scholarly journals Modeling Optimal Location Distribution for Deployment of Flying Base Stations as On-Demand Connectivity Enablers in Real-World Scenarios

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5580
Author(s):  
Jiri Pokorny ◽  
Pavel Seda ◽  
Milos Seda ◽  
Jiri Hosek
Keyword(s):  
Differential Evolution ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
High Capacity ◽  
Network Capacity ◽  
Base Stations ◽  
Set Covering ◽  
Covering Problem ◽  
Network Service ◽  
Aerial Vehicles

The amount of internet traffic generated during mass public events is significantly growing in a way that requires methods to increase the overall performance of the wireless network service. Recently, legacy methods in form of mobile cell sites, frequently called cells on wheels, were used. However, modern technologies are allowing the use of unmanned aerial vehicles (UAV) as a platform for network service extension instead of ground-based techniques. This results in the development of flying base stations (FBS) where the number of deployed FBSs depends on the demanded network capacity and specific user requirements. Large-scale events, such as outdoor music festivals or sporting competitions, requiring deployment of more than one FBS need a method to optimally distribute these aerial vehicles to achieve high capacity and minimize the cost. In this paper, we present a mathematical model for FBS deployment in large-scale scenarios. The model is based on a location set covering problem and the goal is to minimize the number of FBSs by finding their optimal locations. It is restricted by users’ throughput requirements and FBSs’ available throughput, also, all users that require connectivity must be served. Two meta-heuristic algorithms (cuckoo search and differential evolution) were implemented and verified on a real example of a music festival scenario. The results show that both algorithms are capable of finding a solution. The major difference is in the performance where differential evolution solves the problem six to eight times faster, thus it is more suitable for repetitive calculation. The obtained results can be used in commercial scenarios similar to the one used in this paper where providing sufficient connectivity is crucial for good user experience. The designed algorithms will serve for the network infrastructure design and for assessing the costs and feasibility of the use-case.

scholarly journals Effective Heuristic Algorithms Solving the Jobshop Scheduling Problem with Release Dates

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1221
Author(s):  
Tao Ren ◽  
Yan Zhang ◽  
Shuenn-Ren Cheng ◽  
Chin-Chia Wu ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Optimal Schedule ◽  
Differential Evolution Algorithm ◽  
Developed Countries ◽  
Productivity Level ◽  
Jobshop Scheduling

Manufacturing industry reflects a country’s productivity level and occupies an important share in the national economy of developed countries in the world. Jobshop scheduling (JSS) model originates from modern manufacturing, in which a number of tasks are executed individually on a series of processors following their preset processing routes. This study addresses a JSS problem with the criterion of minimizing total quadratic completion time (TQCT), where each task is available at its own release date. Constructive heuristic and meta-heuristic algorithms are introduced to handle different scale instances as the problem is NP-hard. Given that the shortest-processing-time (SPT)-based heuristic and dense scheduling rule are effective for the TQCT criterion and the JSS problem, respectively, an innovative heuristic combining SPT and dense scheduling rule is put forward to provide feasible solutions for large-scale instances. A preemptive single-machine-based lower bound is designed to estimate the optimal schedule and reveal the performance of the heuristic. Differential evolution algorithm is a global search algorithm on the basis of population, which has the advantages of simple structure, strong robustness, fast convergence, and easy implementation. Therefore, a hybrid discrete differential evolution (HDDE) algorithm is presented to obtain near-optimal solutions for medium-scale instances, where multi-point insertion and a local search scheme enhance the quality of final solutions. The superiority of the HDDE algorithm is highlighted by contrast experiments with population-based meta-heuristics, i.e., ant colony optimization (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). Average gaps 45.62, 63.38 and 188.46 between HDDE with ACO, PSO and GA, respectively, are demonstrated by the numerical results with benchmark data, which reveals the domination of the proposed HDDE algorithm.

scholarly journals Solving Set Cover and Dominating Set via Maximum Satisfiability

2020 ◽  
Vol 34 (02) ◽  
pp. 1569-1576 ◽  
Author(s):  
Zhendong Lei ◽  
Shaowei Cai
Keyword(s):  
Local Search ◽  
Large Scale ◽  
Dominating Set ◽  
Scoring Function ◽  
Initial Solution ◽  
Set Covering ◽  
Set Cover ◽  
Covering Problem ◽  
Greedy Heuristic ◽  
Maximum Satisfiability

The Set Covering Problem (SCP) and Dominating Set Problem (DSP) are NP-hard and have many real world applications. SCP and DSP can be encoded into Maximum Satisfiability (MaxSAT) naturally and the resulting instances share a special structure. In this paper, we develop an efficient local search solver for MaxSAT instances of this kind. Our algorithm contains three phrase: construction, local search and recovery. In construction phrase, we simplify the instance by three reduction rules and construct an initial solution by a greedy heuristic. The initial solution is improved during the local search phrase, which exploits the feature of such instances in the scoring function and the variable selection heuristic. Finally, the corresponding solution of original instance is recovered in the recovery phrase. Experiment results on a broad range of large scale instances of SCP and DSP show that our algorithm significantly outperforms state of the art solvers for SCP, DSP and MaxSAT.

