Trustworthiness Of Sensor Data Research Articles

Ambient Air Quality Measurement with Low-Cost Optical and Electrochemical Sensors: An Evaluation of Continuous Year-Long Operation

Sensor technology is attractive to the public due to its availability and ease of use. However, its usage raises numerous questions. The general trustworthiness of sensor data is widely discussed, especially with regard to accuracy, precision, and long-term signal stability. The VSB-Technical University of Ostrava has operated an air quality sensor network for more than two years, and its large sets of valid results can help in understanding the limitations of sensory measurement. Monitoring is focused on the concentrations of dust particles, NO2, and ozone to verify the impact of newly planted greenery on the reduction in air pollution. The sensor network currently covers an open field on the outskirts of Ostrava, between Liberty Ironworks and the nearby ISKO1650 monitoring station, where some of the worst air pollution levels in the Czech Republic are regularly measured. In the future, trees should be allowed to grow over the sensors, enabling assessment of the green barrier effect on air pollution. As expected, the service life of the sensors varies from 1 to 3 years; therefore, checks are necessary both prior to the measurement and regularly during operation, verifying output stability and overall performance. Results of the PMx sensory measurements correlated well with the reference method. Concentration values measured by NO2 sensors correlated poorly with the reference method, although timeline plots of concentration changes were in accordance. We suggest that a comparison of timelines should be used for air quality evaluations, rather than particular values. The results showed that the sensor measurements are not yet suitable to replace the reference methods, and dense sensor networks proved useful and robust tools for indicative air quality measurements (AQM).

Measurement and security trust in WSNs: a proximity deviation based approach

Quality of communication and measurement accuracy are of prime importance in WSN-based applications, as the sensors have to report real-time measurements to enable efficient decision-making. These sensors are often subject to measurement errors, such as noise, nonlinearity, and deviation caused by rapid changes. Sensors are prone to failures and can be targeted by attacks that aim to modify their outputs. To address these drawbacks, measurement should be checked, sensor functions should be protected, and accuracy should be analyzed all the time. In this paper, we propose a trust management framework based on three metrics: the true measurement deviation, group deviation, and security metrics to analyze the accuracy and trustworthiness of sensor data. The third metric considers the attacks detectable at the communication architecture layers. We have derived an analytical model (i) to calibrate the sensors periodically by deciding on their trustworthiness, and (ii) to modify their outputs periodically, whenever needed based on the deviation in measurement. Our simulation results for a real-time fire monitoring system show that the proposed trust framework is efficient in producing 95% accurate and trusted measurements by limiting the frequency of sensor calibrations to a minimal value and by setting a lower boundary of 5% deviation from the true and group value metrics with its added trust at the micro-level by disproving attacks at the protocol layer.

PROVENANCE FORGERY AND PACKET DROP ATTACKS DETECTION IN WIRELESS NETWORKS ,

large scale sensor networks are deployed in numerous application domains, and the data they collect are used in decision-making for critical infrastructures. Data are streamed from multiple sources through intermediate processing nodes that aggregate information. A malicious adversary may introduce additional nodes in the network or compromise existing ones. Therefore, assuring high data trustworthiness is crucial for correct decision-making. Data provenance represents a key factor in evaluating the trustworthiness of sensor data. Provenance management for sensor networks introduces several challenging requirements, such as low energy and bandwidth consumption, efficient storage and secure transmission. In this paper, we propose a novel lightweight scheme to securely transmit provenance for sensor data. The proposed technique relies on in packet Bloom filters to encode provenance. We introduce efficient mechanisms for provenance verification and reconstruction at the base station. In addition, we extend the secure provenance scheme with functionality to detect packet drop attacks staged by malicious data forwarding nodes. We evaluate the proposed technique both analytically and empirically, and the results prove the effectiveness and efficiency of the lightweight secure provenance scheme in detecting packet forgery and loss attacks.

