Shiv Sutar

@mitwpu.edu.in

Asst. Prof. School of Computer Engineering and Technology
MIT World Peace University Pune

Shiv Sutar


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https://researchid.co/shiv.sutar

RESEARCH INTERESTS

WSN IOT
7

Scopus Publications

332

Scholar Citations

7

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • Intelligent Congestion Control Mechanism for IoT-Enabled Wireless Sensor Networks Using Hybrid Aggregation and Scheduling Technique
    Shiv H. Sutar, Y. Bevish Jinila, Kailas Patil, Sital Dash, Shrikant Jadhav
    Journal of Visualized Experiments, 2026
    Congestion in IoT-enabled wireless sensor networks (WSNs) degrades packet delivery, latency, and energy usage, impairing the network, especially under bursty and heterogeneous traffic conditions. This protocol illustrates an intelligent congestion control technique that combines hybrid data aggregation, adaptive scheduling, and a neuro-fuzzy decision engine to efficiently handle network load. The method involves first generating simulation data, creating topologies of different node densities, and setting up traffic patterns using NS-2.35. Packet traces are obtained for each scenario to allow reproducible evaluation. The protocol workflow refers to the combination of two mechanisms: (1) hybrid aggregation, which combines packets in time- and count-based windows while retaining priority labels, and (2) adaptive scheduling, which handles dual priority queues via weighted round robin. A neuro-fuzzy controller always evaluates buffer occupancy, link quality, channel utilization, residual energy, and traffic priority. Taking these inputs, it regulates aggregation depth, queue weights, and transmission decisions by fuzzy inference and neuro-adaptive learning. Performance measurement tasks encompass the calculation of packet delivery ratio, end-to-end latency, throughput, node-level energy consumption, and network lifetime. Statistical analyses are performed across multiple runs to check the reliability of the results. The approach reveals better performance in the simulation compared to the baseline schemes. This protocol offers a reproducible framework for exploring hybrid congestion control methods that enable energy-efficient, scalable, and QoS-aware operation in IoT-enabled WSN environments.
  • Enhancing IoT-Enabled Wireless Sensor Network Performance through Adaptive Congestion Control: Investigation of Hybrid Aggregation and Scheduling Techniques
    Shiv H. Sutar
    Communications on Applied Nonlinear Analysis, 2024
    In the rapidly expanding domain of the Internet of Things (IoT), Wireless Sensor Networks (WSNs) have become indispensable, supporting applications ranging from environmental monitoring to industrial automation. However, as the IoT ecosystem continues to burgeon with an array of devices and applications, the effective management of data transmission and congestion control within these networks presents an escalating challenge. To address this, this paper introduces a ground-breaking Optimal Congestion Control Mechanism tailored explicitly for IoT-enabled Wireless Sensor Networks. This innovative mechanism incorporates a Hybrid Aggregation and Scheduling technique to tackle the dual hurdles of congestion relief and energy efficiency in WSNs. By seamlessly blending data aggregation with dynamic scheduling, this approach endeavors to optimize network resources and alleviate congestion-related issues. Data aggregation intelligently consolidates multiple data packets into a single transmission, reducing overhead and maximizing the con- strained bandwidth of wireless channels. Concurrently, dynamic scheduling adapts the transmission schedule in real-time based on network conditions, ensuring the timely delivery of critical data while minimizing congestion. To achieve an optimal configuration, the mechanism employs an intelligent decision-making algorithm that considers factors like data priority, network traffic, and energy constraints. Furthermore, machine learning techniques, notably reinforcement learning, can be leveraged to enhance the algorithm’s adaptability over time. The efficacy of the proposed mechanism undergoes rigorous assessment through simulations and real-world experiments, validating its ability to diminish congestion, enhance data delivery, and prolong the operational life of the network. The outcomes underscore the significant potential of this Optimal Congestion Control Mechanism to elevate the reliability and efficiency of IoT-enabled Wireless Sensor Networks. By harnessing the combined advantages of data aggregation and dynamic scheduling, the proposed mechanism offers a comprehensive solution for efficiently managing congestion and optimizing network resource utilization.
  • IoT Innovations in Cotton Plant Disease Detection for Sustainable Agriculture
    International Journal of Intelligent Systems and Applications in Engineering, 2024
  • An Automated System for Waste Segregation Using Deep Learning Algorithms
    R. Radhakrishnan Nair, Pratham Prabhu, Chirag Panchal, Labdhi Sarnot, Sukhada Bhingarkar, Amit Savyanavar, Shiv Sutar, Mrunal Annadate, Nikhil Mhala
    2024 3rd International Conference for Innovation in Technology Inocon 2024, 2024
