scholarly journals An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhihua Zha ◽  
Chaoqun Li ◽  
Jing Xiao ◽  
Yao Zhang ◽  
Hu Qin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithm ◽  
Task Allocation ◽  
Large Scale ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Sensor Nodes ◽  
High Yield ◽  
The Road ◽  
Parameter Coordination

Research on intelligent transportation wireless sensor networks (ITWSNs) plays a very important role in an intelligent transportation system. ITWSNs deploy high-yield and low-energy-consumption traffic remote sensing sensor nodes with complex traffic parameter coordination on both sides of the road and use the self-organizing capabilities of each node to automatically establish the entire network. In the large-scale self-organization process, the importance of tasks undertaken by each node is different. It is not difficult to prove that the task allocation of traffic remote sensing sensors is an NP-hard problem, and an efficient task allocation strategy is necessary for the ITWSNs. This paper proposes an improved adaptive clone genetic algorithm (IACGA) to solve the problem of task allocation in ITWSNs. The algorithm uses a clonal expansion operator to speed up the convergence rate and uses an adaptive operator to improve the global search capability. To verify the performance of the IACGA for task allocation optimization in ITWSNs, the algorithm is compared with the elite genetic algorithm (EGA), the simulated annealing (SA), and the shuffled frog leaping algorithm (SFLA). The simulation results show that the execution performance of the IACGA is higher than EGA, SA, and SFLA. Moreover, the convergence speed of the IACGA is faster. In addition, the revenue of ITWSNs using IACGA is higher than those of EGA, SA, and SFLA. Therefore, the proposed algorithm can effectively improve the revenue of the entire ITWSN system.

Fuel-optimal path finding algorithm using traffic information at urban intersection

2019 ◽  
Author(s):  
Jooin Lee ◽  
Hyeongcheol Lee
Keyword(s):  
Fuel Consumption ◽  
Intelligent Transportation System ◽  
Optimal Path ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Traffic Model ◽  
Traffic Information ◽  
Traffic Condition ◽  
Decision Algorithm ◽  
The Road

Intelligent Transportation System (ITS) is actively studied as the sensor and communication technology in the vehicle develops. The Intelligent Transportation System collects, processes, and provides information on the location, speed, and acceleration of the vehicles in the intersection. This paper proposes a fuel optimal route decision algorithm. The algorithm estimates traffic condition using information of vehicles acquired from several ITS intersections and determines the route that minimizes fuel consumption by reflecting the estimated traffic condition. Simplified fuel consumption models and road information (speed limit, average speed, etc.) are used to estimate the amount of fuel consumed when passing through the road. Dynamic Programming (DP) is used to determine the route that fuel consumption can be minimized. This algorithm has been verified in an intersection traffic model that reflects the actual traffic environment (Korea Daegu Technopolis) and the corresponding traffic model is modeled using AIMSUN.

Nodes' Adjacency Relation of Video Sensor Network Based on GIS and GPS

2014 ◽  
Vol 687-691 ◽  
pp. 3675-3678
Author(s):  
Guo Liang Tang ◽  
Zhi Jing Liu ◽  
Jing Xiong
Keyword(s):  
Sensor Network ◽  
Road Network ◽  
Large Scale ◽  
Sensor Nodes ◽  
Abnormal Behavior ◽  
Sorting Algorithm ◽  
The Road ◽  
Video Sensors ◽  
Video Sensor ◽  
Relationship Of

As far as the large-scale video surveillance sensor network in urban road and highway, the relay-surveillance on abnormal behavior or particular targets is one of the hot focuses of in recent researches, while the establishment of adjacency relationship of the neighbor sensor nodes is the basis of the sensor scheduling for the relay-surveillance. The topology of a road network is generated according to the road information, which has already existed in the geographic information systems (GIS) regarding the road intersections as nodes and the section between the two intersections as the edge. The initial topology of relay-adjacency relationship between sensors is built by that each video sensor is deployed at the each intersection and the section of the each two intersections is regarded as the basis of adjacency between the each two sensors. When a new video sensor is to be deployed in a section of a road, the related deployed sensors in same section are searched by using the spatial index of GIS based on its GPS information, and then the adjacency relationship between the new sensor and the related ones is generated by using the sorting algorithm according to their GPS information. By using the road network information that has already existed in the GIS system, the algorithm on establishing the relay-adjacency relationship of video sensors is simple and simpler to implement, and it can be used in the construction of sensor relay-surveillance topology such as automatic real-time tracking on abnormal behavior or the analysis of the escape routes and so on in city roads, highway, smarter cities and smarter planet.

