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2021 | Book

Advances in Computing and Network Communications

Proceedings of CoCoNet 2020, Volume 1

Editors: Dr. Sabu M. Thampi, Dr. Erol Gelenbe, Dr. Mohammed Atiquzzaman, Dr. Vipin Chaudhary, Dr. Kuan-Ching Li

Publisher: Springer Singapore

Book Series : Lecture Notes in Electrical Engineering


About this book

This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Conference on Computing and Network Communications (CoCoNet'20), October 14–17, 2020, Chennai, India. The papers presented were carefully reviewed and selected from several initial submissions. The papers are organized in topical sections on Signal, Image and Speech Processing, Wireless and Mobile Communication, Internet of Things, Cloud and Edge Computing, Distributed Systems, Machine Intelligence, Data Analytics, Cybersecurity, Artificial Intelligence and Cognitive Computing and Circuits and Systems. The book is directed to the researchers and scientists engaged in various fields of computing and network communication domains.

Table of Contents


Communications, Control and Signal Processing

Cost-Effective Device for Autonomous Monitoring of the Vitals for COVID-19 Asymptomatic Patients in Home Isolation Treatment

Ashwin, V. Menon, Athul Devagopal, A. M. Nived, P. A. Gopinath, Athira Gayathri, G. Sai Shibu, N. B.As the number of COVID-19 cases keeps growing exponentially in the world, the use of the combination of wearable technology and IoT technologies opens up a wide variety of possibilities. An IoT-enabled healthcare device is useful for proper monitoring of COVID-19 patients to increase safety and reduce spreading. The healthcare device is connected to a large cloud network to obtain desirable solutions for predicting diseases at an early stage. This paper presents the design of a healthcare system that makes use of these technologies in a cost-effective and intuitive way which highlights the application of these technologies in the battle against the pandemic. The wearable can give real-time analysis reports of body vitals so that necessary precautions can be taken in case of infection. The wearable is designed in such a way that it can be used as a precautionary measure for people who are not infected with the virus and as a monitoring device for affected patients during the curse of their treatment. This low-cost design can not only be used to prevent the community spread of the virus but also for the early prediction of the disease.

V. Ashwin, Athul Menon, A. M. Devagopal, P. A. Nived, Athira Gopinath, G. Gayathri, N. B. Sai Shibu
Predictive Modeling and Control of Clamp Load Loss in Bolted Joints Based on Fractional Calculus

Shah, PriteshSekhar, RaviSafety of bolted joints in industrial machinery is of paramount importance. In this paper, fractional calculus-based predictive modeling has been investigated to control clamping force losses in bolted joints under service loads. Clamp load loss occurs in bolted joints due to application and subsequent removal of an externally applied separating service load on a fastener preloaded beyond its elastic limit. In this work, five different model structures were tried for system identification-based predictive modeling of joint clamp load loss. These structures were the first-order integer, second-order integer, first generation CRONE, fractional integral and fractional-order models. These models were validated by statistical parameters such as FIT, $$R^{2}$$ R 2 , mean squared error, mean absolute error, and maximum absolute error. The fractional-order model with three parameters provided most accurate estimate of the system performance. It also took minimum iterations to reach the optimum controller parameter settings. This model was controlled using PID and fractional PID controllers. Fractional PID controller was designed to minimize integral of squared error (ISE) and toward the convergence of gain/order parameters. The PID controller response exhibited better time domain characteristics as compared to the fractional PID, but suffered from a maximum overshoot as well. In a physical bolted joint, clamp load loss and external service load overshoots may lead to joint failures. Maximum overshoot was totally eliminated by fractional PID controller, proving its safe applicability to the bolted joint system. By choosing a realistic set point for clamp load loss, the maximum permissible external service loading conditions were predicted successfully.

Pritesh Shah, Ravi Sekhar
Resource Allocation for 5G RAN—A Survey

Resource allocation (RA) is a fundamental task in the design and management of wireless signal processing and communication networks. In a wireless communication, we must wisely allocate some available radio resources like time slots, transmission power, frequency band, and transmission waveforms or codes across multiple interfering links as to accomplish a better framework execution while guaranteeing user fairness and quality of service (QoS). In fifth generation (5G) of wireless communication system provides a better mobile service with improved QoS everywhere. Considering the dense deployment and more number of network nodes, RA and interference management are the important research issues in heterogeneous mobile networks. In this, we need to utilize the available radio resources efficiently, for that the RA is of much importance in future wireless communication systems (5G/6G). In this survey, we consider various resource allocation methods for different radio access network (RAN) architecture; several authors have implemented some techniques and algorithms to achieve better resource allocation with the help of existing literature survey, we explore ways to allocate the radio resources for next generation wireless communication.

G. Shanmugavel, M. S. Vasanthi
Wearable PIFA for Off-Body Communication: Miniaturization Design and Human Exposure Assessment

A miniaturized Planar Inverted-F Antenna (PIFA) design tailored for wearable devices is presented in this work. The proposed antenna operates in the ISM band (from 2.4 to 2.5 GHz) used by common wireless communication standards. A felt textile substrate is used to allow easy integration into everyday clothing. A side-fed coaxial cable is also adopted to give a low profile. To assess human exposure, SAR analysis is conducted on the designed antenna and simulated results are presented. The SAR level of the antenna is successfully limited to comply with international guidelines, by introducing significant modifications on the antenna parameters.

Sandra Costanzo, Adil Masoud Qureshi, Vincenzo Cioffi
Generalized Symbolic Dynamics Approach for Characterization of Time Series

Suriyaprabhaa, S.Gopinath, GreeshmaSangeerthana, R. Alfiya, S.Asha, P.Satheesh Kumar, K.Various nonlinear methods have been developed to analyze the underlying dynamics of a nonlinear time series. Dynamic characterization using symbolic dynamics approach has been found to be a good alternative for the analysis of chaotic time series. As per this method, the given time series is first transformed into a single bit binary series. The single bit encoding limits its ability to capture the dynamics faithfully. This paper aims to provide a generalization of the symbolic dynamics method for better capturing the dynamical characteristics such as Lyapunov exponents of a time series. The effectiveness of the generalized method is demonstrated by employing a logistic map. The results of the analysis indicate that higher-order encoding can capture the bifurcation diagram more effectively compared to the original single bit encoding used in symbolic dynamics.

S. Suriyaprabhaa, Greeshma Gopinath, R. Sangeerthana, S. Alfiya, P. Asha, K. Satheesh Kumar
Smart Mirror-Based Personal Healthcare System

A smart mirror is a device that is an extended and enhanced version of a conventional mirror. It is a two-way mirror with an inbuilt display behind the glass. It allows the user to access and interact with various features that can be found in smart devices, like smart phones and tablets, etc. A smart mirror can display images, videos, current time, weather forecast, news feed, upcoming appointments, and all kinds of data supported by a smart device. The existing smart mirror frameworks were developed just to show time, date, and weather. After some updates, it contained schedules, alerts, and notices, and later, it got updated with music player and voice acknowledgment. The smart mirror will be redefined by remembering each and every aspect and disadvantages of the existing systems. The smart mirror offers unique features to improve the user experience and system security through biometric authentication, multimedia capabilities, and customized user profiles. This device can replace a wide range of household utilities like clocks, calendars, and external virtual assistants like Amazon Echo, Google Home, etc. The mirror will provide personalized healthcare services for each of its users. This includes analyzing varying health patterns of the user, like sleep patterns and body parameters and providing suggestions to improve their lifestyle. It also displays the medicine timetables of each user. Thus, it will be beneficial for elderly people and anybody with a busy routine.

