2011
DOI: 10.1002/wcm.1202
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A novel sine‐curve mobility model for intrusion detection in wireless sensor networks

Abstract: Intrusion detection is prominently important for civil and military applications in wireless sensor networks (WSNs). To date, related works address the problem by assuming a straight‐line intrusion path and a Boolean sensing model. However, a straight‐line intrusion path is often not the case in reality, and the Boolean sensing model cannot resemble a real‐world sensor precisely. Results based on these assumptions are therefore not applicable with desirable accuracy in practice. In view of this, we propose a n… Show more

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Cited by 4 publications
(3 citation statements)
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“…It then frequently moves to an area or position that is further away from the destination and results in the worst escape probability. This observation coincides with the results in a related work [16,30], "for an intruder, following a sine-curve intrusion path can be more beneficial than following a straight-line path as the probability of being detected can be substantially decreased, however, with a side effect of reducing intrusion progress towards the destination to some extent". Namely, a linear intruder moves directly toward the destination without avoiding nearby sensors, and hence, it has a higher probability of escaping the WSN as compared to the pinball intruder.…”
Section: Impact Of Node Density λsupporting
confidence: 90%
“…It then frequently moves to an area or position that is further away from the destination and results in the worst escape probability. This observation coincides with the results in a related work [16,30], "for an intruder, following a sine-curve intrusion path can be more beneficial than following a straight-line path as the probability of being detected can be substantially decreased, however, with a side effect of reducing intrusion progress towards the destination to some extent". Namely, a linear intruder moves directly toward the destination without avoiding nearby sensors, and hence, it has a higher probability of escaping the WSN as compared to the pinball intruder.…”
Section: Impact Of Node Density λsupporting
confidence: 90%
“…This work adopts and extends the analytical framework used in [21] and assumes a linear intrusion path. Different from adopting a linear path, Wang et al [25], [32] propose a Sine-curve mobility model that can simulate different intrusion paths by adjusting its features (amplitude, frequency, and phase) and examine the interplays between network settings and the intruders mobility patterns. It is found that an intruder following a Sine-curve intrusion path can be more beneficial than following a straight-line path as the probability of being detected can be decreased, however with a side effect of reducing intrusion progress toward the destination to some extent.…”
Section: Related Workmentioning
confidence: 99%
“…For example, based on a more realistic exponential sensing model as described in [24], [25], the sensor can sense an intruder with probability 1 if d r min and with probability e fðdÞ if r min < d r max , where d is the distance between a sensor and the intruder in the exponential sensing model, and fðdÞ denotes a function of d and simulates the decay of sensing probability. Replacing r s with r min and r max in our analysis can be used to compute the lower and upper bounds of intrusion detection probability, respectively, in a practical WSN.…”
Section: Remarks On the Modeling And Derivationmentioning
confidence: 99%