Mixed Broadcast Techniques of Leisure Information in Vehicular Ad-Hoc Networks

Telecommunication Systems - Vehicle-to-vehicle communication and probabilistic broadcast are important means for information dissemination in vehicular ad-hoc networks (VANETs). In contrast to...     

Using Ontology and RFID Technology to Develop an Agent-Based System for Campus-Safety Management

This study proposes a distributed multi-agent system combining radio-frequency identification press technology with an ontology for special-education students in support of dynamic campus-safety management. Safety management on campus typically involves relevant supporting measures and manuals, but serious safety-related incidents continue to take place on campuses. The goal of campus security should be to identify an incident and resolve it effectively and in real time before the incident evolves into an actual crisis. Our purpose here is to improve the traceability of students involving in campus activities—that is, to identify students’ whereabouts throughout the entire school day. When an incident occurs, our system will notify the relevant parties immediately. In the present study, the agents real-time event processing increases their situational awareness and reduces the likelihood and the severity of unwanted outcomes. Basically, in this paper, we aim to demonstrate the feasibility and applicability of utilizing agent-based technologies to the special-education students.     

An Adaptive Cluster-Based Routing Mechanism for Energy Conservation in Mobile Ad Hoc Networks

In recent years, many studies have proposed various ways to strengthen wireless networks’ performance. In this paper, we identify some drawbacks arising from protocol improved location-aided routing (ILAR). Nodes around the borders of a given broadcast coverage might quickly and easily move out, resulting in a broken routing path. Another problem in ILAR is that, although it may have a better forwarding node available for use a relay node could fail. Therefore we propose a novel routing protocol integrated power balance scheme to help resolve the problems mentioned above. Furthermore, the scheme employs position information and a power-saving scheme to balance the energy of each node and to lengthen the entire network’s life. To achieve these goals, there must be a reduction in the simultaneous loading of networks. Therefore, we also propose a cluster-based routing mechanism in mobile ad hoc networks. This mechanism can reduce the loading of networks, energy conservation, and increase the lifetime of nodes and networks.     

Using Non-cooperative Game Theory for Taxi-Sharing Recommendation Systems

This paper presents a recommendation mechanism for taxi-sharing. The first aim of our model is to respectively recommend taxis and passengers for picking up passengers quickly and finding taxis easily. The second purpose is providing taxi-sharing service for passengers who want to save the payment. In our method, we analyze the historical global positioning system trajectories generated by 10,357 taxis during 110 days and present the service region with time-dependent R-Tree. We formulate the problem of choosing the paths among the taxis in the same region by using non-cooperative game theory, and find out the solution of this game which is known as Nash equilibrium. The simulation of SUMO, MOVE, and TraCI are adopted to fit our model to verify the proposed recommendation mechanism. The results show that our method can find taxis and passengers efficiently. In addition, applying our method can reduce the payment of passengers and increase the taxi revenue by taxi-sharing.     

The Adaptive Recommendation Mechanism for Lane-Changing at Safe Distances in Vehicular Environments

This paper proposes a novel eight-direction mechanism that can receive location information about nearby vehicles to perform self-analysis for lane-changing activities. In this work, we assume that each vehicle creates $3\times 3$ grids called safety-distance fields, and that the central grid is based on the given vehicle’s location. Also assuming one on board unit in each vehicle, this work uses location information of nearby vehicles as input for a cellular automata (CA) model that calculates whether or not vehicles in nearby lanes are a safe distance from the vehicle for which the calculations are being performed. The current study evaluates this approach’s performance by conducting computer simulations. Simulation results reveal the strengths of the proposed CA model in terms of increased safety distance and increased collision-avoidance for urban vehicular environments.     

The Adaptive Road Routing Recommendation for Traffic Congestion Avoidance in Smart City

A fuzzy neural network (FNN) calculates the traffic-light system and extends or terminates the green signal according to the traffic situation at the given junction while also computing from adjacent intersections. In the presence of public transports, the system decides which signal(s) should be red and how much of an extension should be given to green signals for the priority-based vehicle. Using fuzzy logic, we propose a model with a neural network for public transport, normal cars, and motorcycles. The model controls traffic-light systems to reduce traffic congestion and help vehicles with high priority pass through. The system also monitors the density of car flows and makes real-time decisions accordingly. In order to verify the proposed design algorithm, we adapted the simulations of Simulation of Urban MObility, ns2, and green light district simulation method to our model, and further results depict the performance of the proposed FNN in handling traffic congestion and priority-based traffic. The promising results present the efficiency and the scope of the proposed multi-module architecture for future development in traffic control.     

Classifier Learning and Decision Making for a Connection Manager on a Heterogeneous Network

An attractive feature of the Connection Manager Intelligence Agent is its use of network traffic and multi-attribute behavior to locate the best network devices. This study has integrated this agent with a user interface; a network connection handoff; wired and wireless network device drivers; network management applications of the (plug-in) play interface; module-to-module communication authentication; and a DBus for added versatility. To reduce the time that developers of embedded systems spend on the software engineering of this module and to achieve rapid operational efficiency, an Open Source platform, such as MeeGo or Android, must be used. This study has implemented an interactive interface through the function (based on Fuzzy-AHP) of acquisition user behavior and machine designers, boosting iterations for User-Case. The algorithm maintains a set of weights as a distribution class table of cases, as in the parameter learning by user-case; it is quite possible that the expectation–maximization of maximum probability model can be classified by user behavior. In this study, user interaction showed that the agent satisfactorily matched user intent.