DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments

Patch-based Hybrid Modelling of Spatially Distributed Systems by Using Stochastic HYPE - ZebraNet as an Example

Individual-based hybrid modelling of spatially distributed systems is usually expensive. Here, we consider a hybrid system in which mobile agents spread over the space and interact with each other when in close proximity. An individual-based model for this system needs to capture the spatial attributes of every agent and monitor the interaction between each pair of them. As a result, the cost of simulating this model grows exponentially as the number of agents increases. For this reason, a patch-based model with more abstraction but better scalability is advantageous. In a patch-based model, instead of representing each agent separately, we model the agents in a patch as an aggregation. This property significantly enhances the scalability of the model. In this paper, we convert an individual-based model for a spatially distributed network system for wild-life monitoring, ZebraNet, to a patch-based stochastic HYPE model with accurate performance evaluation. We show the ease and expressiveness of stochastic HYPE for patch-based modelling of hybrid systems. Moreover, a mean-field analytical model is proposed as the fluid flow approximation of the stochastic HYPE model, which can be used to investigate the average behaviour of the modelled system over an infinite number of simulation runs of the stochastic HYPE model.Comment: In Proceedings QAPL 2014, arXiv:1406.156

A Hierarchical Dynamic Monitoring Mechanism for Mobile Agent Location

[[abstract]]Mobile agent location monitoring is a necessary mechanism for mobile agent system. Most mobile agent systems do not provide a mobile agent location monitoring mechanism, e.g. Mole (J. Baumann et al., 1998), D'Agents, Concordia (1998) and Grasshdoper (2001). Even though the IBM Aglets system provides a location monitoring mechanism, it has adopted the centralized monitoring mechanism. The centralized monitoring machine is not suitable for huge mobile agents because location information processing exists as a bottleneck issue, and also the system lacks scalability. In this paper, the method proposed is a distributive mechanism for delivering MMA (mobile monitor agent) to the network node (agent server) of a mobile agent system. The MMA can be treated as a regional monitoring platform. It is responsible for collecting a mobile agent's information instantly in its monitoring area. Furthermore, the users can also inquire about their own mobile agent's current location and working status. The above mentioned mechanism is to distribute MMA dynamically to solve the hierarchical monitoring mechanism scalability issue, and still keeps the advantages of hierarchical monitoring mechanism to decrease information processing bottleneck issue which the centralized monitoring mechanism encounters.[[sponsorship]]IEEE Computer Society Technical Committee on Distributed Processing (TCDP); Tamkung University[[notice]]補正完畢[[conferencetype]]國際[[conferencetkucampus]]淡水校園[[conferencedate]]20050328~20050330[[iscallforpapers]]Y[[conferencelocation]]臺北縣, 臺

Prototype of Fault Adaptive Embedded Software for Large-Scale Real-Time Systems

This paper describes a comprehensive prototype of large-scale fault adaptive embedded software developed for the proposed Fermilab BTeV high energy physics experiment. Lightweight self-optimizing agents embedded within Level 1 of the prototype are responsible for proactive and reactive monitoring and mitigation based on specified layers of competence. The agents are self-protecting, detecting cascading failures using a distributed approach. Adaptive, reconfigurable, and mobile objects for reliablility are designed to be self-configuring to adapt automatically to dynamically changing environments. These objects provide a self-healing layer with the ability to discover, diagnose, and react to discontinuities in real-time processing. A generic modeling environment was developed to facilitate design and implementation of hardware resource specifications, application data flow, and failure mitigation strategies. Level 1 of the planned BTeV trigger system alone will consist of 2500 DSPs, so the number of components and intractable fault scenarios involved make it impossible to design an `expert system' that applies traditional centralized mitigative strategies based on rules capturing every possible system state. Instead, a distributed reactive approach is implemented using the tools and methodologies developed by the Real-Time Embedded Systems group.Comment: 2nd Workshop on Engineering of Autonomic Systems (EASe), in the 12th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems (ECBS), Washington, DC, April, 200

An adaptive distributed Intrusion detection system architecture using multi agents