V. B. Aanandhi, Anshida Das, Melissa Grace Melchizedek, Nived Priyadarsan, A. Binu Jose
On–off Thinning in Linear Antenna Arrays Using Binary Dragonfly Algorithm

The aim of this work is to study the suitability of two newly introduced bio-inspired algorithms, namely the dragonfly algorithm (DA) and the salp swarm algorithm (SSA) for thinning a linear antenna array. In array thinning, a fully populated array is chosen as a starting point, and a thinned array is obtained through careful deactivation of select sensors such that the residual active sensors enable the array to achieve a desired side-lobe performance. In this paper, we apply the binary versions of DA and SSA, namely the binary dragonfly algorithm (BDA), and the binary salp swarm algorithm (BSSA) to thin a symmetric linear array with uniform inter-element spacing of half wavelength. Extensive simulations were performed in MATLAB by considering arrays of different sizes. The results obtained from BDA and BSSA were compared against those obtained from the binary versions of two benchmark algorithms, namely the genetic algorithm (GA) and the gray wolf optimizer (GWO). Relative side-lobe level (RSLL) and filling percentage were used as performance comparison metrics. It has been observed that both BDA and BSSA offer promising results in line with BGA and BGWO. More specifically, BDA was found to be faster than BSSA.

Ashish Patwari, Medha Mani, Sneha Singh, Gokul Srinivasan
Reduction in Average Distance Cost by Optimizing Position of ONUs in FiWi Access Network using Grey Wolf Optimization Algorithm

Fiber-Wireless is the promising next generation broadband access network. FiWi integrates the technical merits of the optical access network and wireless access network. ONU placement is the most important issue in FiWi as it affects the network cost and network performance. The present research work considers the ONU placement issue and proposes a novel algorithm for finding an optimum position of ONUs. For this, a nature-inspired grey wolf optimization (GWO) algorithm is applied in the FiWi network. To the best of our knowledge, this algorithm has not been used for the ONU placement problem in the FiWi network. GWO provides the optimum position of every ONU, where the average distance cost (ADC) is minimum. ADC is the average of the distance of ONU and its associated wireless routers. To check the effectiveness of the proposed work, simulation is done for varying numbers of wireless routers. The proposed work is compared with well-known algorithm, namely teaching learning-based optimization (TLBO) algorithm. The result shows the reduction in ADC after applying the GWO algorithm than the initial placement and TLBO algorithm for all the cases considered for simulation. Hence, to deploy a cost-efficient FiWi network, proposed work may be one of the best solutions.

Nitin Chouhan, Uma Rathore Bhatt, Raksha Upadhyay
Performance Analysis of Individual Partial Relay Selection Protocol Using Decode and Forward Method for Underlay EH—CRN

The paper investigates the performance of the underlay cognitive radio network. We propose a relay selection protocol to enhance the throughput and to obtain reduced outage probability in the ad hoc network. The proposed work is based on relay selection aided with energy harvesting to serve communication between the secondary nodes. We have formulated a closed-form expression for the proposed work and differentiated the outage probability and throughput with other partial relay selection techniques. Further, decode and forward (DF) relaying with Rayleigh fading channel is considered in this work to improve the end-to-end channel gain. The performance evaluation indicates that the proposed cooperative relay selection scheme has marginally enhanced by increasing the number of relay node.

G. Kalaimagal, M. S. Vasanthi
Building a Cloud-Integrated WOBAN with Optimal Coverage and Deployment Cost

Increasing demand of new services and application by the users poses a challenge on the communication network. Cloud computing serves this purpose by providing a shared pool of resources such as storage, servers, services, etc. Such technology uses backbone network for every applications to be served and thus results in high latency. To overcome such problem, cloudlets are used which are deployed in decentralized way. Cloudlets are clusters of computers which are connected to the users either directly or in maximum two wireless hops without affecting the latency of the network. There is another important factor, cost efficiency, which plays a very important role in the deployment of cloudlets in cloud-integrated wireless optical broadband access network (CIW). In this paper, we proposed an algorithm to find the optimum position in the network for the deployment of cloudlets by taking coverage and cost as a trade-off.

Mausmi Verma, Uma Rathore Bhatt, Raksha Upadhyay
VR Classroom for Interactive and Immersive Learning with Assessment of Students Comprehension

Jaya Sudha, J. S.Nandakumar, NandagopalRaveendran, SarathSandeep, SidharthThe virtual classroom environment is created using virtual reality that enables multiple students to enter as if in a real class but with better learning environment. Conventional learning is currently limited in the current model of textbook teaching. An interactive and visual environment provided for learning enhances the rate at which the student grasps concepts. Even though many modern online teaching methods are available today, it is not possible to check whether a student is paying attention or not. Technology is evolving at a very fast rate, and this research is an apt integration of two modern technologies: machine learning and virtual reality, so as to increase the quality of education for students. A shared VR environment, optimised for learning, will be created. Students can wear a head-mounted display and select an avatar for themselves, which will be seen by other students and teachers. The VR environment is created using Unity3D software. Students will also have to wear an EEG scanner on their heads. The output of this scanner will be fed to the machine learning subpart. Neural networks are used to identify whether the student is paying attention or not. If a student is not paying attention, the teacher will be informed about it, with a message near the student’s avatar. It has many advantages over traditional learning techniques, like usage of multiple senses and inclusivity for differently abled students.

J. S. Jaya Sudha, Nandagopal Nandakumar, Sarath Raveendran, Sidharth Sandeep
Localization of Self-driving Car Using Particle Filter

Autonomous system or self-driving car needs to localize itself very frequently or sometimes continuously to determine its proper location that is essential to perform its navigation functionality. The probabilistic models are among the best methods for providing a real-time solution to the localization problem. Current techniques still face some issues connected to the type of representation used for the probability densities. In this paper, we attempt to localize the self-driving car using particle filter with low variance resampling. Particle filter is a recursive Bayes filter, non-parametric approach, which models the distribution by samples [1–3]. A specially modified Monte Carlo localization method is used for extracting the local features as the virtual poles [4, 5]. Simulations results demonstrate the robustness of the approach, including kidnapping of the robot’s field of view [6]. It is faster, more accurate, and less memory-intensive than earlier grid-based methods.

