A Novel Approach Towards Selection of Role Model Cluster Head for Power Management in WSN

In this chapter, the authors present an innovative, smart controller to sustain mobility in wireless sensor networks (WSNs). Principally, the focal point is dependent on the arrangement of fuzzy input variables (i.e., remaining battery power [RBP], mobility, and centrality solution) to crucial usages, similar to personnel safety in an industrialized atmosphere. A mobility controller dependent upon type-1 fuzzy logic (T1FL) is planned to support sensor mobile nodes (MN). Here, a role model cluster head (RMCH) is picked out among the cluster heads (CHs) that may simply convey the message to the mobile base station (BS) by determining the appropriate type-1 fuzzy (T1F) descriptors such as RBP, mobility of the sink, and the centrality of the clusters. Type-1 fuzzy inference system (Mamdani's rule) is utilized to opt for the possibility to be RMCH. The validity of the introduced model is carried out by means of multiple linear regressions.

[1]  Davide Brunelli,et al.  Wireless Sensor Networks , 2012, Lecture Notes in Computer Science.

[2]  Haifeng Lin,et al.  A fuzzy inference and big data analysis algorithm for the prediction of forest fire based on rechargeable wireless sensor networks , 2017, Sustain. Comput. Informatics Syst..

[3]  A. Vojdani,et al.  Smart Integration , 2008, IEEE Power and Energy Magazine.

[4]  Abdulmughni Y. Hamzah,et al.  Energy-Efficient Fuzzy-Logic-Based Clustering Technique for Hierarchical Routing Protocols in Wireless Sensor Networks , 2019, Sensors.

[5]  Mohammad Shokouhifar,et al.  Optimized sugeno fuzzy clustering algorithm for wireless sensor networks , 2017, Eng. Appl. Artif. Intell..

[6]  Vasos Vassiliou,et al.  Wireless sensor networks mobility management using fuzzy logic , 2014, Ad Hoc Networks.

[7]  Saurabh Chanana,et al.  Demand Response from Residential Air Conditioning Load Using a Programmable Communication Thermostat , 2013 .

[8]  Boubaker Daachi,et al.  Application of fuzzy inference systems to detection of faults in wireless sensor networks , 2012, Neurocomputing.

[9]  Jiming Chen,et al.  Building-Environment Control With Wireless Sensor and Actuator Networks: Centralized Versus Distributed , 2010, IEEE Transactions on Industrial Electronics.

[10]  Paul K. Wright,et al.  Design architecture for multi-zone HVAC control systems from existing single-zone systems using wireless sensor networks , 2007, SPIE Micro + Nano Materials, Devices, and Applications.

[11]  Shu-Chin Wang,et al.  An Integrated Intrusion Detection System for Cluster-based Wireless Sensor Networks , 2011, Expert Syst. Appl..

[12]  Kamin Whitehouse,et al.  The smart thermostat: using occupancy sensors to save energy in homes , 2010, SenSys '10.

[13]  Therese Peffer,et al.  Why Occupancy-Responsive Adaptive Thermostats Do Not Always Save - and the Limits for When They Should , 2014 .

[14]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[15]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[16]  Padmalaya Nayak,et al.  A Fuzzy Logic-Based Clustering Algorithm for WSN to Extend the Network Lifetime , 2016, IEEE Sensors Journal.

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

[18]  Ravneet Kaur,et al.  Comparative Analysis Of Leach And Its Descendant Protocols In Wireless Sensor Network , 2013 .

[19]  Xue Wang,et al.  Prediction-based Dynamic Energy Management in Wireless Sensor Networks , 2007, Sensors (Basel, Switzerland).

[20]  Manfred Morari,et al.  Use of model predictive control and weather forecasts for energy efficient building climate control , 2012 .

[21]  Waqas Ahmed,et al.  A low complexity online controller using fuzzy logic in energy harvesting WSNs , 2019, Science China Information Sciences.