Deploying a Communicating Automatic Weather Station on an Alpine Glacier

The cost and effort of installing and maintaining an automatic weather station (AWS) on a glacier may be mitigated by the possibility of gathering sensor data in near real-time, and of controlling and programming the station remotely. In this paper we report our experience with upgrading an existing AWS, operating over an Italian glacier, from a mere datalogger into a networked sensing station. Design choices, energy constraints and power-aware programming of the station determined by harsh environment are discussed. Deployment operations and results are described. The upgraded AWS provides low-power connectivity from a remote location and is able to serve as a base station for a wireless sensor network working in the glacier

A Hierarchical Framework Using Approximated Local Outlier Factor for Efficient Anomaly Detection

AbstractAnomaly detection aims to identify rare events that deviate remarkably from existing data. To satisfy real-world appli- cations, various anomaly detection technologies have been proposed. Due to the resource constraints, such as limited energy, computation ability and memory storage, most of them cannot be directly used in wireless sensor networks (WSNs). In this work, we proposed a hierarchical anomaly detection framework to overcome the challenges of anomaly detection in WSNs. We aim to detect anomalies by the accurate model and the approximated model learned at the re- mote server and sink nodes, respectively. Besides the framework, we also proposed an approximated local outlier factor algorithm, which can be learned at the sink nodes. The proposed algorithm is more efficient in computation and storage by comparing with the standard one. Experimental results verify the feasibility of our proposed method in terms of both accuracy and efficiency