TY - JOUR
T1 - Extended Watchdog Mechanism for Wireless Sensor Networks
AU - 
JO - Journal of Information and Computing Science
VL - 1
SP - 21
EP - 38
PY - 2024
DA - 2024/01
SN - 3
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22781.html
KW -  Distributed  Sampling  System
KW -   Stochastic  Expected  Value  Optimization
KW -   Stochastic  Chance 
Constrained Optimization.  

AB - Flow-level traffic measurement is important for network traffic accounting, traffic engineering, 
and network security. However, flow-level measurement in high speed networks poses great challenges due 
to the requirements of high packet processing speed and large memory size (high time/space complexity). To 
reduce these demanding requirements, sampling is usually used and samplers are deployed in the network. 
But sampling incurs information loss. To address this issue, this paper studies the tradeoff between sampling 
loss  and  complexity  in  distributed  sampling  system.  We  formulate  the  distributed  sampling  problem  as  a 
constrained optimization  problem;  specifically,  maximizing  the  measurement  coverage  (i.e.,  the  percentage 
of sampled traffic among the total traffic) and minimizing the complexity/budget. Considering the stochastic 
nature  of  traffic  flows,  we  further  formulate  the  optimization  problem  under  two  stochastic  criteria: 
Stochastic  Expected  Value  Optimization  criterion  (which  is  concerned  with  average  performance)  and 
Stochastic  Chance  Constrained  Optimization  criterion  (which  is  concerned  with  the  distribution  of 
performance  measure).  Then  we  propose  a  Hybrid  Intelligent  algorithm  to  decide  the  optimal  deployment 
strategy  for  monitors’  placement  and  the  sampling  rate  at  each  monitor.  Equipped  with  the  proposed 
algorithm, we are able to address the optimal tradeoff between measurement coverage and deployment cost 
for  networks  with  random  traffic,  which  has  not  been  studied  before.  The  extensive  simulations  and 
experiments demonstrate the effectiveness of our models and algorithm: with careful deployment, monitoring 
over a small fraction of nodes in a high speed network is sufficient to maintain a high level of measurement 
coverage.