Abstract
Localization of access points has become an important research problem due to the wide range of applications it addresses such as dismantling critical security threats caused by rogue access points or optimizing wireless coverage of access points within a service area. Existing proposed solutions have mostly relied on theoretical hypotheses or computer simulation to demonstrate the efficiency of their methods. The techniques that rely on estimating the distance using samples of the received signal strength usually assume prior knowledge of the signal propagation characteristics of the indoor environment in hand and tend to take a relatively large number of uniformly distributed random samples. This paper presents an efficient and practical collaborative approach to detect the location of an access point in an indoor environment without any prior knowledge of the environment. The proposed approach comprises a swarm of wirelessly connected mobile robots that collaboratively and autonomously collect a relatively small number of non-uniformly distributed random samples of the access point’s received signal strength. These samples are used to efficiently and accurately estimate the location of the access point. The experimental testing verified that the proposed approach can identify the location of the access point in an accurate and efficient manner.
Highlights
Wi-Fi networks are almost everywhere nowadays, at homes, workplaces, and even in public places like malls, parks, and bus stations
To evaluate the performance of using a collaborative mobile robot swarm in speeding up the detection of the Access Point (AP) location, the distribute versions of Where is My Access Point (WiMAP) and DWiMAP were implemented within the multi-robot system, one at a time, and were tested experimentally
This paper proposes one important application of a collaborative mobile robot swarm, which is identifying the location of an access point in a relatively large indoor obstructed environment
Summary
Wi-Fi networks are almost everywhere nowadays, at homes, workplaces, and even in public places like malls, parks, and bus stations. Several different techniques have been recently proposed to provide robust and reliable localization accuracy (e.g., [2,3,4,5,6,7,8,9]). Most of these techniques either require special hardware to estimate the distance to the transmitter or extra cost of data collection, storage resources, and/or intensive processing of complex algorithms in order to estimate the location
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