Dr. Sanjeev J Wagh
@gcekarad.ac.in
Professor and Head Information Technology
Government College of Engineering Karad
RESEARCH INTERESTS
Wireless Sensor Networks
Data Science and Analytics
Internet of Things
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Pradnya Patil and Sanjeev J. Wagh
Wiley
Vandana Tulshidas Chavan and Sanjeev J. Wagh
Wiley
Sanjeev J. Wagh, Manisha Sunil Bhende, and Anuradha D. Thakare
CRC Press
Swapnil R. Pokharkar, Sanjeev J. Wagh, and Sachin N. Deshmukh
IEEE
Internet of Things refers to the way that more and more physical devices are collecting and exchanging data over the internet. Internet of Things will have an increasing impact on bandwidth. Many Internet of Things devices operate wirelessly, while others are connected. Most IoT devices use less bandwidth, but many devices going online mean high bandwidth will be needed. As IoT grows, it will be necessary to have a platform which can accommodate this huge change. Due to the development of technology amount of data that is transmitted by devices is increased, which will need for increased bandwidth. For example, when smartphones start transmitting images and streaming video, need for bandwidth increases tremendously. There is no particular solution available for spectrum predictions. In this paper, we propose a machine learning prediction algorithm for internet bandwidth.
Sagar Rane, Sanjeev Wagh, and Arati Dixit
Springer International Publishing
Many organizations are widely using cloud for their day to day business activities. But several attackers and malicious users are targeting cloud for their personal benefits. It is very important to collect and preserve admissible evidences of various activities happened in cloud securely in spite of multi-stakeholder collusion problem. Logs are one of the utmost vital elements to trace the malicious activities happened in cloud computing environment. Thus, forensic investigations involving logs face a grave challenge of making sure that the logs being investigated are consistent and not tampered with. A lot of research has been performed in this field; however with the advent of blockchain and Interplanetary File System (IPFS) new innovative approaches can be applied to secure trustworthy evidences in cloud. In this paper, we used blockchain and IPFS to build a system which stores the logs of cloud users’ activities and assurances the trustworthiness and recovery of such logs to aid in forensic investigation. The integrity of the trustworthy log evidences is assured with the help of blockchain. Using versioning nature of IPFS our system can track the modification of log files. In earlier work, the systems could assure whether a log has been altered with or not, but none provided a mechanism to recover metadata of tampered logs to their original state. With the help of IPFS our proposed technique extend the existing work by providing the original logs for interfered logs.
Sagar Rane, Sanjeev Wagh, and Arati Dixit
ACM
Adoption of cloud computing services greatly reduce the cost of managing businesses and increase the productivity. But, due to complex network configurations of cloud, it is a vector for various malicious attacks. Logs are the most valuable element which can be helpful in revealing the insights of any activity happened in cloud. Experienced attackers and malicious users always targets to destroy logging service first, after their attacks to remain untraceable. The existing logging techniques, which consider logger as a trusted stakeholder cannot be applied in cloud as there is possibility of collusion in between logger of cloud i.e. cloud service provider and fraud cloud service consumer or cloud forensic investigators to falsify the logs.
Pramod D. Ganjewar, Selvaraj Barani, Sanjeev J. Wagh, and Santosh S. Sonavane
Springer International Publishing
In real time, everything requires monitoring and controlling, especially in case of the protecting food from getting spoiled. In this paper, an Internet of Thing (IoT) based framework for food monitoring is proposed to protect food from getting spoiled due to changes in the environmental conditions during storage. In the existing scenario the prediction has been done based on the recorded sensed data and detailed analysis have been done to identify the factors affecting the food to get spoiled. Automated controlling mechanism is proposed in this work for controlling the environmental parameters with adaptive Naive Byes prediction and IoT. In the proposed work, environmental parameters like temperature, humidity, moisture, light, etc., which affect on the quality of the nutritional values of food are considered which spoil the food, if they are not in the advisable range of their values. In this work online analysis would be done to predict the nutritional condition of the food to avoid the spoilage of the food. This will help to save food from getting spoiled and reduces the incidental losses in the business. All the sensed data will be stored on a cloud and the analysis would be performed for prediction of the environmental condition at the storage place to avoid food spoilage by changing to the suitable environmental condition, at the place. In the proposed work adaptive Naive Bayes method is used for prediction of environmental condition at the place where food is stored and the harmful changes are monitored and action will be taken to provide advisable condition at the stored location.
