90,746 research outputs found
Adaptive service discovery on service-oriented and spontaneous sensor systems
Service-oriented architecture, Spontaneous networks, Self-organisation, Self-configuration, Sensor systems, Social patternsNatural and man-made disasters can significantly impact both people and environments. Enhanced effect can be achieved through dynamic networking of people, systems and procedures and seamless integration of them to fulfil mission objectives with service-oriented sensor systems. However, the benefits of integration of services will not be realised unless we have a dependable method to discover all required services in dynamic environments. In this paper, we propose an Adaptive and Efficient Peer-to-peer Search (AEPS) approach for dependable service integration on service-oriented architecture based on a number of social behaviour patterns. In the AEPS network, the networked nodes can autonomously support and co-operate with each other in a peer-to-peer (P2P) manner to quickly discover and self-configure any services available on the disaster area and deliver a real-time capability by self-organising themselves in spontaneous groups to provide higher flexibility and adaptability for disaster monitoring and relief
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
Semi-automatic semantic enrichment of raw sensor data
One of the more recent sources of large volumes of generated data is sensor devices, where dedicated sensing equipment is used to monitor events and happenings in a wide range of domains, including monitoring human biometrics. In recent trials to examine the effects that key moments in movies have on the human body, we fitted fitted with a number of biometric sensor devices and monitored them as they watched a range of dierent movies in groups. The purpose of these experiments was to examine the correlation between humans' highlights in movies as observed from biometric sensors, and highlights in the same movies as identified by our automatic movie analysis techniques. However,the problem with this type of experiment is that both the analysis of the video stream and the sensor data readings are not directly usable
in their raw form because of the sheer volume of low-level data values generated both from the sensors and from the movie analysis. This work describes the semi-automated enrichment of both video analysis and sensor data and the mechanism used to query the data in both centralised
environments, and in a peer-to-peer architecture when the number of sensor devices grows to large numbers. We present and validate a scalable means of semi-automating the semantic enrichment of sensor data, thereby providing a means of large-scale sensor management
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
Identity and Search in Social Networks
Social networks have the surprising property of being "searchable": Ordinary
people are capable of directing messages through their network of acquaintances
to reach a specific but distant target person in only a few steps. We present a
model that offers an explanation of social network searchability in terms of
recognizable personal identities: sets of characteristics measured along a
number of social dimensions. Our model defines a class of searchable networks
and a method for searching them that may be applicable to many network search
problems, including the location of data files in peer-to-peer networks, pages
on the World Wide Web, and information in distributed databases.Comment: 4 page, 3 figures, revte
Agent-oriented Modeling for Collaborative Learning Environments: A Peer-to-Peer Helpdesk Case Study
In this paper, we present the analysis and modelling of Help&Learn, an agent-based peer-to-peer helpdesk system to support extra-class interactions among students and teachers. Help&Learn expands the student’s possibility of solving problems, getting involved in a cooperative learning experience that transcends the limits of classrooms. To model Help&Learn, we have used Agent-Object-Relationship Modeling Language (AORML), an UML extension for agent-oriented information systems modeling. The aim of this research is two-fold. On one hand, we aim at modeling the variety of roles and the complexity of their interactions and activities within the Help&Learn system. On the other hand, we aim at showing the expressive power and the modeling strengths of AORML
Porqpine: a peer-to-peer search engine
In this paper, we present a fully distributed and collaborative search
engine for web pages: Porqpine. This system uses a novel query-based model
and collaborative filtering techniques in order to obtain user-customized
results. All knowledge about users and profiles is stored in each user
node?s application. Overall the system is a multi-agent system that runs on
the computers of the user community. The nodes interact in a peer-to-peer
fashion in order to create a real distributed search engine where
information is completely distributed among all the nodes in the network.
Moreover, the system preserves the privacy of user queries and results by
maintaining the anonymity of the queries? consumers and results? producers.
The knowledge required by the system to work is implicitly caught through
the monitoring of users actions, not only within the system?s interface but
also within one of the most popular web browsers. Thus, users are not
required to explicitly feed knowledge about their interests into the system
since this process is done automatically. In this manner, users obtain the
benefits of a personalized search engine just by installing the application
on their computer. Porqpine does not intend to shun completely conventional
centralized search engines but to complement them by issuing more accurate
and personalized results.Postprint (published version
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