Michalis P. Michaelides

SNAP: Fault Tolerant Event Location Estimation in Sensor Networks Using Binary Data

This paper investigates the use of wireless sensor networks for estimating the location of an event that emits a signal that propagates over a large region. In this context, we assume that the sensors make binary observations and report the event (positive observations) if the measured signal at their location is above a threshold; otherwise, they remain silent (negative observations). Based on the sensor binary beliefs, a likelihood matrix is constructed whose maximum value points to the event location. The main contribution of this work is Subtract on Negative Add on Positive (SNAP), an estimation algorithm that provides an efficient way of constructing the likelihood matrix by simply adding \pm 1 contributions from the sensor nodes depending on their alarm state (positive or negative). This simple estimation procedure provides very accurate results and turns out to be fault tolerant even when a large percentage of the sensor nodes report erroneous observations.

Fault Tolerant Localization and Tracking of Multiple Sources in WSNs Using Binary Data

This paper investigates the use of a Wireless Sensor Network for localizing and tracking multiple event sources (targets) using only binary data. Due to the simple nature of the sensor nodes, sensing can be tampered (accidentally or maliciously), resulting in a significant number of sensor nodes reporting erroneous observations. Therefore, it is essential that any event tracking algorithm used in Wireless Sensor Networks (WSNs) exhibits fault tolerant behavior in order to tolerate misbehaving nodes. The main contribution of this paper is the development and analysis of a low-complexity, distributed, real-time algorithm that uses the binary observations of the sensors for identifying, localizing, and tracking multiple targets in a fault tolerant way. Specifically, our results indicate that the proposed algorithm retains its performance in tracking accuracy in the presence of noise and faults, even when a large percentage of sensor nodes (25 percent) report erroneous observations.

Event Detection Using Sensor Networks

This paper investigates the use of a sensor network for detecting the presence of an event. The sensors monitor the signal emitted from the source and report the existence of the event when the received signal strength is above a certain threshold. In this paper, we derive analytical expressions for the probability of false alarms and the probability of no detection as functions of the threshold. Subsequently, we determine the optimal threshold that trades off the probability of false alarms and the probability of no detection

Fault Tolerant Maximum Likelihood Event Localization in Sensor Networks Using Binary Data

This paper investigates Wireless Sensor Networks (WSNs) for achieving fault tolerant localization of an event using only binary information from the sensor nodes. In this context, faults occur due to various reasons and are manifested when a node outputs a wrong decision. The main contribution of this paper is to propose the Fault Tolerant Maximum Likelihood (FTML) estimator. FTML is compared against the Centroid (CE) and the classical maximum likelihood (ML) estimators and is shown to be significantly more fault tolerant. Moreover, this paper compares FTML against the SNAP (Subtract on Negative Add on Positive) algorithm and shows that in the presence of faults the two can achieve similar performance; FTML is slightly more accurate while SNAP is computationally less demanding and requires fewer parameters.

Fault Tolerant Fingerprint-Based Positioning

The increasing demand for indoor location-based services has motivated the development of positioning methods that exploit the existing wireless network infrastructure. Accuracy is an important requirement, however fault tolerance is also highly desirable in case of failures or malicious attacks. We investigate the fault tolerance of fingerprint-based methods under a variety of fault or attack scenarios. We study the Subtract on Negative Add on Positive (SNAP) algorithm and modify it appropriately for the WLAN setup. Our results indicate that SNAP achieves adequate accuracy with very low computational complexity and exhibits smoother performance degradation in the presence of faults compared to other methods.