Pramod Ganjewar, Barani S., and Sanjeev J. Wagh
Elsevier BV
Abstract A network of sensor nodes forms WSN, where the nodes observe the environment and transfer the sensed data to the sink node. Constraints on various resources, like energy, bandwidth, and memory, are usual in WSN, which the researchers attempt to solve. This paper presents a transmission technique with data reduction using Hierarchical Fractional Least-Mean-Square (HFLMS), in WSN. The proposed HFLMS filter is a prediction method that attempts to predict the sensed data based on an error estimate. The filter design of HFLMS extends Hierarchical Least-Mean-Square (HLMS) by modifying its weight update using Fractional Calculus (FC). The proposed adaptive filter reduces energy constraints in WSN by allowing the sensor nodes to transmit only the required data to the sink. Thus, HFLMS with integrated FC prolongs the lifespan of the network preserving the energy. Two evaluation parameters, energy and prediction error, are utilized to measure the performance of the algorithm. The experimental results performed using two datasets from UCI machine learning, show that HFLMS has better results than the existing without prediction, LMS, and HLMS techniques, with the energy of 0.1202 at 500th round and minimum prediction error of 0.0253.
Deepak C. Mehetre, S. Emalda Roslin, and Sanjeev J. Wagh
Springer Science and Business Media LLC
In the recent era, security is the major problem in sensor networks. Wireless sensor networks (WSNs) are mostly used for various real-world applications. However, WSNs face a lot of insider and outsider attacks, and it is complex to identify and protect towards insider attacks. Generally, an insider attack, in which the intruders choose several received data packets to drop, threatens the clustered WSNs. This situation has occurred because of the unattended clustered environments in the network. To overcome this problem, this paper proposes a trustable and secure routing scheme using two-stage security mechanism, and dual assurance scheme, for selecting the node and securing the data packet for WSNs. Both schemes are based on Active Trust to protect several kinds of attacks, such as black hole attack, and selective forwarding attack, during routing. Therefore, this paper identifies the trusted path and provides the secure routing paths using trust and Cuckoo search algorithm. Energy is the performance parameter utilized in the proposed scheme. The experimental result proves that proposed system provides the assurance to prolong the network lifespan and the probability of secure routing path in the network.
Varsha sid, Deepak C Mehetre, S. Emalada Roslin, and Sanjeev J Wagh
IEEE
A self arranged called as Wireless Sensor Network (WSN) is a network as well as it is includes huge quantity of resources and also a wireless ad hoc network. In Wireless Sensor Networks information transmitting direct to sink node creates several problems. The primary task of wireless sensor network is data aggregation technique. Here aggregated information is transmitted from sensor nodes to a base station (BS) within a wireless sensor network. These are most important parts of sensor network. Network lifetime is a crucial challenge for efficient outline of data aggregation method for sensor networks inside WSN. The most energy of remote sensor network is utilized by transmission of information from sensor node to sink node. We have tendency to solve this problem by implementing efficient data aggregation technique inside the system. Some of the previous systems utilizes cluster as well as tree structures for aggregating data from cluster heads inside the clusters. This may help in sending the aggregated data to base station as well as enhances the lifetime of the entire wireless network. In contribution if attack is found in CH then cluster members transfer data to the DCN. In system DCN share data with the clusters DCN in tree structure. If attack is found in Data Collection Node (DCN) then Base Station recovered the data from other DCN and prevent data loss. Due to this system can forward data securely and efficiently and improve the accuracy of the network and minimize the data loss.
Pramod D. Ganjewar, S. Barani, and Sanjeev J. Wagh
World Scientific Pub Co Pte Lt
Various Wireless Sensor Network (WSN) applications require the common task of collecting the data from the sensor nodes using the sink. Since the procedure of collecting data is iterative, an effective technique is necessary to obtain the data efficiently by reducing the consumption of nodal energy. Hence, a technique for data reduction in WSN is presented in this paper by proposing a prediction algorithm, called Hierarchical Fractional Bidirectional Least-Mean Square (HFBLMS) algorithm. The novel algorithm is designed by modifying Hierarchical Least-Mean Square (HLMS) algorithm with the inclusion of BLMS for bidirectional-based data prediction and Fractional Calculus (FC) in the weight update process. Data redundancy is achieved by transmitting only those data required based on the data predicted at the sensor node and the sink. Moreover, the proposed HFBLMS algorithm reduces the energy consumption in the network by the effective prediction attained by BLMS. Two metrics, such as energy consumption and prediction error, are used for the evaluation of performance of the HFBLMS prediction algorithm, where it can attain energy values of 0.3587 and 0.1953 at the maximum number of rounds and prediction errors of just 0.0213 and 0.0095, using air quality and localization datasets, respectively.
