R. Nandhini

Bio: R. Nandhini is an academic researcher. The author has contributed to research in topics: Wireless sensor network & Data aggregator. The author has an hindex of 2, co-authored 2 publications receiving 9 citations.

Intelligent Query-Based Data Aggregation Model and Optimized Query Ordering for Efficient Wireless Sensor Network

Abstract: Data aggregation algorithms play a primary role in WSN, as it collects and aggregates the data in an energy efficient manner so that the life expectancy of the network is extended. This paper intends to develop a query-based aggregation model for WSN using the advanced optimization algorithm called group search optimization (GSO). The proposed model is constructed in such a way that the querying order (QO) can be ranked based on latency and throughput. Accordingly, the main objective of the proposed GSO-based QO is to minimize the latency and maximize the throughput of WSN. The proposed data aggregation model facilitates the network administrator to understand the best queries so that the performance of the base station can be improved. After framing the model, it compares the performance of GSO-based QO with the traditional PSO-based QO, FF-based QO, GA-based QO, ABC-based QO and GSO-based QO in terms of idle time and throughput. Thus the data aggregation performance of proposed GSO-based QO is superior to the traditional algorithms by attaining high throughput and low latency.

APDA: Adaptive pruning & data aggregation algorithms for query based wireless sensor networks

Abstract: The research contribution presents data reporting using the adaptive dynamic pruning and data aggregation algorithms for query based wireless sensor network called as APDA. The aim of APDA is to reduce the network communication overheads through dynamic sub-network pruning to save the energy, minimize the unnecessary extra non relevant data transmissions. We construct tree rooted at base station BS. Mainly, proposed work includes three algorithms: 1) network formation using MST, 2) data aggregation using normalization, and 3) pruning technique with multi-cast communication. Aggregation tree includes dominator nodes (DN) which perform certain operations like data aggregation, multicast communication and pruning the sub network using normalized values in the range of 0 to 1 and at lower layer i.e. Inferior nodes (IN) sense the requested information and reports to its upstream DN nodes. The objective of APDA is to prolong the network lifetime and reduce query latency while reducing the communication runtime overheads. The solutions are well demonstrated through extensive simulations to prove the validity of proposed approaches to minimize the query response time and to get in time delivery of requested data.

A Robust Network Connection Across Internet of Things Devices Inside the Room with Visible Light Communication

Abstract: In order to better monitor and engage with the communal assets of smart cities, the amount of Internet of Things (IoT) devices that have been installed has been continuously rising. This might result in a reduction in network efficiency as a consequence of the increasing volume of network traffic and connections brought about by different Internet of Things devices. In order to address these challenges, one approach that shows promise is to take advantage of the close physical proximity of communicating devices and to facilitate communication between individual devices in order to achieve low latency, efficient use of bandwidth, and reliable service. Taking use of new technologies like visible light and ultrasound, our study aims to improve the performance of indoor Internet of Things (IoT) connectivity in settings such as smart homes and small office and home offices (SOHO). This method expands the capacity of the network, ensures that network connections among IoT devices are stable, and offers effective ways to allow distance-bounding services. We have constructed communication modules for visible light and ultrasonic using off-the-shelf components, and we are now evaluating their network performance as well as their energy usage.

To Optimizing Energy Utility Using Cell-Leach Protocol in Wireless Sensor Network

Abstract: In this work, we analyze the effectiveness of the Cell-LEACH protocol in optimizing energy-constrained wireless sensor networks. A WSN consisting of independent sensor that communicate with each other in a distributed manner to have an overview of the environment. The sensors are connected to the microcontroller and powered by a battery. The goal of a wireless sensor network is to have high reliability and long life and higher coverage. LEACH is the first hierarchical routing approach for wireless sensor networks. Wireless Sensor Network (WSN) has an important role in Cell-Leach based approach where WSN is known as Wireless Sensor Network. In a WSN that has multiple nodes and many sensors connected to each node. In WSN, which integrates various circuits, several computer embedded systems, many sensors, distributes large wireless communication, some advanced networks, provides technology acquisition, and allocates multiple information processors. Wireless sensor networks have battery-powered sensor nodes and are used to transmit information through environmental monitors. At this point, energy efficiency is an important issue for many WSNs. As a result, various routing techniques have advanced, such as improving network lifetime, achieving the greatest scalability, and also increasing the highest reliability. On the other hand, WSN uses a common hierarchical clustering protocol called LEACH and runs a standard algorithm. The proposed algorithm used is Cell-LEACH and it is developed as Cell Low Energy Adaptive Clustering Hierarchy. Numerous sensors are built into each of the cell heads. No pulling and regrouping is done in this formation. Here, the cell head sends all data at a certain time using TDM. In this case, the Cell head performs data aggregation and sends the processed data to the cluster heads, while performing a similar function and transmitting data to the base stations, while performing a similar function and transmitting data to the base station.