Nalini C. Iyer, Akash Kulkarni, Raghavendra Shet, U. Keerthan
Convex Combination of Maximum Versoria Criterion-Based Adaptive Filtering Algorithm for Impulsive Environment

This paper elaborates convex combination approach of two maximum versoria criteria-based adaptive filters for impulsive environment. The maximum versoria criteria-based adaptive filter performs better than minimum mean square error and maximum correntropy criteria under impulsive environment. The main drawback with the current approach is that there is trade-off in the speed of convergence and steady-state mean square error. In order to overcome this trade-off, convex combination method is adopted in this paper. A new update rule is also proposed to make the algorithm to have more robustness. Experiments were conducted for echo cancellation and system identification applications to validate the performance improvement of the proposed approach.

S. Radhika, A. Chandrasekar, K. Ishwarya Rajalakshmi
Verifying Mixed Signal ASIC Using SVM

KLEEL2020 is an in-house developed event logger. The ASIC is implemented in TSMC 0.18 µm CMOS mixed signal technology, 3.3/1.8 V. The focus of this paper is to achieve functional precision of the design before the tape-out. The process of verification is critical stage in the design flow because any bug not detected at earlier stage will lead to overall failure of the design process. In this paper, the authors present a framework for the complete verification of KLEEL2020 using System Verilog Methodology (SVM). The proposed SV environment allows I2C protocol as communication means with DUT. Different test scenarios are developed, and reused to verify the ASIC. Event logger is verified for various test cases. This verification attempt helped identify 05 RTL bugs in the design.

H. R. Aishwaraya, Saroja V. Siddamal, Aishwaraya Shetty, Prateeksha Raikar
Design of High-Speed Turbo Product Code Decoder

Shivanna, Gautham Yamuna, B. Balasubramanian, Karthi Mishra, DeepakIn the field of digital communication, there has always been a requirement for an efficient, low complex, and high-speed error control encoder and decoder. Many such encoders and decoders for different error control codes have been proposed in the literature by researchers. However, developing such CODECs whose performance can be suitable for the requirements of modern communication systems is still an open research problem. In this paper, one such decoder, namely fast Chase decoder proposed in the literature, has been studied. The hardware design of the decoder has been done and verified with results from MATLAB simulations. An attempt has been made to improve the speed by replacing the ripple carry adder in the design with a fast adder. The hardware architecture is implemented in Xilinx XC7A35T platform, and an increase in computation speed of 5% has been achieved.

Gautham Shivanna, B. Yamuna, Karthi Balasubramanian, Deepak Mishra

Data Analytics

Extraction and Analysis of Facebook Public Data and Images

Gutam, Bala Gangadhara Subhash Chandra Mouli, D. Majjari, SudhakarSocial networks play vital role in human communication and to improve the business applications. The Facebook is one of the most popular social networking application. However, the Facebook generates huge amount of data in the form of text, advertisements, posts, images, and videos. By analyzing Facebook data, we can find the location where our business promising in the real world. While sharing personal data, users demand security. Trust becoming an essential parameter in social networking. In this paper, a new technique is presented to identify duplicate logo images and profile pictures to prevent the fraud in business by keeping secret information in the profile picture or logo without distraction along with theoretical description of Facebook.

Bala Gangadhara Gutam, D. Subhash Chandra Mouli, Sudhakar Majjari
Subspace Clustering Using Matrix Factorization

Harikumar, Sandhya Joseph, ShilpaHigh-dimensional data suffers from the curse of dimensionality and sparsity problems. Since all samples seem equidistant from each other in high-dimensional space, low-dimensional structures need to be found for cluster formation. This paper proposes a top-down approach for subspace clustering called projective clustering to identify clusters in low-dimensional subspaces using best low-rank matrix factorization strategy, singular value decomposition. The advantages of this approach are twofold. First is to obtain multiple low-dimensional substructures using the best low-rank approximation, thereby reducing the storage requirements. Second is the usage of the obtained projective clusters to retrieve approximate results of a given query in time-efficient manner. Experimentation on six real-world datasets proves the feasibility of our model for approximate information retrieval.

Sandhya Harikumar, Shilpa Joseph
A Data-Driven Approach for Peer Recommendation to Reduce Dropouts in MOOC

Massive open online course (MOOC) is an online mode for learning aimed at unlimited participation. A characteristic feature of MOOC is reduced availability of social interaction, which is often responsible for learners feeling isolated. Although to facilitate interaction, MOOC has functionalities like discussion forum, group assignment and peer grading; however, to use these functionalities, the learner has to extensively search for the right person to interact from a large pool of learners. The isolation among learners is one of the significant factors contributing to high learner dropout rate, a major concern for MOOC. In this paper, we present an approach to reduce the dropout rate of MOOC by solving the problem of isolation. A potential solution to this problem is to encourage peer learning, by supporting learners to find other learners for interaction purposes. In this paper, we propose a user similarity-based peer recommendation approach that makes use of learners’ scores and their demographic attributes, to provide recommendations on potential learning peers. To date, however, the main focus of traditional approaches for peer recommendations is on providing recommendations to all learners, including the ones who were not feeling isolated. Furthermore, these approaches provide peer recommendations to learners without considering their actual cause of isolation. To overcome these limitations, we use adaptive interventions to first identify the isolated learners and then recommend peer learners based on their cause of isolation. The proposed approach for peer recommendation is evaluated on the basis of scalability and coverage. The publicly available MIT Harvard database has been used for experimental purpose.

Manika Garg, Anita Goel
Bag of Science: A Query Structuring and Processing Model for Recommendation Systems

Technological advancements and the changing needs drive the process workflow, meeting the need-of-the-hour requirements, and calibrating system components. While the perception evolves, the fundamental principles stay put and wrap around generational disparities. In a changing scenario of the physical market to an e-commerce site, the recommendation systems have had substantial roles. The present systems customarily use the item or user profiles for recommendations. The existing recommendation systems rely heavily on data and learning algorithms. An improved recommendation system given by considering the query’s semantics rather than using only historical data of numerous worldwide queries can create a paradigm shift in the technologies involved in computer recommendations. Bag of science attempts to take on this challenge. The model dwells on inferring a query’s meaning in all contexts to create an order in which the words relate. By constructing a word definition graph, the methodology explores the possibility of enhancing the recommendation systems to improve the e-commerce platforms’ business. The paper presents the model’s architecture with its chief components, including a parser and scraper, graph generator, graph traversal, and results. The model presents the traversal results and analysis of the constructed e-commerce graphs using hops as the threshold metric. The paper also presents the model’s abstract data type to make it applicable and extend to other domain contexts that involve query engines and need recommendations.

Prakash Hegade, Vibha Hegde, Sourabh Jain, Rajaram M. Joshi, K. L. Vijeth
A Novel Design Approach Exploiting Data Parallelism in Serverless Infrastructure

Serverless computing has emerged as a new application design and execution model. The serverless application is decomposed into granular logical functional units that run on small, low cost, and short-lived compute containers. These containers are dynamically managed by FaaS service providers. Users are charged only for the compute and storage resources needed for the execution of their piece of code. Cloud functions have restrictions on memory usage and execution time-out as imposed by their service providers. Due to this limitation, compute intensive tasks time-out before their completion and hence unable to harness the power of serverless computing. In this paper, we propose a design approach for serverless applications. It exploits data parallelism in embarrassingly parallel computations. Using our approach, compute bound tasks that are implemented in conventional design and fail in serverless environment can get executed successfully without worrying about the limitations imposed by serverless platforms. For this, several extensive experimentations using Amazon’s AWS Lambda service have been performed. Further, a serverless application designed using our approach exploits the auto-scalability feature of serverless computing to achieve faster execution benefit.