Sanjesh S. Pawale, Sanjeev J. Wagh, and Ranjana S. Jadhav
IEEE
The tremendous growth of internet demands effective congestion control mechanism to be converted into successful. The most of the internet traffic is based on TCP which guarantees End-to-end transmission. TCP compact with congestion with AIMD and packet conservation principal at plinth, slow start in highly congested scenario, fast Retransmission and fast recovery for multiple packets lost within same window. This paper is discussed about the modification to TCP's NewReno with multiple packet failure in the absence of SACK option. We observe that refinement of TCP NewReno reactive algorithm enhances the performance with change in congestion window to delve with all lost packet. This paper presents the methodology which enhances performance of the TCP's popular algorithm: TCP's NewReno. The methodology projected is implemented in NS2 (Network Simulator version 2) and compared ENewReno [11] and TCP NewReno. The simulation results show that proposed methodology enhances throughput as performance and packet loss.
SOUMITRA DAS, S BARANI, SANJEEV WAGH, and S S SONAVANE
Springer Science and Business Media LLC
Abstract
In this paper a multi-sensor data fusion approach for wireless sensor network based on bayesian methods and ant colony optimization techniques has been proposed. In this method, each node is equipped with multiple sensors (i.e., temperature and humidity). Use of more than one sensor provides additional information about the environmental conditions. The data fusion approach based on the competitive-type hierarchical processing is considered for experimentation. Initially the data are collected by the sensors placed in the sensing fields and then the data fusion probabilities are computed on the sensed data. In this proposed methodology, the collected temperature tand humidity data are processed by multi-sensor data fusion techniques, which help in decreasing the energy consumption as well as communication cost by fusing the redundant data. The multiple data fusion process improves the reliability and accuracy of the sensed information and simultaneously saves energy, which was our primary objective. The proposed algorithms were simulated using Matlab
. The executions of proposed arnd low-energy adaptive clustering hierarchy algorithms were carried out and the results show that the proposed algorithms could efficiently reduce the use of energy and were able to save more energy, thus increasing the overall network lifetime.
Priyanka Kamthe and Sanjeev J. Wagh
IEEE
Wireless sensor networks commonly implemented in such areas like volcano, fire observing, remote sensing and in army etc. Communication cost as well as energy utilization is minimized by implementing strategy called as data aggregation in wireless sensor networks. At the time of data routing, partial outcomes are consolidated by the data aggregation method from lower layer nodes. Related work illustrates number of researchers are proposed different aggregation methods such as synopsis diffusion, predicate count and sum technique. Issue is that these aggregation techniques don't have capability to recognize fake sub aggregate of compromise nodes. Problem is that this can causes huge errors at base station. Logical OR operation is performed by this system for aggregation of the digest at mediatory node. This system causes more risk at the time of information modification. To solve this problem, our system implements appending method to decrease the risk in data aggregation. In our proposed system, base station has capability to recognize the optional path through eliminating the attacker by using collected MAC. Experimental outcomes are demonstrates the proposed system execution is best as compare with the previous system.
Soumitra Das, Barani S, Sanjeev Wagh, and S.S. Sonavane
IEEE
In today's world Wireless Sensor Networks (WSNs) has gained a lot of recognition because of its wide-ranging areas of applications. Sensor nodes in WSNare connected to each other by networks, mainly powered by a battery source. These sensor nodes have lesser amount of power and computational capabilities. Typically, sensor nodes are deployed in remote areas where replacement of their batteries once exhausted becomes extremely difficult and cumbersome. Battery power is crucial aspect of the network (while designing a protocol). To maximize the lifespan of sensor nodes we recommend a genetic algorithm-based model. In this model, energy is distributed among all the sensor nodes and network performance is enriched by choosing the cluster head and clustering on the basis of 3 important factors: a. residual energy, b. distance from the sink and c. trust of the node. The trust of the node helps to detect malicious (abnormal) nodes in the neighborhood. To further maximize the network lifetime, the proposed model implements a multihop routing mechanism for data dissemination from source to the sink. To prove the real-time effectiveness of the proposed model, we simulated it using Matlab and compared with “Design and Implementation of a New Energy Efficient Clustering Algorithm using Genetic Algorithm for Wireless Sensor Networks (DINEECAGA)”[11]. The algorithm was evaluated in the range of 20 to 60 sensor nodes. Our results prove that the proposed model is far better in terms of maximizing the network lifetime than the DINEECAGA.
