36,058 research outputs found
Group-In: Group Inference from Wireless Traces of Mobile Devices
This paper proposes Group-In, a wireless scanning system to detect static or
mobile people groups in indoor or outdoor environments. Group-In collects only
wireless traces from the Bluetooth-enabled mobile devices for group inference.
The key problem addressed in this work is to detect not only static groups but
also moving groups with a multi-phased approach based only noisy wireless
Received Signal Strength Indicator (RSSIs) observed by multiple wireless
scanners without localization support. We propose new centralized and
decentralized schemes to process the sparse and noisy wireless data, and
leverage graph-based clustering techniques for group detection from short-term
and long-term aspects. Group-In provides two outcomes: 1) group detection in
short time intervals such as two minutes and 2) long-term linkages such as a
month. To verify the performance, we conduct two experimental studies. One
consists of 27 controlled scenarios in the lab environments. The other is a
real-world scenario where we place Bluetooth scanners in an office environment,
and employees carry beacons for more than one month. Both the controlled and
real-world experiments result in high accuracy group detection in short time
intervals and sampling liberties in terms of the Jaccard index and pairwise
similarity coefficient.Comment: This work has been funded by the EU Horizon 2020 Programme under
Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The
content of this paper does not reflect the official opinion of the EU.
Responsibility for the information and views expressed therein lies entirely
with the authors. Proc. of ACM/IEEE IPSN'20, 202
An objective based classification of aggregation techniques for wireless sensor networks
Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented
No NAT'd User left Behind: Fingerprinting Users behind NAT from NetFlow Records alone
It is generally recognized that the traffic generated by an individual
connected to a network acts as his biometric signature. Several tools exploit
this fact to fingerprint and monitor users. Often, though, these tools assume
to access the entire traffic, including IP addresses and payloads. This is not
feasible on the grounds that both performance and privacy would be negatively
affected. In reality, most ISPs convert user traffic into NetFlow records for a
concise representation that does not include, for instance, any payloads. More
importantly, large and distributed networks are usually NAT'd, thus a few IP
addresses may be associated to thousands of users. We devised a new
fingerprinting framework that overcomes these hurdles. Our system is able to
analyze a huge amount of network traffic represented as NetFlows, with the
intent to track people. It does so by accurately inferring when users are
connected to the network and which IP addresses they are using, even though
thousands of users are hidden behind NAT. Our prototype implementation was
deployed and tested within an existing large metropolitan WiFi network serving
about 200,000 users, with an average load of more than 1,000 users
simultaneously connected behind 2 NAT'd IP addresses only. Our solution turned
out to be very effective, with an accuracy greater than 90%. We also devised
new tools and refined existing ones that may be applied to other contexts
related to NetFlow analysis
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