scholarly journals REDUCING THE TIME TO GET EMERGENCY ASSISTANCE FOR ACCIDENT VEHICLES ON THE ROAD THROUGH AN INTELLIGENT TRANSPORTATION SYSTEM

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 827-832
Author(s):  
A. H. Nourbakhsh ◽  
M. R. Delavar ◽  
M. Jadidi ◽  
B. Moshiri
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
Intelligent System ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Dijkstra Algorithm ◽  
Notification System ◽  
Smart Transportation ◽  
The Road ◽  
On The Road

Abstract. Intelligent Transportation Systems (ITS) is one of the main components of a smart city. ITS have several purposes including the increase of the safety and comfort of the passengers and the reduction of the road accidents. ITS can enhance safety in three modes before, within and after the collision by preventing accident via assistive system, sensing the collision situation and calculating the time of the collision and providing the emergency response in a timely manner. The main objective of this paper is related to the smart transportation services which can be provided at the time of the collision and after the accident. After the accident, it takes several minutes to hours for the person to contact the emergency department. If an accident takes place for a vehicle in a remote area, this time increases and that may cause the loss of life. In addition, determination of the exact location of the accident is difficult by the emergency centres. That leads to the possibility of erroneous responder act in dispatching the rescue team from the nearest hospital. A new assistive intelligent system is designed in this regard that includes both software and hardware units. Hardware unit is used as an On-Board Unit (OBU), which consists of GPS, GPRS and gyroscope modules. Once OBU detects the accident, a notification system designed and connected to OBU will sent an alarm to the server. The distance to the nearest emergency center is calculated using Dijkstra algorithm. Then the server sends a request for assistance to the nearest emergency centre. The proposed system is developed and tested at local laboratory conditions. The results show that this system can reduce Ambulance Arrival Time (AAT). The preliminary results and architecture of the system have been presented. The inclination angle determined by the proposed system along with the car position identified by the installed GPS sensor assists the crash/accident warning part of the system to send a help request to the nearest road emergency centre. These results verified that the probability of having a remote and smart car crash/accident decision support system using the proposed system has been improved compared to that of the existing systems.

scholarly journals SMF-GA: Optimized Multitask Allocation Algorithm in Urban Crowdsourced Transportation

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Pengfei Wang ◽  
Ruiyun Yu
Keyword(s):  
Genetic Algorithm ◽  
Greedy Algorithm ◽  
Task Allocation ◽  
Large Scale ◽  
Hard Problem ◽  
Optimal Result ◽  
Real Dataset ◽  
Np Hard Problem ◽  
Allocation Algorithms ◽  
Real Vehicle

Urban crowdsourced transportation, which can solve traffic problem within city, is a new scenario where citizens share vehicles to take passengers and packages while driving. Differing from the traditional location based crowdsourcing system (e.g., crowdsensing system), the task has to be completed with visiting two different locations (i.e., start and end points), so task allocation algorithms in crowdsensing cannot be leveraged in urban crowdsourced transportation directly. To solve this problem, we first prove that maximizing the crowdsourcing system’s profit (i.e., maximizing the total saved distance) is an NP-hard problem. We propose a heuristic greedy algorithm called Saving Most First (SMF) which is simple and effective in assigning tasks. Then, an optimized SMF based genetic algorithm (SMF-GA) is devised to jump out of the local optimal result. Finally, we demonstrate the performance of SMF and SMF-GA with extensive evaluations, based on a large scale real vehicle traces. The evaluation with large scale real dataset indicates that both SMF and SMF-GA algorithms outperform other benchmark algorithms in terms of saved distance, participant profits, etc.