Urmil Bharti, Deepali Bajaj, Anita Goel, S. C. Gupta
A LoRa-Based Data Acquisition System for Wildfire Early Detection

A new original LoRa-based data acquisition system for wildfire detection is developed and presented. The emphasis in the paper is pointed towards hardware design concepts, physical system architecture, and implementation as well as embedded firmware structural details and algorithms. The main purpose of the proposed design is to propose techniques whose goals are to improve upon existing WSN fire hazard detection system designs by reducing the end-device power consumption. These techniques and the data analytical steps are described in detail, and evaluation on the basis of testing of their overall system performance improvement has been shown.

Stefan Rizanov, Anna Stoynova, Dimitar Todorov
Announcer Model for Inter-Organizational Systems

From barter systems to shopping online, markets have evolved with institutional design characteristics with the objective of providing a platform for buying and selling. The technological investment portfolio has brought in significant changes to market dynamics. Though there are apprehensions to migrate the offline features online, along with online benefits, there are also inherent challenges to be managed. In supply chain management, which manages from raw materials to customers, inventory management has a substantial role and acts as a key player affecting the entire chain directly or indirectly. Nevertheless of automation, inventory management is a tedious task and could use a computational helping hand. The announcer model proposes an alternative to computationally solve resource management between the business cycle's various stages through this paper. It attempts to establish a strong relationship between the intermittent by announcing the iterative status flags via tags and further utilizing it to improve work efficiency. The model eases the interaction and provides an automated channel for communication. This paper proposes the model and discusses its architecture and a sample workflow from a simulated industry transaction. The announcer space can also be integrated to live web data, making the system dynamic and self-learning to current market needs. The learning capability of the announcer contemplates modern challenges. The system attempts to achieve a natural order by balancing the system components’ workflow through the announcer model. The announcer model promises to provide an intellectual space for coordination and collaboration.

Prakash Hegade, Nikhil Lingadhal, Usman Khan, Tejaswini Kale, Srushti Basavaraddi
Evaluation of Attributed Network Embedding Algorithms for Patent Analytics

Jose, Jinesh Mary Saira Bhanu, S.Patent analytics is a specialized branch of data analytics where patent documents are analysed to understand behavioural information. Citation network analysis is one of the common techniques to examine the importance of a patent by studying its citations. Typical patent citation network (PCN) will have millions of attributed nodes and edges. Inferencing on such a large network necessitates the use of attributed network embedding (ANE) techniques to bring down the computational requirements by reducing the dimensionality of the network data. Identifying the suitable ANE algorithm for PCN analytics is the purpose of this study. Multiple ANE algorithms are applied on the patent dataset to create low-dimensional embeddings, and these embeddings are used as the input for performing the innovation value prediction using linear regression model. Mean square error (MSE) is calculated between the predicted innovation values and the actual innovation values. MSE values obtained with different ANE algorithms are analysed to identify the most suitable ANE algorithm for patent analytics. GraphSAGE with mean-based aggregator resulted in the least MSE compared to all other ANE algorithms evaluated for patent analytics.

Jinesh Jose, S. Mary Saira Bhanu
A Comparative Analysis of Garbage Collectors and Their Suitability for Big Data Workloads

Nair, Advithi Sriram, Aiswarya Simon, Alka Kalambur, Subramaniam Sitaram, DinkarBig data applications tend to be memory intensive, and many of them are written in memory managed languages like Java/Scala. The efficiency of the garbage collector (GC) plays an important role in the performance of these applications. In our paper, we perform a comparative analysis of Java garbage collectors for three commonly used big data workloads to check the choice of the garbage collector for each of the workloads. The garbage collectors under scrutiny are garbage first, parallel and ConcurrentMarkSweep. We demonstrate (a) the relative difference between existing Java workloads that are used to study garbage collectors and big data workloads and (b) the selection of the right garbage collector for a given workload. We find that the garbage first collector gives a performance uplift of up to 15% in certain workloads.

Advithi Nair, Aiswarya Sriram, Alka Simon, Subramaniam Kalambur, Dinkar Sitaram

Networked Systems and Security

An Innovative and Inventive IoT-Based Navigation Device—An Attempt to Avoid Accidents and Avert Confusion

Many routes being created each day; it is a very tiresome job to remember every route. This is the reason maps are created for making our job easier. Due to an increase in technology and lowering of data rates in many countries, these maps are accessible to most of the people in the daily commute. When we want to go to a new route, we cannot remember the whole route by seeing the route provided by the map. Therefore, we need to check the route at regular intervals. This method of the commute is very much suited for pedestrians because they can hold their mobile phone in hand and can follow the route, and for some type of four-wheeler drivers as they can dock it to the dashboard and can drive the dour wheeler. The problem comes in the case of two-wheeler riders because they cannot hold their phone and drive or cannot dock the phone to their bike as it causes serious distraction from the traffic. So, to solve this situation, we have designed a device that can show the directions of the upcoming turn without using a mobile phone while driving.

Chennuru Vineeth, Shriram K. Vasudevan, J. Anudeep, G. Kowshik, Prashant R. Nair
Deploy—Web Hosting Using Docker Container

Sunny, Minto Shaji, Sen Sabu, Sheen Uthaman, Udith George, GeminiIn traditional web hosting, websites/web applications are configured on a bare metal server a virtual private server. For hosting multiple websites, directories are created for each website and a Linux user is created corresponding to each website. This means that a single web server/application server daemon process is responsible for serving all these websites. This is called shared web hosting. This is not a suitable solution if your application handles secret or sensitive data such as credit card numbers and bank account information. If any application handles secret/sensitive data, such applications must be deployed on dedicated servers, this is costly. This paper presents the docker containers technology which is currently being used in many production environments to package their applications in isolated environment. Further, the work elaborates how docker technology has overcome the previous issues which includes building and deploying large applications. The docker container-based deployments on the other hand isolate a website/web application and it’s dependencies into self-contained units which we can run anywhere. With docker-based deployment, we can achieve a docker cloud where we can horizontally scale up and scale down the containers dynamically based on the traffic volume. Further, we can run a large monolith application or a micro-service on a docker container.

