Performance Optimization in IoT-Based Next-Generation Wireless Sensor Networks

In this paper, we propose a novel framework for performance optimization in Internet of Things (IoT)-based next-generation wireless sensor networks. In particular, a computationally-convenient system is presented to combat two major research problems in sensor networks. First is the conventionally-tackled resource optimization problem which triggers the drainage of battery at a faster rate within a network. Such drainage promotes inefficient resource usage thereby causing sudden death of the network. The second main bottleneck for such networks is the data degradation. This is because the nodes in such networks communicate via a wireless channel, where the inevitable presence of noise corrupts the data making it unsuitable for practical applications. Therefore, we present a layer-adaptive method via 3-tier communication mechanism to ensure the efficient use of resources. This is supported with a mathematical coverage model that deals with the formation of coverage holes. We also present a transform-domain based robust algorithm to effectively remove the unwanted components from the data. Our proposed framework offers a handy algorithm that enjoys desirable complexity for real-time applications as shown by the extensive simulation results.

[1]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[2]  Dinggang Shen,et al.  Reconstruction of 7T-Like Images From 3T MRI , 2016, IEEE Transactions on Medical Imaging.

[3]  Aleksejs Jurenoks,et al.  Analysis of wireless sensor network structure and life time affecting factors , 2017, 2017 Communication and Information Technologies (KIT).

[4]  André Frank Krause,et al.  Shape Recognition Through Tactile Contour Tracing - A Simulation Study , 2015, Trans. Comput. Collect. Intell..

[5]  Li Qing,et al.  Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks , 2006, Comput. Commun..

[6]  Nadeem Javaid,et al.  SEEC: Sparsity-Aware Energy Efficient Clustering Protocol for Underwater Wireless Sensor Networks , 2016, 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA).

[7]  Azer Bestavros,et al.  SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks , 2004 .

[8]  Nadeem Javaid,et al.  Design and Development of a Low Cost Ubiquitous Tracking System , 2014, FNC/MobiSPC.

[9]  Muzammil Behzad,et al.  M-BEHZAD: Minimum distance Based Energy efficiency using Hemisphere Zoning with Advanced Divide-and-Rule Scheme for Wireless Sensor Networks , 2018, ArXiv.

[10]  Silviu Folea,et al.  Analysis of Three IoT-Based Wireless Sensors for Environmental Monitoring , 2017, IEEE Transactions on Instrumentation and Measurement.

[11]  Wendi B. Heinzelman,et al.  Application-specific protocol architectures for wireless networks , 2000 .

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

[13]  Wenhan Yang,et al.  Image super-resolution via nonlocal similarity and group structured sparse representation , 2015, 2015 Visual Communications and Image Processing (VCIP).

[14]  Licheng Jiao,et al.  Comparing Noisy Patches for Image Denoising: A Double Noise Similarity Model , 2015, IEEE Transactions on Image Processing.

[15]  Constantin Grumazescu,et al.  WSN solutions for communication challenges in military live simulation environments , 2016, 2016 International Conference on Communications (COMM).

[16]  Mengdi Wang,et al.  Group-based hyperspectral image denoising using low rank representation , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[17]  Muzammil Behzad,et al.  Technology-Embedded Hybrid Learning , 2018 .

[18]  Allen Gersho,et al.  On the structure of vector quantizers , 1982, IEEE Trans. Inf. Theory.

[19]  Tareq Y. Al-Naffouri,et al.  Image denoising via collaborative support-agnostic recovery , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[20]  Shivashankar B. Nair,et al.  On a Multi-agent Distributed Asynchronous Intelligence-Sharing and Learning Framework , 2015, Trans. Comput. Collect. Intell..

[21]  A. Manjeshwar,et al.  TEEN: a routing protocol for enhanced efficiency in wireless sensor networks , 2001, Proceedings 15th International Parallel and Distributed Processing Symposium. IPDPS 2001.

