Radio Access Techniques for Energy Effcient and Energy Harvesting based Wireless Sensor Networks

Traditional Wireless Sensor Networks (WSN) rely on batteries with finite stored energy. In the future with billions of such devices, it will be difficult to replace and dispose their batteries, which can cause a huge environmental threat. Hence, research is being done to eliminate batteries from sensor devices and replace them with harvesters. These harvesters can power the sensor network nodes by extracting energy from ambient sources. Harvesters are already being implemented in many real-life applications like structural health monitoring, environment monitoring and body area networks. A sensor network of multiple energy harvesting enabled devices is known as Energy Harvesting based Wireless Sensor Network (EH-WSN). For uninterrupted operation of EH-WSN, radio protocols must consider the energy harvesting constraints; (i) energy harvesting process unpredictability and; (ii) energy harvesting rate variations in time and space. EH-WSN comes with unique traits which discourage the use of existing WSNs radio protocols, as most of existing protocols are focussed on decreasing the energy consumption and increasing the network lifetime. This thesis work focusses on modifying an existing energyefficient Multipath Rings (MPR) routing protocol for low-power and low-bandwidth EH-WSN and evaluating its performance through simulations. Firstly, the topology setup phase is revised by implementing a new ring formation scheme for better data reliability. Secondly, controlled flooding of data packets is used by enabling selective forwarding, which leads to decrease in network traffic and overall energy consumption. Lastly, every node is equipped with a neighbors’ table on-board which helps in making energy-related routing decisions in multi-hop networks. A periodic energy update packet transmission helps in keeping latest neighbor information. This modified version of MPR routing protocol is called Energy Harvesting based Multipath Rings (EH-MPR) routing. This work also provides a comprehensive survey on existing MAC and Routing protocols for energy efficient and energy harvesting based WSNs. Through this work, the main constraints on using existing energy-efficient protocols for EH-WSN are discussed and depicted with the help of network simulations. The effects of using fixed duty cycle for energy harvesting enabled sensor nodes are outlined by simulating T-MAC (adaptive duty cycle) against S-MAC (fixed duty cycle). For all evaluation metrics, T-MAC outperformed S-MAC. Using Castalia’s realistic wireless channel and radio model, EH-MPR is simulated for low-power, low-data rate and low bandwidth (1 MHz) networks where satisfactory results are obtained for sub-GHz frequencies (433 MHz and 868 MHz). Next, the modified EH-MPR protocol is compared with original MPR routing under practical deployment scenarios. The metrics in consideration are successful packet transmissions, energy consumption, energy harvested-to-consumed ratio and failed packets. After thorough simulations, it was concluded that although the packet success rate is approximately equal for both protocols, EH-MPR has advantages over original MPR routing protocol in terms of energy cost and uninterrupted operations

A methodology for memory chip stress levels prediction

The reliability of electronic component plays an important role in proper functioning of the electronic devices. The manufacturer tests electronic components before they are used by end users. Still at times electronic devices fail due to undue stresses existing inside the microelectronic components such as memory chips, microcontrollers, resistors etc. The stresses can be caused by variation in the operating voltage, variation in the usage frequency of the particular chip and other factors. This variation leads to variation in chip temperature, which can be made evident from thermal profiles of these chips. In this thesis, effort was made to study two different kind of stress existing in the electronic board, namely signal stress based on variation in duty cycle/frequency of chip usage and the voltage stress. Memory chips were stressed using these stresses causing change in heating rates, which were captured by infrared camera. This data was then extracted and plotted to obtain different curves for the heating rate. The same experiment was done time and again for a large number of chips to get heating rate data. This data consisting of average heating rate for large number of chips was used to build Neural Network model (NN). Back Propagation algorithm was used for modeling because of its advantage of converging to solution faster compared to other algorithms. To develop a prediction model, data sets were divided into two-third and one-third parts. This two-thirds of the data was used to build the prediction model and remaining one third was used to evaluate the model. The designed model would predict the stress levels existing in the chips based on the heating rates of the chips. Results obtained suggested 1. There is difference in heating rate for chips stressed at different stress levels. 2. Accuracy of the model to predict the stress is high (greater than 90 %). 3. Model is robust enough that is it can yield efficient results even if there is presence of noise in the data. 4. Generic methodology can be proposed based on the experiments. This work is a progress in direction of making predictive model, for a complete electronic device, which can predict the stress level existing on any component in the device and will provide an opportunity to either protect the data or removal of the defected components timely before it even fails

