A Hybrid Fuzzy-Genetic Algorithm for Performance Optimization of Cyber Physical Wireless Body Area Networks

The use of fuzzy decision-making in datapath selection extends the sensor network lifetime with a uniform distribution of routing load among network nodes. Fuzzy-logic based routing protocols are mostly designed for general wireless sensor networks (WSN). However, such protocols are not compatible with a Wireless Body Area Network (WBAN) comprised of biosensor nodes. WBAN nodes carry inferior computational, communication and energy resources as compared to general WSN nodes. A WBAN routing protocol needs to be designed as per IEEE 802.15.6 WBAN standards to meet high-end QoS requirements of medical applications. This paper presents a fuzzy-logic-based clustering protocol for data routing in WBANs. Nodes are grouped into clusters and cluster head nodes are selected through a Fuzzy-Genetic Algorithm termed as EB- f g -MADM. EB- f g -MADM makes an assessment of dual attributes of each cluster node in terms of node residual energy and CH selection cost. CH selection cost of a node is the forecasted value of network energy consumption if the node acts as a cluster head. EB- f g -MADM utilizes a fuzzy-TOPSIS function which makes a quantitative comparison of cluster nodes and selects the cluster head node possessing the aforementioned attributes closest to their ideally desired values. A Genetic Algorithm-based optimization process adapts the attribute weights for cluster head selection. EB- f g -MADM provides enhanced network lifetime with a uniform distribution of routing load. Protocol performance is obtained in terms of network lifetime, throughput and latency. Results are compared with existing WBAN routing protocols and are found to be better.

[1]  Kamalrulnizam Abu Bakar,et al.  Routing protocols in wireless body sensor networks: A comprehensive survey , 2017, J. Netw. Comput. Appl..

[2]  Wendi Heinzelman,et al.  Energy-efficient communication protocol for wireless microsensor networks , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[3]  Javed Iqbal,et al.  Traffic priority based delay-aware and energy efficient path allocation routing protocol for wireless body area network , 2019, J. Ambient Intell. Humaniz. Comput..

[4]  Peter C. Fishburn,et al.  Letter to the Editor - Additive Utilities with Incomplete Product Sets: Application to Priorities and Assignments , 1967, Oper. Res..

[5]  Vinod Kumar Jain,et al.  Energy-Efficient Network Protocol for Precision Agriculture: Using threshold sensitive sensors for optimal performance. , 2017, IEEE Consumer Electronics Magazine.

[6]  S. Balaji,et al.  Development of Fuzzy based Energy Efficient Cluster Routing Protocol to Increase the Lifetime of Wireless Sensor Networks , 2019, Mob. Networks Appl..

[7]  Abbas Jamalipour,et al.  Body Node Coordinator Placement Algorithms for Wireless Body Area Networks , 2015, IEEE Internet of Things Journal.

[8]  Wook Hyun Kwon,et al.  Computational complexity of general fuzzy logic control and its simplification for a loop controller , 2000, Fuzzy Sets Syst..

[9]  Moosa Ayati,et al.  A fuzzy three-level clustering method for lifetime improvement of wireless sensor networks , 2018, Ann. des Télécommunications.

[10]  Chiara Buratti,et al.  A Survey on Wireless Body Area Networks: Technologies and Design Challenges , 2014, IEEE Communications Surveys & Tutorials.

[11]  S. K. Sathya Lakshmi Preeth,et al.  An adaptive fuzzy rule based energy efficient clustering and immune-inspired routing protocol for WSN-assisted IoT system , 2018, Journal of Ambient Intelligence and Humanized Computing.

[12]  Nadeem Javaid,et al.  Co-LAEEBA: Cooperative link aware and energy efficient protocol for wireless body area networks , 2015, Comput. Hum. Behav..

[13]  Leghris Cherkaoui,et al.  Adaptive Routing Protocol for Lifetime Maximization in Multi-Constraint Wireless Sensor Networks , 2018, Journal of Communications and Information Networks.

[14]  Z. Abbas,et al.  M-ATTEMPT: A New Energy-Efficient Routing Protocol for Wireless Body Area Sensor Networks , 2012, ANT/SEIT.

