Communication concepts for mobile agent systems

Baumann, J.; Hohl, Fritz; Radouniklis, Nikolaos; Rothermel, Kurt; Straßer, Markus

doi:10.1007/3-540-62803-7_29

Cited by 77 publications

(26 citation statements)

References 3 publications

(2 reference statements)

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“…However, such approach would require some form of synchronous or asynchronous inter-agent communication (e.g. a communication language such as KQML for synchronous communication or a blackboard system for asynchronous communication [3]) raising manageability issues and resulting in increased overhead upon the nodes serving as meeting points. Also, it would require an algorithm for near-optimal selection of suitable nodes to serve as meeting points.…”

Section: The Noid Algorithmmentioning

confidence: 99%

An approach for near-optimal distributed data fusion in wireless sensor networks

et al. 2009

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In wireless sensor networks (WSNs), a lot of sensory traffic with redundancy is produced due to massive node density and their diverse placement. This causes the decline of scarce network resources such as bandwidth and energy, thus decreasing the lifetime of sensor network. Recently, the mobile agent (MA) paradigm has been proposed as a solution to overcome these problems. The MA approach accounts for performing data processing and making data aggregation decisions at nodes rather than bring data back to a central processor (sink). Using this approach, redundant sensory data is eliminated. In this article, we consider the problem of calculating nearoptimal routes for MAs that incrementally fuse the data as they visit the nodes in a WSN. The order of visited nodes (the agent's itinerary) affects not only the quality but also the overall cost of data fusion. Our proposed heuristic algorithm adapts methods usually applied in network design problems in the specific requirements of sensor networks. It computes an approximate solution to the problem by suggesting an appropriate number of MAs that minimizes the overall data fusion cost and constructs nearoptimal itineraries for each of them. The performance gain of our algorithm over alternative approaches both in terms of cost and task completion latency is demonstrated by a quantitative evaluation and also in simulated environments through a Java-based tool.

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Section: The Noid Algorithmmentioning

confidence: 99%

An approach for near-optimal distributed data fusion in wireless sensor networks

et al. 2009

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“…It supports rich communication semantics (location independency, synchronous, asynchronous and multicast). Mole project at University of Stuttgart was one of the first academic efforts in mobile agents in Java [4]. It collaborates with a few industrial partners, such as Siemens and Tandem.…”

Section: Related Workmentioning

confidence: 99%

“…Compared to mobile objects, mobile agents also represent someone; they can perform autonomous actions on behalf of a user or another agent. A number of academic systems (such as Agent Tcl [20], Mole [4], Ara [27] and Tacoma [18]) and industrial systems (such as Telescript [34], Aglets [1], Concordia [9] and Voyager [33]) exist. The products using mobile agents have started to appear, such as Guideware [16].…”

Section: Introductionmentioning

confidence: 99%

Mobile Objects and Agents (MOA)

Milojicic

LaForge²,

Chauhan³

1998

Distrib. Syst. Engng.

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This paper describes the design and implementation of the Mobile Objects and Agents (MOA) project at the Open Group Research Institute. MOA was designed to support migration, communication and control of agents. It was implemented on top of the Java Virtual Machine, without any modifications to it. The initial project goals were to support communication across agent migration, as a means for collaborative work; and to provide extensive resource control, as a basic support for countering denial of service attacks. In the course of the project we added two further goals: compliance with the Java Beans component model which provides for additional configurability and customization of agent system and agent applications; and interoperability which allows cooperation with other agent systems.This paper analyzes the architecture of MOA, in particular the support for mobility, naming and locating, communication, and resource management. Object and component models of MOA are discussed and some implementation details described. We summarize the lessons learned while developing and implementing MOA and compare it to related work. IntroductionMobility has always attracted researchers in computer science. This interest spans from general observations, such as "if it weren't for mobility, we would still be trees" [10], and the analogies with the real world "migrating birds and nomadic tribes moving due to the lack of resources", to purely technical reasons, such as improving locality of reference and difference between local and remote semantics.One of the first incarnations of software mobile entities. is worms [30], which could spread across nodes and arbitrarily clone. Unrestricted implementations of worms and viruses have received negative connotations, due to security breaches and denial of service attacks [13].The next generation of mobile entities, known as process migration, were implemented at the operating sysThis work was supported in part by the Advanced Research Projects Agency and the Rome Laboratory of the Air Force Materiel Command. tem (OS) level. There were many implementations of process and object migration [3,12,19,31], but none has achieved wide acceptance. Due to inherent complexity, it was hard to introduce process migration without impacting the stability and robustness of the underlying OS.Mobile objects and agents have attracted significant attention recently. In addition to mobile code (such as applets), agents consist of data and non-transient system state that can travel between the nodes in a distributed system (intranet or Internet). Compared to mobile objects, mobile agents also represent someone; they can perform autonomous actions on behalf of a user or another agent. This paper describes the Mobile Objects and Agents (MOA) project at the Open Group Research Institute. The obvious question is why yet another mobile agent system? There were a few reasons. None of the existing systems at the time of starting the project were mature enough to be used as a starting point for our wor...

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“…Under certain circumstances mobile agents can benefit from sending communication messages instead of migrating to remote platforms [9], [13].…”

Section: Introductionmentioning

confidence: 99%

Mobile Agent Communication in Highly Dynamic Networks: A Self-Adaptive Architecture inspired by the Honey Bee Colony

Nguyen

Schau²,

Rossak³

2014

EAI Endorsed Transactions on Context-aware Systems and Applicat

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Communication is considered as a building block for mobile agent systems. In highly dynamic networks, thanks to environmental stimuli such as changes in connection quality and network topology, performance of communication between mobile agents may be degraded considerably. With focus on attaining fault tolerance and reliability, we propose a context-aware architecture for agent communication model inspired by the honey bee colony. To validate the hypothesis, a software prototype has been designed and implemented according to the proposed mechanism. Encouraging experimental results on a test system show that our approach brings benefits to a colony of agent platforms.

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Communication concepts for mobile agent systems

Cited by 77 publications

References 3 publications

An approach for near-optimal distributed data fusion in wireless sensor networks

An approach for near-optimal distributed data fusion in wireless sensor networks

Mobile Objects and Agents (MOA)

Mobile Agent Communication in Highly Dynamic Networks: A Self-Adaptive Architecture inspired by the Honey Bee Colony

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