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2018 | Buch

Detection and Prevention of DDoS Attacks in Wireless Sensor Networks Kapitel lesen Erstes Kapitel lesen

Networking Communication and Data Knowledge Engineering

Volume 1

herausgegeben von: Prof. Gregorio Martinez Perez, Dr. Krishn K. Mishra, Prof. Shailesh Tiwari, Dr. Munesh C. Trivedi

Verlag: Springer Singapore

Buchreihe : Lecture Notes on Data Engineering and Communications Technologies

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Data science, data engineering and knowledge engineering requires networking and communication as a backbone and have wide scope of implementation in engineering sciences. Keeping this ideology in preference, this book includes the insights that reflect the advances in these fields from upcoming researchers and leading academicians across the globe. It contains high-quality peer-reviewed papers of ‘International Conference on Recent Advancement in Computer, Communication and Computational Sciences (ICRACCCS 2016)’, held at Janardan Rai Nagar Rajasthan Vidyapeeth University, Udaipur, India, during 25–26 November 2016. The volume covers variety of topics such as Advanced Communication Networks, Artificial Intelligence and Evolutionary Algorithms, Advanced Software Engineering and Cloud Computing, Image Processing and Computer Vision, and Security. The book will help the perspective readers from computer industry and academia to derive the advances of next generation communication and computational technology and shape them into real life applications.

Inhaltsverzeichnis

Frontmatter
Erratum to: Design of Low Cost Blood Glucose Sensing System Using Diffused Reflectance Near-Infrared Light
Jyoti Yadav, Asha Rani, Vijander Singh, Bhaskar Mohan Murari