Financial Tracker using NLP

NLP (Natural Language Processing) is a mechanism that helps computers to know natural languages like English. In general, computers can understand data, tables etc. which are well formed. But when it involves natural languages, it's unacceptable for computers to spot them. NLP helps to translate the tongue in such a fashion which will be easily processed by modern computers. Financial Tracker is an approach which will use NLP as a tool and can differentiate the user messages in various categories. the appliance of the approach will be seen at multiple levels. At a personal level, this permits users to filtrate useful financial messages from an large junk of text messages. On the opposite hand, from an industrial point of view, this can be useful in services like online loan disbursal, which are hitting the market nowadays. These services attempt to provide online loans to individuals in an exceedingly faster and quicker manner. But when it involves business view, loan recovery from customers becomes a really important & crucial aspect. As most such services can’t take strict legal actions against the fraud customers, it becomes a requirement that loan should be provided only to those customers who deserve it. At that time, this model can come under the image. As a business we will find the user’s messages from their inbox (after taking permission from the users). These messages are often filtered using NLP which might help to differentiate various types of messages within the user's inbox which might further be used as a content for further prediction and analysis on user’s behaviour in terms of cash related transactions

A Survey on Explainability of Graph Neural Networks

Graph neural networks (GNNs) are powerful graph-based deep-learning models that have gained significant attention and demonstrated remarkable performance in various domains, including natural language processing, drug discovery, and recommendation systems. However, combining feature information and combinatorial graph structures has led to complex non-linear GNN models. Consequently, this has increased the challenges of understanding the workings of GNNs and the underlying reasons behind their predictions. To address this, numerous explainability methods have been proposed to shed light on the inner mechanism of the GNNs. Explainable GNNs improve their security and enhance trust in their recommendations. This survey aims to provide a comprehensive overview of the existing explainability techniques for GNNs. We create a novel taxonomy and hierarchy to categorize these methods based on their objective and methodology. We also discuss the strengths, limitations, and application scenarios of each category. Furthermore, we highlight the key evaluation metrics and datasets commonly used to assess the explainability of GNNs. This survey aims to assist researchers and practitioners in understanding the existing landscape of explainability methods, identifying gaps, and fostering further advancements in interpretable graph-based machine learning.Comment: submitted to Bulletin of the IEEE Computer Society Technical Committee on Data Engineerin

Handwritten Digits and Optical Characters Recognition

The process of transcribing a language represented in its spatial form of graphical characters into its symbolic representation is called handwriting recognition. Each script has a collection of characters or letters, often known as symbols, that all share the same fundamental shapes. Handwriting analysis aims to correctly identify input characters or images before being analysed by various automated process systems. Recent research in image processing demonstrates the significance of image content retrieval. Optical character recognition (OCR) systems can extract text from photographs and transform that text to ASCII text. OCR is beneficial and essential in many applications, such as information retrieval systems and digital libraries

Predicting Information Pathways Across Online Communities

The problem of community-level information pathway prediction (CLIPP) aims at predicting the transmission trajectory of content across online communities. A successful solution to CLIPP holds significance as it facilitates the distribution of valuable information to a larger audience and prevents the proliferation of misinformation. Notably, solving CLIPP is non-trivial as inter-community relationships and influence are unknown, information spread is multi-modal, and new content and new communities appear over time. In this work, we address CLIPP by collecting large-scale, multi-modal datasets to examine the diffusion of online YouTube videos on Reddit. We analyze these datasets to construct community influence graphs (CIGs) and develop a novel dynamic graph framework, INPAC (Information Pathway Across Online Communities), which incorporates CIGs to capture the temporal variability and multi-modal nature of video propagation across communities. Experimental results in both warm-start and cold-start scenarios show that INPAC outperforms seven baselines in CLIPP.Comment: In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'23