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Preeti Kale

Bio: Preeti Kale is an academic researcher from Defence Institute of Advanced Technology. The author has contributed to research in topics: Wireless sensor network & Data aggregator. The author has an hindex of 3, co-authored 8 publications receiving 27 citations.

Papers
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Journal ArticleDOI
TL;DR: Three algorithms for Scheduling DATs using Local Heuristics with Ordering (SDLHO), with Randomization and with Tree factor techniques are proposed to increase the survivability of the network and address imperfect link quality.

15 citations

Proceedings ArticleDOI
22 Mar 2017
TL;DR: An algorithm to reestablish a communication path between disconnected nodes and the BS is proposed and the reestablishment of paths add fault-tolerance; and facilitate retention of connectivity of the nodes in the network to enable communication with the BS.
Abstract: In in-network data aggregation in Wireless Sensor Networks (WSN), selection of communication paths and aggregation points are critical network requirements. They enable efficient use of network resources in constrained environments. Base Station (BS) driven routing protocols are prime enablers to implement such network requirements. In Base Station (BS) driven routing protocols the BS proactively finds a path to communicate with each node in the network. However, if a node in the network fails or goes to sleep; the communication path between the BS and some nodes in the network is disabled and these nodes are disconnected from the BS. In this paper, an algorithm to reestablish a communication path between these disconnected nodes and the BS, is proposed. The reestablishment of paths add fault-tolerance; and facilitate retention of connectivity of the nodes in the network to enable communication with the BS. The results demonstrate the merit of the proposed algorithm.

9 citations

Proceedings ArticleDOI
01 Mar 2019
TL;DR: This work explores QoS of DATs with Network Lifetime (NL) as a parameter for QoS and proposes an algorithm to enhance QoS for Data Aggregation Trees with Deterministic Network Model (DNM).
Abstract: Wireless Sensor Networks (WSNs) are instrumental facilitators in the development of the Internet of Things (IoT). In the smart world of IoT, energy efficient data gathering is an essential requirement to extend the sustainability of the network. In WSNs, Data Aggregation Trees (DATs) are employed for energy efficient data gathering. While DAT gathers data, it is imperative that the network provides enhanced Quality of Service (QoS). Constructing a DAT that caters to the requirements of the application results in improved QoS. This work explores QoS of DATs with Network Lifetime (NL) as a parameter for QoS. QDD, an algorithm to enhance QoS of DATs with Deterministic Network Model(DNM) is proposed. In DNM, any two sensors either communicate or they do not. However, in practical scenarios the communication between a pair of sensors is probabilistic and is represented using the Probabilistic Network Model (PNM). QDP, an algorithm to enhance QoS of DATs with PNM is proposed. Simulation results show the effectiveness of the proposed algorithms and demonstrates improved QoS through DAT path refinements.

5 citations

Book ChapterDOI
14 Feb 2019
TL;DR: An algorithm Quality Aware Data Aggregation Tree (QADAT) to construct a quality aware DAT is proposed and results show the effectiveness of the proposed algorithm and demonstrates quality awareness through DAT adaptability.
Abstract: Wireless Sensor Networks (WSNs) are key enablers for IoT and pervasive computing paradigm. While devices are being seamlessly enabled with connection and communication capabilities, exploring techniques to quantify and improve quality has gathered significance. This work explores quality of a Data Aggregation Tree (DAT) in sensor networks. DATs are building blocks for data collection in WSNs. In this work Quality of Experience (QoE) and Quality of Service (QoS) of DATs is evaluated using data aggregation ratio \(\alpha \) and generated data \(\delta \) respectively. An algorithm Quality Aware Data Aggregation Tree (QADAT) to construct a quality aware DAT is proposed. QADAT adapts the DAT to network and user expectation dynamics. Simulation results show the effectiveness of the proposed algorithm and demonstrates quality awareness through DAT adaptability.

3 citations

Proceedings ArticleDOI
01 Oct 2019
TL;DR: The study presents the techniques to estimate the communication and computation costs incurred for DAT construction, asymptotically and demonstrates the utilization of the proposed techniques to estimates the best and worst cases for communication and computations cost to meet the design objective of adhoc WSN deployments.
Abstract: In Wireless Sensor Networks (WSNs), Data Aggregation Trees (DATs) are employed for energy efficient data gathering. Energy efficient data collection is a primary requirement in the smart world of Internet of Things (IoT) as it facilitates to extend the survivability of the network. DATs gather data efficiently by employing data aggregation functions at the aggregator nodes. The employed aggregation function influences the cost of communication and cost of computation at a node. The study in this paper presents the techniques to estimate the communication and computation costs incurred for DAT construction, asymptotically. The strength of the proposed techniques is its ability to enable the estimation of best, average and worst case cost of DAT construction and rescheduling scenarios. Based on the asymptotic analysis, the study in this paper demonstrates the utilization of the proposed techniques to estimate the best and worst cases for communication and computation cost to meet the design objective of adhoc WSN deployments.