Advanced Communication Networks

Frontmatter
Detection and Prevention of DDoS Attacks in Wireless Sensor Networks
Abstract
Wireless Sensor Networks are emerging at a great pace due to their cost effective solutions for the sensitive and remote applications like military, medical and environmental applications (Chatterjee and Pandey in Int J Sci Eng Res 5, 2014) [1]. But due to limited range, memory, processing and power supply, gathering of important remote data from wireless sensors is really challenging. The use of ad hoc network and radio waves for data transmission has also increased the chance for attackers to attack on such networks. Various schemes have been proposed in the past to fight against the attacks in WSN (Sahu and Pandey in Mod Educ Comput Sci 1:65–71, 2014) [2], (Paul et al. in Wireless Sensor Network Security: A Survey. Auerbach Publications, Florida, 2006) [3]. In this paper two methods have been introduced, one is light weight two way authentication method that will prevent majority of attacks in WSN and other is traffic analysis based data filtering method that will detect and prevent DDoS attacks in WSN. The results have been verified using the Network Simulator 2 (NS2) on several performance metrics i.e. throughput, delay, lost packets, energy consumption and PDR.
Shivam Dhuria, Monika Sachdeva
Wired LAN and Wireless LAN Attack Detection Using Signature Based and Machine Learning Tools
Abstract
There are various attack which is possible in the network, it may be from externally or internally. But internal attacks are more dangerous than external. So, my mainly concern upon Wireless LAN and Wired LAN attacks which occurs internally. There are various Signature based tools, IDS/IPS (Intrusion detection or prevention system) available now-a-days for detecting these types of attacks but these are not sufficient due to high false alarm rate. So, I detect these types of attacks with three ways: through Wireshark, with signature based tools (Snort and Kismet) and with machine learning tools (WEKA). In wired LAN attack, my mainly concern on PING scan or PING flood, NMAP scan (portsweep) and ARP spoofing attacks. In wireless LAN attacks, I take care of Deauthentication attack, Disassociation attack and Access point (AP) spoofing attack. Signature based tools detect these types of the attacks based on the stored signature and timing threshold. But machine learning tools take several different feature to detect these types of attacks with more accuracy and low false positive rate.
Jaspreet Kaur
Enhancing WSN Lifetime Using TLH: A Routing Scheme
Abstract
A Wireless Sensor Network (WSN) consists of sensor nodes which are distributed over the network, where each sensor node composed of sensing unit along with limited computational power, low storage capacity and limited non-rechargeable battery source, resulting WSN to be energy constrained. In the proposed work a heterogeneous network is considered, in which uniformly placed nodes are divided into two levels of heterogeneity on the basis of energy. For the sake of long network lifetime, transmission to BS is done via Cluster Heads (CH’s) resulting in faster drainage of CH node. So to manage the load on CH’s, a new static clustering technique gained into its inception which includes CH’s of higher energy, that in addition to increase network lifetime, also will reduce hop count to BS. The simulation result shows a significant improvement in stability as well as network lifetime of the proposed technique.
Gaurav Bathla, Rajneesh Randhawa
IP Traceback Schemes for DDoS Attack
Abstract
Nowadays the Internet is exposed to a span of web threats. In the modernized era, multifarious types of attacks are discovered on the Internet, along with the utmost disastrous attack, Distributed Denial of Service (DDoS) attacks. In such course of attacks, an immense number of settle arrangement tie in with one another to make the services baseless for honest users. These composed systems frequently mask their existence by counterfeit technique. IP traceback is a way used to catch the real path of web packets in such scenario. This paper provides a schematized investigation of various IP traceback approaches with their fruitful domain and doorway for forthcoming research in this thrust expanse of IP traceback.
Vipul Mandhar, Virender Ranga
Adaptability of Various Mobility Models for Flying AdHoc Networks—A Review
Abstract
Researchers worldwide have immensely contributed towards the field of MANETs. In recent years, Unmanned Aerial Vehicles (UAVs) are being extensively used to create ad hoc networks due to their ability to be used in tactical as well as civilian areas. Interestingly, the research focus is being extended from one large UAV based network to multi-UAV ad hoc network consisting mini or micro UAVs, commonly known as Flying Ad hoc Network (FANET). There are various mobility models that are being used in MANETs and it is the right time to explore the capabilities of those mobility models for their use in FANETs. The underlying mobility model plays a vital role in simulating the performance of a routing protocol. In this paper, an analysis of various mobility models have been carried on parameters such as dependencies, connectivity metrics and real time applications with an eye on the adaptability of these models in FANETs.
Kuldeep Singh, Anil Kumar Verma
Reliable Packet Delivery in Vehicular Networks Using WAVE for Communication Among High Speed Vehicles
Abstract
This paper by way of simulation based on NS3 and SUMO shows multi-channel working of WAVE which in turn helps in defining communication among high speed vehicles and their surrounding environment. NS3 being a popular tool for simulation supports WAVE and its extension modules. The main objective is to lower the delivery time of packet among connected network of vehicles. The roads are divided based on the assumption of traffic density i.e. high and low respectively. The vehicles share their knowledge of position and fastest possible delivery path. Dijkstra based calculations are used for shortest path with weights linked to traffic density. For simplification of calculation two strategies are used to find shortest path, one relays information to the nearest road with high density, second is to simply carry and forward the information to the destination. Prior gained knowledge by a vehicle about its surrounding is used for selection of best path. The paper shows the reliable packet delivery to conventional methods such as Vehicle Assisted Data Delivery (VADD) in city traffic conditions.
