Making Wireless Sensor Network Simulators Cooperate

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A Joint Model for IEEE 802.15.4 Physical and Medium Access Control Layers

Many studies have tried to evaluate wireless networks and especially the IEEE 802.15.4 standard. Hence, several papers have aimed to describe the functionalities of the physical (PHY) and medium access control (MAC) layers. They have highlighted some characteristics with experimental results and/or have attempted to reproduce them using theoretical models. In this paper, we use the first way to better understand IEEE 802.15.4 standard. Indeed, we provide a comprehensive model, able more faithfully to mimic the functionalities of this standard at the PHY and MAC layers. We propose a combination of two relevant models for the two layers. The PHY layer behavior is reproduced by a mathematical framework, which is based on radio and channel models, in order to quantify link reliability. On the other hand, the MAC layer is mimed by an enhanced Markov chain. The results show the pertinence of our approach compared to the model based on a Markov chain for IEEE 802.15.4 MAC layer. This contribution allows us fully and more precisely to estimate the network performance with different network sizes, as well as different metrics such as node reliability and delay. Our contribution enables us to catch possible failures at both layers.Comment: Published in the proceeding of the 7th International Wireless Communications and Mobile Computing Conference (IWCMC), Istanbul, Turkey, 201

Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams

Wildfires are frequent, devastating events in Australia that regularly cause significant loss of life and widespread property damage. Fire weather indices are a widely-adopted method for measuring fire danger and they play a significant role in issuing bushfire warnings and in anticipating demand for bushfire management resources. Existing systems that calculate fire weather indices are limited due to low spatial and temporal resolution. Localized wireless sensor networks, on the other hand, gather continuous sensor data measuring variables such as air temperature, relative humidity, rainfall and wind speed at high resolutions. However, using wireless sensor networks to estimate fire weather indices is a challenge due to data quality issues, lack of standard data formats and lack of agreement on thresholds and methods for calculating fire weather indices. Within the scope of this paper, we propose a standardized approach to calculating Fire Weather Indices (a.k.a. fire danger ratings) and overcome a number of the challenges by applying Semantic Web Technologies to the processing of data streams from a wireless sensor network deployed in the Springbrook region of South East Queensland. This paper describes the underlying ontologies, the semantic reasoning and the Semantic Fire Weather Index (SFWI) system that we have developed to enable domain experts to specify and adapt rules for calculating Fire Weather Indices. We also describe the Web-based mapping interface that we have developed, that enables users to improve their understanding of how fire weather indices vary over time within a particular region.Finally, we discuss our evaluation results that indicate that the proposed system outperforms state-of-the-art techniques in terms of accuracy, precision and query performance.Comment: 20pages, 12 figure

Une approche d'ontologie pour la modélisation des connaissances et l’interrogation des capteurs de réseaux sans fil

International audienceWireless sensor networks (WSNs) generate large volumes of raw data which increases the difficulty for applications to manage and query sensor data. WSNs are normally application specific with no sharing or reusability of sensor data among applications. In order for applications to be developed independently of particular WSNs, sensor data need to be enriched with semantic information. Ontologies are widely used as a means for solving the information heterogeneity problems because of their capability to provide explicit meaning to the information. This paper presents our work towards the development of a wireless sensor network ontology. Based on the proposed ontology we use the SPARQL query language to enable querying of sensor data. We present the description of the development of the proposed ontology, partial evaluation of the early prototype ontology, a discussion of design and implementation issues, and directions for future research works.Les réseaux de capteurs sans fil (WSN) génèrent de gros volumes de données brutes, ce qui complique la gestion et l'interrogation des données des capteurs par les applications. Les WSN sont normalement spécifiques à une application, sans partage ni possibilité de réutilisation des données de capteur entre les applications. Pour que les applications puissent être développées indépendamment de certains WSN, les données des capteurs doivent être enrichies d'informations sémantiques. Les ontologies sont largement utilisées pour résoudre les problèmes d'hétérogénéité de l'information en raison de leur capacité à donner un sens explicite à l'information. Cet article présente nos travaux en vue du développement d’une ontologie de réseau de capteurs sans fil. Sur la base de l'ontologie proposée, nous utilisons le langage de requête SPARQL pour permettre l'interrogation des données du capteur. Nous présentons la description du développement de l'ontologie proposée, une évaluation partielle de l'ontologie du prototype initial, une discussion des problèmes de conception et de mise en œuvre et des orientations pour les travaux de recherche futurs

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs