scholarly journals Centralized Unmanned Aerial Vehicle (UAV) Mesh Networks Placement Scheme: A Multi-Objective Evolutionary Algorithm Approach

2018 ◽  
Author(s):  
Sérgio Sabino ◽  
António Grilo
Keyword(s):  
Evolutionary Algorithm ◽  
Mesh Networks ◽  
Signal To Noise Ratio ◽  
Geographical Area ◽  
Cost Effective ◽  
Data Rate ◽  
Mesh Network ◽  
Target Area ◽  
Nsga Ii ◽  
Multi Objective

In the past, Unmanned Aerial Vehicles (UAVs) were mostly used in the military operations to prevent pilot losses. Nowadays, the fast technological evolution enables the production of a class of cost-effective UAVs which can service a plethora of public and civilian applications, specially when configured to work cooperatively to accomplish a task. However, designing a communication network among the UAVs is challenging task. In this article, we propose a centralized UAV placement strategy, where UAVs are used as flying access points forming a mesh network, providing connectivity to ground nodes deployed in a target area. The geographical placement of UAVs is optimized based on a Multi-Objective Evolutionary Algorithm (MOEA). The goal of the proposed scheme is to cover all ground nodes using a minimum number of UAVs, while maximizing the fulfillment of their data rate requirements. The UAVs can employ different data rates depending on the channel conditions, which are expressed by the Signal-to-Noise-Ratio (SNR). In this work, elitist Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is used to find a set of optimal positions to place UAVs, given the positions of the ground nodes. We evaluate the trade-off between the number of UAVs used to cover the target area and the data rate requirement of the ground nodes. Simulation results show that the proposed algorithm can optimize the UAV placement given the requirement and the positions of the ground nodes in the geographical area.

scholarly journals Centralized Unmanned Aerial Vehicle Mesh Network Placement Scheme: A Multi-Objective Evolutionary Algorithm Approach

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4387 ◽  
Author(s):  
Sérgio Sabino ◽  
Nuno Horta ◽  
António Grilo
Keyword(s):  
Evolutionary Algorithm ◽  
Signal To Noise Ratio ◽  
Geographical Area ◽  
Cost Effective ◽  
Data Rate ◽  
Mesh Network ◽  
Target Area ◽  
Military Operations ◽  
Nsga Ii ◽  
Multi Objective

In the past, Unmanned Aerial Vehicles (UAVs) were mostly used in military operations to prevent pilot losses. Nowadays, the fast technological evolution has enabled the production of a class of cost-effective UAVs that can service a plethora of public and civilian applications, especially when configured to work cooperatively to accomplish a task. However, designing a communication network among the UAVs is a challenging task. In this article, we propose a centralized UAV placement strategy, where UAVs are used as flying access points forming a mesh network, providing connectivity to ground nodes deployed in a target area. The geographical placement of UAVs is optimized based on a Multi-Objective Evolutionary Algorithm (MOEA). The goal of the proposed scheme is to cover all ground nodes using a minimum number of UAVs, while maximizing the fulfillment of their data rate requirements. The UAVs can employ different data rates depending on the channel conditions, which are expressed by the Signal-to-Noise-Ratio (SNR). In this work, the elitist Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is used to find a set of optimal positions to place UAVs, given the positions of the ground nodes. We evaluate the trade-off between the number of UAVs used to cover the target area and the data rate requirement of the ground nodes. Simulation results show that the proposed algorithm can optimize the UAV placement given the requirement and the positions of the ground nodes in the geographical area.

scholarly journals Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multi-Objective Evolutionary Algorithm

2021 ◽  
pp. 1-34
Author(s):  
Joost Huizinga ◽  
Jeff Clune
Keyword(s):  
Evolutionary Algorithm ◽  
Function Optimization ◽  
Stepping Stones ◽  
Nsga Ii ◽  
Navigation Problem ◽  
Multi Objective ◽  
Robot Locomotion ◽  
Important Challenge ◽  
Maze Navigation ◽  
Multimodal Problems

Abstract An important challenge in reinforcement learning is to solve multimodal problems, where agents have to act in qualitatively different ways depending on the circumstances. Because multimodal problems are often too difficult to solve directly, it is often helpful to define a curriculum, which is an ordered set of sub-tasks that can serve as the stepping stones for solving the overall problem. Unfortunately, choosing an effective ordering for these subtasks is difficult, and a poor ordering can reduce the performance of the learning process. Here, we provide a thorough introduction and investigation of the Combinatorial Multi-Objective Evolutionary Algorithm (CMOEA), which allows all combinations of subtasks to be explored simultaneously. We compare CMOEA against three algorithms that can similarly optimize on multiple subtasks simultaneously: NSGA-II, NSGA-III and ϵ-Lexicase Selection. The algorithms are tested on a function-optimization problem with two subtasks, a simulated multimodal robot locomotion problem with six subtasks and a simulated robot maze navigation problem where a hundred random mazes are treated as subtasks. On these problems, CMOEA either outperforms or is competitive with the controls. As a separate contribution, we show that adding a linear combination over all objectives can improve the ability of the control algorithms to solve these multimodal problems. Lastly, we show that CMOEA can leverage auxiliary objectives more effectively than the controls on the multimodal locomotion task. In general, our experiments suggest that CMOEA is a promising algorithm for solving multimodal problems.

scholarly journals A Multi-Objective Modeling Method of Multi-Satellite Imaging Task Planning for Large Regional Mapping

2020 ◽  
Vol 12 (3) ◽  
pp. 344
Author(s):  
Yaxin Chen ◽  
Miaozhong Xu ◽  
Xin Shen ◽  
Guo Zhang ◽  
Zezhong Lu ◽  
...  
Keyword(s):  
Modeling Method ◽  
Target Area ◽  
Regional Mapping ◽  
Task Planning ◽  
Large Area ◽  
Nsga Ii ◽  
Satellite Imaging ◽  
Multi Objective ◽  
Core Requirements ◽  
Application Fields

