Outlier detection techniques for wireless sensor networks: A survey

In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree

Integrating sensor streams in pHealth networks

Personal Health (pHealth) sensor networks are generally used to monitor the wellbeing of both athletes and the general public to inform health specialists of future and often serious ailments. The problem facing these domain experts is the scale and quality of data they must search in order to extract meaningful results. By using peer-to-peer sensor architectures and a mechanism for reducing the search space, we can, to some extent, address the scalability issue. However, synchronisation and normalisation of distributed sensor streams remains a problem in many networks. In the case of pHealth sensor networks, it is crucial for experts to align multiple sensor readings before query or data mining activities can take place. This paper presents a system for clustering and synchronising sensor streams in preparation for user queries

Worst-Case Routing Performance Evaluation of Sensor Networks

Successful sensor network applications depends heavily on the ability of these networks to reliably and reasonably perform under the worst-case scenarios, extreme and unusual events for which many such networks are designed to detect. One of the key performance measures is the network's ability to route measurement data from the sensor nodes to the destination node(s). This paper introduces a general framework with which worst-case routing performance of different sensor networks can be evaluated and compared. Our method can either be used as a design optimization tool, or a decision making tool to select and price contending sensor network designs and applications

A resilient key predistribution scheme for multiphase wireless sensor networks

In wireless sensor networks, sensor nodes eventually die due to battery depletion. Wireless sensor networks (WSNs) in which new nodes are periodically redeployed with certain intervals, called generations, to replace the dead nodes are called multi-phase wireless sensor networks. In the literature, there are several key predistribution schemes proposed for secure operation of WSNs. However, these schemes are designed for single phase networks which are not resilient against continuous node capture attacks; even under temporary attacks on the network, the harm caused by the attacker does not heal in time. However, the periodic deployments in multi-phase sensor networks could be utilized to improve the resiliency of the WSNs by deploying nodes with fresh keys. In the literature, there is limited work done in this area. In this paper, we propose a key predistribution scheme for multi-phase wireless sensor networks which is highly resilient under node capture attacks. In our scheme, called RGM (random generation material) key predistribution scheme, each generation of deployment has its own random keying material and pairwise keys are established between node pairs of particular generations. These keys are specific to these generations. Therefore, a captured node cannot be abused to obtain keys of other generations. We compare the performance of our RGM scheme with a well-known multi-phase key predistribution scheme and showed that RGM achieves up to three-fold more resiliency. Even under heavy attacks, our scheme's resiliency performance is 50% better in steady state

SIR: A New Wireless Sensor Network Routing Protocol Based on Artificial Intelligence

Currently, Wireless Sensor Networks (WSNs) are formed by hundreds of low energy and low cost micro-electro-mechanical systems. Routing and low power consumption have become important research issues to interconnect this kind of networks. However, conventional Quality of Service routing models, are not suitable for ad hoc sensor networks, due to the dynamic nature of such systems. This paper introduces a new QoS-driven routing algorithm, named SIR: Sensor Intelligence Routing. We have designed an artificial neural network based on Kohonen self organizing features map. Every node implements this artificial neural network forming a distributed intelligence and ubiquitous computing system