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Prachi Sarode

Bio: Prachi Sarode is an academic researcher from VIT University. The author has contributed to research in topics: Wireless sensor network & Key distribution in wireless sensor networks. The author has an hindex of 4, co-authored 6 publications receiving 35 citations.

Papers
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Journal ArticleDOI
TL;DR: The various parameters (root causes of congestion), which help to avoid and control the congestion in the wireless sensor network are discussed, which are input/output flow rate, node density, non-linear or unbalanced distribution of load, processing / service time of node and reliability of network.
Abstract: In wireless sensor networks data, which get generated, is not always same; some data may be more important than others and having different priorities. As deployment sizes and data rates grow, congestion becomes a major problem in these networks. The congestion results in arbitrary dropping of data packets that reduce the overall network throughput. In this paper, we discuss the various parameters (root causes of congestion), which help us to avoid and control the congestion in the wireless sensor network. The parameters consider in this paper are input/output flow rate, node density, non-linear or unbalanced distribution of load, processing / service time of node and reliability of network. Categories and Subject Descriptors Wireless Communication General Terms Algorithms, Reliability Keywords Wireless Sensor Network, Congestion control and avoidances. 1. INTRODUCTION Wireless sensor Network consist of small microcontroller fitted with sensors and some means of communication radios. They are distributed over wide area and transmit gathered data to one or many central nodes called as Sink or also know as base station. Wireless sensor network (WSN) is one of the emerging research areas that provide designated services such as disaster prevention, environment monitoring, medical monitoring, habitat monitoring, military surveillance, inventory tracking, intelligent logistics, and health monitoring. These networks deliver numerous types of traffic, from simple periodic reports to unpredictable bursts of messages due to this congestion control in wireless sensor network is very rare event. Congestion occurs in WSN due to i) Radio channel interference, ii) Addition and removal of sensor nodes, iii) Lastly sensed event cause bursts of messages. WSNs have some problems to be overcome, for example energy-conservation congestion control, reliability data dissemination, and security. These problems often involve in one or several layers top-down from application layer to physical layer, and can be studies separately in each corresponding layer, or collaboratively cross each layer. One of the important problem, congestion control may involve in only transport layer, but energy-conservation may be related to physical layer, data link layer, network layer, and high layers. Many researchers recently turn their attentions to transport layer protocols, which are important for reliable data dissemination and energy-conservation for WSNs. Congestion causes many problems when sensors receives more packets than that its buffer space, the excess packets has to be dropped energy consumed by sensor nodes on the packet is wasted. And if further packet has traveled, the more waste is, which in turn diminish the network throughput and reliable data transmissions. Congestion control studies how to recover from congestion. Congestion avoidance studies how to prevent congestion from happening for this we have to monitor the parameters which can helps us to avoid congestion in WSN which is the subject of this paper.The remainder of the paper is organized as follows. Section 2 lists existing congestion control protocol. Section 3 present parameters, which help to avoid congestion. And lastly Section 4 concludes the whole paper on the note of future work.

16 citations

01 Jan 2012
TL;DR: This paper presents a survey of various approaches which help to minimize query response time mainly for wireless sensor networks.
Abstract: Wireless sensor network is an application specific network. There are various factors which affects reliability in Wireless Sensor Network some of them are energy consumption, high packet loss, congestion, and large response time. There are different parameters, which affects query response time. Whenever Sink (Base Station) needs data, it sends query to source node and node response back to sink. In Wireless Sensor Networks (WSNs) query response time depends on number of parameters like data caching, routing algorithm, node deployment, topology, data availability, query aggregation, query processing, packet loss and congestion. This paper presents a survey of various approaches which help us to minimize query response time mainly for wireless sensor networks.

10 citations

Journal ArticleDOI
TL;DR: The proposed query-based aggregation model for WSN using the advanced optimization algorithm called group search optimization (GSO) is constructed in such a way that the querying order (QO) can be ranked based on latency and throughput.
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.