Regional remote sensing image products are playing an important role in an increasing number of application fields. Aiming at multi-satellite imaging task planning for large-area image acquisition, this paper proposes a multi-objective modeling method. First, we analyzed the core requirements of regional mapping for multi-satellite imaging mission planning: Full coverage of the target area and low consumption of satellite resources. Second, an optimization model with two objective functions, namely the maximum target area coverage and minimum satellite resource utilization, was established. Using the selection of imaging strips and their swing angles as two types of decision variables, the regional decomposition and satellite resource allocation were integrated into the planning model. Third, two efficient algorithms, Vatti and non-dominated sorting genetic algorithm (NSGA-II), were used for objective function calculation and model solving, respectively. Finally, the experiments used Hubei, Finland, and Congo as the target areas and GF1, GF6, ZY1-02C, and ZY3 as imaging satellites to verify the modeling method proposed in this paper. The experiments showed that the proposed multi-objective modeling method could complete the coverage of regional targets with fewer satellite resources and improve the satellite application efficiency significantly.

Priority Scheduling Algorithm for Traffic in a LTE-based Defence Mesh Network Incorporating Centralised Scheduling Architecture

2017 ◽  
Vol 67 (5) ◽  
pp. 581
Author(s):  
Sidharth Shukla ◽  
Vimal Bhatia
Keyword(s):  
Packet Loss ◽  
Mesh Networks ◽  
Scheduling Algorithm ◽  
Data Rate ◽  
Mesh Network ◽  
High Data Rate ◽  
Priority Scheduling ◽  
High Data ◽  
Wireless Mesh

<p>Wireless mesh networks (WMN) are the networks of future and can operate on multiple protocols ranging from WiFi, WiMax to long term evolution (LTE). As a recent trend defence networks are incorporating off-the-shelf, state of the art commercial protocols to enhance the capability of their networks. LTE is one such commercially available protocol which is easy to deploy and provide high data rate which can be ideally implemented in WMN for defence networks. To enable these high data rate services LTE-based defence mesh networks (DMN) are the requirement of the day and future. However, LTE-based DMN are prone to congestion at times of active operations or full-fledged war. The congestion scenarios may lead to LTE packet loss. Hence, it is pertinent that these networks amalgamate information grooming algorithms to alleviate the throughput of the network in peak hour conditions. An efficient priority scheduling algorithm based on class of service prioritisation, data rate consumption and location of origin of traffic in the DMN is proposed. The simulations demonstrate that by incorporating the proposed priority scheduling algorithm, the overall packet loss of priority packets in the DMN reduces substantially.</p>

Evaluating the ε-Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions

2005 ◽  
Vol 13 (4) ◽  
pp. 501-525 ◽  
Author(s):  
Kalyanmoy Deb ◽  
Manikanth Mohan ◽  
Shikhar Mishra
Keyword(s):  
Steady State ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Computational Time ◽  
Test Problems ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Nsga Ii ◽  
Multi Objective

Since the suggestion of a computing procedure of multiple Pareto-optimal solutions in multi-objective optimization problems in the early Nineties, researchers have been on the look out for a procedure which is computationally fast and simultaneously capable of finding a well-converged and well-distributed set of solutions. Most multi-objective evolutionary algorithms (MOEAs) developed in the past decade are either good for achieving a well-distributed solutions at the expense of a large computational effort or computationally fast at the expense of achieving a not-so-good distribution of solutions. For example, although the Strength Pareto Evolutionary Algorithm or SPEA (Zitzler and Thiele, 1999) produces a much better distribution compared to the elitist non-dominated sorting GA or NSGA-II (Deb et al., 2002a), the computational time needed to run SPEA is much greater. In this paper, we evaluate a recently-proposed steady-state MOEA (Deb et al., 2003) which was developed based on the ε-dominance concept introduced earlier (Laumanns et al., 2002) and using efficient parent and archive update strategies for achieving a well-distributed and well-converged set of solutions quickly. Based on an extensive comparative study with four other state-of-the-art MOEAs on a number of two, three, and four objective test problems, it is observed that the steady-state MOEA is a good compromise in terms of convergence near to the Pareto-optimal front, diversity of solutions, and computational time. Moreover, the ε-MOEA is a step closer towards making MOEAs pragmatic, particularly allowing a decision-maker to control the achievable accuracy in the obtained Pareto-optimal solutions.

scholarly journals Application of an Queuing Model to WiMAX-Based Wireless Mesh Networks: Grid Topology Case Study

2012 ◽  
Vol 2012 ◽  
pp. 1-22
Author(s):  
Vahid Sattari Naeini ◽  
Naser Movahhedinia
Keyword(s):  
High Performance ◽  
Performance Metrics ◽  
Mesh Networks ◽  
Cost Effective ◽  
Mesh Network ◽  
Queuing Model ◽  
Basic Step ◽  
Broadband Internet ◽  
Mesh Structure ◽  
Wireless Mesh

Wireless mesh networking is an effective approach to reach high performance in the last mile of broadband Internet access. The mesh structure is the basic step toward providing cost-effective, dynamic, and high-bandwidth wireless connection. In this paper, WiMAX-like wireless mesh network is considered, emphasizing the grid arrangement which is the general topology described in the literature. To evaluate the performance of the conventional and proposed scheduling algorithms, each link is modeled using an M/D/1 queue and a virtual node concept is introduced to describe comparable performance metrics for the system. Performance measures of the system in addition to the simulation results are assessed in terms of the network length and the arrival rates.

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