Efficient Power Control and Clustering Method Based on Hybrid Clonal Genetic Algorithm in Wireless Sensor Networks

2012 ◽  
Vol 263-266 ◽  
pp. 889-897
Author(s):  
Xiang Xian Zhu ◽  
Su Feng Lu
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Clonal Selection ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Clonal Selection Algorithm ◽  
Clustering Methods

Wireless sensor networks (WSNs) lifetime for large-scale surveillance systems is defined as the time span that all targets can be covered. How to manage the combination of the sensor nodes efficiently to prolong the whole network’s lifetime while insuring the network reliability, it is one of the most important problems to research in WSNs. An effective optimization framework is then proposed, where genetic algorithm and clonal selection algorithm are hybridized to enhance the searching ability. Our goal can be described as minimizing the number of active nodes and the scheduling cost, thus reducing the overall energy consumption to prolong the whole network’s lifetime with certain coverage rate insured. We compare the proposed algorithm with different clustering methods used in the WSNs. The simulation results show that the proposed algorithm has higher efficiency and can achieve better network lifetime and data delivery at the base station.

Implementation of Vehicle Detection System Using WSN

2013 ◽  
Vol 411-414 ◽  
pp. 1459-1464
Author(s):  
Yun Long Li ◽  
Chun Xin Wang ◽  
Xiao Li Zhou ◽  
Huan Juan Wang ◽  
Ya Kun Liu
Keyword(s):  
Road Traffic ◽  
Detection System ◽  
Vehicle Detection ◽  
Intelligent Transportation ◽  
Detection Algorithm ◽  
State Machine ◽  
Sensor Nodes ◽  
Guidance System ◽  
The Road ◽  
On The Road

Vehicle Detection System plays a basic role in the field of intelligent transportation, and is the cornerstone of constructing modern intelligent transportation system. This paper presents a new vehicle detection algorithm using WSN that called the adaptive state machine. The algorithm can adaptively update the threshold and baseline; use the state machine to achieve the aim of the accurate and efficient vehicle detection. It can be used for the detection of road traffic flow, and can be used in large parking vehicle guidance system. On the road, we have deployed 76 Sensor Nodes to evaluate the performance. We observe the accurate of the road vehicle detection rate of vehicle detection system is nearly 98%.

scholarly journals Emergency Situation Prediction Mechanism: A Novel Approach for Intelligent Transportation System Using Vehicular Ad Hoc Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
P. Ganeshkumar ◽  
P. Gokulakrishnan
Keyword(s):  
Ad Hoc ◽  
Intelligent Transportation System ◽  
Road Traffic ◽  
Emergency Situation ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Status Report ◽  
The Road ◽  
Death Toll ◽  
Prediction Mechanism

In Indian four-lane express highway, millions of vehicles are travelling every day. Accidents are unfortunate and frequently occurring in these highways causing deaths, increase in death toll, and damage to infrastructure. A mechanism is required to avoid such road accidents at the maximum to reduce the death toll. An Emergency Situation Prediction Mechanism, a novel and proactive approach, is proposed in this paper for achieving the best of Intelligent Transportation System using Vehicular Ad Hoc Network. ESPM intends to predict the possibility of occurrence of an accident in an Indian four-lane express highway. In ESPM, the emergency situation prediction is done by the Road Side Unit based on (i) the Status Report sent by the vehicles in the range of RSU and (ii) the road traffic flow analysis done by the RSU. Once the emergency situation or accident is predicted in advance, an Emergency Warning Message is constructed and disseminated to all vehicles in the area of RSU to alert and prevent the vehicles from accidents. ESPM performs well in emergency situation prediction in advance to the occurrence of an accident. ESPM predicts the emergency situation within 0.20 seconds which is comparatively less than the statistical value. The prediction accuracy of ESPM against vehicle density is found better in different traffic scenarios.

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