5 citations

Journal ArticleDOI
TL;DR: The two-stage optimization process with NN for query ordering is compared over the conventional methods in terms of performance measures like Latency, throughput, and data freshness to validate the improved performance of the proposed model.
Abstract: Query processing can be briefly defined as a database that comprises of an organized collection of data for one or more users either in digital form or in analog form such that it can portray exactly. A Wireless Sensor Network (WSN) is a specialized network of minimum cost, and power sensor nodes that can be described as the ability of performing some processing, gathering sensory information and communicating with each other. Query ordering with data aggregation is the process of scheduling of the nodes to receive the useful data from sensors. Data aggregation is considered as one of the fundamental processing procedures for saving the energy. In WSN data aggregation is an effective way to save the limited resources. This paper proposes a novel query-based data aggregation model with the aid of intelligent techniques. The framing of the query order takes place and the frames are ranked on the basis of a multi-objective function. The newly developed multi-objective function includes Latency, Throughput, and Data freshness. Initially, the solution corresponding to query order is trained in NN using the proposed Fitness-Mated Lion Algorithm (FM-LA). The optimally generated query order from NN is further given for second-level solution generation, which is again applied to FM-LA for subsequent query order optimization. Hence the two-stage optimization process with NN for query ordering is compared over the conventional methods in terms of performance measures like Latency, throughput, and data freshness. Hence, substantiated performance and comparative analysis validate the improved performance of the proposed model.

4 citations

Proceedings ArticleDOI
01 Nov 2016
TL;DR: The research contribution presents data reporting using the adaptive dynamic pruning and data aggregation algorithms for query based wireless sensor network called as APDA to prolong the network lifetime and reduce query latency while reducing the communication runtime overheads.
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.

4 citations


Cited by
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Journal Article
TL;DR: In this article, Stann et al. present RMST (Reliable Multi-Segment Transport), a new transport layer for Directed Diffusion, which provides guaranteed delivery and fragmentation/reassembly for applications that require them.
Abstract: Appearing in 1st IEEE International Workshop on Sensor Net Protocols and Applications (SNPA). Anchorage, Alaska, USA. May 11, 2003. RMST: Reliable Data Transport in Sensor Networks Fred Stann, John Heidemann Abstract – Reliable data transport in wireless sensor networks is a multifaceted problem influenced by the physical, MAC, network, and transport layers. Because sensor networks are subject to strict resource constraints and are deployed by single organizations, they encourage revisiting traditional layering and are less bound by standardized placement of services such as reliability. This paper presents analysis and experiments resulting in specific recommendations for implementing reliable data transport in sensor nets. To explore reliability at the transport layer, we present RMST (Reliable Multi- Segment Transport), a new transport layer for Directed Diffusion. RMST provides guaranteed delivery and fragmentation/reassembly for applications that require them. RMST is a selective NACK-based protocol that can be configured for in-network caching and repair. Second, these energy constraints, plus relatively low wireless bandwidths, make in-network processing both feasible and desirable [3]. Third, because nodes in sensor networks are usually collaborating towards a common task, rather than representing independent users, optimization of the shared network focuses on throughput rather than fairness. Finally, because sensor networks are often deployed by a single organization with inexpensive hardware, there is less need for interoperability with existing standards. For all of these reasons, sensor networks provide an environment that encourages rethinking the structure of traditional communications protocols. The main contribution is an evaluation of the placement of reliability for data transport at different levels of the protocol stack. We consider implementing reliability in the MAC, transport layer, application, and combinations of these. We conclude that reliability is important at the MAC layer and the transport layer. MAC-level reliability is important not just to provide hop-by-hop error recovery for the transport layer, but also because it is needed for route discovery and maintenance. (This conclusion differs from previous studies in reliability for sensor nets that did not simulate routing. [4]) Second, we have developed RMST (Reliable Multi-Segment Transport), a new transport layer, in order to understand the role of in- network processing for reliable data transfer. RMST benefits from diffusion routing, adding minimal additional control traffic. RMST guarantees delivery, even when multiple hops exhibit very high error rates. 1 Introduction Wireless sensor networks provide an economical, fully distributed, sensing and computing solution for environments where conventional networks are impractical. This paper explores the design decisions related to providing reliable data transport in sensor nets. The reliable data transport problem in sensor nets is multi-faceted. The emphasis on energy conservation in sensor nets implies that poor paths should not be artificially bolstered via mechanisms such as MAC layer ARQ during route discovery and path selection [1]. Path maintenance, on the other hand, benefits from well- engineered recovery either at the MAC layer or the transport layer, or both. Recovery should not be costly however, since many applications in sensor nets are impervious to occasional packet loss, relying on the regular delivery of coarse-grained event descriptions. Other applications require loss detection and repair. These aspects of reliable data transport include the provision of guaranteed delivery and fragmentation/ reassembly of data entities larger than the network MTU. Sensor networks have different constraints than traditional wired nets. First, energy constraints are paramount in sensor networks since nodes can often not be recharged, so any wasted energy shortens their useful lifetime [2]. This work was supported by DARPA under grant DABT63-99-1-0011 as part of the SCAADS project, and was also made possible in part due to support from Intel Corporation and Xerox Corporation. Fred Stann and John Heidemann are with USC/Information Sciences Institute, 4676 Admiralty Way, Marina Del Rey, CA, USA E-mail: fstann@usc.edu, johnh@isi.edu. 2 Architectural Choices There are a number of key areas to consider when engineering reliability for sensor nets. Many current sensor networks exhibit high loss rates compared to wired networks (2% to 30% to immediate neighbors)[1,5,6]. While error detection and correction at the physical layer are important, approaches at the MAC layer and higher adapt well to the very wide range of loss rates seen in sensor networks and are the focus of this paper. MAC layer protocols can ameliorate PHY layer unreliability, and transport layers can guarantee delivery. An important question for this paper is the trade off between implementation of reliability at the MAC layer (i.e. hop to hop) vs. the Transport layer, which has traditionally been concerned with end-to-end reliability. Because sensor net applications are distributed, we also considered implementing reliability at the application layer. Our goal is to minimize the cost of repair in terms of transmission.

