Fine-grained analysis of packet loss symptoms in wireless sensor networks

Shebaro, Bilal; Midi, Daniele; Bertino, Elisa

doi:10.1145/2517351.2517408

Cited by 3 publications

(5 citation statements)

References 3 publications

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“…Consider the update() logic for a sensor in the normal state, shown in Listsing 5 (lines [10][11][12][13][14][15][16]. The method queries the corresponding Analyzer for the average and standard deviation of the sensor's reporting intervals (lines 3-4).…”

Section: Listing 5 Statuschecker Update() Methods Ation Of These Intementioning

confidence: 99%

“…The maximum reporting interval is calculated based on the current average and the numberOfStdDevs parameter, which controls the sensitivity of acceptability. The system places the sensor in the abnormal state if the observed reporting interval is outside of allowable, and the timer corresponding to that sensor is then reset (lines [11][12][13][14]. When the sensor is moved to the abnormal state, the sensor's expectedInterval is set to the observed reporting interval (line 13).…”

Section: Listing 5 Statuschecker Update() Methods Ation Of These Intementioning

confidence: 99%

“…Once a sensor enters the normal state, the check() method follows the actions in Listing 6 on wake-up (lines [12][13][14][15][16]. allowable is calculated as before, in the update() method.…”

Section: Listing 5 Statuschecker Update() Methods Ation Of These Intementioning

confidence: 99%

“…Shebaro et al [12] focus on determining the cause of packet loss. Their method employs comparisons of RSSI and LQI values.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Detecting Reporting Anomalies in Streaming Sensing Systems

Hughes

Hallstrom

2015

International Conferences on Software Engineering and Knowledge Engineering

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Sensor networks must be monitored to identify and correct problems as they occur. We present a comparison of two approaches to monitoring deployed sensors. The first relies on configuration parameters to define expected reporting behavior. The second automatically identifies normal reporting patterns based on a combination of configuration parameters and an analysis of reporting times. Using these patterns, the system notifies personnel of possible malfunctions. We present empirical evaluations in the context of the Intelligent River R system [11].

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Section: Listing 5 Statuschecker Update() Methods Ation Of These Intementioning

confidence: 99%

Section: Listing 5 Statuschecker Update() Methods Ation Of These Intementioning

confidence: 99%

Section: Listing 5 Statuschecker Update() Methods Ation Of These Intementioning

confidence: 99%

“…Shebaro et al [12] focus on determining the cause of packet loss. Their method employs comparisons of RSSI and LQI values.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Reporting Anomalies in Streaming Sensing Systems

Hughes

Hallstrom

2015

International Conferences on Software Engineering and Knowledge Engineering

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“…thresholds). For example, Fine-grained Analysis [25] detects security attacks when RSSI changes exceed the measured maximum fluctuation occurred during the initial training phase. In the statistical measure category, there are two often used techniques: (1) CDF-based thresholding (or percentile-based thresholding), and (2) Chebyshev inequality-based thresholding.…”

Section: Related Workmentioning

confidence: 99%

RADIUS: A System for Detecting Anomalous Link Quality Degradation in Wireless Sensor Networks

Fu,

Shih,

Jiang

et al. 2017

Preprint

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To ensure proper functioning of a Wireless Sensor Network (WSN), it is crucial that the network is able to detect anomalies in communication quality (e.g., RSSI), which may cause performance degradation, so that the network can react accordingly. In this paper, we introduce RADIUS, a lightweight system for the purpose. The design of RADIUS is aimed at minimizing the detection error (caused by normal randomness of RSSI) in discriminating good links from weak links and at reaching high detection accuracy under diverse link conditions and dynamic environment changes. Central to the design is a threshold-based decision approach that has its foundation on the Bayes decision theory. In RADIUS, various techniques are developed to address challenges inherent in applying this approach. In addition, through extensive experiments, proper configuration of the parameters involved in these techniques is identified for an indoor environment. In a prototype implementation of the RADIUS system deployed in an indoor testbed, the results show that RADIUS is accurate in detecting anomalous link quality degradation for all links across the network, maintaining a stable error rate of 6.13% on average.

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Node or Link? Fine-Grained Analysis of Packet-Loss Attacks in Wireless Sensor Networks

Midi

Bertino

2016

ACM Trans. Sen. Netw.

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In wireless sensor networks, packet losses are often an indicator of possible undergoing attacks. Therefore, security solutions aiming at providing comprehensive protection must include functions for packet-loss detection. Determining the actual cause of these losses is crucial to a quick and automated reaction to the attack, be it a simple reporting of the attack or a more sophisticated action such as packet rerouting and retransmission. Packet losses in wireless sensor networks can be caused by either attacks affecting the nodes or attacks focused on the wireless links. The efficacy of the response to such attacks is highly dependent on an accurate identification of the actual attack cause. Therefore, approaches to correctly identifying the cause of packet losses are needed. The work presented in this article addresses this problem by designing and implementing a fine-grained analysis (FGA) tool that investigates packet-loss events and reports their most likely cause. Our FGA tool profiles the wireless links between the nodes, as well as their neighborhood, by leveraging resident parameters, such as RSSI and LQI, available within every received packet. The design of the system is fully distributed and event-driven, and its low overhead makes it suitable for resource-constrained entities such as wireless motes. We have validated our approach through real-world experiments, showing that our FGA tool is effective in differentiating between the various attacks that may affect nodes and links.

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Fine-grained analysis of packet loss symptoms in wireless sensor networks

Cited by 3 publications

References 3 publications

Detecting Reporting Anomalies in Streaming Sensing Systems

Detecting Reporting Anomalies in Streaming Sensing Systems

RADIUS: A System for Detecting Anomalous Link Quality Degradation in Wireless Sensor Networks

Node or Link? Fine-Grained Analysis of Packet-Loss Attacks in Wireless Sensor Networks

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