MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

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.

School of Computer Engineering & Technology

Engineering and Technology

World University root node

Wireless Personal Communications

Combination of Fitness-Mated Lion Algorithm with Neural Network for Optimal Query Ordering Data Aggregation Model in WSN

The data aggregation highly reduces the energy consumption in the network and hence their is a requirement for involving aggregation of data from multiple nodes. These techniques are considered as remove the crucial for successful applications in Wireless Sensor Network (WSN). This paper proposes Group Search Optimization (GSO) algorithm with Neural Network (NN) for developing the novel query-based data aggregation model. The Querying Order (QO) model in the scheme uses Query Order that can be ranked on the basis of latency and throughput. The paper aims to build up the QO model with low latency and high throughput thereby enhancing existing data aggregation techniques. Thus it can assist the network administrator to get the knowledge regarding the best queries for enhancing the performance of the sink. Further, it compares the performance of conventional methods by analyzing the latency as well as throughput of the network. It reveals that the data aggregation performance of the proposed model outperforms the conventional models as it minimizes latency and maximizes throughput.

Ad-hoc & Sensor wireless network

Optimized Query Ordering Data Aggregation Model using neural Networks and Group Search Optimization in Wireless Sensor Network

Now a day’s mobility in sink has been renowned by the researchers. In normal conditions the data is collected by the sink with single or multi hop structure. A concept is introduced for sink with the mobility. A lot of research is going on in the field of data collection using mobile sink. The researchers talked about sink mobility where the sink is moving in the specified area and get the data from the sensor node. For collecting the data from the sensor node the mobile sink may or may not take different path on each journey. If route is not fixed then path should be calculated which should be a shortest path .For data collection methods it may use single sink or multi sink. Sink mobility basically focuses on various factors like energy consumption, life time of the network, cost of the network , hotspot problem etc .This paper contains a survey of different methods used for data collection using mobile sink.

Universal Review

Data Collection Methods to improve the lifetime of WSNUsing Mobile Sink: A Survey

Countering Cyber Attacks and Preserving the Integrity and Availability of {\ldots}

Study on Query-Based Information Extraction in IoT-Integrated Wireless Sensor Networks

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.

Wireless Personal Communications-Springer

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

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.

Proceedings - International Conference on Global Trends in Signal Processing, Information Computing and Communication, ICGTSPICC 2016

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

IJEIT

Analysis and Implementation of Top k Query Response Time Optimization Approach for Reliable Data Communication in Wireless Sensor Networks

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.