    In current society, the issue of a proper waste disposal system is one of the major concerns for a clean and green environment. The most common method of waste disposal has always been dumping and burning regardless of the waste category. A better method of waste disposal can be implemented, if waste is categorically separated and is then disposed of using various methods, and if salvageable then may be reused or recycled thus reducing wastage and improving sustainability. Traditional automated waste detection systems only employ sensors that cannot accurately categorize waste into the ideal category for proper waste disposal. This paper proposes a waste detection system that uses image processing, categorizes waste into a few categories and then segregates them as per the division assigned to them. A large dataset is used to train a Convolutional Neural and then waste is identified by applying image processing techniques. The object then moves down the line on a conveyor belt after it is identified and a corresponding shaft is activated which pushes the object down to the bucket assigned to the waste category. The entire system is integrated using an Arduino and is remotely controlled. This system is used to facilitate simplifying the process of recycling resources that may have been disposed of and then turn back these resources into reusable resources. This will improve the process of waste segregation and disposal and automate end to end process as opposed to having any human involvement thus improving the efficiency. The CNN model is trained on a dataset with 5 main categories and gives an accuracy of 93%.
  • Congestion control for better performance of WSN based iot ecosystem using KHA mechanism
    Shiv Sutar, Y. Bevish Jinila, S Shah, Z Chen, F Yin, et al.
    International Journal of Recent Technology and Engineering, 2019
    Since last two decades, Wireless Sensor Network and Mobile Communication success rate is raised too high and demanding for overall communication network as it consists of heterogeneous wireless networks. These networks provides universal wireless access change, also provide dynamic scheduling between devices, networks and whole world for communication. It is applicable for prominently highly demanding domains like medical devices, sensors used for military applications, smart buildings, societies, and cities so on. At the same time new technological domain named IoT (Internet of Things) is making high impact on communication domain. It is an integration of several technologies. It is interchangeably can also be used as Wireless Sensor Network only the difference is that the IoT devices and networks has limited storage and computing capabilities and works on the internet. As IoT Ecosystem and WSN used for numerous Computation, Communication over the internet which ultimately increased network traffic and leads to congestion in the network which affects the Quality of Service (QoS) parameters of the network. In this paper, a novel method is proposed to handle the increased network traffic and Congestion network using the Krill Herd Aggregation Mechanism.
  • Performance analysis of wireless sensor networks for QoS
    Yash Yamsanwar, Shiv Sutar
    2017 International Conference on Big Data Iot and Data Science Bid 2017, 2017
    Wireless sensor network (WSN) is a dynamic self — configured device which is used for the communicational purpose to monitor different scenarios. To the best of our knowledge, the main challenge faced in the field of wireless sensor network is computing, monitoring and the size of the sensor. In this paper we underscore the fact of analysis of the behavior of wireless sensor network using different factors and protocols. By analysis we can predict the behavior and analyze the superficiality.
  • Integration of Smart Phone and IOT for development of smart public transportation system
    Shiv. H. Sutar, Rohan Koul, Rajani Suryavanshi
    2016 International Conference on Internet of Things and Applications Iota 2016, 2016
    As population is burgeoning, there is an increase in the number of vehicles on the road and hence an upsurge in the problems associated with traffic management, especially the Public Transport. There is also an increase in the number of accidents and various other traffic related issues. Intelligent Transportation System (ITS) provides the solution to most of these problems by integrating existing technologies with the underlying infrastructure. With the advent of mobile technology and the ubiquitous cellular network, real time vehicle tracking for efficient transport management has become viable. The futile long wait for a bus to arrive can be avoided by Intelligent Public Transportation System. The omnipresence of Smart Phones and their ever increasing power at a very economical price makes them one of the most attractive options for developing IOT applications. Here, an approach based on the combination of technologies like GPS and Android is discussed which can assuage passengers who commute by the means of public transport. The user is furnished with explicit information about the current location of nearest buses approaching the bus-stop on a mobile application. Using readily available Android API's, technologies like 3G network and SMS based services in the existing mobile phones can reduce the cost and size of hardware required, as well as lead to a better output.