Intrusion detection systems are used for monitoring the network data, analyze them and find the intrusions if any. The major issues with these systems are the time taken for analysis, transfer of bulk data from one part of the network to another, high false positives and adaptability to the future threats. These issues are addressed here by devising a framework for intrusion detection. Here, various types of co-operating agents are distributed in the network for monitoring, analyzing, detecting and reporting. Analysis and detection agents are the mobile agents which are the primary detection modules for detecting intrusions. Their mobility eliminates the transfer of bulk data for processing. An algorithm named territory is proposed to avoid interference of one analysis agent with another one. A communication layout of the analysis and detection module with other modules is depicted. The inter-agent communication reduces the false positives significantly. It also facilitates the identification of distributed types of attacks. The co-ordinator agents log various events and summarize the activities in its network. It also communicates with co-ordinator agents of other networks. The system is highly scalable by increasing the number of various agents if needed. Centralized processing is avoided here to evade single point of failure. We created a prototype and the experiments done gave very promising results showing the effectiveness of the system

Monitoring of a virtual infrastructure testbed

This paper presents a SNMP-based Monitoring Agents for Multi-Constrain Resource Scheduling in Grids (SBLOMARS) as an effective solution for resource usage monitoring in virtual network environments. SBLOMARS is different to current large-scale distributed monitoring systems in three essential aspects: Firstly, it reaches a high level of generality by the integration of the SNMP protocol and thus, facilitates to handle heterogeneous operating platforms. Secondly, it is able to self-configure the polling periods of the resources to be monitored depending of network context and finally, it makes use of dynamic software structures to interface with third parties, allowing to be deployed in a wide range of devices, from simple mobile access devices to robust multiprocessor systems or clusters with even multiple hard disks and storage partitions. SBLOMARS has been deployed in EmanicsLab, a virtual laboratory constituted by fourteen nodes distributed in seven European Universities. Although the research is not yet concluded, available results confirm its suitability to deal with the challenges of monitoring virtual networks.Postprint (published version

Automated Syndromic Surveillance using Intelligent Mobile Agents

Current syndromic surveillance systems utilize centralized databases that are neither scalable in storage space nor in computing power. Such systems are limited in the amount of syndromic data that may be collected and analyzed for the early detection of infectious disease outbreaks. However, with the increased prevalence of international travel, public health monitoring must extend beyond the borders of municipalities or states which will require the ability to store vasts amount of data and significant computing power for analyzing the data. Intelligent mobile agents may be used to create a distributed surveillance system that will utilize the hard drives and computer processing unit (CPU) power of the hosts on the agent network where the syndromic information is located. This thesis proposes the design of a mobile agent-based syndromic surveillance system and an agent decision model for outbreak detection. Simulation results indicate that mobile agents are capable of detecting an outbreak that occurs at all hosts the agent is monitoring. Further study of agent decision models is required to account for localized epidemics and variable agent movement rates

Adding an ontology to a standardized QoS-based MAS middleware

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-02481-8_12In a Multi-Agent system, middleware is one of the components used to isolate control and communications. The use of standards in the implementation of an intelligent distributed system is always advantageous. This paper presents a middleware that provides support to a multi-agent system. Middleware is based on the standard Data Distribution Services (DDS), proposed by Object Management Group (OGM). Middleware organizes information by tree based ontology and provides a set of quality of service policies that agents can use to increase efficiency. DDS provides a set of quality of service policy. Joining quality of service policy and the ontology allows getting many advantages, among others the possibility of to conceal some details of the communications system to agents, the correct location of the agents in the distributed system, or the monitoring agents in terms of quality of service. For modeling the middleware architecture it has used UML class diagrams. As an example it has presented the implementation of a mobile robot navigation system through agents that model behaviors.The MAS architecture described in this article is a part of the coordinated project SIDIRELI: Distributed Systems with Limited Resources. Control Kernel and Coordination. Education and Science Department, Spanish Government. CICYT: MICINN: DPI2008-06737-C02-01/02.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2009). Adding an ontology to a standardized QoS-based MAS middleware. En Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. Springer. 83-90. doi:10.1007/978-3-642-02481-8_12S8390Coulouris, G., Dollimore, J., Kindberg, T.: Distributed systems, concepts and design, 3rd edn. Addison Wesley, Reading (2001)Hapner, M., Sharma, R., Fialli, J., Stout, K.: JMS specification, vol. 1.1. Sun Microsystems Inc., Santa Clara (2002)Lewis, R.: Advanced Messaging Applications with MSMQ and MQ Series. Que Publishing (1999)OMG. Real-Time Corba Specification version 1.1. Document formal /02-08-02 (2002)FIPA. Specfication. Part 2, Agent Communication Language. Foundation for Intelligent Physical Agents (1997)Vogel, A., Kerherve, B., von Bochmann, G., Gecsei, J.: Distributed Multimedia and QoS: A Survey. IEEE Multimedia 2(2), 10–19 (1995)Smith, B.: Beyond concepts, or: Ontology as reality representation. In: Formal Ontology in Information Systems (FOIS 2004), pp. 73–84 (2004)Gruber, T.R.: Toward Principles for the Design of Ontologies Used for Knowledge Sharing. International Journal Human-Computer Studies 43(5-6), 907–928 (1995)Pardo-Castellote, G.: OMG Data-Distribution Service: architectural overview. In: Proceedings of 23rd International Conference on Distributed Computing Systems Workshops, Providence, USA, vols. 19-22, pp. 200–206 (2003)Object Management Group (OMG). Unified Modeling Language Specification, v1.4.2, ISO/IEC 19501 (2001)Poza, J.L., Posadas, J.I., Simó, J.E.: Distributed agent specification to an Intelligent Control Architecture. In: 6th International Workshop on Practical Applications of Agents and Multiagent Systems, Salamanca (2007)Poza, J.L., Posadas, J.l., Simó, J.E.: QoS-based middleware archi-tecture for distributed control systems. In: International Symposium on Distributed Computing and Artificial Intelligence, Salamanca (2008