Assessing Algorithmic Performance by Frontier Analysis

2018 ◽  
Vol 9 (1) ◽  
pp. 78-94
Author(s):  
Jose Humberto Ablanedo-Rosas ◽  
Cesar Rego
Keyword(s):  
Performance Metrics ◽  
Heuristic Algorithms ◽  
Analysis Of Algorithms ◽  
Statistical Significance ◽  
Individual Performance ◽  
Set Covering ◽  
Covering Problem ◽  
Frontier Analysis ◽  
Surrogate Constraint ◽  
Significance Levels

In Combinatorial Optimization the evaluation of heuristic algorithms often requires the consideration of multiple performance metrics that are relevant for the application of interest. Traditional empirical analysis of algorithms relies on evaluating individual performance metrics where the overall assessment is conducted by subjective judgment without the support of rigorous scientific methods. The authors propose an analytical approach based on data envelopment analysis (DEA) to rank algorithms by their relative efficiency scores that result from combining multiple performance metrics. To evaluate their approach, they perform a pilot study examining the relative performance of ten surrogate constraint algorithms for different classes of the set covering problem. The analysis shows their DEA-based approach is highly effective, establishing a clear difference between the algorithms' performances at appropriate statistical significance levels, and in consequence providing useful insights into the selection of algorithms to address each class of instances. Their approach is general and can be used with all types of performance metrics and algorithms.

scholarly journals Optimization of Base Location and Patrol Routes for Unmanned Aerial Vehicles in Border Intelligence, Surveillance and Reconnaissance

2018 ◽  
Author(s):  
Yao Liu ◽  
Zhong Liu ◽  
Jianmai Shi ◽  
Guohua Wu ◽  
Chao Chen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Base Station ◽  
Base Stations ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Solution Approach ◽  
Location Routing ◽  
Aerial Vehicles

The location routing problem of unmanned aerial vehicles (UAV) in border patrol for intelligence, surveillance and reconnaissance is investigated, where the location of UAV base stations and the UAV flying routes for visiting the targets in border area are jointly optimized. The capacity of the base station and the endurance of the UAV are considered. A binary integer programming model is developed to formulate the problem, and two heuristic algorithms combined with local search strategies are designed for solving the problem. The experiment design for simulating the distribution of stations and targets in border is proposed for generating random test instances. Also, an example based on the Sino-Vietnamese border is presented to illustrate the problem and the solution approach. The performance of the two algorithms are analyzed and compared through randomly generated instances.

scholarly journals mmWave Backhaul Testbed Configurability Using Software-Defined Networking

2019 ◽  
Vol 2019 ◽  
pp. 1-24 ◽  
Author(s):  
Ricardo Santos ◽  
Konstantin Koslowski ◽  
Julian Daube ◽  
Hakim Ghazzai ◽  
Andreas Kassler ◽  
...  
Keyword(s):  
Channel Assignment ◽  
Large Scale ◽  
High Capacity ◽  
Software Defined Networking ◽  
Adaptive Mesh ◽  
Base Stations ◽  
Small Cells ◽  
Data Traffic ◽  
Wireless Backhaul ◽  
The Impact

Future mobile data traffic predictions expect a significant increase in user data traffic, requiring new forms of mobile network infrastructures. Fifth generation (5G) communication standards propose the densification of small cell access base stations (BSs) in order to provide multigigabit and low latency connectivity. This densification requires a high capacity backhaul network. Using optical links to connect all the small cells is economically not feasible for large scale radio access networks where multiple BSs are deployed. A wireless backhaul formed by a mesh of millimeter-wave (mmWave) links is an attractive mobile backhaul solution, as flexible wireless (multihop) paths can be formed to interconnect all the access BSs. Moreover, a wireless backhaul allows the dynamic reconfiguration of the backhaul topology to match varying traffic demands or adaptively power on/off small cells for green backhaul operation. However, conducting and precisely controlling reconfiguration experiments over real mmWave multihop networks is a challenging task. In this paper, we develop a Software-Defined Networking (SDN) based approach to enable such a dynamic backhaul reconfiguration and use real-world mmWave equipment to setup a SDN-enabled mmWave testbed to conduct various reconfiguration experiments. In our approach, the SDN control plane is not only responsible for configuring the forwarding plane but also for the link configuration, antenna alignment, and adaptive mesh node power on/off operations. We implement the SDN-based reconfiguration operations in a testbed with four nodes, each equipped with multiple mmWave interfaces that can be mechanically steered to connect to different neighbors. We evaluate the impact of various reconfiguration operations on existing user traffic using a set of extensive testbed measurements. Moreover, we measure the impact of the channel assignment on existing traffic, showing that a setup with an optimal channel assignment between the mesh links can result in a 44% throughput increase, when compared to a suboptimal configuration.