2 citations


Cited by
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Proceedings ArticleDOI
02 Apr 2020
TL;DR: An effective cluster based data gathering technique is developed and the optimal path for mobile sink (MS) is done by Ant Colony Optimization (ACO) algorithm, which provides efficient path along with MS to collect the data along with Cluster centroid.
Abstract: A data aggregation is an essential process in the field of wireless sensor network to deal with base station and sink node. In current data gathering mechanism, the nearest nodes to the sink receives data from all the other nodes and shares it to the sink. The data aggregation process is utilized to increase the capability and efficiency of the existing system. In existing technique, the possibility of data loss is high this may leads to energy loss therefore; the efficiency and performance are damaged. In order to overcome these issues, an effective cluster based data gathering technique is developed. Here the optimal cluster heads are selected which is used for transmission with low energy consumption. The optimal path for mobile sink (MS) is done by Ant Colony Optimization (ACO) algorithm. It provides efficient path along with MS to collect the data along with Cluster centroid. The performance of the proposed method is analyzed in terms of delay, throughput, lifetime, etc.

15 citations

Journal ArticleDOI
TL;DR: Three algorithms for Scheduling DATs using Local Heuristics with Ordering (SDLHO), with Randomization and with Tree factor techniques are proposed to increase the survivability of the network and address imperfect link quality.

15 citations

Journal ArticleDOI
TL;DR: A cluster-based routing scheme for heterogeneous network (CRSH) is proposed which performs the clustering of nodes and data aggregation in the network and performs the data aggregation to eliminate the redundant data packets.
Abstract: The lifetime of the wireless sensor network is treated as a concerning topic for every network related application as the sensors are provided with limited battery capacity. In order to improve the network life, the energy of the nodes should be utilized in a well-organized way. The existing cluster-based routing protocols and data aggregation approaches have helped in enhancing the network's lifetime. The data aggregation approaches minimize the redundant data packets in the network which improves the network lifespan. In this paper, a cluster-based routing scheme for heterogeneous network (CRSH) is proposed which performs the clustering of nodes and data aggregation in the network. The proposed scheme uses the node’s energy level for choosing the most energy-efficient node as cluster head and performs the data aggregation to eliminate the redundant data packets. The simulation of the proposed scheme is performed in the MATLAB simulator. The proposed scheme is compared with the existing protocols using various performance parameters for measuring its effectiveness.

11 citations

Journal ArticleDOI
TL;DR: The primary intention of the study is to design an energy embedded routing protocol based on optimal updation of the mobile sink, and the results will be analyzed and compared with existing routing protocols to ensure the efficiency.
Abstract: The utilization of mobile sink in spite of its points of interest carries new difficulties to WSNs. The principle disputes are the position update of sink node to the hub. Every sensor hub should know about the sink position all together that it can move its information to the sink. Existing Flooding technique proposed that the portable sink needs to consistently spread its situation all through the system to advise sensor hubs regarding the sink position. In any case, visit position refreshes from the sink can prompt both maximum power utilization and amplified crashes in the network. To diminish the updation of sink position, different types of routing structures can be utilized. A routing mechanism dependent on the mobile sink is effective if it limits the power utilization and delays in the system network. The primary intention of the study is to design an energy embedded routing protocol based on optimal updation of the mobile sink. Here, the most recent sink position will be spared in the hubs developing the implicit environment. Accordingly, in Embedded routing, the position of the sink node is proliferated to the sensor hubs situated at the discs instead of the solicitation of hubs in the system. The remainder of the hubs can locate the most recent sink position by forwarding solicitation information to the closest disc. Based on the received information, the receiver of the message is identified. This will be performed by optimal fuzzy based clustering technique. The optimization can be done by Oppositional grey wolf optimization (OGWO) algorithm. The efficiency is analyzed by maximum network lifetime, minimum delay etc. The results will be analyzed and compared with existing routing protocols to ensure the efficiency.

8 citations