A mobile agent approach for distributed train control and monitoring system.

by Wong, Wan-Lung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 88-92).Abstract also in Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Mobile Agent Systems --- p.1Chapter 1.2 --- Distributed Control Systems --- p.2Chapter 1.3 --- Motivation of the Dissertation --- p.3Chapter 1.4 --- Related Work --- p.3Chapter 1.5 --- Overview of the Dissertation --- p.5Chapter 2 --- Mobile Agents --- p.6Chapter 2.1 --- Definition of an Agent --- p.7Chapter 2.1.1 --- A Weak Notion of Agents --- p.8Chapter 2.1.2 --- A Stronger Notion of Agents --- p.9Chapter 2.1.3 --- Other Attributes of Agents --- p.9Chapter 2.2 --- Characteristics of Mobile Agents --- p.10Chapter 2.3 --- Programming Languages for Mobile Agents --- p.11Chapter 3 --- A Mobile Agent Framework --- p.16Chapter 3.1 --- The Framework --- p.16Chapter 3.1.1 --- Agent Operations --- p.19Chapter 3.1.2 --- Agent Life Cycle --- p.23Chapter 3.1.3 --- Agent Migration Server --- p.26Chapter 3.1.4 --- Communication Server --- p.28Chapter 3.1.5 --- Facilitator --- p.30Chapter 3.2 --- April as a Mobile Agent Language --- p.30Chapter 4 --- An Agent Based Distributed Train Control and Monitoring Sys- tem --- p.32Chapter 4.1 --- Introduction to DiTCAMS --- p.33Chapter 4.2 --- Terminology in DiTCAMS --- p.34Chapter 4.3 --- Architecture of DiTCAMS --- p.34Chapter 4.3.1 --- Active Agents --- p.36Chapter 4.3.2 --- Passive Agents --- p.38Chapter 4.4 --- Agent Collaborations --- p.41Chapter 4.4.1 --- Track Resource Allocation --- p.41Chapter 4.4.2 --- Sensor Triggering --- p.42Chapter 4.4.3 --- Hardware Control --- p.42Chapter 4.4.4 --- Train Migration --- p.42Chapter 4.5 --- Other Implementation Issues --- p.46Chapter 4.5.1 --- Track Resource Management --- p.47Chapter 4.5.2 --- Railway Topology Encoding --- p.50Chapter 4.5.3 --- Train Location Determination --- p.54Chapter 4.5.4 --- Train Speed Control --- p.62Chapter 4.5.5 --- Collision Prevention and Recovery --- p.64Chapter 4.5.6 --- Improving Efficiency of April for Real-time Execution --- p.65Chapter 5 --- Discussions --- p.72Chapter 5.1 --- On Enabling Mobile Agents --- p.72Chapter 5.2 --- Cost in Achieving Mobile Agents --- p.74Chapter 5.3 --- On Using April as a Mobile Agent Language --- p.75Chapter 5.4 --- History of DiTCAMS --- p.76Chapter 6 --- Concluding Remarks --- p.79Chapter 6.1 --- Contributions --- p.79Chapter 6.2 --- Limitations --- p.80Chapter 6.3 --- Future Work --- p.81Chapter A --- Hardware Components --- p.83Chapter B --- A Concurrent Administrator Based Train System Using C --- p.85Bibliography --- p.8