RECENT SCHOLAR PUBLICATIONS

  • Intelligent Congestion Control Mechanism for IoT-Enabled Wireless Sensor Networks Using Hybrid Aggregation and Scheduling Technique
    SH Sutar, YB Jinila, K Patil, S Dash, S Jadhav
    JoVE (Journal of Visualized Experiments), e69909 , 2026
    2026.0
  • An automated system for waste segregation using deep learning algorithms
    RR Nair, P Prabhu, C Panchal, L Sarnot, S Bhingarkar, A Savyanavar, ...
    2024 3rd International Conference for Innovation in Technology (INOCON), 1-6 , 2024
    2024.0
    Citations: 8
  • IoT Innovations in Cotton Plant Disease Detection for Sustainable Agriculture
    SH Katti, J. , Dharmale, G. , Ubale, S.A. , Deoghare, R. , Sutar
    International Journal of Intelligent Systems and Applications in Engineering … , 2024
    2024.0
    Citations: 5
  • STAR-GALAXY CLASSIFICATION USING MACHINE LEARNING ALGORITHMS AND DEEP LEARNING
    MMSHS Amit S
    International Journal on Information Technologies and Security 15 (2) , 2023
    2023.0
    Citations: 12
  • Artificial Intelligence Based Techniques to Prevent the Spread of the COVID 19 pandemic
    LP Sutar Shiv H, Pawar R
    International Journal of Gird and Distributed Computing 13 (2), 1226-1238 , 2020
    2020.0
  • Congestion control for better performance of Wsn based IoT ecosystem using Kha mechanism
    SH Sutar, YB Jinila
    Int. J. Recent Technol. Eng 8 , 2019
    2019.0
    Citations: 4
  • Performance analysis of wireless sensor networks for QoS
    Y Yamsanwar, S Sutar
    2017 International Conference on Big Data, IoT and Data Science (BID), 120-123 , 2017
    2017.0
    Citations: 7
  • Integration of Smart Phone and IOT for development of smart public transportation system
    SH Sutar, R Koul, R Suryavanshi
    2016 international conference on internet of things and applications (IOTA … , 2016
    2016.0
    Citations: 239
  • Recommendation System for Outfit Selection (RSOS)
    SH Sutar, AH Khade
    arXiv preprint arXiv:1402.6692 , 2014
    2014.0
    Citations: 7
  • Early disaster warning & evacuation system on mobile phones using google street map
    A Soni, A Sharma, P Kumar, V Verma, S Sutar
    International Journal of Engineering and Technical Research 2 (4), 1-3 , 2014
    2014.0
    Citations: 12
  • C/C++ Cloud Compiler Using MainFrame
    SB Tambe, S Sutar, MD Nirmal
    International Journal of Computer Technology and Electronics Engineering … , 2013
    2013.0
    Citations: 3
  • Monitoring and Control System for Precision Farming using WSN
    S Sutar, S Patil, J Sarnaik
    Networking and Communication Engineering, 16-21 , 2012
    2012.0
  • Irrigation and fertilizer control for precision agriculture using WSN: energy efficient approach
    S Sutar, KPMITCOE Swapnita-Jayesh, M Pune
    International Journal of Advances in Computing and Information Researches 1 (1) , 2012
    2012.0
    Citations: 23
  • Network Based Virtual Compilation–C/C++ and JAVA
    S Sutar, U Mergu, S Thale
    Networking and Communication Engineering, 281-284 , 2011
    2011.0
  • Cascaded algorithm: DWT-DCT-SVD in watermarking
    N Gholve, S Sutar, S Dhokey, S Hande
    Digital Image Processing, 814-819 , 2011
    2011.0
  • EDCAM: Early Detection Congestion Avoidance Mechanism
    JB Helonde, V Wadhai, V Deshpande, S Sutar
    International Journal of Computer Applications 7 (2), 11-14 , 2010
    2010.0
    Citations: 12
  • Optical Packet Switching in High Performance Computer Networks: A Survey
    VS Deshpande, S Sutar
    Networking and Communication Engineering, 383-391 , 2009
    2009.0
  • SMART EXOSKELETON PRIMARILY FOR LIGHT WEIGHTING OF LOAD
    S Sutar, A Joshi, S Pawar, A Kalyanshetti, R Abhang, Y Yamsanwar
  • INTELLIGENT MONITORING SYSTEMS FOR EVALUATING AND IMPROVING DRIVER BEHAVIOR: A SURVEY
    A Mali, K Nirgude, M Ruikar, A Raut, S Sutar, P Joshi
  • LANDMINE DETECTOR DRONE’S USING MACHINE EARNING
    N Venkatesan, S Sutar, A Joshi, S Kapare

