Dimitrios A. Karras

An improved multi-agent simulation methodology for modelling and evaluating wireless communication systems resource allocation algorithms

Multi-Agent Systems (MAS) constitute a well known approach in modelling dynamical real world systems. Recently, this technology has been applied to Wireless Communication Systems (WCS), where efficient resource allocation is a primary goal, for modelling the physical entities involved, like Base Stations (BS), service providers and network operators. This paper presents a novel approach in applying MAS methodology to WCS resource allocation by modelling more abstract entities involved in WCS operation, and especially the concurrent network procedures (services). Due to the concurrent nature of a WCS, MAS technology presents a suitable modelling solution. Services such as new call admission, handoff, user movement and call termination are independent to one another and may occur at the same time for many different users in the network. Thus, the required network procedures for supporting the above services act autonomously, interact with the network environment (gather information such as interference conditions), take decisions (e.g. call establishment), etc, and can be modelled as agents. Based on this novel simulation approach, the agent cooperation in terms of negotiation and agreement becomes a critical issue. To this end, two negotiation strategies are presented and evaluated in this research effort and among them the distributed negotiation and communication scheme between network agents is presented to be highly efficient in terms of network performance. The multi-agent concept adapted to the concurrent nature of large scale WCS is, also, discussed in this paper.

On the implementation of ant colony optimization scheme for improved channel allocation in wireless communications

Channel allocation in wireless communication systems is one of the fundamental issues. The corresponding allocation schemes can not be static due to the dynamically changing traffic conditions and network performance. Thus, more sophisticated strategies adapted to current network conditions must be investigated and applied. Recently, various approaches have been proposed for channel allocation based on intelligent techniques such as multi-agent technology and genetic algorithms. These approaches constitute heuristic solutions to resource management problem. On the other hand, the ant colony optimization approach has been proposed for solving optimization problems but this approach has not been proposed so far for solving the channel allocation problem in wireless communication systems. In this paper, a comprehensive heuristic approach for solving the channel allocation problem based on intelligent techniques such as multi-agents and ant colony optimization is proposed. Moreover, important implementation issues such as thread execution sequence are also presented. Finally, the simulation results show the performance improvement of the proposed ant colony optimization algorithm as well as the multi-agent modeling approach.

On Multi Agent based modeling and control in large scale wireless communication systems for improved resource allocation performance

The goal of this paper is to investigate the application of the Multi Agent technology in modeling and simulating large scale wireless communication systems. There are very few studies using the multi agent concept as an alternative approach for designing and simulating resource allocation in cellular systems and especially in large scale ones. Thus, a novel modeling methodology of wireless network services exploiting multi agent technology and investigating in depth critical agent issues is proposed in this paper. It is shown that the multi agent concept proves to be a suitable solution for modeling and implementing cellular network services simulation even in the case of large scale wireless networks. The whole investigation is divided in two phases: (a) multi agent modeling analysis and (b) multi agent model development for controlling network performance. Simulation results show the effectiveness of the proposed multi agent model when is applied in combination with the state of the art event scheduling mechanism.

Efficient non linear time series prediction using non linear signal analysis and neural networks in chaotic diode resonator circuits

A novel non linear signal prediction method is presented using non linear signal analysis and deterministic chaos techniques in combination with neural networks for a diode resonator chaotic circuit. Multisim is used to simulate the circuit and show the presence of chaos. The Time series analysis is performed by the method proposed by Grasberger and Procaccia, involving estimation of the correlation and minimum embedding dimension as well as of the corresponding Kolmogorov entropy. These parameters are used to construct the first stage of a one step / multistep predictor while a back-propagation Artificial Neural Network (ANN) is involved in the second stage to enhance prediction results. The novelty of the proposed two stage predictor lies on that the backpropagation ANN is employed as a second order predictor, that is as an error predictor of the non-linear signal analysis stage application. This novel two stage predictor is evaluated through an extensive experimental study.

On Cellular Network Channels Data Mining and Decision Making through Ant Colony Optimization and Multi Agent Systems Strategies

Finding suitable channels to allocate in order to serve increasing user demands in a cellular network, which is a dynamical system, constitute the most important issue in terms of network performance since they define the bandwidth management methodology. In modern cellular networks these strategies become challenging issues especially when advanced services are applied. The effectiveness of decision making for channel allocation in a cellular network is strongly connected to current traffic and wireless environment conditions. Moreover, in large scale environments, network states change dynamically and the network performance prediction is a hard task. In the recent literature, the network adaptation to current real user needs seems it could be achieved through computational intelligence based channel allocation schemes mainly involving genetic algorithms. In this paper, a quite new approach for communication channels decision making, based on ant colony optimization, which is a special form of swarm intelligence, modelled through multi agent methodology is presented. The main novelty of this research lies on modelling this optimization scheme through multi agent systems. The simulation model architecture which includes network and ant agents are also presented as well as the performance results based on the above techniques. Finally, the current study, also, shows that there is a great field of research concerning intelligent techniques modelled through multi-agent methodologies focused on channels decision making and bandwidth management in wireless communication systems.