Minto Sunny, Sen Shaji, Sheen Sabu, Udith Uthaman, Gemini George
Enhancement of VerticalThings DSL with Learnable Features

Ghanta, Sandesh Surya Chaitanya, P. V. Ganti, Sai Sarath Chandra Roshan Patnaik, M. P. V. Gopakumar, G.Resource-efficient ML for edge and endpoint IoT devices is a field of active research and increasing development. Libraries have been providing support for machine learning enthusiasts to run ML algorithms in the cloud. Executing ML algorithms on motes is a challenge, as resources are highly constrained. To optimally use these resources, the developer often needs to have complete knowledge of the underlying architecture. VerticalThings is a domain-specific language (DSL) developed for programming ML-based embedded applications. The language offers constructs for key platform functions such as resource management, concurrency, task isolation, and security. This enables static analysis of (a) important safety and security properties, and (b) timing and power considerations. To enhance this DSL further, we developed a DSL named FieryIce which provides intelligent learning of parameters based on sensor data. We would integrate both the DSLs within the IDE developed for VerticalThings. The learnable parameters are learnt at compile time avoiding the use of scarce memory of the embedded systems. This paper shows the capabilities of a domain-specific language (DSL) named FieryIce which is designed to help embedded developers use VerticalThings and develop ML-based embedded applications with ease. The contributions of this article are: (a) A domain-specific language (DSL) named FieryIce and it is capabilities to perform machine learning-related tasks; (b) how FieryIce helps students better their understanding of machine learning algorithms.

Sandesh Ghanta, P. V. Surya Chaitanya, Sai Sarath Chandra Ganti, M. P. V. Roshan Patnaik, G. Gopakumar
Demand-Based Dynamic Slot Allocation for Effective Superframe Utilization in Wireless Body Area Network

Justin Gopinath, A. Nithya, B.Wireless body area network (WBAN) is an emerging technology for remotely monitoring the critically affected patients regularly, which is a utility platform for a medical pandemic like COVID-19. IEEE 802.15.6 medium access control (MAC) defines the communication standard to pillar the quality requirements of the sensor nodes. Most of the existing works are focused on optimizing the conventional MAC by adopting dynamic scheduled access and efficient contention scheme to utilize the superframe structure. However, utilizing the entire slots based on demand from different priority sensor nodes is a challenging task. To address this issue, an efficient time slot allocation method, namely the demand-based dynamic slot allocation (DDSA) algorithm, is proposed. DDSA computes sensor node priority based on the run-time parameters such as critical index, remaining energy, and delivery demand. The slot assignment is proportional to the priority order, and the critical index factor resolves slot conflict. This guarantees data priority preservation with fair allocation for critical and non-critical medical data. The simulation is carried out using the Castalia-OMNeT++ simulator, and the results are shown that the proposed DDSA algorithm outperforms priority-based MAC and the conventional method in terms of packet reception rate, energy efficiency, and latency.

A. Justin Gopinath, B. Nithya
A Survey on Congestion Control Algorithms of Wireless Body Area Network

Nowadays, research on wireless body area network (WBAN) touches its extremity as the need arising more for the present mundane lifestyle of the world. Exclusive demand for WBAN is mainly due to its special properties such as its mobility, tiny size, and network topology, etc. WBAN is a specialized technology designed to monitor a remote patient (or a subject—as WBAN is not limited to human being), and it grabs attention from researchers as it is emergency-aware. Due to the nature of the WBN, the collisions among data packets are inevitable which in turn increases congestion in the network by triggering more number of retransmissions. To eradicate these issues, several congestion control (CC) algorithms are proposed in the literature. This paper surveys some of the recent CC algorithms and stretches a detailed comparative study of these algorithms. This survey reveals the strength and weakness of these algorithms and the future research direction in this research field.

Vamsikiran Mekathoti, B. Nithya
Applications of RSSI Preprocessing in Multi-Domain Wireless Networks: A Survey

Today’s age of communication has been looking for technologies and techniques to support high data rate applications with required quality of services. Advanced communication network architectures like Internet of things (IoT), fifth generation (5G), and long term evolution (LTE) with supporting high end transmission and reception processes have evolved to meet present requirements. It is also observed that to further enhance network performance, incorporation of received signal strength indicator (RSSI)/channel state information (CSI)-based preprocessing techniques have been exhibiting substantial impact. Physical layer key generation in wireless networks, localization of nodes in wireless networks, signal identification, human activity recognition, etc., are few such applications, using RSSI/CSI preprocessing for their performance improvement in multi-domain wireless networks. Hence, this paper describes above-mentioned applications using different preprocessing techniques of RSSI, which is not investigated comprehensively in literature so far. Therefore, the purpose of this paper is to reveal the impact of RSSI preprocessing techniques in system performance enhancement as per the need of application. As an outcome, we find the possibility of applying other preprocessing techniques in existing and upcoming applications in future to achieve desired system performance.

Tapesh Sarsodia, Uma Rathore Bhatt, Raksha Upadhyay
Exploring IoT-Enabled Multi-Hazard Warning System for Disaster-Prone Areas

Menon, Vishal Arjun Rathya , R. Prasad, Abhiram Gopinath, Athira Sai Shibu, N. B. Gayathri, G.Natural disaster in India has become a great challenge in the recent years. Each year the rates have been escalating affecting both the social as well as economic progress of the country. India’s topographic/climatic and socio-economic features make the country most vulnerable to the devastating effects of such calamities. Hence, it is the need of the hour to come with a system capable of long term as well as quick prediction of disaster. This can be useful for early preparedness and developing well-planned mitigation/relief system which can reduce the effects of such disaster and can be also useful in channelizing the finding in a right way during calamities. The proposed system consists of modules for prediction of: Weather pattern, flood, earthquake, landslide, fire and gas leakage. The sensor node deployed at various disaster-prone areas transmits sensor data to a local aggregator that pre-process the data and relays to remote monitoring servers. The remote monitoring platform has algorithms for prediction of disaster as well as suggestions for quick response. Hence, the possibility of disaster can be predicted prior to the onset of these calamities.

Vishal Menon, R. Arjun Rathya, Abhiram Prasad, Athira Gopinath, N. B. Sai Shibu, G. Gayathri
IOT Based Smart and Secure Surveillance System Using Video Summarization

Nowadays, security is important for every commercial property to prevent robberies and thefts and to ensure secure safe business operations. In CCTV (Closed-circuit television) systems, the data is non-intelligently recorded which produces huge volumes. It makes it difficult to search for the desired content from the big data. It is found from the literature that limited work is done in the field of a secure surveillance system using real-time videos. Therefore, there is a need for video summarization, classification (action recognition), and encryption. This paper aims to make decisions about abnormal events like suspicious activity detection in surveillance applications incorporating the above-said techniques. This IoT (Internet of Things) based smart secure surveillance system allows for reduced storage of unwanted data and helps to protect the confidential data to be sent to the user by cryptographic methods.

M. Surya Priya, D. Diana Josephine, P. Abinaya
An Efficient and Innovative IoT-Based Intelligent Real-Time Staff Assessment Wearable

The traditional method for assessing the performance of the workers in industrial workspaces is by measuring their inertial movements. Presently in industries at the workspaces, every 20–30 workers will have an in-charge to monitor their work. Although workers are being assessed by those in-charges, there are still many mishaps happening in the industrial workspaces. Workers with poor work performance are earning the wages that are same as that of a worker with good performance. These expenditures may seem to be minimal in number, but recent statistics reveal that this affects the company's total productivity in a disastrous way. Some studies state that on an average, companies are losing $3,156 on the workers due to their idleness (Duffy J, Productivity report|bridging research and practice on personal productivity). Forbes magazines revealed that 31% of the workers are roughly wasting 1 h of time per day at their work apart from their allotted leisure times (Wasting time at work: the epidemic continues—the fobs report). Many companies in the UK with industrial workspaces claim that they lost 15.4 billion dollars annually only due to worker illness and their maintenance (Number of workplace injury and work-related ill health cases|Page 8—HSE, UK report). However, unfortunately, besides having distinct inertial measurement unit (IMU) systems in place, companies are facing many difficulties in identifying and estimating the worker performances and the health of the worker. Therefore, we have developed a system for overcoming the difficulties faced by using these IMUs using the power of IoT and Android application.

