scholarly journals UAV-Based Intelligent Transportation System for Emergency Reporting in Coverage Holes of Wireless Networks

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6371
Author(s):  
Abdullah M. Almasoud
Keyword(s):  
Emergency Response ◽  
Critical Condition ◽  
Intelligent Transportation System ◽  
Path Loss ◽  
Optimal Solution ◽  
Computational Time ◽  
Trajectory Design ◽  
Coverage Hole ◽  
Aerial Vehicle ◽  
Coverage Holes

During critical moments, disaster and accident victims may not be able to request help from the emergency response center. This may happen when the victim’s vehicle is located within a coverage hole in a wireless network. In this paper, we adopt an unmanned aerial vehicle (UAV) to work as an automatic emergency dispatcher for a user in a vehicle facing a critical condition. Given that the UAV is located within a coverage hole and a predetermined critical condition is detected, the UAV becomes airborne and dispatches distress messages to a communication network. We propose to use a path loss map for UAV trajectory design, and we formulate our problem mathematically as an Integer Linear Program (ILP). Our goals are to minimize the distress messages delivery time and the UAV’s mission completion time. Due to the difficulty of obtaining the optimal solution when the scale of the problem is large, we proposed an efficient algorithm that reduces the computational time significantly. We simulate our problem under different scenarios and settings, and study the performance of our proposed algorithm.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

scholarly journals Autonomous Terrestrial Image Segmentation and Sensor Node Localization for Disaster Management using Plant Growth Simulation Algorithm

2017 ◽  
Author(s):  
Deblina Bhattacharjee ◽  
Anand Paul ◽  
Won-Hwa Hong ◽  
HyunCheol Seo ◽  
Karthik S.
Keyword(s):  
Plant Growth ◽  
Emergency Response ◽  
Sensor Nodes ◽  
Simulation Algorithm ◽  
Sources Of Information ◽  
Node Localization ◽  
Growth Simulation ◽  
Aerial Vehicle ◽  
Disaster Monitoring ◽  
Plant Growth Simulation Algorithm

The use of unmanned aerial vehicle (UAV) during emergency response of a disaster has been widespread in recent years and the terrain images captured by the cameras on board these vehicles are significant sources of information for such disaster monitoring operations. Thus, analyzing such images are important for assessing the terrain of interest during such emergency response operations. Further, these UAVs are mainly used in disaster monitoring systems for the automated deployment of sensor nodes in real time. Therefore, deploying and localizing the wireless sensor nodes optimally, only in the regions of interest that are identified by segmenting the images captured by UAVs, hold paramount significance thereby effecting their performance. In this paper, the highly effective nature-inspired Plant Growth Simulation Algorithm (PGSA) has been applied for the segmentation of such terrestrial images and also for the localization of the deployed sensor nodes. The problem is formulated as a multi-dimensional optimization problem and PGSA has been used to solve it. Furthermore, the proposed method has been compared to other existing evolutionary methods and simulation results show that PGSA gives better performance with respect to both speed and accuracy unlike other techniques in literature.

scholarly journals Analysis of Firefly–Fuzzy Hybrid Algorithm for Navigation of Quad-Rotor Unmanned Aerial Vehicle

Inventions ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 48
Author(s):  
Brijesh Patel ◽  
Bhumeshwar Patle
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Fuzzy Controller ◽  
Optimal Solution ◽  
Obstacle Detection ◽  
Optimization Techniques ◽  
Multiple Features ◽  
Hybrid Controller ◽  
Aerial Vehicle ◽  
Single Controller ◽  
The Individual

In the present scenario for the development of the unmanned aerial vehicle (UAV), artificial intelligence plays an important role in path planning and obstacle detection. Due to different environments, it is always a task to achieve the proper moment for achieving the target goal while avoiding obstacles with minimum human interference. To achieve the goal with the avoidance of obstacles, individual optimization techniques with metaheuristic algorithms such as fuzzy, particle swarm optimization (PSO), etc. were implemented in various configurations. However, the optimal solution was not attained. Thus, in order to achieve an optimal solution, a hybrid model was developed by using the firefly algorithm and the fuzzy algorithm, establishing multiple features of the individual controller. The path and time optimization were achieved by a standalone controller and a hybrid firefly–fuzzy controller in different conditions, whereby the results of the controller were validated by simulation and experimental results, highlighting the advantages of the hybrid controller over the single controller.

