Modeling Quality of Information in Multi-sensor Surveillance Systems

Hossain, M. Shamim; Atrey, Pradeep K.; Saddik, A. El

doi:10.1109/icdew.2007.4400968

Cited by 20 publications

(20 citation statements)

References 12 publications

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“…For example, QOI includes: Accuracy, Timeliness, Certainty, and Integrity [44]. QOI integrity measures whether the data has not been manipulated as it impacts the shared situational awareness [45].…”

Section: Quality Of Service/information Researchmentioning

confidence: 99%

Measures of effectiveness for high-level fusion

Blasch

Valin

Bossé

2010

2010 13th International Conference on Information Fusion

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Current advances in technology, sensor collection, data storage, and data distribution have afforded more complex, distributed, and operational information fusion systems (IFSs). IFSs notionally consist of low-level (data collection, registration, and association in time and space) and high-level fusion (user coordination, situational awareness, and mission control). Low-level IFSs typically rely on standard metrics for evaluation such as timeliness, accuracy, and confidence. Given the broader use of IFSs, it is also important to look at high-level fusion processes and determine a set of metrics to test IFSs, such as workload, throughput, and cost. Three types of measures (measures of performance MOP, measures of effectiveness MOE, and measures of merit MOM) are summarized. In this paper, we seek to describe MOEs for High-Level Fusion (HLF) based on developments in Quality of Service (QOS) and Quality of Information (QOI) that support the user and the machine, respectively. We define a HLF MOE based on (1) information quality, (2) robustness, and (3) information gain. We demonstrate the HLF MOE based for a maritime domain situation awareness example.

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Section: Quality Of Service/information Researchmentioning

confidence: 99%

Measures of effectiveness for high-level fusion

Blasch

Valin

Bossé

2010

2010 13th International Conference on Information Fusion

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“…In the literature, different metrics are used for QoI with respect to timeliness and confidence. Bisdikian [3] analyzes the impact of signal and system parameters on QoI, such as the sampling rate, while Hossain et al [4] propose to use certainty, accuracy, timeliness and integrity as QoI attributes and model them statistically. In our paper, the QoI at each sensor reflects the recognition accuracy as a function of the sending rates.…”

Section: Sensor Qoi Function and Sensor Fusionmentioning

confidence: 99%

Quality and energy aware data acquisition for activity and locomotion recognition

Việt

Munthe-Kaas

Plagemann

2013

2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)

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With the advent of wireless sensors and pervasive environments, autonomic human activity recognition has received substantial attention in research. In such environments, many sensors are deployed on each object with the purpose to collect sufficient data to recognize the activities of the object. To perform activity recognition, low-level data streams from the sensors are combined at the sink. A key challenge is to recognize efficiently and with high accuracy the object's activities based on the low-level sensor data. However, there is a trade-off between high accuracy and efficiency, caused by the cost of delivering data samples from sensors to the sink. The challenge is to determine sampling rates that satisfy the required accuracy and minimizes the communication cost. We formalize this problem of choosing sampling rates that satisfy the required accuracy and minimize the communication cost. We formalize this problem as an integer programming problem and solve it by using Lagrangian relaxation with branch-andbound method. Evaluation results with a publicly available dataset demonstrate the potential applicability of our approach.

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“…Key aspects of measuring effectiveness come from quality of service (QoS) metrics that can be utilized for hard-soft semantic information fusion [71,72,73,74]. Another perspective includes quality of information, or rather information quality (IQ), metrics to combine different types of uncertainty to an established affect.…”

Section: Challenge Problems For Large Format Imagerymentioning

confidence: 99%