Elsevier

Ad Hoc Networks

Volume 10, Issue 4, June 2012, Pages 709-722
Ad Hoc Networks

Using fuzzy logic for robust event detection in wireless sensor networks

https://doi.org/10.1016/j.adhoc.2011.06.008Get rights and content

Abstract

Event detection is a central component in numerous wireless sensor network (WSN) applications. Nevertheless, the area of event description has not received enough attention. The majority of current event description and detection approaches rely on using precise values to specify event thresholds. However, we believe that crisp values cannot adequately handle the often imprecise sensor readings. In this paper we demonstrate that using fuzzy values instead of crisp ones significantly improves the accuracy of event detection. We also show that our fuzzy logic approach provides higher event detection accuracy than two well-established classification algorithms.

A disadvantage of using fuzzy logic is the exponentially growing size of the fuzzy logic rule-base. As sensor nodes have limited memory, storing large rule-bases could be a challenge. To address this issue, we have developed a number of techniques that help reduce the size of the rule-base by more than 70%, while preserving the event detection accuracy.

Introduction

Event detection is one of the main components in numerous wireless sensor networks (WSNs). WSNs for military application are deployed to detect the invasion of enemy forces, health monitoring sensor networks are deployed to detect abnormal patient behavior, fire detection sensor networks are deployed to set an alarm if a fire starts somewhere in the monitored area. Regardless of the specific application, the network should be able to detect if particular events of interest, such as fire, have occurred or are about to. However, similar to many other human-recognizable events, the phenomenon fire has no real meaning to a sensor node. Therefore, we need suitable techniques that would allow us to describe events in ways that sensor nodes would be able to “understand”. The area of event description and detection in WSNs, however, has not been explored much.

Most previous work on event description in WSNs uses precise, also called crisp, values to specify the parameters that characterize an event. For example, we might want to know if the temperature drops below 5 °C or the humidity goes above 46%. However, sensor readings are not always precise. In addition, different sensors, even if located close to each other, often vary in the values they register. Consider an example scenario where we want the air conditioning in a room to be turned on if the temperature goes above 5 °C. Two sensors, A and B, measure the temperature in the room. The average of their values is used to determine if an action should be taken. At some point, sensor A reports 5.1 °C and sensor B reports 4.8 °C. The average, 4.95 °C, is below our predefined threshold and the cooling remains off. However, if sensor B’s measurement is inaccurate and, therefore, lower than the actual temperature, we have made the wrong decision. The situation becomes even more complex when more than two sensor measurements are involved. This makes determining the precise event thresholds an extremely hard task which has led us to believe that using crisp values to describe WSN events is not the most suitable approach. Fuzzy logic, on the other hand, might be able to address these challenges better than crisp logic.

Fuzzy logic has a number of properties that make it suitable for describing WSN events:

  • It can tolerate unreliable and imprecise sensor readings.

  • It is much closer to our way of thinking than crisp logic. For example, we think of fire as an event described by high temperature and smoke rather than an event characterized by temperature above 55 °C and smoke obscuration level above 15%.

  • Compared to other classification algorithms based on probability theory, fuzzy logic is much more intuitive and easier to use.

A disadvantage of using fuzzy logic is that storing the rule-base might require a significant amount of memory. The number of rules grows exponentially to the number of variables. With n variables each of which can take m values, the number of rules in the rule-base is mn. Adding spatial and temporal semantics to the decision process further increases the number of rules. Since sensor nodes have limited memory, storing a complete rule-base on every node might not be reasonable. In addition, constantly traversing a large rule-base might considerably slow down the event detection. To address this problem, we have designed a number of techniques that reduce the size of the rule-base. A key property of these techniques is that they do not decrease the event detection accuracy of the system.

This paper has three main contributions. First, we show that using fuzzy logic results in more accurate event detection than when either crisp values or well established classification algorithms, such as Naive Bayes classifiers or decision trees, are used. Second, we incorporate event semantics into the fuzzy logic rule-base to further improve the accuracy of event detection. Third, we have designed techniques that can be used to prevent the exponential growth of the rule-base without compromising the accuracy of event detection.