[15]  Nadeem Javaid,et al.  LAEEBA: Link Aware and Energy Efficient Scheme for Body Area Networks , 2014, 2014 IEEE 28th International Conference on Advanced Information Networking and Applications.

[16]  Jean-Michel Redoute,et al.  An Autonomous Wireless Body Area Network Implementation Towards IoT Connected Healthcare Applications , 2017, IEEE Access.

[17]  Mohamed Elhoseny,et al.  Balancing Energy Consumption in Heterogeneous Wireless Sensor Networks Using Genetic Algorithm , 2015, IEEE Communications Letters.

[18]  Jin-Shyan Lee,et al.  Fuzzy-Logic-Based Clustering Approach for Wireless Sensor Networks Using Energy Predication , 2012, IEEE Sensors Journal.

[19]  Feng Zhang,et al.  A Clustering Routing Protocol for WSN Based on Type-2 Fuzzy Logic and Ant Colony Optimization , 2015, Wireless Personal Communications.

[20]  Jian Liu,et al.  Classification of Daily Activities for the Elderly Using Wearable Sensors , 2017, Journal of healthcare engineering.

[21]  Honggang Wang,et al.  Interference Mitigation for Cyber-Physical Wireless Body Area Network System Using Social Networks , 2013, IEEE Transactions on Emerging Topics in Computing.

[22]  Nadeem Javaid,et al.  SIMPLE: Stable Increased-Throughput Multi-hop Protocol for Link Efficiency in Wireless Body Area Networks , 2013, 2013 Eighth International Conference on Broadband and Wireless Computing, Communication and Applications.

[23]  Nadeem Javaid,et al.  A Relay Based Routing Protocol for Wireless In-Body Sensor Networks , 2015, Wirel. Pers. Commun..

[24]  Bashir Alam,et al.  Fuzzy based enhanced cluster head selection (FBECS) for WSN , 2020 .

[25]  Tin-Yu Wu,et al.  Low-SAR Path Discovery by Particle Swarm Optimization Algorithm in Wireless Body Area Networks , 2015, IEEE Sensors Journal.

[26]  Zahid Ullah,et al.  DSCB: Dual sink approach using clustering in body area network , 2019, Peer-to-Peer Netw. Appl..

[27]  Francisco Rodrigues Lima Junior,et al.  A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection , 2014, Appl. Soft Comput..

[28]  Navneet Kaur,et al.  Optimized cost effective and energy efficient routing protocol for wireless body area networks , 2017, Ad Hoc Networks.

[29]  Rupert Young,et al.  Fuzzy-TOPSIS based Cluster Head selection in mobile wireless sensor networks , 2018, Journal of Electrical Systems and Information Technology.

[30]  Rahat Ali Khan,et al.  An Energy Efficient Routing Protocol for Wireless Body Area Sensor Networks , 2018, Wireless Personal Communications.

[31]  Jianfeng Wang,et al.  Applications, challenges, and prospective in emerging body area networking technologies , 2010, IEEE Wireless Communications.

[32]  Ghulam Muhammad,et al.  Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms , 2017, Journal of healthcare engineering.

[33]  Vidushi Sharma,et al.  Cluster Head Selection in Wireless Sensor Networks under Fuzzy Environment , 2013 .

[34]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[35]  Patrick T. Hester,et al.  An Analysis of Multi-Criteria Decision Making Methods , 2013 .

[36]  Nadeem Javaid,et al.  iM-SIMPLE: iMproved stable increased-throughput multi-hop link efficient routing protocol for Wireless Body Area Networks , 2015, Comput. Hum. Behav..

[37]  María Teresa Lamata,et al.  The LTOPSIS: An alternative to TOPSIS decision-making approach for linguistic variables , 2012, Expert Syst. Appl..

[38]  Dimitrios D. Vergados,et al.  Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[39]  Yi Huai Yang.  Channel Modelling for WBANs , 2012 .

[40]  Nadeem Javaid,et al.  Distance Aware Relaying Energy-Efficient: DARE to Monitor Patients in Multi-hop Body Area Sensor Networks , 2013, 2013 Eighth International Conference on Broadband and Wireless Computing, Communication and Applications.