53 research outputs found
A Lightweight and Flexible Mobile Agent Platform Tailored to Management Applications
Mobile Agents (MAs) represent a distributed computing technology that
promises to address the scalability problems of centralized network management.
A critical issue that will affect the wider adoption of MA paradigm in
management applications is the development of MA Platforms (MAPs) expressly
oriented to distributed management. However, most of available platforms impose
considerable burden on network and system resources and also lack of essential
functionality. In this paper, we discuss the design considerations and
implementation details of a complete MAP research prototype that sufficiently
addresses all the aforementioned issues. Our MAP has been implemented in Java
and tailored for network and systems management applications.Comment: 7 pages, 5 figures; Proceedings of the 2006 Conference on Mobile
Computing and Wireless Communications (MCWC'2006
Clustering of Mobile Ad Hoc Networks: An Adaptive Broadcast Period Approach
Organization, scalability and routing have been identified as key problems
hindering viability and commercial success of mobile ad hoc networks.
Clustering of mobile nodes among separate domains has been proposed as an
efficient approach to address those issues. In this work, we introduce an
efficient distributed clustering algorithm that uses both location and energy
metrics for cluster formation. Our proposed solution mainly addresses cluster
stability, manageability and energy efficiency issues. Also, unlike existing
active clustering methods, our algorithm relieves the network from the
unnecessary burden of control messages broadcasting, especially for relatively
static network topologies. This is achieved through adapting broadcast period
according to mobile nodes mobility pattern. The efficiency, scalability and
competence of our algorithm against alternative approaches have been
demonstrated through simulation results.Comment: 7 pages, 9 figures; IEEE International Conference on Communications,
2006. ICC '0
Smart Cities: Recent Trends, Methodologies, and Applications
Guest Editorial
SMARTBUY dataset
The dataset represents a compilation of user interaction data generated by users who participated in the project's pilot activities in Patras, Greece. Data was generated by users in the SMARTBUY app and includes information about users, stores, product categories, professions, and events. The dataset comprises the following data: - users: user account data for the Patras pilot users - occupation: all possible occupations that the pilot users could choose from - stores: stores which participated in the Patras pilot - sel_products_cat: products uploaded to the SMARTBUY platform by retailers - events: geo-stamped and time-stamped descriptions of a user interaction event (for instance, "user_id 67 rated product_id 722 with rating 4 at location x1 at datetime y1", or "user_id 91 denoted product_id 78 as favorite at location x2 at datetime y2") - event_types: all possible event types captured by the SMARTBUY platform ('Product searches', 'Product views', 'Featured product', 'Products near you views', 'Product photos browsed', 'Product ratings', 'Clicks on Read More button to read product reviews', 'Clicks on Open map button', 'Clicks on Send this info by email button', 'Products denoted as Favorite') Privacy-sensitive information such as user names, retailer owner names and store names and keywords searched are anonymized.Datasetet representerar en sammanstĂ€llning av anvĂ€ndarinteraktionsdata som genererats av anvĂ€ndare som deltog i projektets pilotverksamheten i Patras, Grekland. Data genererades av anvĂ€ndare i SMARTBUY-appen och omfattar information om anvĂ€ndare, butiker, produktkategorier, yrken och evenemang. Datasetet innehĂ„ller följande data: - anvĂ€ndare: anvĂ€ndarkontodata för Patras pilotanvĂ€ndare - yrke: alla möjliga yrken som pilotanvĂ€ndarna kan vĂ€lja mellan - butiker: butiker som deltog i Patras-piloten - sel_products_cat: produkter som laddas upp till SMARTBUY-plattformen av Ă„terförsĂ€ljare - hĂ€ndelser: geo-stĂ€mplade och tidsstĂ€mplade beskrivningar av en anvĂ€ndarinteraktionshĂ€ndelse (till exempel "user_id 67 rankade produkt_id 722 med betyg 4 pĂ„ plats x1 vid tidpunkt y1", eller "user_id 91 betecknade produkt_id 78 som favorit pĂ„ plats x2 vid datetime y2 ") - event_types: alla möjliga hĂ€ndelsetyper som fĂ„ngats av SMARTBUY-plattformen ('Produktsökningar', 'Produktvyer', 'Utvalda produkter', 'Produkter nĂ€ra dig visningar', 'Produktfoton blĂ€ddrade', 'Produktbetyg', 'Klicka pĂ„ LĂ€s Mer knapp för att lĂ€sa produktrecensioner, "Klick pĂ„ knappen Ăppna karta", "Klicka pĂ„ Skicka denna information via e-postknappen", "Produkter betecknade som favorit") IntegritetskĂ€nslig information som anvĂ€ndarnamn, detaljistĂ€garnamn och butiksnamn och sökord som sökts har anonymiserats
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