TY - GEN
T1 - RL-MADP
T2 - 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020
AU - Mustafa, Iqra
AU - Aslam, Shahzad
AU - Aslam, Sheraz
AU - Qureshi, Muhammad Bilal
AU - Ashraf, Nouman
AU - Mohsin, Syed Muhammad
AU - Mustafa, Hasnain
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Wireless Sensor Networks (WSNs) provide noteworthy advantages over conventional methods for various real-time applications, i.e., healthcare, temperature sensing, smart homes, homeland security, and environmental monitoring. However, limited resources, short life-time network constraints, and security vulnerabilities are the challenging issues in the era of WSNs. Besides, WSNs performance is susceptible to network anomalies, particularly to misdirection attacks. The above-mentioned issues pose our attentions to produce a security-aware application. In this work, therefore, we present a Reinforcement Learning (RL) algorithm for Misdirection Attack Detection and Prevention (RL-MADP) in WSNs. In our proposed approach, other than the flat architecture configuration for WSN, Markov Decision Process (MDP) from RL is considered. Where, each sensor node is fully aware of its environment. It is an online method and incurs minimal computation cost, and performs load-balancing with higher residual energy to prolong the network lifetime.
AB - Wireless Sensor Networks (WSNs) provide noteworthy advantages over conventional methods for various real-time applications, i.e., healthcare, temperature sensing, smart homes, homeland security, and environmental monitoring. However, limited resources, short life-time network constraints, and security vulnerabilities are the challenging issues in the era of WSNs. Besides, WSNs performance is susceptible to network anomalies, particularly to misdirection attacks. The above-mentioned issues pose our attentions to produce a security-aware application. In this work, therefore, we present a Reinforcement Learning (RL) algorithm for Misdirection Attack Detection and Prevention (RL-MADP) in WSNs. In our proposed approach, other than the flat architecture configuration for WSN, Markov Decision Process (MDP) from RL is considered. Where, each sensor node is fully aware of its environment. It is an online method and incurs minimal computation cost, and performs load-balancing with higher residual energy to prolong the network lifetime.
KW - Markov Decision Process
KW - Misdirection Attack
KW - Policy Iteration
KW - Reinforcement Learning
KW - Wireless Sensor Network
KW - WSN Security
UR - http://www.scopus.com/inward/record.url?scp=85089698938&partnerID=8YFLogxK
U2 - 10.1109/IWCMC48107.2020.9148445
DO - 10.1109/IWCMC48107.2020.9148445
M3 - Conference contribution
AN - SCOPUS:85089698938
T3 - 2020 International Wireless Communications and Mobile Computing, IWCMC 2020
SP - 721
EP - 726
BT - 2020 International Wireless Communications and Mobile Computing, IWCMC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 June 2020 through 19 June 2020
ER -