Contaminant Event Monitoring in Intelligent Buildings Using a Multi-Zone Formulation

Abstract The dispersion of contaminants from sources (events) inside a building can compromise the indoor air quality and influence the occupants’ comfort, health, productivity and safety. These events could be the result of an accident, faulty equipment or a planned attack. Under these safety-critical conditions, immediate event detection should be guaranteed and the proper actions should be taken to ensure the safety of the people. In this paper, we consider an event as a fault in the process that disturbs the normal system operation. Furthermore, we demonstrate how the problem of monitoring the indoor air quality in intelligent buildings against the presence of contaminant sources fits the usual framework of fault detection, isolation, identification and accommodation. Specifically, we develop a multi-zone formulation using state space equations that enables the use of fault diagnosis and fault tolerant control techniques for monitoring contaminant events inside the building environment. We demonstrate our proposed formulation for the problem of isolating multiple contaminant sources using an estimation scheme in a nine zone building setting.

Fault tolerant detection and tracking of multiple sources in WSNs using binary data

This paper investigates the use of a Wireless Sensor Network for detecting and tracking the location of multiple event sources (targets) using only binary data. Due to the simple nature of the sensor nodes, sensing can be tampered (accidentally or maliciously), resulting in a significant number of sensor nodes reporting erroneous observations. Therefore, it is essential that any event tracking algorithm used in Wireless Sensor Networks (WSNs) exhibits fault tolerant behavior in order to tolerate misbehaving nodes. The main contribution of this paper is the development of a simple and decentralized algorithm that uses the binary observations of the sensors for tracking multiple targets in a fault-tolerant way. Furthermore, tracking is performed in real-time by the alarmed sensor nodes that are elected as leaders, utilizing only information from their neighbors.

Improved coverage in WSNs by exploiting spatial correlation: the two sensor case

One of the main applications of Wireless Sensor Networks (WSNs) is area monitoring. In such problems, it is desirable to maximize the area coverage. The main objective of this work is to investigate collaborative detection schemes at the local sensor level for increasing the area coverage of each sensor and thus increasing the coverage of the entire network. In this article, we focus on pairs of nodes that are closely spaced and can exchange information to decide their collective alarm status in a decentralized manner. By exploiting their spatial correlation, we show that the pair can achieve a larger area coverage than the two individual sensors acting alone. Moreover, we analyze the performance of different collaborative detection schemes for a pair of sensor nodes and show that the area coverage achieved by each scheme depends on the distance between the two sensors.

Subtract on Negative Add on Positive (SNAP) Estimation Algorithm for Sensor Networks

This paper investigates the use of wireless sensor networks for estimating the location of an event that emits a signal that propagates over a large region. In this context we assume that the sensors make binary observations and report the event (positive observations) if the measured signal at their location is above a threshold; otherwise they remain silent (negative observations). Based on the sensor binary beliefs, a likelihood matrix is constructed whose maximum value points to the event location. The main contribution of this work is SNAP (Subtract on Negative Add on Positive), an estimation algorithm that provides an efficient way of constructing the likelihood matrix by simply adding plusmn1 contributions from the sensor nodes depending on their observation state (positive or negative). This simple and efficient estimation procedure provides very accurate results and turns out to be fault tolerant even when a large percentage of the sensor nodes report erroneous observations.

ftTRACK: Fault-Tolerant Target Tracking in Binary Sensor Networks

The provision of accurate and reliable localization and tracking information for a target moving inside a binary Wireless Sensor Network (WSN) is quite challenging, especially when sensor failures due to hardware and/or software malfunctions or adversary attacks are considered. Most tracking algorithms assume fault-free scenarios and exploit all binary sensor observations, thus their accuracy may degrade when faults are present in the field. Spatiotemporal information available while the target is traversing the sensor field can be used not only for tracking the target, but also for detecting certain types of faults that appear highly correlated both in time and space. Our main contribution is ftTRACK, a target tracking architecture that is resilient to sensor faults and consists of three main components, namely the sensor health-state estimator, a fault-tolerant localization algorithm, and a location smoothing component. The key idea in the ftTRACK architecture lies in the sensor health-state estimator that leverages spatiotemporal information from previous estimation steps to intelligently choose which sensors to employ in the localization and tracking tasks. Simulation results indicate that ftTRACK maintains a high level of tracking accuracy, even when a large number of sensors fail.