MOST CITED SCHOLAR PUBLICATIONS

  • Integration of Smart Phone and IOT for development of smart public transportation system
    SH Sutar, R Koul, R Suryavanshi
    2016 international conference on internet of things and applications (IOTA … , 2016
    2016.0
    Citations: 239
  • Irrigation and fertilizer control for precision agriculture using WSN: energy efficient approach
    S Sutar, KPMITCOE Swapnita-Jayesh, M Pune
    International Journal of Advances in Computing and Information Researches 1 (1) , 2012
    2012.0
    Citations: 23
  • STAR-GALAXY CLASSIFICATION USING MACHINE LEARNING ALGORITHMS AND DEEP LEARNING
    MMSHS Amit S
    International Journal on Information Technologies and Security 15 (2) , 2023
    2023.0
    Citations: 12
  • Early disaster warning & evacuation system on mobile phones using google street map
    A Soni, A Sharma, P Kumar, V Verma, S Sutar
    International Journal of Engineering and Technical Research 2 (4), 1-3 , 2014
    2014.0
    Citations: 12
  • EDCAM: Early Detection Congestion Avoidance Mechanism
    JB Helonde, V Wadhai, V Deshpande, S Sutar
    International Journal of Computer Applications 7 (2), 11-14 , 2010
    2010.0
    Citations: 12
  • An automated system for waste segregation using deep learning algorithms
    RR Nair, P Prabhu, C Panchal, L Sarnot, S Bhingarkar, A Savyanavar, ...
    2024 3rd International Conference for Innovation in Technology (INOCON), 1-6 , 2024
    2024.0
    Citations: 8
  • Performance analysis of wireless sensor networks for QoS
    Y Yamsanwar, S Sutar
    2017 International Conference on Big Data, IoT and Data Science (BID), 120-123 , 2017
    2017.0
    Citations: 7
  • Recommendation System for Outfit Selection (RSOS)
    SH Sutar, AH Khade
    arXiv preprint arXiv:1402.6692 , 2014
    2014.0
    Citations: 7
  • IoT Innovations in Cotton Plant Disease Detection for Sustainable Agriculture
    SH Katti, J. , Dharmale, G. , Ubale, S.A. , Deoghare, R. , Sutar
    International Journal of Intelligent Systems and Applications in Engineering … , 2024
    2024.0
    Citations: 5
  • Congestion control for better performance of Wsn based IoT ecosystem using Kha mechanism
    SH Sutar, YB Jinila
    Int. J. Recent Technol. Eng 8 , 2019
    2019.0
    Citations: 4
  • C/C++ Cloud Compiler Using MainFrame
    SB Tambe, S Sutar, MD Nirmal
    International Journal of Computer Technology and Electronics Engineering … , 2013
    2013.0
    Citations: 3
  • Intelligent Congestion Control Mechanism for IoT-Enabled Wireless Sensor Networks Using Hybrid Aggregation and Scheduling Technique
    SH Sutar, YB Jinila, K Patil, S Dash, S Jadhav
    JoVE (Journal of Visualized Experiments), e69909 , 2026
    2026.0
  • Artificial Intelligence Based Techniques to Prevent the Spread of the COVID 19 pandemic
    LP Sutar Shiv H, Pawar R
    International Journal of Gird and Distributed Computing 13 (2), 1226-1238 , 2020
    2020.0
  • Monitoring and Control System for Precision Farming using WSN
    S Sutar, S Patil, J Sarnaik
    Networking and Communication Engineering, 16-21 , 2012
    2012.0
  • Network Based Virtual Compilation–C/C++ and JAVA
    S Sutar, U Mergu, S Thale
    Networking and Communication Engineering, 281-284 , 2011
    2011.0
  • Cascaded algorithm: DWT-DCT-SVD in watermarking
    N Gholve, S Sutar, S Dhokey, S Hande
    Digital Image Processing, 814-819 , 2011
    2011.0
  • Optical Packet Switching in High Performance Computer Networks: A Survey
    VS Deshpande, S Sutar
    Networking and Communication Engineering, 383-391 , 2009
    2009.0
  • SMART EXOSKELETON PRIMARILY FOR LIGHT WEIGHTING OF LOAD
    S Sutar, A Joshi, S Pawar, A Kalyanshetti, R Abhang, Y Yamsanwar
  • INTELLIGENT MONITORING SYSTEMS FOR EVALUATING AND IMPROVING DRIVER BEHAVIOR: A SURVEY
    A Mali, K Nirgude, M Ruikar, A Raut, S Sutar, P Joshi
  • LANDMINE DETECTOR DRONE’S USING MACHINE EARNING
    N Venkatesan, S Sutar, A Joshi, S Kapare