A Lightweight Secure Scheme for Detecting Provenance Forgery and Packet DropAttacks in Wireless Sensor Networks

Large-scale sensor networks are deployed in numerous application domains, and the data they collect are used in decision-making for critical infrastructures. Data are streamed from multiple sources through intermediate processing nodes that aggregate information. A malicious adversary may introduce additional nodes in the network or compromise existing ones. Therefore, assuring high data trustworthiness is crucial for correct decision-making. Data provenance represents a key factor in evaluating the trustworthiness of sensor data. Provenance management for sensor networks introduces several challenging requirements, such as low energy and bandwidth consumption, efficient storage and secure transmission. In this paper, we propose a novel lightweight scheme to securely transmit provenance for sensor data. The proposed technique relies on in-packet Bloom filters to encode provenance. We introduce efficient mechanisms for provenance verification and reconstruction at the base station. In addition, we extend the secure provenance scheme with functionality to detect packet drop attacks staged by malicious data forwarding nodes. We evaluate the proposed technique both analytically and empirically, and the results prove the effectiveness and efficiency of the lightweight secure provenance scheme in detecting packet forgery and loss attacks.

Mining sensor data in cyber-physical systems

A Cyber-Physical System (CPS) integrates physical devices (i.e., sensors) with cyber (i.e., informational) components to form a context sensitive system that responds intelligently to dynamic changes in real-world situations. Such a system has wide applications in the scenarios of traffic control, battlefield surveillance, environmental monitoring, and so on. A core element of CPS is the collection and assessment of information from noisy, dynamic, and uncertain physical environments integrated with many types of cyber-space resources. The potential of this integration is unbounded. To achieve this potential the raw data acquired from the physical world must be transformed into useable knowledge in real-time. Therefore, CPS brings a new dimension to knowledge discovery because of the emerging synergism of the physical and the cyber. The various properties of the physical world must be addressed in information management and knowledge discovery. This paper discusses the problems of mining sensor data in CPS: With a large number of wireless sensors deployed in a designated area, the task is real time detection of intruders that enter the area based on noisy sensor data. The framework of IntruMine is introduced to discover intruders from untrustworthy sensor data. IntruMine first analyzes the trustworthiness of sensor data, then detects the intruders' locations, and verifies the detections based on a graph model of the relationships between sensors and intruders.

A practical approach for provenance transmission in wireless sensor networks

Assessing the trustworthiness of sensor data and transmitters of this data is critical for quality assurance. Trust evaluation frameworks utilize data provenance along with the sensed data values to compute the trustworthiness of each data item. However, in a sizeable multi-hop sensor network, provenance information requires a large and variable number of bits in each packet, resulting in high energy dissipation due to the extended period of radio communication. In this paper, we design energy-efficient provenance encoding and construction schemes, which we refer to as Probabilistic Provenance Flow (PPF). Our work demonstrates the feasibility of adapting the Probabilistic Packet Marking (PPM) technique in IP traceback to wireless sensor networks. We design two bit-efficient provenance encoding schemes along with a complementary vanilla scheme. Depending on the network size and bit budget, we select the best method based on mathematical approximations and numerical analysis. We integrate PPF with provenance-based trust frameworks and investigate the trade-off between trustworthiness of data items and transmission overhead. We conduct TOSSIM simulations with realistic wireless links, and perform testbed experiments on 15–20TelosB motes to demonstrate the effectiveness of PPF. Our results show that the encoding schemes of PPF have identical performance with a low bit budget (∼32-bit), requiring 33% fewer packets and 30% less energy than PPM variants to construct provenance. With a twofold increase in bit budget, PPF with the selected encoding scheme reduces energy consumption by 46–60%.

Trusted Integration of Wireless Sensor Networks into Herd Control Monitoring Application

Integration of trusted information is an important requirement from business application. As business applications adaption relies on sensor data, that information must be trustworthy enough in order to prevent any misuse of sensor data. For example, the use of un-trustworthy information in healthcare system can have severe consequence on patient life. But it appears that sensors are not tamper resistant enough in order to prevent any capture of cryptographic material stored on the nodes. Therefore, a soft security alternative of hardening security mechanisms is to ensure trustworthiness of sensor data. From its production on the node to its delivery to the business application, and including its processing and forwarding, the authors propose to assess sensor data along its life-cycle. In this paper, the authors propose a framework for the assessment of sensor data trustworthiness, from the acquisition at the node to their delivery to business applications, including any intermediary routing or processing. The assessment aims at identifying erroneous sensor data caused intentionally or unintentionally. In addition, the authors evaluate their approach with real sensor data acquired in the scope of the WASP project (http://www.wasp-project.org - Wirelessly Accessible Sensor Populations - IST 034963) during a herd control experiment.