An Online Outlier Detection Technique for Wireless Sensor Networks using Unsupervised Quarter-Sphere Support Vector Machine

The main challenge faced by outlier detection techniques designed for wireless sensor networks is achieving high detection rate and low false alarm rate while maintaining the resource consumption in the network to a minimum. In this paper, we propose an online outlier detection technique with low computational complexity and memory usage based on an unsupervised centered quarter-sphere support vector machine for real-time environmental monitoring applications of wireless sensor networks. The proposed approach is completely local and thus saves communication overhead and scales well with increase of nodes deployed. We take advantage of spatial correlations that exist in sensor data of adjacent nodes to reduce the false alarm rate in real-time. Experiments with both synthetic and real data collected from the Intel Berkeley Research Laboratory show that our technique achieves better mining performance in terms of parameter selection using different kernel functions compared to an earlier offline outlier detection technique designed for wireless sensor networks

Wireless Sensor Network for Aircraft Cabin Environment Sensing

Wireless sensor networks consist of physically distributed autonomous sensor nodes that cooperatively monitor physical or environmental conditions. One of the greatest benefits of wireless sensor networks is that they are capable of generating a more complete view of the sensed environment by acquiring larger quantities of correlated data than independent sensor monitors. The aircraft cabin is a highly dynamic environment which necessitates the use of more advanced sensing systems. It is with the motivation of painting a better picture of the aircraft cabin environment that such a wireless sensor network is being designed and prototyped. This paper discusses the design considerations required for wireless sensor networks in the aircraft cabin environment, as well as an overview of past and present systems developed for use in aircraft cabin environmental sensing. In addition to the sensor network, supporting tools are also discussed to enable analysis of the data collected. The primary goal of this research is to provide sensing tools to enable better characterization of the aircraft cabin environment

Security Threats and Challenges for Wireless Sensor Network

A wireless sensor network is a network of a large number of independently working small sensing units which can communicate wirelessly. The basic plan of a Wireless sensor network (WSN) is to structural distribute self-determining devices using sensors to monitor physical or environmental conditions. Wireless communication technology performance different forms of security threats. WSN need effective security mechanisms because of these networks deployed in untended environments. Due to fixed limitations in wireless sensor networks, security is a crucial issue. The intent of this paper is to investigate the security-related threats and challenges in wireless sensor networks. The threats faced by this WSN are similar but not limited to those observed in a simple network of computers or Internet.We identify thesensorsecuritythreats, review proposed security mechanisms for wireless sensor networks

A Review on Wireless Sensor Networks

The study of wireless sensor networks is challenging in that it requires an enormous breadth of knowledge. A wireless sensor network (WSN) is a computer network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants at different locations. The development of wireless sensor networks was originally motivated by military applications such as battlefield surveillance. However, wireless sensor networks are now used in many civilian application areas, including environment and habitat monitoring, healthcare applications, home automation and traffic control. Keywords: WSN, Senso

Development of a long range wireless sensor platform

Wireless Sensor Networks have emerged as an exciting field in recent years. There have been numerous studies on how to improve and standardise different aspects of wireless sensor networks. This paper aims to develop a wireless sensor network suitable for environmental monitoring applications. More specifically this paper aims to address the limited communication range of the existing wireless sensor technology. In order to achieve the desired objectives, we have initially developed a hardware platform and then integrated the hardware with a long range RF radio module to achieve the goals. The system is further enhanced with mesh networking capabilities to increase the communication range and overall reliability of the network. The developed wireless sensor network is composed of sensors, microcontroller, RF radio module, antenna and expansion connectors for additional sensors and peripheral devices. The developed wireless sensor network has been rigorously tested under three different scenarios to ensure the correct operation of the mesh network, communication range and effect of environmental obstacles such as vegetation and trees. The developed wireless sensor network has been proven to be a suitable platform for environmental monitoring applications and the modular design has made it very easy to optimise it for different applications

Evaluation of the ZigBee based wireless soil moisture sensor network SoilNet

A remaining challenge in hydrology is to explain the observed patterns of hydrological behaviour over multiple spacetime scales as a result of interacting environmental factors. The large spatial and temporal variability of soil water content is determined by factors like atmospheric forcing, topography, soil properties and vegetation, which interact in a complex nonlinear way (e.g. Western et al., 2004). A promising new technology for environmental monitoring is the wireless sensor network (Cardell-Oliver et al., 2005). The wireless sensor network technology allows the real-time soil water content monitoring at high spatial and temporal resolution for observing hydrological processes in small water-sheds (0.1-80 sqkm). Although wireless sensor networks can still be considered as an emerging research field, the supporting communication technology for low cost, low power wireless networks has matured greatly in the past decade (Robinson et al., 2008). Wireless environmental sensor networks will play an important role in the emerging terrestrial environmental observatories (Bogena et al., 2006), since they are able to bridge the gap between local (e.g. lysimeter) and regional scale measurements (e.g. remote sensing). This paper presents a first application of the novel wireless soil water content network SoilNet, which was developed at the Forschungszentrum Jülich using the new low-cost ZigBee radio network

A Robust Frame of WSN Utilizing Localization Technique

Wireless sensor networks are becoming increasingly popular due to their low cost and wide applicability to support a large number of diverse application areas. Localization of sensor nodes is a fundamental requirement that makes the sensor data meaningful. A wireless sensor network (WSN) consist of spatially distributed autonomous devices using sensors to monitor cooperatively physical or environmental conditions such as temperature, sound, vibration, pressure, motion or pollutants at different locations. The development of wireless sensor networks was originally motivated by a military application like battlefield surveillance. Node localization is required to report the origin of events, assist group querying of sensors, routing and to answer questions on the network coverage. So one of the fundamental challenges in wireless sensor network is node localization. This paper discusses different approaches of node localization discovery in wireless sensor networks. The overview of the schemes proposed by different scholars for the improvement of localization in wireless sensor networks is also presented. Keywords: Localization, Particle Swarm Optimization, Received Signal Strength, Angle of Arrival