Passive diagnosis for wireless sensor networks
Kebin Liu,Mo Li,Yunhao Liu,Minglu Li,Zhongwen Guo,Feng Hong +5 more
- pp 113-126
TLDR
This work introduces a probabilistic inference model that encodes internal dependencies among different network elements for online diagnosis of an operational sensor network system, capable of additively reasoning root causes based on passively observed symptoms.Abstract:
Network diagnosis, an essential research topic for traditional networking systems, has not received much attention for wireless sensor networks. Existing sensor debugging tools like sympathy or EmStar rely heavily on an add-in protocol that generates and reports a large amount of status information from individual sen-sor nodes, introducing network overhead to a resource constrained and usually traffic sensitive sensor network. We report in this study our initial attempt at providing a light-weight network diag-nosis mechanism for sensor networks. We propose PAD, a prob-abilistic diagnosis approach for inferring the root causes of ab-normal phenomena. PAD employs a packet marking algorithm for efficiently constructing and dynamically maintaining the inference model. Our approach does not incur additional traffic overhead for collecting desired information. Instead, we introduce a prob-abilistic inference model which encodes internal dependencies among different network elements, for online diagnosis of an operational sensor network system. Such a model is capable of additively reasoning root causes based on passively observed symptoms. We implement the PAD design in our sea monitoring sensor network test-bed and validate its effectiveness. We further evaluate the efficiency and scalability of this design through ex-tensive trace-driven simulations.read more
Figures
Figure 11 (a) Node failure detection rate Figure 11 (b) Node failure false positive ratio 
Figure 4. The Belief Network 
Figure 12 (a) Detection rate for multiple faults Figure 12 (b) False positive ratio for multiple faults 
Figure 5. Belief Network constructed from the communication topology 
Figure 10 (a) Sensing failure detection rate Figure 10 (b) Sensing failure false positive ratio 
Figure 9. Convergence time on varying network sizes
Citations
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FlockLab: a testbed for distributed, synchronized tracing and profiling of wireless embedded systems
TL;DR: FlockLab is presented, a testbed that overcomes limitation by allowing multiple services to run simultaneously and synchronously against all nodes under test in addition to the traditional serial port service: tracing of GPIO pins to record logical events occurring on a node, actuated pins to trigger actions on a nodes, and high-resolution power profiling.
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CitySee: Urban CO 2 monitoring with sensors
TL;DR: This work proposes efficient and effective approaches to sensor deployment, and proves that their scheme uses additional relay nodes at most twice of the minimum, and successfully applies this design into CitySee, a large-scale wireless sensor network consisting of 1096 relay nodes and 100 sensor nodes in Wuxi City, China.
Journal ArticleDOI
Beyond trilateration: on the localizability of wireless ad hoc networks
TL;DR: This study proposes a novel approach that inherits the simplicity and efficiency of trilateration and improves the performance by identifying more localizable nodes and proves the correctness and optimality of this design by showing that it is able to locally recognize all one-hopLocalizable nodes.
Proceedings ArticleDOI
KleeNet: discovering insidious interaction bugs in wireless sensor networks before deployment
Raimondas Sasnauskas,Olaf Landsiedel,Muhammad Hamad Alizai,Carsten Weise,Stefan Kowalewski,Klaus Wehrle +5 more
TL;DR: KleeNet as mentioned in this paper is a debugging environment that automatically injects non-deterministic failures in unmodified sensor network applications on symbolic input and generates distributed execution paths at high-coverage, including low-probability corner-case situations.
Proceedings ArticleDOI
Combinatorial auction with time-frequency flexibility in cognitive radio networks
TL;DR: This paper designs an auction mechanism with near-optimal winner determination algorithm, whose worst-case approximation ratio reaches the upper bound √m, m is the number of time-frequency slots, and devise a truthful payment scheme under the approximation winner determination algorithms to guarantee that all the bids submitted by SUs reflect their true valuation of the spectrum.
References
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Book
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Proceedings ArticleDOI
A wireless sensor network For structural monitoring
Ning Xu,Sumit Rangwala,Krishna Chintalapudi,Deepak Ganesan,Alan S. Broad,Ramesh Govindan,Deborah Estrin +6 more
TL;DR: Wisden incorporates two novel mechanisms, reliable data transport using a hybrid of end-to-end and hop-by-hop recovery, and low-overhead data time-stamping that does not require global clock synchronization.
Journal ArticleDOI
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Paul Dagum,Michael Luby +1 more
TL;DR: It is shown that the existence of a polynomial-time relative approximation algorithm for major classes of problem instances implies that NP ⊆ P is NP -hard.
An Energy-Efficient Surveillance System Using Wireless Sensor Networks
Tian He,Sudha Krishnamurthy,John A. Stankovic,Tarek Abdelzaher,Liqian Luo,Ting Yan,Lin Gu,Jonathan W. Hui,Bruce H. Krogh +8 more
TL;DR: In this paper, the authors describe the design and implementation of a running system for energy-efficient surveillance, which allows a group of cooperating sensor devices to detect and track the positions of moving vehicles in an energyefficient and stealthy manner.
Proceedings ArticleDOI
Energy-efficient surveillance system using wireless sensor networks
Tian He,Sudha Krishnamurthy,John A. Stankovic,Tarek Abdelzaher,Liqian Luo,Radu Stoleru,Ting Yan,Lin Gu,Jonathan W. Hui,Bruce H. Krogh +9 more
TL;DR: The design and implementation of a running system that allows a group of cooperating sensor devices to detect and track the positions of moving vehicles in an energy-efficient and stealthy manner and achieves a significant extension of network lifetime is described.
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