SUNITI PURBEY
@gmrit.irins.org
Assistant professor
GMRIT
EDUCATION
PhD Pursuing (Amity University)
RESEARCH INTERESTS
IoT, WSN
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Suniti Purbey, Brijesh Khandelwal, and Ashutosh Kumar Choudhary
Springer Science and Business Media LLC
Ashutosh Kumar Choudhary, Surendra Rahamatkar, and Suniti Purbey
Springer Science and Business Media LLC
Suniti Purbey, Rika Sharma, and Brijesh Khandelwal
Springer Science and Business Media LLC
Suniti Purbey, Rika Sharma, Brijesh Khandelwal, and Ashutosh Kumar Choudhary
Springer Nature Singapore
Meesala Sravani, Suniti Purbey, Burada Chakradhar, and Ashutosh Kumar Choudhary
IEEE
Heterogeneity of data is a common issue in data retrieval in Internet of Things (IoT) Networks, when dealing with data from multiple sensors & sources. To retrieve data from heterogeneous sources, there are several techniques proposed by researchers, which include, Data integration, Data federation, Semantic mapping, Management of Metadata, and efficient Visualization of Data Samples. But most of the existing models that perform these tasks either have limited scalability or showcase lower processing efficiency on the collected data samples. To overcome these issues, this text proposes design of an efficient multidomain augmented data aggregation model to solve heterogeneity issues for IoT deployments. The proposed model initially converts all input data samples into 1D vectors via convolutional flatting operations. These vectors are further converted into multidomain feature sets via a combination of Frequency, Gabor, Entropy, Wavelet and Convolutional analysis. These feature sets assist in identification of differential & spatial data patterns, which can be used for further analysis. To demonstrate efficiency of the proposed model, the collected IoT datasets were given to an Auto Regressive Integrated Moving Average (ARIMA) Model for prediction of temperature and humidity levels. These predicted levels were compared with existing models, and it was observed that the proposed model was able to improve the accuracy of prediction by 8.3%, while improving the precision by 5.9%, and recall by 2.5% under real-time deployment data samples.
Suniti Purbey, Ashutosh Kumar Choudhary, and Bhupesh Kumar Dewangan
IEEE
This Blockchains are considered as high security linked-list based models that can be used to incorporate immutability, distributed processing, high transparency, and high trustworthiness for scalable deployments. But as the chain length increases, these blockchains require larger mining delays and higher energy consumption which limits their applicability for low-power use cases. Existing low-power blockchains require complex miner-level evaluations, which increases their computational complexity levels. To overcome these issues, this text proposes design of a novel low-power blockchain via iterative sharding for IoT (Internet of Things) deployments. The proposed model initially collects temporal information about the current IoT scenario and uses an Elephant Herding optimization (EHO) based model to form low-energy & low-delay sidechains. The length of these chains is iteratively modified, which assists in reducing computational complexity even under large scale networks. The EHO Model formulates a mining-delay & mining-energy based fitness function, that allows the model to select optimal chain lengths for context-specific use cases. Due to these enhancements, the proposed model is able to reduce the computational delay by 8.3%, and mining energy by 4.5% when compared with standard blockchain & sidechain models under different realtime use cases. This also allows the model to improve communication throughput and reduce the number of delay jitters for large-scale communication scenarios.
Suniti Purbey and Brijesh Khandelwal
Springer International Publishing
Suniti Purbey and Brijesh Khandelwal
Springer International Publishing
Suniti Purbey and Archana Raut
IEEE
Under many research and development cell it has been noted that establishing the wireless sensor nodes network and let them work as a cluster, but the major problem with wireless sensor nodes cluster is monitoring of cluster head through wireless technology has limitation of range. Using existing mobile network one can achieve the task but again it has a limitation over world wide access and support of roaming everywhere due to non-standardized communication between multiple mobile service provider. By utilizing the advantage, ease of access and the unlimited long distance accessibility we can take full control over wireless sensor node cluster or network. Hence here we are proposing the system for wireless node cluster control and statistics monitoring using web enabled interface and the internet. In system cluster node will be responsible to communicate with all nodes and also work as a middle ware between individual node and web interface. In this paper we are discussing technology used for monitoring and controlling of wireless sensor node network.