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;

&nbsp;

MIT World Peace University

World University root node

School of Computer Engineering & Technology

Engineering and Technology

Lecture Notes in Electrical Engineering

Probabilistic Harmony Search Algorithm: Fitness Proportionate Selection Variants

In Sensor Networks (SNs), Network Lifetime (NL) enhancement using Data Aggregation Trees (DATs) is an extensively studied NP complete problem. The study in this paper investigates the existing DAT scheduling heuristics and proposes a metric termed as Brevis Factor ρ to evaluate the merit of the employed heuristics. Then, the ρ metric is applied to the state-of-art DAT scheduling heuristics which are typically based on Residual Energy (RE) parameter of the sensor nodes. Further, this paper proposes a new heuristics parameter for DAT scheduling based on the Number of Neighboring nodes (NN). Performance of NN-based heuristics is evaluated with rigorous simulations, and the results demonstrate improvement in NL as compared with the state-of-art. The values of ρ are observed for the existing RE-based heuristics as well as the proposed NN-based heuristics. The results demonstrate how ρ metric values evaluate the DAT scheduling heuristics. In addition, the results facilitate to understand the trade-off associated with the overheads to reconstruct DATs while improving NL. © 2021 John Wiley & Sons Ltd.

International Journal of Communication Systems

Brevis factor ρ to evaluate data aggregation tree scheduling heuristics in sensor networks

Stock price forecasting is a very interesting topic in financial studies. Technical analysis indicators are mathematical models/calculations that help us predict stock price direction. The share market is not an ideal place for prediction as the share market is very volatile and share prices keep on fluctuating based on multiple factors. According to business reports, more than 90-95% of traders lose their money in the share market. The main objective of this work is to apply understanding of technical analysis indicators like RSI, exponential moving averages, Heiken Ashi candlesticks, price-volume analysis, MACD, decision tree, random forest analysis, Naïve Bayes classifier, neural network and KNN. The work in this research paper compares and analyses short-term profit considering 5-year data (2015-2019) for high-traded stocks based on accuracy. © 2021 IEEE.

2021 12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021

Technical Analysis Indicators in Stock Market Using Machine Learning: A Comparative Analysis

Demand forecasting is a crucial part of any company or supply chain. It aims at predicting and estimating the future demand of products to help in better decision-making. This paper is a literature review on different demand forecasting methodologies which are used in different industries. The industries which are mainly focused in this literature review are restaurants, retail stores, drug stores, supermarkets and supply chains. It is observed that different organizations employ different forecasting techniques based on their requirement from these methodologies: traditional statistical models, machine learning, deep learning models and hybrid models. Latest research in demand forecasting using these models is briefly described and their advantages and limitations are discussed. Furthermore, models are classified on the basis of characteristics of data and forecasting time periods. It is observed that these individual models do not always work efficiently with all kinds of data and therefore an integrated approach should be employed where all these models are implemented, evaluated and then the model giving the best accuracy is considered for that particular industry dataset. © 2021 IEEE.

Demand Forecasting: Literature Review On Various Methodologies

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.

Lecture Notes on Data Engineering and Communications Technologies

Quality Aware Data Aggregation Trees in Sensor Networks

Increasing network lifetime (NL) is an important requirement in wireless sensor networks (WSNs). One of the techniques to extend NL is to use Data Aggregation Trees (DATs). DATs improve NL by combining the energy efficiency benefits of both Data Aggregation (DA) and tree-based routing. While centralized and distributed strategies for DAT construction are widely used, we propose a combined approach for DAT construction to improve NL. The approach reduces the communication overhead and relaxes the requirement of complete network information at the sink. In the proposed work, this collaborative approach is termed as Extended Local View (ELV) approach. Two ELV-based DAT construction algorithms termed as ELV with Fixed sink (ELVF) and ELV with Random sink (ELVR) are proposed. Both ELVF and ELVR use heuristics-based technique of Local Path Reestablishment (LPR) and greedy-based technique of Extended Path Reestablishment (EPR). Using these techniques a sequence of DATs are scheduled that collectively improve NL and also reduce the associated DAT reconstruction overhead. Performance of ELVF and ELVR is evaluated with rigorous experiments and the simulation results show that the proposed algorithms have improved NL and are scalable across different DA ratio values. DAT schedule analysis further demonstrates reduced DAT reconstruction overhead of the proposed algorithms which illustrates its suitability for hostile and critical environments.

