellular Networks in Licensed and Unlicensed Spectrum: Matching-Iteration-Based Joint User Access and Resource Allocation

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 80
Author(s):  
Qiuqi Han ◽  
Guangyuan Zheng ◽  
Chen Xu
Keyword(s):  
Resource Allocation ◽  
Cellular Networks ◽  
System Capacity ◽  
System Throughput ◽  
Unlicensed Spectrum ◽  
Resource Allocation Algorithm ◽  
User Access ◽  
Limited Spectrum

Device-to-Device (D2D) communications, which enable direct communication between nearby user devices over the licensed spectrum, have been considered a key technique to improve spectral efficiency and system throughput in cellular networks (CNs). However, the limited spectrum resources cannot be sufficient to support more cellular users (CUs) and D2D users to meet the growth of the traffic data in future wireless networks. Therefore, Long-Term Evolution-Unlicensed (LTE-U) and D2D-Unlicensed (D2D-U) technologies have been proposed to further enhance system capacity by extending the CUs and D2D users on the unlicensed spectrum for communications. In this paper, we consider an LTE network where the CUs and D2D users are allowed to share the unlicensed spectrum with Wi-Fi users. To maximize the sum rate of all users while guaranteeing each user’s quality of service (QoS), we jointly consider user access and resource allocation. To tackle the formulated problem, we propose a matching-iteration-based joint user access and resource allocation algorithm. Simulation results show that the proposed algorithm can significantly improve system throughput compared to the other benchmark algorithms.

scholarly journals Standards of Proof and Civil Litigation: A Game-Theoretic Analysis

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Alice Guerra ◽  
Barbara Luppi ◽  
Francesco Parisi
Keyword(s):  
Critical Role ◽  
Policy Instrument ◽  
Civil Litigation ◽  
Access To Justice ◽  
Standard Of Proof ◽  
Game Theoretic Analysis ◽  
Standards Of Proof ◽  
Game Theoretic ◽  
Truth Finding ◽  
Selection Of

AbstractIn litigation models, the parties’ probability to succeed in a lawsuit hinge upon the merits of the parties’ claims and their litigation efforts. In this paper we extend this framework to consider an important procedural aspect of the legal system: the standard of proof. We recast the conventional litigation model to consider how alternative standards of proof affect litigation choices. We analyze the interrelation between different standards of proof, the effectiveness of the parties’ efforts, and the merits of the case. We study how these factors jointly affect the parties’ litigation expenditures, the selection of cases brought to the courts, pretrial bargain solutions and preemptive strategies. Our results show that standards of proof are not only instrumental to balancing the competing goals of access to justice and judicial truth-finding, but they also play a critical role in affecting parties’ litigation investments and settlement choices, and in sorting the mix of cases that will actually be filed and defended in courts. The understanding of the sorting effect of standards of proof sheds light on their role as a policy instrument in civil litigation.

Improved Fuzzy FCM-LI Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Feature Analysis ◽  
Cluster Center ◽  
High Dimensional ◽  
Fuzzy C Means ◽  
Sample Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

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