J. Anudeep, Shriram K. Vasudevan, G. Kowshik, Chennuru Vineeth, Prashant R. Nair
Performance Evaluation of WebRTC for Peer-to-Peer Communication

In this era of the Internet and developing technology, there are numerous ways of interacting with each other and a plenty of services available to make it possible. These include social media platforms, email, VoIP, messaging applications, etc. One of the important aspects here would be real-time communication (RTC), which means interacting with people all around the world as if they were face-to-face. The advancement in RTC has led to the development of a new innovative technology called Web real-time communication (WebRTC), which enables an easy streaming of audio and video content over the Web. This powerful tool currently revolutionizing Web communication has introduced RTC capabilities into browsers as well as mobile applications. The study of this WebRTC technology and its implementation has been carried out in this paper. The WebRTC standards specified protocols, signaling techniques, and WebRTC communication flow between the peers are discussed. WebRTC is supported on two major browsers Google Chrome and Mozilla Firefox, and experiments are conducted on devices running on these browsers with different configurations. Performance has been measured in terms of peer connection establishment, peer communication, user data transfer, and video streaming parameters.

Kiran Jadhav, D. G. Narayan, Mohammed Moin Mulla
Scalable Blockchain Framework for a Food Supply Chain

Of late, in a food supply chain (FSC) management, many incidents related to the mislabeling and mishandling of food items are found to occur frequently, which often leaves the customers with a question of how safe and reliable is the food they buy and consume. Since the information regarding the tracking of food items is distributed across different locations widely, and the data is vulnerable to being recorded wrongly, the reliability of tracking of the food items through FSC is suspected. Further, an FSC has various stakeholders that interact and transact continuously. Thus, the scalability issue also plays a vital role in making FSC more efficient. The present-day traceability solutions lack efficiency in terms of scalability and reliability. The scalability can be categorized as throughput, cost, capacity, and response time. In this paper, the proposed method for the traceability solution of a food supply chain (FSC) is based on Blockchain, which plays a vital role in providing transparency and integrity along with other salient features like decentralization, immutability, and verifiability. The proposed FSC is made scalable in terms of throughput and cost using state channeling off-chain algorithm for Blockchain implementation.

Manjula K. Pawar, Prakashgoud Patil, P. S. Hiremath, Vaibhav S. Hegde, Shyamsundar Agarwal, P. B. Naveenkumar
Maximizing Lifetime of Mobile Ad-Hoc Networks with Optimal Cooperative Routing

Research in MANET is a challenging task because topology changes frequently and results in link breakages due to node mobility and fast over tiredness of node energy due to limited battery capacity. Therefore, the topology, node mobility, and energy are main important factors that have an impact over the performance of a routing protocol and decreases the overall lifetime of the network. In order to enhance the lifetime of the network, a cooperative communication scheme have been proposed in this paper. Cooperative communication requires cooperative table, relay table, and cooperative neighbor table to store the topological information and implement cooperative transmission among the nodes thereby improving the robustness against the node mobility. Cooperative communication uses multi-hop transmission between the source and destination nodes in order to save energy and thus enhancing the lifetime of the network using minimum energy consumption selection decode and forward (MESDF) routing protocol. The proposed scheme chooses the best relays with minimum energy consumption in a cooperative and distributed manner and considers the link break probability and energy harvesting techniques, to determine the optimal route across a cooperative network. Simulation results clearly shows that the robustness of proposed method increases against the node mobility and saves 21% of node energy in a selected route which in turn increases the lifetime of the network when compared to the existing cooperative and non-cooperative routing methods.

K. C. Kullayappa Naik, Ch. Balaswamy, Patil Ramana Reddy
On-Demand Multi-mobile Charging Scheduling Scheme for Wireless Rechargeable Sensor Networks

Kodi, Charan Ramtej Das, Debjit Shekar, ShashiThe sensor nodes in the network senses and processes the data, and each sensor usually has different task burdens due to the environmental change, which results in dynamic change of the energy consumption rate at different nodes. To provide real-time on-demand charging to these sensors is a real challenge. Based on the certain threshold, each sensor node requests for charging, and these charging requests are taken in to the matrix and processed accordingly based on the selection rate by mobile charging vehicle (MCV). This paper deals with wireless charging in sensor networks and explores efficient policies to perform simultaneous multi-mobile charging power transfer through a mobile charging vehicle. The proposed solution, called on-demand multi-mobile charging scheduling scheme (MMCSS), features selection rate (SR) based on which selection of the next charging request node is selected efficiently by considering important parameters. After selecting the node based on the SR, it is checked whether the charging is possible or not based on the next charging node possible (NNCP). Then, the shortest path is given from the MCV to selected node using Dijkstra algorithm. Various MCV charging conditions are discussed below.

Charan Ramtej Kodi, Debjit Das, Shashi Shekar
CRAWL: Cloud-Based Real-Time Interconnections of Agricultural Water Sources Using LoRa

Sree Harshitha, P. VaraPrasad, Raja Venkataraman, HrishikeshWater wells are traditional sources of water for agricultural needs. Particularly, due to ever-increasing demand from the exponentially growing population, there arises a need to balance the water demand and conserve water resources. The current systems adopted are very rudimentary and cannot be scaled. Hence, a major challenge is to investigate equitable allocation of water from excess sources to deficit ones. In this regard, this work proposes an Internet of things (IoT)-based technique to effectively manage and utilize water resources by connecting wells, ponds, lakes, etc., with a smart network of pipelines. The interconnected wells are configured with a sensors-actuation mechanism and communication devices that sense the water scarcity among wells in a network and then redistribute water accordingly. A low-cost and low-power IoT technology is used for data acquisition from sensors to auto control the actuators. A long-range wireless communication between water sources is achieved by deploying long-range (LoRa) modules, a prototype is developed, and a cloud-based app is deployed. The CRAWL—cloud-based real-time interconnections of agricultural water sources using LoRa system—is scalable and hence is capable of being developed as a rugged and robust system that can solve problems of floods, burst of rains and water shortages at not only panchayat/Taluka level, but also scaled upto district and state levels in the country.

