1,172 research outputs found
DOH: A Content Delivery Peer-to-Peer Network
Many SMEs and non-pro¯t organizations su®er when their Web
servers become unavailable due to °ash crowd e®ects when their web site
becomes popular. One of the solutions to the °ash-crowd problem is to place
the web site on a scalable CDN (Content Delivery Network) that replicates
the content and distributes the load in order to improve its response time.
In this paper, we present our approach to building a scalable Web Hosting
environment as a CDN on top of a structured peer-to-peer system of collaborative
web-servers integrated to share the load and to improve the overall
system performance, scalability, availability and robustness. Unlike clusterbased
solutions, it can run on heterogeneous hardware, over geographically
dispersed areas. To validate and evaluate our approach, we have developed a
system prototype called DOH (DKS Organized Hosting) that is a CDN implemented
on top of the DKS (Distributed K-nary Search) structured P2P
system with DHT (Distributed Hash table) functionality [9]. The prototype
is implemented in Java, using the DKS middleware, the Jetty web-server, and
a modi¯ed JavaFTP server. The proposed design of CDN has been evaluated
by simulation and by evaluation experiments on the prototype
Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)
Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber world. The Internet of Things (IoT) consists of the network-enabled devices and communication technologies that allow connectivity and integration of physical objects (Things) into the digital world (Internet). Enormous amounts of dynamic IoT data are collected from Internet-connected devices. IoT data are usually multi-variant streams that are heterogeneous, sporadic, multi-modal, and spatio-temporal. IoT data can be disseminated with different granularities and have diverse structures, types, and qualities. Dealing with the data deluge from heterogeneous IoT resources and services imposes new challenges on indexing, discovery, and ranking mechanisms that will allow building applications that require on-line access and retrieval of ad-hoc IoT data. However, the existing IoT data indexing and discovery approaches are complex or centralised, which hinders their scalability. The primary objective of this article is to provide a holistic overview of the state-of-the-art on indexing, discovery, and ranking of IoT data. The article aims to pave the way for researchers to design, develop, implement, and evaluate techniques and approaches for on-line large-scale distributed IoT applications and services
Efficient Processing of Continuous Join Queries using Distributed Hash Tables
International audienceThis paper addresses the problem of computing approximate answers to continuous join queries. We present a new method, called DHTJoin, which combines hash-based placement of tuples in a Distributed Hash Table (DHT) and dissemination of queries using a gossip style protocol. We provide a performance evaluation of DHTJoin which shows that DHTJoin can achieve significant performance gains in terms of network traffic
Sensor Search Techniques for Sensing as a Service Architecture for The Internet of Things
The Internet of Things (IoT) is part of the Internet of the future and will
comprise billions of intelligent communicating "things" or Internet Connected
Objects (ICO) which will have sensing, actuating, and data processing
capabilities. Each ICO will have one or more embedded sensors that will capture
potentially enormous amounts of data. The sensors and related data streams can
be clustered physically or virtually, which raises the challenge of searching
and selecting the right sensors for a query in an efficient and effective way.
This paper proposes a context-aware sensor search, selection and ranking model,
called CASSARAM, to address the challenge of efficiently selecting a subset of
relevant sensors out of a large set of sensors with similar functionality and
capabilities. CASSARAM takes into account user preferences and considers a
broad range of sensor characteristics, such as reliability, accuracy, location,
battery life, and many more. The paper highlights the importance of sensor
search, selection and ranking for the IoT, identifies important characteristics
of both sensors and data capture processes, and discusses how semantic and
quantitative reasoning can be combined together. This work also addresses
challenges such as efficient distributed sensor search and
relational-expression based filtering. CASSARAM testing and performance
evaluation results are presented and discussed.Comment: IEEE sensors Journal, 2013. arXiv admin note: text overlap with
arXiv:1303.244
Data semantic enrichment for complex event processing over IoT Data Streams
This thesis generalizes techniques for processing IoT data streams, semantically enrich data with contextual information, as well as complex event processing in IoT applications. A case study for ECG anomaly detection and signal classification was conducted to validate the knowledge foundation
Marrying Big Data with Smart Data in Sensor Stream Processing
Widespread deployments of spatially distributed sensors are continuously generating data that require advanced analytical processing and interpretation by machines. Devising machine-interpretable descriptions of sensor data is a key issue in building a semantic stream processing engine. This paper proposes a semantic sensor stream processing pipeline using Apache Kafka to publish and subscribe semantic data streams in a scalable way. We use the Kafka Consumer API to annotate the sensor data using the Semantic Sensor Network ontology, then store the annotated output in an RDF triplestore for further reasoning or semantic integration with legacy information systems. We follow a Design Science approach addressing a Smart Airport scenario with geolocated audio sensors to evaluate the viability of the proposed pipeline under various Kafka-based configurations. Our experimental evaluations show that the multi-broker Kafka cluster setup supports read scalability thus facilitating the parallelization of the semantic enrichment of the sensor data
06431 Abstracts Collection -- Scalable Data Management in Evolving Networks
From 22.10.06 to 27.10.06, the Dagstuhl Seminar 06431 ``Scalable Data Management in Evolving Networks\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Data Centric Peer-to-Peer Communication in Power Grids
We study the use of peer-to-peer based declarative data management to enable efficient monitoring and control of power transmission and distribution networks.
We propose methods and an architecture for data centric communication in power networks; a proof-of-concept decentralized communication infrastructure is presented that uses and advances state of the art peer-to-peer and distributed data management protocols to provide real time access to network state information. We propose methods for adaptive network reconfiguration and self-repair mechanisms
to handle fault situations. To efficiently handle complex queries, we present a centralized metadata index, and propose a query language and execution method that allows us to handle high volume data streams in-network
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