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TY  - JOUR <br>
AU  - Roy, Arijit<br>
AU  - Kar, Pushpendu<br>
AU  - Misra, Sudip<br>
AU  - Obaidat, Mohammad S.<br>
C7  - e2913<br>
C8  - dac.2913<br>
TI  - D3: distributed approach for the detection of dumb nodes in wireless sensor networks<br>
JO  - International Journal of Communication Systems<br>
JA  - Int J Commun Syst<br>
VL  - 30<br>
IS  - 1<br>
SN  - 1074-5351<br>
UR  - https://doi.org/10.1002/dac.2913<br>
DO  - doi:10.1002/dac.2913<br>
SP  - e2913<br>
KW  - dumb node<br>
KW  - environmental effect<br>
KW  - detection<br>
KW  - CUSUM<br>
KW  - Markov chain<br>
PY  - 2017<br>
AB  - Summary In this work, we propose D3?a distributed approach for the detection of ?dumb? nodes in a wireless sensor network (WSN). A dumb node can sense its surroundings, but is unable to transmit these sensed data to any other node, due to the sudden onset of adverse environmental effects. However, such a node resumes its normal operations with the resumption of favorable environmental conditions. Due to the presence of dumb nodes, the network is unable to provide the expected services. Therefore, it is prudent to re-establish connectivity between dumb and other nodes, so that sensed data can be reliably transmitted to the sink. Before the re-establishment of connectivity, a node needs to confirm its actual state of being dumb. Dumb behavior is dynamic in nature, and is, thus, distinct from the traditional node isolation problem considered in stationary WSNs. Therefore, the existing schemes for the detection of other misbehaviors are not applicable for detecting a dumb node in a WSN. Considering this temporal behavior of a dumb node, we propose an approach, D3, for the detection of dumb nodes. In the propose scheme, we uses cumulative sum test, which helps in detecting the dumb behavior. The simulation results show that there is 56% degradation in detection percentage with the increment in the detection threshold, whereas energy consumption and the message overhead increase by 40% with the increment in detection threshold.<br>
ER  - 


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@article{doi:10.1002/dac.2913,
author = {Roy, Arijit and Kar, Pushpendu and Misra, Sudip and Obaidat, Mohammad S.},
title = {D3: distributed approach for the detection of dumb nodes in wireless sensor networks},
journal = {International Journal of Communication Systems},

volume = {30},
number = {1},
pages = {e2913},
keywords = {dumb node, environmental effect, detection, CUSUM, Markov chain},
doi = {10.1002/dac.2913},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.2913},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/dac.2913},
note = {e2913 dac.2913},
abstract = {Summary In this work, we propose D3—a distributed approach for the detection of ‘dumb’ nodes in a wireless sensor network (WSN). A dumb node can sense its surroundings, but is unable to transmit these sensed data to any other node, due to the sudden onset of adverse environmental effects. However, such a node resumes its normal operations with the resumption of favorable environmental conditions. Due to the presence of dumb nodes, the network is unable to provide the expected services. Therefore, it is prudent to re-establish connectivity between dumb and other nodes, so that sensed data can be reliably transmitted to the sink. Before the re-establishment of connectivity, a node needs to confirm its actual state of being dumb. Dumb behavior is dynamic in nature, and is, thus, distinct from the traditional node isolation problem considered in stationary WSNs. Therefore, the existing schemes for the detection of other misbehaviors are not applicable for detecting a dumb node in a WSN. Considering this temporal behavior of a dumb node, we propose an approach, D3, for the detection of dumb nodes. In the propose scheme, we uses cumulative sum test, which helps in detecting the dumb behavior. The simulation results show that there is 56\% degradation in detection percentage with the increment in the detection threshold, whereas energy consumption and the message overhead increase by 40\% with the increment in detection threshold.},
year = {2017}
}