A Systematic Review of Analytical Management Techniques in Business Process Modelling for SMEs Beyond What-if-Analysis and Towards a Framework for Integrating Them with BPM

Unquestionably, Business Process Modelling (BPM) is an increasingly popular research area for both organisations and enterprises due to its effectiveness in enabling better planning of resources, business reengineering and optimized business performance. The understanding of Business Process modelling is an essential approach for an Organization or Enterprise to achieve set objectives and improve its operations. Recent development has shown the importance of representing processes to carry out continuous improvement. The modelling and simulation of Business Processes has been able to show Business Analysts, and Managers where bottleneck exists in the system, how to optimize the Business Process to reduce cost of running the Organization, and the required resources needed for an Organization. Although large scale organizations have already been involved in such BPM applications, on the other hand, Small Medium Enterprises (SME) have not drawn much attention with this respect. It seems that SME need more practical tools for modelling and analysis with minimum expenses if possible. One approach to make BPM more applicable to SME but, also, to larger scale organizations would be to properly integrate it with analytical management computational techniques, including the game-theoretic analysis, the probabilistic modelling, the Markov-chain modelling and the Cognitive Maps methodology. In BPM research the Petri Nets methodology has already been involved in theory, applications and BPM Software tools. However, this is not the case in the previously mentioned as well as to other analytical management techniques. It is, therefore, important in BPM research to take into account such techniques. This paper presents an overview of some important analytical management computational techniques, as the above, that could be integrated in the BPM framework. It provides an overview along with examples of the applicability of such methods in the BPM field. The major goal of this systematic overview is to propose steps for the integration of such analytical techniques in the BPM framework so that they could be widely applied especially for SME since currently are well suited to smaller scale problems.

An in depth comparative analysis of software tools for modelling and simulating business processes

Undoubtedly, Business process modelling is an increasingly popular research area for both organisations and enterprises due to its usefulness in facilitating better planning of resources, business re-engineering and optimized business performance. The understanding of Business Process is an essential approach for an Organization to achieve set objectives and improve its operations. Business Process Modelling is a representation of the processes of the organization. Several modelling techniques have been proposed and used to capture the characteristics of business processes. However, available techniques view business processes from different perspectives and have different features and capabilities. Furthermore, to date limited guidelines exist for selecting appropriate modelling techniques based on the characteristics of the problem and its requirements. Recent development has shown the importance of representing processes to carry out continuous improvement. The modelling and simulation of Business Processes has been able to show Business Analysts, and Managers where bottleneck exists in the system, how to optimise the Business Process to reduce cost of running the organization, and the required resources needed for an organization. This paper presents a comparative analysis of some popular business process modelling techniques. The comparative framework is based on the following major criteria: data flow capability, logical reasoning and understandability, specification of roles, and capability of simulation, flexibility and ease of use, simulation support and scope. The proposed framework can serve as the basis for evaluating further modelling techniques and generating selection procedures focusing on a comparison of existing tools to implement these techniques. This is to enable a potential user/modeller choose the right technique and tool in modelling critical Business Processes in order to analyse and optimise the operations of an organization.

On defining an hierarchical Secure proxy agent architecture for embedded communication network applications

The goal of this paper is to present the design and some implementation aspects of a security oriented proxy agent architecture to be used in embedded applications for network monitoring and remote control tasks. Whereas agents may be misused and lead to outflow of sensitive information on a network, they may also be exploited as a Security Enhancing Technology [1,2]. This paper presents the definition of such an architecture in the form of a fully configurable hierarchical and extensible filtering proxy for Secure Network Monitoring and control regarding a tactical wireless communication network. This approach allows the local network manager to freely specify and dynamically update his monitoring/control preferences/profile, without being limited by particular client-server implementations. It also becomes possible to implement high-level monitoring/control policies, where all technology-specific settings (such as enabling or disabling cookies, scripts, etc) are hidden in favor of more intuitive criteria such as the confidence levels that the local/global network manager is willing to grant to each network site. The proposed approach leads to a hierarchical management scheme with regards to the outlined proxy agent architecture.

On Improved Event Scheduling Mechanisms for Wireless Communications Simulation Modelling

An improved simulation model is presented in this paper for modeling resource allocation strategies in wireless communications based on new event scheduling mechanisms involving real time systems methodologies. Such mechanisms are more suitable than the currently utilized Calendar Queue algorithm for simulating concurrent events in cellular networks. The proposed simulation model is based on a novel real time event scheduling algorithm extending the known in Discrete Event Simulation methodology, Calendar Queue algorithm. The new algorithm is analyzed and its implementation issues involving multithreading technology are discussed. The application of the new scheme in modeling GSM cellular networks shows significant enhancements with regards to network modeling performance compared to rival simulation modeling techniques.

A Framework for Traffic Engineering and Routing in Survivable Multi-service High Bit Rates Optical Networks

This paper presents a general framework for traffic engineering (TE) of primary and backup paths and routing of quality of service (QoS) and best-effort (BE) flows in multi-service high bit rates optical networks capable of surviving against single or multiple node and/or link failure(s). The aforementioned framework involves an off-line and an on-line phase. In the off-line phase either a method based on a integer linear programming (ILP) TE problem or a method based on a heuristic TE algorithm provides the primary and backup routes for the QoS-guaranteed flows, where backup paths are required, and the routes for the BE flows. In the on-line phase the QoS and BE flows are routed based on the outputs of the off-line phase. In the case that one or more primary paths fail the corresponding backup paths are used for restoration. However, if one or more backup paths also fail or a backup path is not defined, dynamic restoration is applied utilising a special source-routing algorithm. Implementation issues are also discussed and test results regarding the two off-line TE methods are presented