A Comparison with Semantic Sensor Network Storages

International Journal of Computer Applications, 2012

More and more WSNs of diverse capabilities are being deployed so as to get data about the physical or environmental conditions they monitor. Even though a large amount of data is produced by these sensor networks, it is generally heterogeneous and un-semantic in nature thereby hindering collaboration between different WSNs. In this paper, we capitalize on the concept of semantic web and explore the use of associating these semantics with this raw data so as to facilitate easy integration of different WSNs, thereby resulting in better experience for the user. We process this data at the sensing node level rather than at gateway node level and provide a theoretical architecture of the sensor node to facilitate this.

2016

The increasing availability of small-size sensor devices during the last few years and the large amount of data that they generate has led to the necessity for more efficient methods regarding data management. In this chapter, we review the techniques that are being used for data gathering and information management in sensor networks and the advantages that are provided through the proliferation of Semantic Web technologies. We present the current trends in the field of data management in sensor networks and propose a three-layer flexible architecture which intends to help developers as well as end users to take advantage of the full potential that modern sensor networks can offer. This architecture deals with issues regarding data aggregation, data enrichment and finally, data management and querying using Semantic Web technologies. Semantics are used in order to extract meaningful information from the sensor’s raw data and thus facilitate smart applications development over large...

2006

A semantic sensor network describes the physical world using the metadata obtained from a sensor network. In this paper, we present our design and implementation of MeT, a real world oriented metadata management system for semantic sensor networks. MeT generates metadata either statically based on the application semantics, or dynamically using inference rules. It then constructs relationships between physical objects through these metadata. The sensor devices in MeT include RFID tags, position sensors and Motes. The users can query MeT about physical objects through either a Graphic User Interace, or Robovie, a communication robot. We give several concrete examples of querying physical objects in MeT to demonstrate the functionality of our system.

2010

Abstract There has been significant progress in the number and capabilities of mobile devices, wireless sensors, and sensor networks. These developments, combined with the improved ability to bridge between the physical and cyber world in a more seamless way, have fostered the broad availability of sensor data capturing the state of the physical world.

2006

Semantic technologies are often proposed as important components of complex, cross-jurisdictional, heterogeneous, dynamic information systems. The needs and opportunities arising from the rapidly growing capabilities of networked sensing devices are a challenging case. It is estimated that today there are 4 billion mobile devices that can act as sensors, including active and passive RFID tags. This is complemented by an even larger number of fixed sensors recording observations of a wide variety of modalities.

Wireless sensor networks (WSN) generate large volumes of raw data which possess natural heterogeneity. WSNs are normally application specific with no sharing or reusability of sensor data among applications. In order for applications and services to be developed independently of particular WSNs, sensor data need to be enriched with semantic information. In this paper, we propose a Semantic Web Architecture for Sensor Networks (SWASN). This information oriented architecture allows the sensor data to be understood and processed in a meaningful way by a variety of applications with different purposes. We develop ontologies for sensor data and use the Jena API for processing which includes querying and inference over sensor data. By studying a building fire emergency scenario, we show that semantic web technologies can provide high level information extraction and inference of sensor data.

2010

1 Executive Summary There has been significant progress in the number and capabilities of mobile devices, wireless sensors, and sensor networks. These developments, combined with the improved ability to bridge between the physical and cyber world in a more seamless way, have fostered the broad availability of sensor data capturing the state of the physical world.

2009 13th Panhellenic Conference on Informatics, 2009

Storage management in wireless sensor networks is an area that has started to attract significant attention, and several methods have been proposed, such as Local Storage (LS), Data-Centric Storage (DCS) and more recently Location-Centric Storage (LCS). Several modern applications, like contextdependent information dissemination for pervasive computing, on-demand warning in surveillance sensor networks and roadway safety warning, require that each originating event is stored around its point of origin. LCS is a suitable approach for such applications. Though, LCS does not take into consideration the origin of the queries, which is equally important to the storage method, because it has immediate influence on the experienced latency. This paper proposes a simple yet effective way of reducing the network latency, namely the Query Sensitive Storage (QSS) protocol. QSS makes certain that not only will the queries be answered, but all subsequent queries that originated in the same area will be answered faster. The experimental evaluation using the J-Sim simulator attests that with the proposed QSS protocol we can achieve smaller network latency at a minimum storage cost as compared to its state-of-the-art competitor, namely LCS.

Proceedings of the 13th International Conference on Web Information Systems and Technologies, 2017

Large collections of sensor data are semantically described using ontologies, e.g., the Semantic Sensor Network (SSN) ontology. Semantic sensor data are RDF descriptions of sensor observations from related sampling frames or sensors at multiple points in time, e.g., climate sensor data. Sensor values can be repeated in a sampling frame, e.g., a particular temperature value can be repeated several times, resulting in a considerable increase in data volume. We devise a factorized compact representation of semantic sensor data using linked data technologies to reduce repetition of same sensor values, and propose algorithms to generate collections of factorized semantic sensor data that can be managed by existing RDF triple stores. We empirically study the effectiveness of the proposed factorized representation of semantic sensor data. We show that the size of semantic sensor data is reduced by more than 50% on average without loss of information. Further, we have evaluated the impact of this factorized representation of semantic sensor data on query execution. Results suggest that query optimizers can be empowered with semantics from factorized representations to generate query plans that effectively speed up query execution time on factorized semantic sensor data.

Semantic Web, 2010

The combination of sensor networks with the Web, web services and database technologies, was named some years ago as the Sensor Web or the Sensor Internet. Most efforts in this area focused on the provision of platforms that could be used to build sensor-based applications more efficiently, considering some of the most important challenges in sensor-based data management and sensor network configuration. The introduction of semantics into these platforms provides the opportunity of going a step forward into the understanding, management and use of sensor-based data sources, and this is a topic being explored by ongoing initiatives. In this paper we go through some of the most relevant challenges of the current Sensor Web, and describe some ongoing work and open opportunities for the introduction of semantics in this context.