Heuristic algorithm for optimal redundancy allocation in complex systems

2019 ◽  
Vol 25 (1) ◽  
pp. 54-64 ◽  
Author(s):  
Sudhanshu Aggarwal
Keyword(s):  
Complex Systems ◽  
Heuristic Algorithm ◽  
Communication Systems ◽  
Heuristic Algorithms ◽  
Real Life ◽  
Optimal Solution ◽  
Systems Design ◽  
Computational Time ◽  
Coherent System ◽  
Content Type

PurposeThe purpose of this paper is to present an efficient heuristic algorithm based on the 3-neighborhood approach. In this paper, search is made from sides of both feasible and infeasible regions to find near-optimal solutions.Design/methodology/approachThe algorithm performs a series of selection and exchange operations in 3-neighborhood to see whether this exchange yields still an improved feasible solution or converges to a near-optimal solution in which case the algorithm stops.FindingsThe proposed algorithm has been tested on complex system structures which have been widely used. The results show that this 3-neighborhood approach not only can obtain various known solutions but also is computationally efficient for various complex systems.Research limitations/implicationsIn general, the proposed heuristic is applicable to any coherent system with no restrictions on constraint functions; however, to enforce convergence, inferior solutions might be included only when they are not being too far from the optimum.Practical implicationsIt is observed that the proposed heuristic is reasonably proficient in terms of various measures of performance and computational time.Social implicationsReliability optimization is very important in real life systems such as computer and communication systems, telecommunications, automobile, nuclear, defense systems, etc. It is an important issue prior to real life systems design.Originality/valueThe utilization of 3-neighborhood strategy seems to be encouraging as it efficiently enforces the convergence to a near-optimal solution; indeed, it attains quality solutions in less computational time in comparison to other existing heuristic algorithms.

scholarly journals Demonstration and application of diffusive and ballistic wave propagation for drone-to-ground and drone-to-drone wireless communications

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Peter J. Burke
Keyword(s):  
Wave Propagation ◽  
Electromagnetic Wave ◽  
Path Loss ◽  
Base Station ◽  
Physical Models ◽  
Mapping Technology ◽  
Aerial Vehicle ◽  
A Cell ◽  
Independent Path ◽  
The Waves

Abstract In order to determine how an electromagnetic wave propagates from a base station to a cell phone or a wirelessly connected device, we use a novel Unmanned Aerial Vehicle (UAV) mapping technology to map the cellular network coverage at various altitudes in various terrains (flat, hilly, mountainous). For the flat terrains, the waves are shown to propagate ballistically: They have an altitude independent path loss consistent with minimal scatter in the propagation from transmitter to (aerial) receiver. In mountainous terrain, the waves are shown to propagate in the diffuse regime, and demonstrate a 10 dB increase in received signal intensity per 100′ of altitude gain, up to 400′. In the intermediate case, evidence of coherent wave interference is clearly observed in altitude independent interference patterns. These general observations can be used to build a physical or empirical model for drone-to-ground and drone-to-drone propagation, for which existing models are shown to fail. While important for building physical models of wave propagation in wireless networks, this method can be used more generally to determine the magnitude and phase of an electromagnetic wave at every point in space, as well as usher in the era of drone-to-ground and drone-to-drone communications.

scholarly journals Boundary Detection Method for Large-Scale Coverage Holes in Wireless Sensor Network Based on Minimum Critical Threshold Constraint

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Rong Jing ◽  
Lingfu Kong ◽  
Liang Kong
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Large Scale ◽  
Optimization Problem ◽  
Boundary Detection ◽  
Wireless Sensor ◽  
Critical Threshold ◽  
Coverage Hole ◽  
Hole Boundary ◽  
Coverage Holes

The existing coverage hole boundary detection methods cannot detect large-scale coverage hole boundary in wireless sensor network quickly and efficiently. Aiming at this problem, a boundary detection method for large-scale coverage holes in wireless sensor network based on minimum critical threshold constraint is proposed. Firstly, the optimization problem of minimum critical threshold is highlighted, and its formulaic description is constructed according to probabilistic sensing model. On the basis of this, the distributed gradient information is used to approximately solve the optimization problem. After that, local-scale rough boundary detection algorithm incorporating the minimum critical threshold and its iterative thinning algorithm are proposed according to blocking flow theory. The experimental results show that the proposed method has low computational complexity and network overhead when detecting large-scale coverage hole boundary in wireless sensor network.

Using Genetic Algorithm and Simplex Method to Stabilize an Oil Treatment Plant Inlet Flow

2000 ◽  
Author(s):  
Adriano C. Silva ◽  
Takaaki Ohishi ◽  
Alexandre S. Mendes ◽  
Fernando A. França ◽  
Eliana A. R. Delgado
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Problem ◽  
Hybrid Approach ◽  
Treatment Plant ◽  
Optimal Solution ◽  
Computational Time ◽  
Processing Plant ◽  
Constant Input ◽  
Pipeline Network ◽  
Network Operation

This paper presents a hybrid approach, composed of a genetic algorithm and a linear programming method, to achieve an efficient pipeline network operation. The pipeline network optimization consists of the determination of pump scheduling over a short-term horizon, usually one or more days ahead. The resulting mathematical problem has a dynamic and combinatorial characteristic, in which a sub-optimal solution was obtained through these two mathematical tools in a short computational time. The approach was applied in a Pipeline Network to a study case based on the Patagonia Argentina, which is comprised of 16 tanks and linked pumps, with 66 kilometers of pipelines, that transport the production of more than 100 wells to a pre-processing plant. The goal was to obtain a constant input flow rate at the plant respecting physical and chemical processes requirements.