650 citations

Journal ArticleDOI
TL;DR: In this article, a dynamic routing protocol is proposed to improve the quality of service by increasing the lifetime of the Wireless Sensor Networks, where the forwarding node is selected based on parameters such as the number of hops towards the sink, remaining energy and distance towards sink node.
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.

19 citations

Journal ArticleDOI
TL;DR: This paper highlights data collection, aggregation and dissemination challenges in WSN and presents a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021.
Abstract: The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in numerous sectors such as manufacturing, smart cities, transportation and Internet of Things by providing pervasive real-time applications. In this survey, we analyze the existing research trends with respect to Artificial Intelligence (AI) methods in WSN and the possible use of these methods for WSN enhancement. The main goal of data collection, aggregation and dissemination algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. In this paper, we highlight data collection, aggregation and dissemination challenges in WSN and present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. We compare and contrast different algorithms on the basis of optimization criteria, simulation/real deployment, centralized/distributed kind, mobility and performance parameters. We conclude with possible future research directions. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to data collection, aggregation and dissemination challenges in WSN. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for the research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new possibilities for future studies.

19 citations

Journal ArticleDOI
TL;DR: In this paper, a generic framework for the IoT search engine is proposed, and a naming service for the system is presented, which is an essential component for an effective 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

17 citations

Journal ArticleDOI
TL;DR: A hybrid algorithm is introduced with the introduction of a hybrid algorithm named Circling Ins-Rider Optimization Algorithm (CI-ROA), which hybrids the concept of Whale OptimizationAlgorithm (WOA) and Rideroptimization Al algorithm (ROA) respectively.
Abstract: Semantic web technology seems to be in the infant stage as only little efforts have been taken on ontology construction with cross-domain application. This paper intends to take an effort on a new workspace, in which the ontology construction model under cross-domain application is performed. The core concern of this work is on two decision-making process namely data filtering and data annotation. Certain process is followed in this work: (i) Preprocessing (ii) Proposed Jaccard Similarity Evaluation (iii) Data filtering and Outlier Detection (iv) Semantic annotation and clustering. More particularly, data filtering is performed based on the evaluated similarity function. The outliers are identified and grouped separately. The data annotation is performed based on the semantics and thereby the clustering process takes place to form the ontology precisely. This clustering process obviously relies to the optimization crisis as the optimal centroid selection becomes the greatest issue. In order to solve this, this paper extends with the introduction of a hybrid algorithm named Circling Insisted-Rider Optimization Algorithm (CI-ROA), which hybrids the concept of Whale Optimization Algorithm (WOA) and Rider Optimization Algorithm (ROA), respectively. Finally, the performance of proposed work is compared and proved over other state-of-the-art models.

10 citations