scholarly journals Optimization of Base Location and Patrol Routes for Unmanned Aerial Vehicles in Border Intelligence, Surveillance and Reconnaissance

2019 ◽  
Author(s):  
Yao Liu ◽  
Zhong Liu ◽  
Jianmai Shi ◽  
Guohua Wu ◽  
Chao Chen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Base Station ◽  
Base Stations ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Solution Approach ◽  
Location Routing ◽  
Aerial Vehicles

The location routing problem of unmanned aerial vehicles (UAV) in border patrol for intelligence, surveillance and reconnaissance is investigated, where the location of UAV base stations and the UAV flying routes for visiting the targets in border area are jointly optimized. The capacity of the base station and the endurance of the UAV are considered. A binary integer programming model is developed to formulate the problem, and two heuristic algorithms combined with local search strategies are designed for solving the problem. The experiment design for simulating the distribution of stations and targets in border is proposed for generating random test instances. Also, an example based on the Sino-Vietnamese border is presented to illustrate the problem and the solution approach. The performance of the two algorithms are analyzed and compared through randomly generated instances.

scholarly journals Hybrid differential evolution algorithms for the optimal camera placement problem

2018 ◽  
Vol 20 (4) ◽  
pp. 446-467 ◽  
Author(s):  
Mathieu Brévilliers ◽  
Julien Lepagnot ◽  
Lhassane Idoumghar ◽  
Maher Rebai ◽  
Julien Kritter
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Set Covering ◽  
Covering Problem ◽  
Placement Problem ◽  
Camera Placement ◽  
Content Type ◽  
Combinatorial Optimization Problems ◽  
Evolution Algorithms ◽  
Problem Instances

PurposeThis paper aims to investigate to what extent hybrid differential evolution (DE) algorithms can be successful in solving the optimal camera placement problem.Design/methodology/approachThis problem is stated as a unicost set covering problem (USCP) and 18 problem instances are defined according to practical operational needs. Three methods are selected from the literature to solve these instances: a CPLEX solver, greedy algorithm and row weighting local search (RWLS). Then, it is proposed to hybridize these algorithms with two hybrid DE approaches designed for combinatorial optimization problems. The first one is a set-based approach (DEset) from the literature. The second one is a new similarity-based approach (DEsim) that takes advantage of the geometric characteristics of a camera to find better solutions.FindingsThe experimental study highlights that RWLS and DEsim-CPLEX are the best proposed algorithms. Both easily outperform CPLEX, and it turns out that RWLS performs better on one class of problem instances, whereas DEsim-CPLEX performs better on another class, depending on the minimal resolution needed in practice.Originality/valueUp to now, the efficiency of RWLS and the DEset approach has been investigated only for a few problems. Thus, the first contribution is to apply these methods for the first time in the context of camera placement. Moreover, new hybrid DE algorithms are proposed to solve the optimal camera placement problem when stated as a USCP. The second main contribution is the design of the DEsim approach that uses the distance between camera locations to fully benefit from the DE mutation scheme.

scholarly journals Optimization of Base Location and Patrol Routes for Unmanned Aerial Vehicles in Border Intelligence, Surveillance, and Reconnaissance

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yao Liu ◽  
Zhong Liu ◽  
Jianmai Shi ◽  
Guohua Wu ◽  
Chao Chen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Base Station ◽  
Base Stations ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Solution Approach ◽  
Location Routing ◽  
Aerial Vehicles

The location-routing problem (LRP) of unmanned aerial vehicles (UAV) in border patrol for Intelligence, Surveillance, and Reconnaissance is investigated, where the locations of UAV base stations and the UAV flying routes for visiting the targets in border area are jointly optimized. The capacity of the base station and the endurance of the UAV are considered. A binary integer programming model is developed to formulate the problem, and two heuristic algorithms combined with local search strategies are designed for solving the problem. The experiment design for simulating the distribution of stations and targets in border is proposed for generating random test instances. Also, an example based on the practical border in Guangxi is presented to illustrate the problem and the solution approach. The performance of the two algorithms is analysed and compared through randomly generated instances.

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