Scheduling data aggregation trees to extend network lifetime in sensor networks

Data gathering is a basic requirement in many applications of Wireless Sensor Networks (WSNs). In tree based data gathering, Data Aggregation Tree (DAT) is constructed by the sink or by the nodes in a distributed manner. In this paper, we study the problem of enhancing Network Lifetime (NL) using hybrid DAT construction methods. In hybrid methods of DAT construction, the sink and the nodes collaboratively construct the DAT. We propose three algorithms for Scheduling DATs using Local Heuristics with Ordering (SDLHO), with Randomization (SDLHR) and with Tree factor (SDLHT) techniques.These techniques avoid disparity in energy levels of the nodes and increase the survivability of the network. In addition, to address imperfect link quality, we propose an algorithm for Scheduling DATs using Local Heuristics with Ordering based on Link Quality (SDLHO-LQ). Rigorous simulation results demonstrate the efficacy of the proposed algorithms; and their ability to scaleup to suit deployment of applications in harsh regions. Further, their performances evaluated to quantify the amount of enhancements of NL with the existing state of art is propitious to suit the distributed environments.

Computer Networks

Scheduling of data aggregation trees using local heuristics to enhance network lifetime in sensor networks

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.

2019 IEEE 5th International Conference for Convergence in Technology, I2CT 2019

Data Aggregation Trees with QoS in Sensor Networks

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.

Proceedings - 2019 International Conference on Computing, Communication and Intelligent Systems, ICCCIS 2019

Asymptotic Cost Estimation for Scheduling Data Aggregation Trees in Sensor Networks

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.

Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017

Path reestablishment in Wireless Sensor Networks

With advances in sequencing technologies, large amounts of biological data is easily accessible for research. Advanced techniques to extract biologically relevant knowledge from this data have gained importance. In this paper, we discuss the computation based disease associations that infer human diseases. The biological knowledge of diseases dictates associations between diseases and biological molecules like genes, miRNAs and proteins as well as the associations between diseases and pathways and phenotypes. It is essential to model and represent this knowledge in a computational form with minimal loss of biological context. Based on biological assumptions and statistical analysis, samples of (disease) affected and normal individuals are compared to generate a hypothesis about the disease as depicted by disease associations. We survey computation based disease associations supported by internal interactions i.e interactions between various biological molecules as well as external interactions i.e interactions between the biological molecules and external factors like environment and drugs.

Proceedings Iwbbio 2014: International Work-Conference on Bioinformatics and Biomedical Engineering, Vols 1 and 2

Computation Based Disease Associations in Disease Inference

Using routing technique, wireless sensor network WSN can route sensor data quickly. As there is only one next hop, transmissions can be fast. However, routed data only traverses its end-to-end path. So routing data to every node is difficult. Additionally, verifying transmission to each destination can be prohibitively expensive. Broadcast technique can scalably reach every node in a sensor network. Current sensor network routing protocols running on the mica2 hardware platform can route across approximately ten hops in a second after considering retransmissions, media access and contention [12], [14], [20]. The trickle broadcast protocol transmits at most once a second to minimize packet losses due to collisions [2]. This study presents a way to combine routing and broadcasting techniques to achieve reliable propagation of data packets. It brings out clearly, the reliability with which the control packets are propagated to all the nodes deployed in the network, as compared to pure broadcast based dissemination. Reliably disseminating a piece of data to every node is a basic requirement for WSNs.