Development of the real time situation identification model for adaptive service support in vehicular communication networks domain

The article discusses analyses and assesses the key proposals how to deal with the situation identification for the heterogeneous service support in vehicular cooperation environment. This is one of the most important topics of the pervasive computing. Without the solution it is impossible to adequately respond to the user's needs and to provide needed services in the right place at the right moment and in the right way. In this work we present our developed real time situation identification model for adaptive service support in vehicular communication networks domain. Our solution is different from the others as it uses additional virtual context information source - information from other vehicles which for our knowledge is not addressed in the past. The simulation results show the promising context exchange rate between vehicles. The other vehicles provided additional context source in our developed model helps to increase situations identification level

Cooperative context data acquisition and dissemination for situation identification in vehicular communication networks

One of the most interesting recent topics in pervasive computing is the smart adaptive service support systems. These systems are impossible without knowing the context with the entity at the moment, what context was in the past and what is possible in the near future. The context alone is not so important in human oriented services provided in connected vehicles. In this environment we need to know not only the exact context but also the higher level information—situation. The situation awareness can be increased by exchanging it to other participating nodes—vehicles. Because the vehicular communication network is a very dynamic environment, it is essential to adequately respond to the user’s needs and to provide all the needed services in the right place at the right moment and in the right way. In this work we present our developed cooperative context data acquisition and dissemination model for situation identification in vehicular communication networks. Our solution is different from others as it uses additional virtual context information source—information from other vehicles is weighted and exchanged using the utility function. The proposed decision support system decides if the message should be transmitted to other vehicles, sent to the cloud, saved locally or dismissed. The simulation results show the promising context exchange rate between vehicles and huge saving on channel utilization.Web of Science851624

2010),The Reinforcement Framework of a Decision Support System for the Localization and Monitoring

This paper analyses the possibilities of the integration of different technological and knowledge representation techniques for the development of reinforcement frameworks for the remote control of multiple agents such as wheelchair-type robots. Some technological solutions are discussed regarding the recognition of localization of moving objects by using mobile technologies. Large-scale multi-dimensional recognitions of emotional diagnoses of disabled persons often generate large amounts of multidimensional data with complex recognition mechanisms, based on the integration of different knowledge representation techniques and complex inference models. The problem is to reveal the main components of a diagnosis as well as to construct flexible decision making models. Sensors can help to record primary data for monitoring objects; however the recognition of abnormal situations, the clustering of emotional stages and resolutions for certain types of diagnoses is an oncoming issue for bio-robot constructors. The prediction criteria of the diagnosis of the emotional situation of disabled persons are described using knowledge based models of neural networks. The research results present the development of a multi-layered framework architecture with the integration of artificial agents and support components for diagnosis recognition and control, or further actions, by using mobile technologies. The method of fuzzy neural network control of the speed of wheelchair-type robots working in real time by providing movement support for disabled individuals is presented. The fuzzy reasoning by using fuzzy logical Petri nets is described in order to define the physiological state of disabled individuals through recognizing their emotions during their different activities. Some new possibilities of the recognition of moving object location are introduced in the system

Advanced Approach of Multiagent Based Buoy Communication

Usually, a hydrometeorological information system is faced with great data flows, but the data levels are often excessive, depending on the observed region of the water. The paper presents advanced buoy communication technologies based on multiagent interaction and data exchange between several monitoring system nodes. The proposed management of buoy communication is based on a clustering algorithm, which enables the performance of the hydrometeorological information system to be enhanced. The experiment is based on the design and analysis of the inexpensive but reliable Baltic Sea autonomous monitoring network (buoys), which would be able to continuously monitor and collect temperature, waviness, and other required data. The proposed approach of multiagent based buoy communication enables all the data from the costal-based station to be monitored with limited transition speed by setting different tasks for the agent-based buoy system according to the clustering information