P. Sree Harshitha, Raja VaraPrasad, Hrishikesh Venkataraman
Link Prediction Analysis on Directed Complex Network

Link prediction helps in the analysis of complex network and predicts the future possible links. Researchers developed various link prediction methods using the network topological information. The topological information depends on different types of network such as undirected network, directed network, weighted network, etc. So, designing a link prediction method based on the types of complex network is a challenging task. Methods which are suitable for undirected network cannot be applied to a directed network. Hence, for every method associated with undirected network, corresponding methods of directed network can be developed by considering the topological information associated with the directed network. In this paper, we have designed a link prediction method known as modified resource allocation (MRA) for directed complex network. In the existing directed resource allocation (DRA) method, the immediate neighbors in the path length of two is considered. Here, this resource allocation index has been extended by considering neighbors in the path length of at most three. The proposed MRA method is primarily designed to predict the probability of formation of links between the disconnected nodes in a directed network by considering the longer path length. Area under the receiver operating characteristic curve (AUC) metric is used for evaluating the performance. Based on parameter $$\sigma$$ σ , the AUC value is calculated and selected the most ideal solution. The comparative analysis of the proposed method with existing link prediction methods is performed to determine that the proposed MRA method provides better results than the existing link prediction models.

Salam Jayachitra Devi, Buddha Singh
Energy-Efficient VM Management in OpenStack-Based Private Cloud

The increase in usage of cloud data centers in recent years has led to high utilization of resources. Allocating of new virtual machines (VMs), disabling of existing hosts, and existing VM being removed are the reasons due to which the resource utilization in data centers vary over time. For efficient resource utilization, detection of the host’s load using prediction techniques is an important issue. Furthermore, host load detection enhances the scheduling which results in higher utilization of the compute, network, and storage resources. Default scheduler in OpenStack uses a worst fit algorithm for VM allocation which leaves large fragments of RAM in compute nodes. In this work, VMs are scheduled based on user request using modified best fit algorithm depending on the prediction results that minimize the unnecessary large fragments of RAM in compute nodes. The prediction of host load is carried out using machine learning and statistical models. Furthermore, as a part of continuous resource monitoring and server consolidation, load balancing is performed based on the prediction result to balance the load on all servers. This helps in the energy-efficient consolidation to optimize energy consumption of hosts. We conduct the experimentation of proposed work using OpenStack based cloud testbed in a multi-node environment. Experimental results show that machine learning model LSTM and the statistical ARIMA model give comparatively good results for PlanetLab CPU trace data set. Also, the results reveal that the load among the servers is fairly distributed, and there is a significant improvement in energy saving.

P. K. Prameela, Priyanka Gadagi, Revathi Gudi, Somashekar Patil, D. G. Narayan
Intelligent Transportation System: The Applicability of Reinforcement Learning Algorithms and Models

Krishnendhu, S. P. Mohandas, PrabuNowadays, many research works that associate real-time data widely use an unsupervised artificial intelligence (AI) technique, namely reinforcement learning (RL). Its fast adaptiveness to the dynamicity drags the attention of researchers who works in real-time traffic signal control systems. The scope of RL in most of the research problems remains remarkable with its peculiar characteristics. This paper reviews the basic concepts of RL, along with RL algorithms and models with an emphasis on traffic signal control (TSC). TSC is one among the trending applications of RL. Traffic congestion control with less human intervention is a challenging task of the intelligent transportation system (ITS). It not only helps traffic managers to get a grip over the traffic operation situation and analyze congestion, but also assists travelers to avoid congestion. Considering its significance, we have chosen TSC as the basis to explain the RL algorithms and models presented in this paper. In addition to such a comprehensive review, we have also provided a list of open challenges which when addressed can take the research in this area to considerable heights.

S. P. Krishnendhu, Prabu Mohandas
Speaker Identification Approach for the Post-pandemic Era of Internet of Things

Saleema, A. Thampi, Sabu M.With the rapid aggravation of COVID-19 pandemic, the organizations, industries, institutions, etc., are forced to rapidly adapt to social distancing by limiting physical contact and thereby limit the person to person contamination. The scenario of Internet of things in the post COVID era will be interesting indeed. Inorder to ensure public health, the social distancing and semi lockdown will continue in the foreseeable future, and therefore, the need of secure remote person authentication methods is being more and more critical especially in the Internet of things which is a multitude of networks consisting of a huge number of uniquely identifiable devices. As far as human_device authentication strategies are concerned, the one which needs less human involvement will be preferable in a post COVID-19 IoT scenario. Moreover, since the range of IoT devices may span from tiny sensors to complex machines, an authentication method which will be adaptable to each and every type of device will be more welcomed. Considering these facts, voice biometric authentication seems to be the most suitable one which can provide a balanced mix of security, adaptivity and convenience to such an advanced world of connectivity. Here, we introduce a lightweight text independent voice biometric method for IoT using extreme learning machines, and we perform a comparative analysis with a deep learning-based method of speaker identification using 3D convolutional neural networks. We have performed experimental study using different datasets and concluded that the extreme learning-based method is more suitable for IoT, considering the trade-off between the recognition accuracy and the training time requirements.

A. Saleema, Sabu M. Thampi
Random Permutation-Based Linear Discriminant Analysis for Cancelable Biometric Recognition

The increased use of biometrics in the present scenario has led to the concerns over security and privacy of the enrolled users. This is because the biometric traits like face, iris, ear, etc., are not cancelable or revocable. In case if the templates are compromised, the imposters may gain illegitimate access. To resolve such issues, a simple yet powerful technique called “random permutation-based linear discriminant analysis” for cancelable biometric recognition has been proposed in this paper. The proposed technique is established on the notion of a cancelable biometric system through which the biometric templates can be revoked and renewed. The proposed technique accepts the cancelable biometric template and a key (called PIN) issued to the user. The user’s identity is recognized only when both cancelable biometric template and PIN are valid, else the user is prohibited. The performance of the proposed technique is demonstrated on the freely available face (ORL), iris (UBIRIS), and ear (IITD) datasets against state-of-the-art methods. The key benefits of the proposed technique are (i) classification accuracy remains unaffected by using random permutation and (ii) robustness across different biometric traits.

P. Punithavathi, S. Geetha
A Deep Learning-Based Framework for Distributed Denial-of-Service Attacks Detection in Cloud Environment

The widespread cyberattack is distributed denial of service (DDoS). The unauthorized users will target the specific server or network infrastructure by flooding with malicious Internet traffic by creating the interruption in the normal traffic. The victim server will not be able to respond to legitimate traffic. The DDoS attacks recognition in real time is one of the challenging problems. The predictable solutions analyze the traffic and detect the different types of activities from captured traffic based on attributes of statistical differences. The alternate approach for identifying the performance of DDoS attacks is the analysis of the statistical features using machine learning algorithms. These detection techniques have a low detection rate and time delay. The new approach for the DDoS attack detection was proposed by capturing different patterns of sequences from the captured traffic and analysis of the high-level features using deep learning and can be used with a high detection rate. The results of the proposed methodology have demonstrated the better performance of long short-term memory (LSTM) approach with good accuracy compared to the convolutional neural network (CNN) and multilayer perceptron (MLP).

