TY - JOUR
T1 - Epidemic theory and data survivability in unattended wireless sensor networks: Models and gaps
AU - Di Pietro, Roberto
AU - Verde, Nino Vincenzo
N1 - Generated from Scopus record by KAUST IRTS on 2023-09-20
PY - 2013/1/1
Y1 - 2013/1/1
N2 - Information survivability is the capability of a system to fulfill its mission, in a timely manner, and even in the presence of attacks, failures, or accidents. In this paper, we provide a preliminary assessment of epidemic-domain inspired approaches to model the information survivability in UWSNs. In particular, we show that epidemic models can be used to devise solutions enabling the information to survive, once the maximal compromising power of an attacker is estimated. However, we also point out that the mere application of these models is not always the right choice. Indeed, our results show that these deterministic models are not accurate enough, and "unlikely" events - usually met when striving to optimize resource usage - can cause the loss of the datum; furthermore, we highlight that when translating these models into real applications, geometric constraints (such as communication radius and deployment area) can hinder the applicability of epidemic models. We propose a simple but effective solution to these issues. Finally, extensive simulations support our results.© 2012 Elsevier B.V. All rights reserved.
AB - Information survivability is the capability of a system to fulfill its mission, in a timely manner, and even in the presence of attacks, failures, or accidents. In this paper, we provide a preliminary assessment of epidemic-domain inspired approaches to model the information survivability in UWSNs. In particular, we show that epidemic models can be used to devise solutions enabling the information to survive, once the maximal compromising power of an attacker is estimated. However, we also point out that the mere application of these models is not always the right choice. Indeed, our results show that these deterministic models are not accurate enough, and "unlikely" events - usually met when striving to optimize resource usage - can cause the loss of the datum; furthermore, we highlight that when translating these models into real applications, geometric constraints (such as communication radius and deployment area) can hinder the applicability of epidemic models. We propose a simple but effective solution to these issues. Finally, extensive simulations support our results.© 2012 Elsevier B.V. All rights reserved.
UR - https://linkinghub.elsevier.com/retrieve/pii/S1574119212000946
UR - http://www.scopus.com/inward/record.url?scp=84878658424&partnerID=8YFLogxK
U2 - 10.1016/j.pmcj.2012.07.010
DO - 10.1016/j.pmcj.2012.07.010
M3 - Article
SN - 1574-1192
VL - 9
SP - 588
EP - 597
JO - Pervasive and Mobile Computing
JF - Pervasive and Mobile Computing
IS - 4
ER -