Mindaugas Kurmis

Prediction based context data dissemination and storage model for cooperative vehicular networks

The vehicle as the context information source generates a huge amount of different information including from physical vehicle and environment sensors. The implementation of an efficient and scalable model for information dissemination in VANETs confronts with major problems. In this dynamic environment, an increasing number of context dissemination messages are increasing channels utilization which affects the network performance. This article discusses analyses and assesses the key proposals how to deal with the context data dissemination and how to decrease the amounts of transferred and stored data in vehicular cooperation environment. This is one of the most important topics of the pervasive computing.

Communication Technologies for the Improvement of Marine Transportation Operations

Abstract The short-sea waterborne transportation makes use of several systems and technologies which are not interconnected neither interoperable, including inner ship mechanical and information, communication systems and complex alert situation and terrorist attack prevention systems located both in ports, inland and on-board the ships. Each single system and piece of technology delivers complex data, other information and functionalities which are vital for the working stability of all waterborne transport operations. The integration of specific decision support functionalities based on information and communication technologies (ICT) in the intelligent ship management systems could deliver new business functionalities and indeed make waterborne transportation more autonomous, efficient and safer. In this paper, current stages of development of the intelligent transportation concept are discussed and a robust ships systems integration and communication system concept is presented for several normal and abnormal situations: high-traffic, potentially dangerous situations or port approaching or ship maintenance, with the capability to solve problems with the limited human interface and with a remote control possibility.

Self-learning adaptive algorithm for maritime traffic abnormal movement detection based on virtual pheromone method

The paper deals with a newly designed self-learning adaptive algorithm enabling detecting any non-standard movement in marine traffic based on the bio-inspired virtual pheromone method. The algorithm detects non-standard vessel movements purely based on the common marine traffic patterns from the Automated Identification System. The proposed approach provides rapid self-learning and fast adaptation characteristics. We verified the algorithms accuracy in two modes, each of which incorporates different learning factors. The dataset for the verification of the proposed algorithm was provided by the marine traffic Automated Identification System of the Klaipeda seaport.

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.

Priority based tag authentication and routing algorithm for intermodal containers RFID sensor network

Abstract Intermodal containers transportation management has always been a serious issue among logistics worldwide companies where the application of secure mobile information technologies (e.g. radio frequency identification systems (RFID) and sensor networks) could significantly improve the current situation by sending managers all the needed transportation conditions information. In this paper, we have focused on improving managerial decision making method by introducing the expert system evaluation functionality in a common software solution CTRMS for additional ICT risks evaluation. The basic risks involved in transportation and the appropriate measures are introduced as well. The pre-defined RFID sensor network was used to develop an optimal tag authentication and routing algorithm where tags and reader authentication protocols were defined and based upon the highest security assurance and the reader to tag response time criterias. A Nearest Neighbor (NN) heuristic approach and a Priority setting me...

Intelligent Containers Network Concept

In this paper, a novel approach is presented to increase the security of shipping containers transportation and storage in container yards. This approach includes wireless sensors networks with programmable modules to increase the effectiveness of the decision support functionality for operators’ onsite. This approach is closely related to the Container Security Initiative and is intended to deepen knowledge in the intelligent transportation research area. This paper examines an urgent challenge secure of cargo transportation in containers, i.e., how quickly it is possible to detect dangerous goods in shipping containers without changing their tightness and hence rationally implements international security regulations all around the world. This paper contributes to the development of new approaches of shipping containers handling and monitoring in terms of smart cities and smart ports (for the development of the Smart Port initiative) for ports that have higher levels of security violations. This contribution is addressed as an informative measure to the general public working in the Information and Communications Technologies (ICT) research area.

Multi-sensor Data Fusion Technique to Detect Radiation Emission in Wireless Sensor Networks

Detection of objects emitting radiation is a classical problem widely analyzed by many authors worldwide; however, new computerized data analysis wireless sensor network and systems are emerging daily and require application of optimal sensors data fusion methods to increase their effectiveness even further. Conventional monitoring and other radiation emission detection systems lack optimal resources allocation in harsh and huge environments with shielding materials preventing in-time easy event detection. That is why many security issues are partially omitted in our daily lives and even in industry, including international cargo transportation operations, due to the need for a larger amount of high precision sensors to be deployed on larger areas with different technological standards and their high procurement and exploitation costs. Such conventional sensor systems are already used in many areas including detection of dirty bombs in containers (US Homeland Security initiative - Container security initiative (CSI)), but only in certain areas with relatively low efficiency. In this paper, a theoretical mathematical model is presented and discussed to lower the radiation emission detection time using DAI-DAO data fusion technique in crucial security systems. A known problem of dirty bomb detection in a container terminal is presented as an example to demonstrate the problem area and the effectiveness of the proposed solution.