Pramod D. Ganjewar, Sanjeev. J. Wagh, and Barani S.
IEEE
Threshold based data reduction is a famous data reduction technique used in data pre-processing. Minimization of energy consumption is a current trend of research. Maximum energy in WSN is utilized for transmission of data from sensor node to sink node. To minimize the energy required for data transmission this technique will be useful. This technique can be applicable to remove redundant data at sensor node in WSN. This technique can applied in any WSN application. The result will vary depending on the type of data collected in that application with respect to time. Here in this paper we have applied this technique on heartbeats data of patients. Using this technique considerable energy can be saved and it proves that network time can be prolonged.
Pramod D. Ganjewar, S. Barani, and Sanjeev J. Wagh
IEEE
A Wireless Sensor Network (WSN) consists of spatially distributed autonomous sensor nodes for monitoring environmental conditions. Energy saving by data reduction in WSN is an emerging trend. Energy saving is essential in WSN as sensor nodes are low powered as they are battery operated. Data reduction is technique of data mining, which identifies the redundant data and remove it. Proposed work combines data mining with Wireless Sensor Network using Incremental Naive Bayes Prediction, to remove the redundant data based on prediction. This helps to reduce the number of data entities to be transferred to sink. This is beneficial for saving the energy required for transmission of data to sink. INBP model is compared with two techniques which are simple naive Bayes prediction model and normal transmission model. Weather forecasting data is used as input in this work Proposed work increases the lifetime of the sensor network by considerable amount energy saving.
Soumitra Das, S. Barani, Sanjeev Wagh, and S. S. Sonavane
Springer Singapore
In this paper a multi-sensor data fusion approach for wireless sensor system based on Bayesian strategies and Ant Colony Optimization procedures have been recommended. In this methodology, every node is furnished with various sensors (i.e. temperature and humidity sensors). Usage of more than one sensor gives additional information about the environmental conditions. The data fusion based on the competitive type hierarchical processing has been considered for experimentation. In the data fusion, the data gathered by the sensors are set in the sensing fields and afterward the data fusion probabilities are registered. In our suggested approach, the gathered temperature and humidity information are prepared by multi-sensor data fusion strategies, which then helps in diminishing the energy utilization and also communication cost through accumulation of the repetitive information. The multiple information merging process is reliable and accurate in addition to being energy-efficient which our primary goal is. The proposed algorithms were simulated utilizing Matlab. The implementation of the proposed algorithms were conducted with and without multi-sensor data fusion and the outcomes demonstrate that the proposed algorithms could reduce the energy- consumption, rather save additional energy, thus enhance the entire lifetime of the system.
Pramod Ganjewar, S. Barani, and Sanjeev J. Wagh
Springer International Publishing
WSN comprises of sensor nodes distributed spatially to accumulate and transmit measurements from environment through radio communication. It utilizes energy for all its functionality (sensing, processing, and transmission) but energy utilization in case of transmission is more. Data compression can work effectively for reducing the amount of data to be transmitted to the sink in WSN. The proposed compression algorithm i.e. Energy Efficient Deflate (EEDeflate) along with fuzzy logic works effectively to prolong the life of Wireless Sensor Network. EEDeflate algorithm saves 7% to 10% of energy in comparison with the data transmitted without compression. It also achieves better compression ratio of average 22% more than Huffman and 8% more than Deflate compression algorithm. This improvements in terms of compression efficiency allows saving energy and therefore extends the life of the sensor network.
Vishwajeet Hari Bhide and Sanjeev Wagh
IEEE
Internet of Things (IoT) is extension of current internet to provide communication, connection, and inter-networking between various devices or physical objects also known as “Things.” In this paper we have reported an effective use of IoT for Environmental Condition Monitoring and Controlling in Homes. We also provide fault detection and correction in any devices connected to this system automatically. Home Automation is nothing but automation of regular activities inside the home. Now a day's due to huge advancement in wireless sensor network and other computation technologies, it is possible to provide flexible and low cost home automation system. However there is no any system available in market which provide home automation as well as error detection in the devices efficiently. In this system we use prediction to find out the required solution if any problem occurs in any device connected to the system. To achieve that we are applying Data Mining concept. For efficient data mining we use Naive Bayes Classifier algorithm to find out the best possible solution. This gives a huge upper hand on other available home automation system, and we actually manage to provide a real intelligent system.