Where has this hard disk been? Extracting geospatial intelligence from digital storage systems

Digital storage systems (DSS) contain an abundance of geospatial data which can be extracted and analysed to provide useful and complex intelligence insights. This data takes a number of forms such as data within text files, configuration databases and in operating system generated files—each of which require particular forms of processing. This paper investigates the breadth of geospatial data available on DSS, the issues and problems involved in extracting and analysing them and the intelligence insights that the visualisation of the data can provide. We describe a framework to extract a wide range of geospatial data from a DSS and resolve this data into geographic coordinates

Centroid Localization of Uncooperative Nodes in Wireless Networks Using a Relative Span Weighting Method

Increasingly ubiquitous wireless technologies require novel localization techniques to pinpoint the position of an uncooperative node, whether the target is a malicious device engaging in a security exploit or a low-battery handset in the middle of a critical emergency. Such scenarios necessitate that a radio signal source be localized by other network nodes efficiently, using minimal information. We propose two new algorithms for estimating the position of an uncooperative transmitter, based on the received signal strength (RSS) of a single target message at a set of receivers whose coordinates are known. As an extension to the concept of centroid localization, our mechanisms weigh each receiver's coordinates based on the message's relative RSS at that receiver, with respect to the span of RSS values over all receivers. The weights may decrease from the highest RSS receiver either linearly or exponentially. Our simulation results demonstrate that for all but the most sparsely populated wireless networks, our exponentially weighted mechanism localizes a target node within the regulations stipulated for emergency services location accuracy