DRP: Dynamic Routing Protocol in Wireless Sensor Networks

Abstract: Routing is the process of identifying the best path from source to sink nodes. The lifetime of nodes in the network is crucial and has to be increased by considering energy of the node. In this paper, Dynamic routing protocol is proposed to improve the Quality of Service by increasing the lifetime of the Wireless Sensor Networks. When a node requires sending the data, it searches for an intermediate node. It is assumed that nodes in network must have the equal sensing range, identical speed and also same energy. The forwarding node is selected by parameters such as the number of hops towards the sink, remaining energy and distance towards the sink node. The source or intermediate node considers itself as located in the origin and divides the area into four quadrants. The nodes that are in the sink quadrant are conceived as eligible forwarding nodes. One of the nodes is selected and used to forward data packet towards the sink. The proposed method performs better than the other existing methods.

Towards Efficient and Intelligent Internet of Things Search Engine

Abstract: The Internet of Things (IoT) has created a novel ecosystem for sensing and actuation throughout our world, enabling intelligently controlled autonomous systems to conserve energy, water crops, manage factories, and provide situation awareness on an unprecedented scale As IoT progresses, the interest in IoT search engines, that is, search engines to find IoT devices and retrieve IoT data, has grown While basic examples of IoT search engines exist, considerable challenges prevent the full realization of an efficient and intelligent IoT search engine that provides universal data service, scalable data communication and retrieval, and efficient querying of massively distributed heterogeneous devices and data In this article, we first propose a generic framework for the IoT search engine, and then present a naming service for the IoT system, an essential component for an effective IoT search engine We also outline some research challenges and possible solutions for building efficiency and intelligence in the IoT search engine Further, we present a case study and seek to address a particular aspect of the query process for IoT search, namely efficient and timely query processing Given the now obvious advances in machine learning, the potential for deep learning-based prediction to improve resource use, and thus query retrieval, is clear In detail, we utilize Long-Short-Term Memory (LSTM) neural network architecture to predict aggregated query volumes to be preemptively applied and stored for immediate response Combining several realistic IoT datasets, we explore the efficacy of simultaneously predicting multiple targets for predictive query retrieval

Novel Computational Heuristics for Wireless Parameters Estimation in Bistatic Radar systems

Abstract: Research in the field of bistatic radars have growing interest with exclusive importance in defence sector, aerospace industry, remote sensing, meteorological and navigation applications. In this work, joint wireless parameters of electromagnetic plane waves impinging on the receiver of bistatic radar are estimated by exploiting the efficacy of bio-inspired heuristics through backtracking search optimization algorithm (BSA). The wireless parameters, the amplitude, elevation and azimuth angles, are estimated form the data received on 1-L and 2-L structured antenna arrays deployed on the receiver of bistatic radar. Mean square error is a performance metric incorporated for fitness evaluation, constructed on the basis of difference between the desired and actual responses of the system. Monte Carlo simulations of the meta-heuristic algorithm BSA are performed for 1-L as well as 2-L structured antenna arrays to validate and verify the worth of the scheme in terms of estimation accuracy, reliability, robustness and proximity effect. Comparative study of BSA with state of art counterparts further demonstrate its effectiveness.

Intelligent Query-Based Data Aggregation Model and Optimized Query Ordering for Efficient Wireless Sensor Network

Abstract: Data aggregation algorithms play a primary role in WSN, as it collects and aggregates the data in an energy efficient manner so that the life expectancy of the network is extended. This paper intends to develop a query-based aggregation model for WSN using the advanced optimization algorithm called group search optimization (GSO). The proposed model is constructed in such a way that the querying order (QO) can be ranked based on latency and throughput. Accordingly, the main objective of the proposed GSO-based QO is to minimize the latency and maximize the throughput of WSN. The proposed data aggregation model facilitates the network administrator to understand the best queries so that the performance of the base station can be improved. After framing the model, it compares the performance of GSO-based QO with the traditional PSO-based QO, FF-based QO, GA-based QO, ABC-based QO and GSO-based QO in terms of idle time and throughput. Thus the data aggregation performance of proposed GSO-based QO is superior to the traditional algorithms by attaining high throughput and low latency.

Optimized Multi-level Data Aggregation Scheme (OMDA) for Wireless Sensor Networks

Abstract: Data aggregation plays an important role over WSN as aggregated data is utilized for decision making/analysis purpose; but due to complex aggregation computations, sensors may consume excessive energy and thus may reduce the network lifespan. So there is requirement to optimize the aggregation process. In this paper, an optimized data aggregation scheme, called optimized multi-level data aggregation scheme (OMDA), is introduced using LEACH protocol. Its performance is analyzed using different performance parameters (throughput/end-to-end delay/energy consumption/network lifespan) under the constraints of sensor node density that varies from 50 to 200.