[22]  Long Bao,et al.  Sequence-to-Sequence Similarity-Based Filter for Image Denoising , 2016, IEEE Sensors Journal.

[23]  Jian Sun,et al.  Image Completion Approaches Using the Statistics of Similar Patches , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Alireza Khadivi,et al.  FTPASC: A Fault Tolerant Power Aware Protocol with Static Clustering for Wireless Sensor Networks , 2006, 2006 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications.

[25]  Nadeem Javaid,et al.  REEC: Reliable Energy Efficient Critical Data Routing in Wireless Body Area Networks , 2014, 2014 Ninth International Conference on Broadband and Wireless Computing, Communication and Applications.

[26]  Manal Abdullah,et al.  Layer-Adaptive Communication and Collaborative Transformed-Domain Representations to Optimize Performance in Next-Generation WSNs , 2018, 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA).

[27]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[28]  Nadeem Javaid,et al.  TSDDR: Threshold Sensitive Density Controlled Divide and Rule Routing Protocol for Wireless Sensor Networks , 2014, 2014 Ninth International Conference on Broadband and Wireless Computing, Communication and Applications.

[29]  V. J. Rayward-Smith,et al.  Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition , 1999 .

[30]  Nadeem Javaid,et al.  Mobility Model for WBANs , 2014, 2014 Ninth International Conference on Broadband and Wireless Computing, Communication and Applications.

[31]  Nadeem Javaid,et al.  Density controlled divide-and-rule scheme for energy efficient routing in Wireless Sensor Networks , 2013, 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).

[32]  Yaning Liu,et al.  Anomaly detection in medical WSNs using enclosing ellipse and chi-square distance , 2014, 2014 IEEE International Conference on Communications (ICC).

[33]  Irfan-Ullah Awan,et al.  Centralized Dynamic Clustering for Wireless Sensor Network , 2009, 2009 International Conference on Advanced Information Networking and Applications Workshops.

[34]  Yao Ge,et al.  Performance Optimization in Wireless Sensor Networks: A Novel Collaborative Compressed Sensing Approach , 2017, 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA).

[35]  Shekhar Verma,et al.  Dynamic Multi-level Hierarchal Clustering Approach for Wireless Sensor Networks , 2009, 2009 11th International Conference on Computer Modelling and Simulation.

[36]  Dongyao Jia,et al.  Dynamic Cluster Head Selection Method for Wireless Sensor Network , 2016, IEEE Sensors Journal.

[37]  Nadeem Javaid,et al.  On Enhancing Network Reliability and Throughput for Critical-range based Applications in UWSNs , 2014, FNC/MobiSPC.

[38]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[39]  Mohammad Patwary,et al.  Universal and Dynamic Clustering Scheme for Energy Constrained Cooperative Wireless Sensor Networks , 2017, IEEE Access.

[40]  M. S. Javaid,et al.  Distributed PCA and Consensus Based Energy Efficient Routing Protocol for WSNs , 2017, J. Inf. Sci. Eng..

[41]  Wen Gao,et al.  Gradient based image/video softcast with grouped-patch collaborative reconstruction , 2014, 2014 IEEE Visual Communications and Image Processing Conference.

[42]  F. Saleem,et al.  IDDR: Improved Density Controlled Divide-and-Rule Scheme for Energy Efficient Routing in Wireless Sensor Networks , 2014, FNC/MobiSPC.

[43]  Victor C. M. Leung,et al.  Recent Advances in Industrial Wireless Sensor Networks Toward Efficient Management in IoT , 2015, IEEE Access.

[44]  Ashwani Sharma,et al.  A Novel Cluster-Based Energy Efficient Routing in Wireless Sensor Networks , 2013, 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA).

[45]  Pragasen Mudali,et al.  Performance evaluation of routing protocols in uniform and normal node distributions using inter-mesh wireless networks , 2015, 2015 World Symposium on Computer Networks and Information Security (WSCNIS).