Agent-based Social Networks for Enterprise Collaboration

Bergenti, Federico; Franchi, Enrico; Poggi, Agostino

doi:10.1109/wetice.2011.12

Cited by 19 publications

(10 citation statements)

References 11 publications

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“…Instead, the radii of circumferences in (13) are range estimates, and they are affected by errors. Hence, circumferences do not intersect in a unique point and, therefore, proper localization algorithms are needed.…”

Section: Two Relevant Localization Algorithmsmentioning

confidence: 99%

“…In this case, the initial system of equations can be obtained from (13) by substituting range estimatesr i,j with the averaged range estimatesr…”

Section: A Two-stage Maximum-likelihood Algorithmmentioning

confidence: 99%

“…The PSO algorithm outlined previously is used to solve the localization problem formulated in (28) and, hence, to solve the localization problem described in (13). The same algorithm can be applied also when the system of equations (24), where averaged range estimatesr K i,j defined in (11) appear, is used.…”

Section: A Two-stage Maximum-likelihood Algorithmmentioning

confidence: 99%

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Experimental Results on Agent-Based Indoor Localization using WiFi Signaling

Monica¹,

Bergenti²

2018

ijacsa

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Abstract-This paper discusses experimental results on the possibility of accurately estimating the position of smart devices in known indoor environments using agent technology. Discussed localization approaches are based on WiFi signaling, which can be considered as an ubiquitous technology in the large majority of indoor environments. The use of WiFi signaling ensures that no specific infrastructures nor special on-board sensors are required to support localization. Localization is performed using range estimates from the fixed access points of the WiFi network, which are assumed to have known positions. The performance of two range-based localization algorithms are discussed. The first, called Two-Stage Maximum-Likelihood algorithm, is well-known in the literature, while the second is a recent optimization-based algorithm that uses particle swarm techniques. Results discussed in the last part of the paper show that a proper processing of WiFi-based range estimates allows obtaining accurate position estimates, especially if the optimization-based algorithm is used.

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Section: Two Relevant Localization Algorithmsmentioning

confidence: 99%

“…In this case, the initial system of equations can be obtained from (13) by substituting range estimatesr i,j with the averaged range estimatesr…”

Section: A Two-stage Maximum-likelihood Algorithmmentioning

confidence: 99%

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Experimental Results on Agent-Based Indoor Localization using WiFi Signaling

Monica¹,

Bergenti²

2018

ijacsa

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“…A message filter is a composition filter [4] whose primary scope is to define the constraints on the reception/sending of messages; however, it can also be used for manipulating messages (e.g., their encryption and decryption) and for the implementation of replication and logging services.…”

Section: Agent Modelmentioning

confidence: 99%

“…Agent-based technologies are well-suited for online social networks [4] , especially considering: (i) the networks massive scale and (ii) the interoperability requirements that are becoming increasingly relevant, especially in the context of pervasive computing. We decided to use the MAADE framework to imp lement a pervasive layer built on top of an OpenSocial Container.…”

Section: Ubamentioning

confidence: 99%

Developing Pervasive and Adaptive Applications with MAADE

Franchi¹,

Poggi²,

Tomaiuolo

2013

JCSA

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Pervasive computing is one of the most active research fields because it promises the creation of environments where computing and communication devices are effectively integrated with users so th at applications can provide largely invisible support for tasks performed by users. This paper presents an environment for software development, called MAADE (Multi Adaptive Agent Development Environment), and aimed at the implementation of mult i-agent systems for pervasive and adaptive applications using both: (i) agents and multi-agent systems properties and (ii) composition filters for driving and dynamically adapting the behaviour of the system. MAADE allows the realization of both intentional and reactive agents, which live in an environment constituted by passive objects exposing their features in the form of available services. Thanks to its modular nature, the framework is proving particularly effective for allowing graduate students to experiment with different models and protocols used in Distributed Systems. Moreover, it is being used for the realization of prototypal applications, including an ubiquitous social networking platform, with dynamical location and proximity groups.

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A Bio-Inspired Approach to WiFi-Based Indoor Localization

Bergenti

Monica

2019

Communications in Computer and Information Science

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Agent-based Social Networks for Enterprise Collaboration

Cited by 19 publications

References 11 publications

Experimental Results on Agent-Based Indoor Localization using WiFi Signaling

Experimental Results on Agent-Based Indoor Localization using WiFi Signaling

Developing Pervasive and Adaptive Applications with MAADE

A Bio-Inspired Approach to WiFi-Based Indoor Localization

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