Krasimira Kapitanova

The price of security in wireless sensor networks

With the increased application of wireless sensor networks (WSNs) to military, commercial, and home environments, securing the data in the network has become a critical issue. Several security mechanisms, such as TinySec, have been introduced to address the need for security in WSNs. The cost of security, however, still mostly remains an unknown variable. To provide a better understanding of this cost we have studied three aspects of WSNs security: encryption algorithms, modes of operation for block ciphers, and message authentication algorithms. We have measured and compared their memory and energy consumption on both MicaZ and TelosB sensor motes. The results of our experiments provide insight into the suitability of different security algorithms for use in WSN environments and could be used by WSN designers to construct the security architecture of their systems in a way that both satisfies the requirements of the application and reasonably uses the constrained sensor resources.

RDDS: A Real-Time Data Distribution Service for Cyber-Physical Systems

One of the primary requirements in many cyber-physical systems (CPS) is that the sensor data derived from the physical world should be disseminated in a timely and reliable manner to all interested collaborative entities. However, providing reliable and timely data dissemination services is especially challenging for CPS since they often operate in highly unpredictable environments. Existing network middleware has limitations in providing such services. In this paper, we present a novel publish/subscribe-based middleware architecture called Real-time Data Distribution Service (RDDS). In particular, we focus on two mechanisms of RDDS that enable timely and reliable sensor data dissemination under highly unpredictable CPS environments. First, we discuss the semantics-aware communication mechanism of RDDS that not only reduces the computation and communication overhead, but also enables the subscribers to access data in a timely and reliable manner when the network is slow or unstable. Further, we extend the semantics-aware communication mechanism to achieve robustness against unpredictable workloads by integrating a control-theoretic feedback controller at the publishers and a queueing-theoretic predictor at the subscribers. This integrated control loop provides Quality-of-Service (QoS) guarantees by dynamically adjusting the accuracy of the sensor models. We demonstrate the viability of the proposed approach by implementing a prototype of RDDS. The evaluation results show that, compared to baseline approaches, RDDS achieves highly efficient and reliable sensor data dissemination as well as robustness against unpredictable workloads.

Using fuzzy logic for robust event detection in wireless sensor networks

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.

Run time assurance of application-level requirements in wireless sensor networks

The current rapid development and deployment of wireless sensor networks (WSNs) and their application in mission critical systems are exacerbating the need for high confidence WSNs. Achieving high confidence WSNs will require new assurance technologies. Most current solutions deal with faults and reliability and not with application level semantics and associated assurances. We propose the use of a novel WSN design and assurance mechanism, run time assurance (RTA), to guarantee that important application-level requirements are met in mission critical applications.

Event Detection in Wireless Sensor Networks – Can Fuzzy Values Be Accurate?

Event detection is a central component in numerous wireless sensor network (WSN) applications. In spite of this, the area of event description has not received enough attention. The majority of current event description 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 detection precision than a couple of well established classification algorithms.

Being SMART about failures: assessing repairs in SMART homes

Inexpensive wireless sensing products are dramatically reducing the cost of in-home sensing. However, these sensors have been found to fail often and prohibitive maintenance costs may negate the cost benefits of inexpensive hardware and do-it-yourself installation. In this paper, we describe a new technique called SMART that uses application-level semantics to detect, assess, and adapt to sensor failures. SMART detects sensor failures at run-time by analyzing the relative behavior of multiple classifier instances trained to recognize the same set of activities based on different subsets of sensors. Once a failure is detected, SMART assesses its importance and adapts the classifier ensemble in attempt to avoid maintenance dispatch. Evaluation on three homes from two public datasets shows that SMART decreases the number of maintenance dispatches by 55% on average, identifies non-fail-stop failures at run-time with more than 85% accuracy, and improves the activity recognition accuracy under sensor failures by 15% on average.

MEDAL: A coMpact event description and analysis language for wireless sensor networks

Event detection plays an important role in wireless sensor network (WSN) applications such as battlefield surveillance and habitat monitoring. However, effective approaches for specifying events in a sensor network remain a challenge. In this paper we present MEDAL, a formal event description language. MEDAL is a modified Petri net which provides a more compact formal language than its predecessor SNEDL. As a system analysis tool, MEDAL can capture the structural, spatial, and temporal properties of a complex event detection system, which can be used to assist system designers in identifying inconsistencies and potential problems. MEDAL can also perform case-specific analyses that can make the debugging phase easier. We present a case study as an example illustrating the features and effectiveness of MEDAL. We also describe an approach for simultaneous detection of multiple events in a single WSN.

Applying Formal Methods to Modeling and Analysis of Real-time Data Streams

Achieving situation awareness is especially challenging for real-time data stream applications because they i) operate on continuous unbounded streams of data, and ii) have inherent realtime requirements. In this paper we showed how formal data stream modeling and analysis can be used to better understand stream behavior, evaluate query costs, and improve application performance. We used MEDAL, a formal specification language based on Petri nets, to model the data stream queries and the quality-of-service management mechanisms of RT-STREAM, a prototype system for data stream management. MEDAL’s ability to combine query logic and data admission control in one model allows us to design a single comprehensive model of the system. This model can be used to perform a large set of analyses to help improve the application’s performance and quality of service.

A token-based admission control and request scheduling in lane reservation systems

In many parts of the world, the ever-expanding traffic congestion problem has become a major source of wasted fuel, economic burden, and environmental pollution. Alleviating traffic congestion is not only a matter of expanding the transportation capacity, such as adding more lanes or building new roads, but also the problem of providing good traffic management and polices. Recently, the concept of road reservation systems has been widely discussed. With this system, in order to use the lanes and roads controlled by the transportation operators, drivers make reservations in advance. The goal of the system is to provide certain quality of services to the drivers, e.g., guaranteed end to end travel delay. In this work, we focus on the admission control and request scheduling for a reservation system. Specifically, we consider a high-priority lane reservation scenario, in which the system has to determine which vehicles could be allowed to enter the high-priority lane, so that the traffic workload does not exceed the lane capacity. We present a token-based admission control policy that implements the reservation scheme. We have also designed an on-line scheduling algorithm that selects which reservation requests can be allowed based on the admission control policy. Using a simulation model to evaluate the system performance in a variety of scenarios, we show that the proposed algorithm can achieve efficient utilization of the high priority lane.

Modeling and Analyzing Real-Time Data Streams

Achieving situation awareness is especially challenging for real-time data stream applications because they i) operate on continuous unbounded streams of data, and ii) have inherent real-time requirements. In this paper we show how formal data stream modeling and analysis can be used to better understand stream behavior, evaluate query costs, and improve application performance. We use MEDAL, a formal specification language based on Petri nets, to model the data stream queries and the Quality-of-Service (QoS) management mechanisms in a data stream system. MEDALs ability to combine query logic and data admission control in one model allows us to design a single comprehensive model of the system. This model can be used to perform a large set of analyses to help improve the applications performance and QoS.