ZiFind: Exploiting cross-technology interference signatures for energy-efficient indoor localization

Gao, Yuhang; Niu, Jianwei; Zhou, Ruogu; Xing, Guoliang

doi:10.1109/infcom.2013.6567105

Cited by 52 publications

(29 citation statements)

References 22 publications

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“…Also Boers et al [21] sample the spectrum for interferer classification but they only target interference occurring at regular intervals. Likewise, Zhou et al [9,13] propose an algorithm that is restricted to detecting WiFi beacons from RSSI traces. Another approach based on spectrum sampling is by Bloessl et al [22].…”

Section: Related Workmentioning

confidence: 99%

“…The value of t p is approximately 100 ms for WiFi beacons, which is the default beaconing interval on most WiFi access points. Algorithm 1, however, is also generally applicable to detect RSSI bursts of any period, in contrast to other approaches [9,13] that explicitly check for predetermined values. This makes it a viable option to detect and classify other forms of interference that include periodic transmissions in 802.15.4 networks [14] as well as microwave bursts [12].…”

mentioning

confidence: 99%

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Detecting and Avoiding Multiple Sources of Interference in the 2.4 GHz Spectrum

Iyer

Hermans²,

Voigt

2015

Lecture Notes in Computer Science

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Abstract. Sensor networks operating in the 2.4 GHz band often face cross-technology interference from co-located WiFi and Bluetooth devices. To enable effective interference mitigation, a sensor network needs to know the type of interference it is exposed to. However, existing approaches to interference detection are not able to handle multiple concurrent sources of interference. In this paper, we address the problem of identifying multiple channel activities impairing a sensor network's communication, such as simultaneous WiFi traffic and Bluetooth data transfers. We present SpeckSense, an interference detector that distinguishes between different types of interference using a unsupervised learning technique. Additionally, SpeckSense features a classifier that distinguishes between moderate and heavy channel traffic, and also identifies WiFi beacons. In doing so, it facilitates interference avoidance through channel blacklisting. We evaluate SpeckSense on common mote hardware and show how it classifies concurrent interference under real-world settings. We also show how SpeckSense improves the performance of an existing multichannel data collection protocol by 30%.

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Detecting and Avoiding Multiple Sources of Interference in the 2.4 GHz Spectrum

Iyer

Hermans²,

Voigt

2015

Lecture Notes in Computer Science

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“…For example, considering the widely large-scale deployment of wireless sensor nodes, the papers [13][14][15][16][17][18] present methods to perform localization for wireless sensor nodes. Also, because the accuracy of data is critical to WSNs' performance, [19] presents a novel approach to identify nodes with fault reading.…”

Section: Wireless Sensor Networkmentioning

confidence: 99%

“…Habitat observation, military surveillance, [3][4][5]7] Road network monitoring, health care supervision [8][9][10][11] House security sentry, wireless LAN performance monitoring [6,12] Auxiliary application Sensor node localization, fault reading identification, [13][14][15][16] Experimental repeatability improvement, [17][18][19][20] On-demand synchronization [22][23][24][25] Neighbor finding acceleration [21,26] Wireless network protocol Efficient packet broadcasting, flooding and forwarding [27][28][29][30] Wireless link property…”

Section: Main Target Applicationmentioning

confidence: 99%

A Survey on Spectrum Utilization in Wireless Sensor Networks

2015

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In recent years, the industrial, scientific, and medical (ISM) bands have been intensively shared with unlicensed wireless communications applications such as wireless sensor networks (WSNs). With flourishing popularity of sensor devices and increasing installation of wireless sensor nodes, the cross technology interference (CTI) has become a considerable realworld problem. Because of CTI, wireless devices suffer significant communication dilemma. Moreover, ISM band, as the main communication medium of WSN, should be reasonably utilized in an efficient and effective manner. Extensive approaches have been proposed to explore spectrum utilization in WSN. However, there is no such one, which systematically organizes these works. In this paper, we present a comprehensive survey on spectrum utilization in WSNs. To achieve this goal, We first illustrate the background of WSN and spectrum utilization. Our concern on CTI is then noted. Later we demonstrate the importance of efficient spectrum utilization. Eventually, through classification and summary of recent related works, we provide an essential structure of research in titled field and detailed intellectual merits of published works. Our survey covers more than 80 studies in the scope of spectrum utilization in WSN.

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“…Many schemes have been proposed to classify WiFi APs by using correlation coefficients of listened RSSI values, such as centroid [24], WDF [25], KNN [28,29], R-KNN [17], WKNN [18]. Among which, WKNN is most reasonable in certain cases by focusing on estimating reference locations and their weights.…”

Section: Related Workmentioning

confidence: 99%

Improving the Indoor Localization Accuracy for CPS by Reorganizing the Fingerprint Signatures

Qiu

Wang

Dai

2014

International Journal of Distributed Sensor Networks

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This paper firstly represents a survey of recent researches on localization of CPS and analyzes the advantages and limitations of the existing schemes. To overcome the limitations, a novel localization scheme is then proposed, which includes site survey, received signal comparison, and location correction phases. The main improvement of our proposed scheme is to filter the noises and interference added to the received RSSI signal; the detailed procedure is then provided with mathematical analysis. Finally, the experiment and simulation results are presented with analysis.

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ZiFind: Exploiting cross-technology interference signatures for energy-efficient indoor localization

Cited by 52 publications

References 22 publications

Detecting and Avoiding Multiple Sources of Interference in the 2.4 GHz Spectrum

Detecting and Avoiding Multiple Sources of Interference in the 2.4 GHz Spectrum

A Survey on Spectrum Utilization in Wireless Sensor Networks

Improving the Indoor Localization Accuracy for CPS by Reorganizing the Fingerprint Signatures

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