The rest of the paper is organized as follows: We discuss the related work in Section 2. Section 3 introduces a brief overview of fuzzy logic and fuzzy systems. Section 4 discusses the spatial and temporal semantics of wireless sensor network events. Section 5 describes the reduction techniques we use to decrease the size of the rule-base. We evaluate and analyze how using fuzzy logic affects the accuracy and timeliness of event detection in Sections 6 Evaluation, 7 Conclusions and future work concludes the paper.

Section snippets

Event detection

Relatively little research has focused on providing methods for event description and detection in WSNs that can support data dependency and collaborative decision making. The prevailing approach is to use SQL-like primitives [1], [2], [3], [4]. The papers that employ this method vary in semantics. In [1], [2], the authors use general SQL primitives to define events in sensor networks. The limitation of this approach is that the events can only be defined by predicates on sensor readings with

Overview of fuzzy logic

Fig. 1 shows the structure of a general fuzzy logic system (FLS). The fuzzifier converts the crisp input variables x  X, where X is the set of possible input variables, to fuzzy linguistic variables by applying the corresponding membership functions. Zadeh defines linguistic variables as “variables whose values are not numbers but words or sentences in a natural or artificial language” [31]. An input variable can be associated with one or more fuzzy sets depending on the calculated membership

Event semantics

Sensors are generally believed to be unreliable and imprecise. Therefore, to increase our confidence in the presence of an event somewhere in the monitored area, we often need readings from multiple sensors and/or readings over some period of time. This could be achieved by instrumenting the event description logic with temporal and spatial semantics. We believe that this can significantly decrease the number of false positives. It will also allow us to describe and detect more complex events.

Decreasing the size of the rule-base

Augmenting the rule-base with temporal and spatial variables increases the number of rules. As mentioned earlier, the size of the rule-base grows exponentially to the number of linguistic variables. In our fire monitoring example, where the only sensor readings we consider are temperature and smoke, the full rule-base has 6561 rules. In more complicated scenarios that require more than two types of sensors, the number of rules in the fuzzy rule-base could be much higher. Storing such rule-bases

Evaluation

We use the FuzzyJ Toolkit for Java [37] to implement the necessary fuzzy logic functionality. To avoid the danger, cost, and non-repeatability of creating fires, we perform trace-based simulations using real fire data publicly available on the National Institute of Standards and Technology (NIST) website [38]. The study they conduct provides sensor measurements from a number of different real fires as well as nuisance scenarios. We have used three of the available real fire scenarios: fire

Conclusions and future work

A disadvantage of the current event detection approaches used in WSNs is that they cannot properly handle the often imprecise sensor readings. In this paper we show that fuzzy logic is a powerful and accurate mechanism which can successfully be applied not only to fire detection but to any event detection sensor network application. Compared to using crisp values, fuzzy logic maintains a high accuracy level despite fluctuations in the sensor values. This helps decrease the number of false

Krasimira Kapitanova is a Ph.D. student in Computer Science at the University of Virginia. She received her B.S. degree in Computer Science and Technologies from Technical University Sofia, Bulgaria, and an M.C.S. degree from the University of Virginia. Her research interests include formal event description in sensor networks, QoS management, and information management and security.

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    Krasimira Kapitanova is a Ph.D. student in Computer Science at the University of Virginia. She received her B.S. degree in Computer Science and Technologies from Technical University Sofia, Bulgaria, and an M.C.S. degree from the University of Virginia. Her research interests include formal event description in sensor networks, QoS management, and information management and security.

    Sang Hyuk Son is a Professor at the Department of Computer Science of University of Virginia. He received the B.S. degree in electronics engineering from Seoul National University, M.S. degree from KAIST, and the Ph.D. in computer science from University of Maryland, College Park, in 1986. He is on the executive board of the IEEE Technical Committee on Real-Time Systems, for which he served as the Chair during 2007–2008. He is currently serving as an Associate Editor for IEEE Transactions on Computers and Real-Time Systems Journal. His research interests include real-time and embedded systems, database and data services, QoS management, wireless sensor networks, and information security.

    Kyoung-Don Kang is an Associate Professor in the Department of Computer Science at the State University of New York at Binghamton. He received his Ph.D. from the University of Virginia in 2003. His research interests include real-time data services, wireless sensor networks, and security and privacy.

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