Using cooperative mobile agents to monitor distributed and dynamic environments☆

Ilarri, Sergio; Mena, Eduardo; Illarramendi, Arantza

doi:10.1016/j.ins.2007.12.015

Cited by 65 publications

(24 citation statements)

References 43 publications

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“…For larger networks and longer lifetimes, we plan to incorporate a mechanism to keep node clocks within a negligibly small time interval of each other. One possibility is [23], a time synchronization protocol (used by TinyDB) which ensures node clocks are approximately within 10 ms of each other; another would be to adopt a relative deadline-based approach, as proposed in [36].…”

Section: Discussionmentioning

confidence: 99%

SNEE: a query processor for wireless sensor networks

Galpin

Brenninkmeijer

Gray

et al. 2010

Distrib Parallel Databases

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A wireless sensor network (WSN) can be construed as an intelligent, largescale device for observing and measuring properties of the physical world. In recent years, the database research community has championed the view that if we construe a WSN as a database (i.e., if a significant aspect of its intelligent behavior is that it can execute declaratively-expressed queries), then one can achieve a significant reduction in the cost of engineering the software that implements a data collection program for the WSN while still achieving, through query optimization, very favorable cost:benefit ratios. This paper describes a query processing framework for WSNs that meets many desiderata associated with the view of WSN as databases. The framework is presented in the form of compiler/optimizer, called SNEE, for a continuous declarative query language over sensed data streams, called SNEEql. SNEEql can be shown to meet the expressiveness requirements of a large class of applications. SNEE can be shown to generate effective and efficient query evaluation plans. More specifically, the paper describes the following contributions: (1) a user-level syntax and physical algebra for SNEEql, an expressive continuous query language over WSNs; (2) example concrete algorithms for physical algebraic operators defined in such a way that the task of deriving memory, time and energy analytical cost-estimation models (CEMs) for them becomes straightforward by reduction to a structural traversal of the pseudocode; (3) CEMs for the concrete algorithms alluded to; (4) an architecture for the optimization of SNEEql queries, called SNEE, building on well-established distributed query processing components where possible, but making enhancements or refinements where necessary to accommodate the WSN context; (5) algorithms that instantiate the components in the SNEE architecture, thereby supporting integrated query planning that includes routing, placement and timing; and (6) an empirical performance evaluation of the resulting framework.

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Section: Discussionmentioning

confidence: 99%

SNEE: a query processor for wireless sensor networks

Galpin

Brenninkmeijer

Gray

et al. 2010

Distrib Parallel Databases

View full text Add to dashboard Cite

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“…Mr. Zhou put forward a model of multi-Agent group (MAG) for monitoring and controlling hazard installations [34]. Sergio Ilarri proposed a loose and fault-tolerant monitoring approach based on mobile agents for tracking of the involved computers, carrying the monitoring tasks [35].…”

Section: (3) Trustworthiness Of Distributed Cooperative Workmentioning

confidence: 99%

Research on Agent-based Human-Information System Trusted Interaction in Distributed Cooperative Work Environment

Zhang¹,

Yi²,

Zhao³

2011

TOAUTOCJ

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Abstract:In order to solve the problem of untrustworthy interactive process in distributed cooperative work environment, this paper, firstly, proposes a notion of "human-information system trusted interaction" and provides the basic research framework. Secondly, this paper illustrates the meaning of this notion and studies the characteristics of the trustworthiness about the human-information system trusted interaction. Finally, we analyses the key factors influencing the human-information system trusted interaction and puts forward a three-layer trustworthy interactive model based on agent. A trustworthy interaction management layer is inserted between users and information system. The management layer includes 5 agents. The Interaction agent and negotiation agent track and collect data of interactive behavior; monitor agent maintains global clock to guaranty temporal consistency; assessment and analysis agents used for determining abnormal interactive behaviors; and feedback agent is used for giving user prompt and early warning. To use this model, we constructed a trustworthy interaction chain of human-information system. Due to the each link of the chain is monitored and managed by the management layer, the trustworthiness of interaction process is guaranteed.

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“…Several classifiers have been developed to consider systems evolutions (Geetha et al 2010;Gil-García and Pons-Porrata 2010;Ilarri et al 2008;Nezhad and Niaki 2010;Yang et al 2010;Bifet and Gavaldà 2009;Sahel et al 2007;Gama and Castillo 2006). In the literature, classifiers are adapted using two approaches (Angstenberger 2000;Angelov 2004;Nakhaeizadeh et al 1997).…”

Section: Adaptive Classifiersmentioning

confidence: 99%

A semi-supervised dynamic version of Fuzzy K-Nearest Neighbours to monitor evolving systems

2010

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Data issued of most real-world applications are evolving; they change constantly over time. In such applications, it is difficult to induce correctly a model (classifier) using traditional classification methods. Thus, it is important to use an adapted classification method to build a classifier and to update its parameters as new data is available. In this paper, we propose an adaptive classification approach based on the Fuzzy K-Nearest Neighbours (FKNN) method to monitor online evolving systems. The developed method, named semi-supervised Dynamic FKNN, comprises the following phases. In the first phase (detection phase), a class evolution can be detected and confirmed after the classification of each new pattern. Then in the second phase (adaptation phase), the parameters of the evolved class are updated incrementally. In the last phase (validation phase) the adapted classes are validated in order to keep only the representative ones. This approach is illustrated using an example of system which switches between several functioning modes.

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Using cooperative mobile agents to monitor distributed and dynamic environments☆

Cited by 65 publications

References 43 publications

SNEE: a query processor for wireless sensor networks

SNEE: a query processor for wireless sensor networks

Research on Agent-based Human-Information System Trusted Interaction in Distributed Cooperative Work Environment

A semi-supervised dynamic version of Fuzzy K-Nearest Neighbours to monitor evolving systems

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