scholarly journals An Algorithm to Manage Transportation Logistics That Considers Sabotage Risk

2018 ◽  
Vol 8 (3) ◽  
pp. 39 ◽  
Author(s):  
Chaiya Chomchalao ◽  
Sasitorn Kaewman ◽  
Rapeepan Pitakaso ◽  
Kanchana Sethanan
Keyword(s):  
Differential Evolution ◽  
Computational Result ◽  
Optimal Solution ◽  
Computational Time ◽  
Location Allocation ◽  
De Algorithm ◽  
Large Numbers ◽  
The Difference ◽  
Location Allocation Problem ◽  
The Mathematical Model

This paper presents an algorithm to solve the multilevel location–allocation problem when sabotage risk is considered (MLLAP-SB). Sabotage risk is the risk that a deliberate act of sabotage will happen in a living area or during the transportation of a vehicle. This can change the way decisions are made about the transportation problem when it is considered. The mathematical model of the MLLAP-SB is first presented and solved to optimality by using Lingo v. 11 optimization software, but it can solve only small numbers of test instances. Second, two heuristics are presented to solve large numbers of test instances that Lingo cannot solve to optimality within a reasonable time. The original differential evolution (DE) algorithm and the extended version of DE—the modified differential evolution (MDE) algorithm—are presented to solve the MLLAP-SB. From the computational result, when solving small numbers of test instances in which Lingo is able to find the optimality, DE and MDE are able to find a 100% optimal solution while requiring much lower computational time. Lingo uses an average 96,156.67 s to solve the problem, while DE and MDE use only 104 and 90 s, respectively. Solving large numbers of test instances where Lingo cannot solve the problem, MDE outperformed DE, as it found a 100% better solution than DE. MDE has an average 0.404% lower cost than DE when using a computational time of 90 min. The difference in cost between MDE and DE changes from 0.08% when using 10 min to 0.54% when using 100 min computational time. The computational result also explicitly shows that when sabotage risk is integrated into the method of solving the problem, it can reduce the average total cost from 32,772,361 baht to 30,652,360 baht, corresponding to a 9.61% reduction.

Is now the time to invest in emergency smart-navigated multiple-response quadcopter fleets?

2019 ◽  
Vol 7 (2) ◽  
pp. 145-155
Author(s):  
Timotius Kartawijaya ◽  
Edwin Townsend ◽  
Kevin Tully ◽  
Paul Isihara ◽  
Danilo R. Diedrichs ◽  
...  
Keyword(s):  
Emergency Response ◽  
Disaster Response ◽  
Fleet Size ◽  
Physical Systems ◽  
Emergency Response System ◽  
Humanitarian Response ◽  
Design Life ◽  
Life Saving ◽  
Aerial Vehicle ◽  
Vehicle Technology

With increased development of unmanned aerial vehicle technology and its application during humanitarian response to emergencies, the issue of smart navigation as a better alternative to manual operators is becoming increasingly significant. In response to a SmartAmerica initiative to design life-saving cyber-physical systems, a prototype Smart Emergency Response System (SERS) was developed in 2013–2014 to coordinate futuristic disaster response by cyber agents including ground and aerial telerobots and biobots. A more immediate application of the SERS system is simulation of quadcopter response to 911 police and fire requests. Tailoring parameters to specific locations, simulations inform decisions about effective quadcopter fleet size and quantify improved operator cost efficiency of a smart-navigated response.

scholarly journals Efficient Coverage Hole Detection Algorithm Based on the Simplified Rips Complex in Wireless Sensor Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jing Zhang ◽  
Han Chu ◽  
Xin Feng
Keyword(s):  
Wireless Sensor Networks ◽  
Detection Algorithm ◽  
Wireless Sensor ◽  
Detection Accuracy ◽  
High Complexity ◽  
Rips Complex ◽  
Coverage Hole ◽  
Coverage Holes ◽  
Coverage Hole Detection ◽  
Hole Detection

The appearance of coverage holes in the network leads to transmission links being disconnected, thereby resulting in decreasing the accuracy of data. Timely detection of the coverage holes can effectively improve the quality of network service. Compared with other coverage hole detection algorithms, the algorithms based on the Rips complex have advantages of high detection accuracy without node location information, but with high complexity. This paper proposes an efficient coverage hole detection algorithm based on the simplified Rips complex to solve the problem of high complexity. First, Turan’s theorem is combined with the concept of the degree and clustering coefficient in a complex network to classify the nodes; furthermore, redundant node determination rules are designed to sleep redundant nodes. Second, according to the concept of the complete graph, redundant edge deletion rules are designed to delete redundant edges. On the basis of the above two steps, the Rips complex is simplified efficiently. Finally, from the perspective of the loop, boundary loop filtering and reduction rules are designed to achieve coverage hole detection in wireless sensor networks. Compared with the HBA and tree-based coverage hole detection algorithm, simulation results show that the proposed hole detection algorithm has lower complexity and higher accuracy and the detection accuracy of the hole area is up to 99.03%.

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