Amit V. Kachavimath, D. G. Narayan
Automation for Furnace in Thermal Power Station Using Public Key Cryptography

Data is facts and statistics collected together for reference or analysis and it must be protected from corruption or unauthorized access. Data security is practice as well as technology of securing or protecting valuable and sensitive information by means of encryption of data. It is also known as information security. Data can be guarded using various hardware and software technologies. Some common ones include antivirus, encryption, firewalls, etc. Safeguarding the sensitive data from corruption and unauthorized access protects from malicious use of the data. Cryptography refers to securing information using mathematical concepts and techniques. Data encryption software enhances data security with more efficiency. To an authorized person, the encrypted form is absolutely unreadable. The main objective of this paper is encryption and decryption of data received by temperature sensor and motion sensor that has been done using the ECC method.

M. Prathyusha, Padmanabha Nikitha, S. Rajashree, B. Prasad Honnavalli
Active Dictionary Attack on WPA3-SAE

Patel, Manthan Amritha, P.P Sam jasper, R.In wireless network, we have different protocols like WEP, WPA, and WPA2. WPA3 is currently used standard protocol in WIFI to authenticate the client with access point. In the WPA3, Simultaneous Authentication of Equals protocol downgrade attack is already discovered. With the downgrade attack, we are able to do offline dictionary attack on WPA3-SAE protocol. WPA3-SAE is also known as WPA3-Personal. Dictionary attack is classified into active dictionary attack and passive dictionary attack. Passive dictionary attack is also known as offline dictionary attack. In this paper, we proposed active attack model in which software will try different password from given dictionary word list until it connect with the Access Point. In this model, computer will change their MAC address continuously so that access point won’t detect as an attack. To speed up the process, we can use multiple virtual machines that will work as a separate wireless client to the access point.

Manthan Patel, P.P Amritha, R. Sam jasper
Multiple Hashing Using SHA-256 and MD5

Message Digest 5 (MD5) is a hashing function with numerous vulnerabilities such as pre-image vulnerability and collision vulnerability which restrict the usage of MD5. Therefore, by using other hashing functions such as SHA prior to hashing with MD5, we can use MD5 for various applications such as data integrity without compromising the security of the hash. MD5 is widely used in file transfer or storage applications because it produces a smaller hash value of 128 bits when compared with other hashing algorithms. Also, it is simpler to implement in hardware and as a program. We propose a technique of hashing the original message (or string) with secure hashing algorithms such as SHA-256 followed by hashing the hash value of SHA-256 with MD5 to get the resultant hash which is less prone to various security attacks such as collision attacks. By hashing the string twice, we make it more secure and tackle the pre-image vulnerability and collision vulnerability of MD5. This makes the hashing algorithm more secure for file transfer applications. Multiple iterations will produce more secure hash values but our simulation uses two iterations, where we upload a file onto a cloud server and check if it has been tampered with or modified.

Gautham P. Reddy, Anoop Narayana, P. Karan Keerthan, B. Vineetha, Prasad Honnavalli
Design and Analysis of a Secure Coded Communication System Using Chaotic Encryption and Turbo Product Code Decoder

Khavya, S. Balasubramanian, Karthi Yamuna, B. Mishra, DeepakErrors in a transmitted message is unavoidable since noise is inevitable in any communication channel. For reliable transmission of messages, the bit error rate has to be kept at an acceptable rate by the use of proper error control coding schemes. To ensure that the transmission is also secure, data encryption is used as an integral part of the system. This paper deals with the design and analysis of a secure and reliable communication system accomplished using logistic map-based chaotic encryption and turbo product codes. The system is simulated using MATLAB and it is shown that the use of encryption for secure communication does not degrade the system performance. The hardware design of the decoder is also done and verified in Verilog using the same set of vectors as obtained from the system simulation. BER performance was analyzed in all the different scenarios and the correctness of the design was established.

S. Khavya, Karthi Balasubramanian, B. Yamuna, Deepak Mishra
Digital Image Transmission Using Combination of DWT-DCT Watermarking and AES Technique

The Internet technology brings big revolution in twenty-first century. It facilitates communication between man-to-man, man-to-machine, machine-to-machine, and vice versa. There are many applications, viz. (i) vehicle-to-vehicle, (ii) vehicle-to-infrastructure, (iii) drone communication, etc., which transmit and receive data in various formats like image data, audio data, video data, text data, etc. Bank system uses data communication technique for transferring money through debit card, net banking, credit card, demand draft, cheques, RTGS, NEFT, etc. Bank cheques are cleared through CTS system. For clearing the cheque, it is scanned, and image is transferred to cheque clearing house. During cheque image transmission, there is a need of security, confidentiality, integrity, authorization, copyright protection, and indexing services. There are many techniques and algorithm which provide this facility to overcome this issue. To solve this issue, combination of DWT-DCT watermarking along with AES technique is used. Its performance and analysis against various attacks are also explained in this research paper.

Sudhanshu S. Gonge
An HTTP DDoS Detection Model Using Machine Learning Techniques for the Cloud Environment

The cloud computing platform has been evolved as an essential computing paradigm for today's world. As the cloud environment mainly focuses on the service model, to ensure the availability of these services to the intended user is an essential requirement. In this paper, an HTTP DDoS detection model for the cloud environment is presented. The proposed system uses machine learning-based classifiers on network flow data. Four tree-based classifiers, i.e., decision tree, random forest, XGBoost, and AdaBoost are applied to the identified parameters. The CIDDS-001 dataset were used for training and evaluation. Results obtained show that the proposed classifier can achieve 99.99% accuracy using the random forest classifier. Comparing the obtained results with the recent works available in the literature shows the proposed model outperforms it in the classification accuracy.

N. Muraleedharan, B. Janet
IoT Device Authentication and Access Control Through Hyperledger Fabric

Kurian, Bibin Subramanian, NarayananInternet of Things (IoT) is one of the sizzling technology that connects everything to everyone, everywhere. Security and privacy with confidentiality, integrity, and availability to data are among the most pressing challenge faced by IoT as well as the Internet. Networks are getting more expanded and are becoming more open, and security practices has to be uplifted to ensure protection of this rapidly growing Internet, its users, and data. In this paper, we propose a new authentication and access control mechanism for the IoT devices through a blazing blockchain technology, Hyperledger Fabric, an open-source distributed ledger platform for developing enterprise-grade permissioned blockchains. Blockchain is typically a hash-chain of blocks consisting of a number of (ordered) transactions. Fabric provides a secure and scalable permissioned platform with plug-in components that support data privacy and smartcontracts, rather than a permission-less system where anybody can access and transact data. The authentication and access control of the IoT devices is achieved by making use of newly introduced features in managing channel, chaincodes, policies, Certificate Authority (CA), and others in Hyperledger Fabric version 2.0. Our architecture has the potential to act upon different layers of the IoT in authentication and access control safeguarding the confidentiality, integrity, and availability of data.

Bibin Kurian, Narayanan Subramanian
Advances in Computing and Network Communications
Dr. Sabu M. Thampi
Dr. Erol Gelenbe
Dr. Mohammed Atiquzzaman
Dr. Vipin Chaudhary
Dr. Kuan-Ching Li
Copyright Year
Springer Singapore
Electronic ISBN
Print ISBN