Arjun Arora, Nitin Rakesh, Krishn K. Mishra
Integration of IEC 61850 MMS and IEEE 802.22 for Smart Grid Communication
Abstract
The reliability of Smart Grid depends on two-way communication between substation and utility. IEC 61850 is an international standard defined to ensure interoperability between Substation Automation System (SAS). IEC 61850 services are mapped on the Manufacturing Message Specification (MMS) especially for meter data management system. MMS is the OSI protocol that run over TCP/IP or OSI networks to support IEC 61850 services. Presently, the MMS uses Ethernet as the layer 2 protocol. However, for long distance communication in remote areas wireless communication is the prefered mode. The Cognitive Radio based IEEE 802.22 is next generation standard for Wireless Regional Area Network (WRAN) that can support long distance wireless communication for low-latency, high-volume, reliable and secure communication. This paper shows the potential to integrate IEC 61850 MMS with IEEE 802.22 for long distance Smart Grid communication.
Vasudev Dehalwar, Akhtar Kalam, Mohan Lal Kolhe, Aladin Zayegh, Anil Kumar Dubey
KRUSH-D Approach for the Solution to Node Mobility Issue in Underwater Sensor Network (UWSN)
Abstract
The most frequently experienced challenge in Underwater Wireless Sensor Network (UWSN) is the mobility issue i.e. the nodes present underwater changes their position from one place to another, therefore affecting the entire communication in the network. In our previous work, we proposed a 2D-based solution namely “Arc moment” for the node mobility. This approach used the Euclidean 2D distance formula and various other assumptions to efficiently maintain the communication. In this paper, we have proposed a 3D based approach named KRUSH-D, which is a further enhancement to our previous 2D approach. The proposed approach that has now been put forward rectifies the mobility issue, and also maintains the communication in the network at the same time. It brings in use the Euclidean 3D distance calculation and the famous KRUSKAL algorithm for path selection. The main objective here is to provide a solution for maintaining reliable communication in UWSN. The proposed work is examined using example(s) in order to provide readers clarity.
Nishit Walter, Nitin Rakesh, Rakesh Matam, Shailesh Tiwari
NS2 Based Structured Network Attack Scrutiny in MANET
Abstract
Wireless networks are widely used in almost areas and the urging of users has inspired the development of Mobile Ad Hoc Network (MANET). MANET is a dynamic Network which does not require any backbone or infrastructure network as it deploys peer to peer connection between nodes. There are numerous implementations of MANET ranging from defense to multiuser gaming which necessitates the need to secure our network from various trespassers and attacks. Various attackers use numerous approaches to degrade the network performance. The paper proposes classification to network attacks on MANET by distinguishing them in a structured way with NS2 simulated results. The objective of this paper is to know various attacks and their respective measures to be taken to detect and prevent these attacks.
Kamal Nayan Chaturvedi, Ankur Kohli, Akshat Kamboj, Shobhit Mendiratta, Nitin Rakesh
EKMT-k-Means Clustering Algorithmic Solution for Low Energy Consumption for Wireless Sensor Networks Based on Minimum Mean Distance from Base Station
Abstract
EKMT-k-means clustering algorithmic solution is one of the well known methods among all the partition based algorithms to partition a data set into group of patterns. This paper presents an energy efficient k-means clustering algorithm named EKMT which is based on concept of finding the cluster head minimizing the sum of squared distances between the closest cluster centers and member nodes and also considers the minimum distance between cluster heads and base station. In the proposed protocol the effort was made to improve the energy efficiency of the protocol by re-selecting the cluster head among the most possible cluster heads on the basis of the least distance between new selected cluster head and the base station thus improves throughput and delay.
Bindiya Jain, Gursewak Brar, Jyoteesh Malhotra
A Review of FIR Filter Designs
Abstract
Digital filters are commonly used as an essential element of Digital Signal Processing (DSP) System. Digital filter can be used for developing many designs, which are impractical or impossible in Analog filter. Digital filters may be more expensive than an equivalent analog filter due to their increased complexity. Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) are the two types of digital filters; FIR filters are preferred over IIR filters due their properties like inherent stability and linear phase. In this paper various techniques for FIR filter has been described and analyzed various factors which effects the performance of FIR filter in communication system especially in multi-standard communication.
Bharat Naresh Bansal, Amanpreet Singh, Jaskarn Singh Bhullar
Analysis of Safety Applications in VANET for LTE Based Network
Abstract
Safety applications in VANET are fundamentally dependent on communicating safety messages. Such messages are delay-critical and transmission parameters need to be kept in check for effective communication among vehicles. As long term evaluation (LTE) is infrastructure based direct communication is not possible among vehicles, such messages need to pass through the infrastructure. In case of safety applications extra care needs to be taken as high speed vehicles are more reactive to delay than slow speed vehicles. This is because high speed vehicles change their positions more frequently. Higher priority needs to be given to such vehicles in order to lower the delay. Characterization of delay requirements is needed which in turn can be used to compute path loss, fading, transmission range, rate and access probability in order to satisfy the delay requirements. In this paper a scheduler is proposed for LTE network suitable for safety applications in VANET. This scheduler takes into account the vehicular speed (VS) and Vehicular Location (VL) to assign priority and resources. High speed vehicles receive a higher priority for allocation of network resources. Simulation of the proposed scheduler (PS) shows that it performs better in comparison to largest weighted delay first (LWDF) algorithm which is considered to be the best choice for development of safety applications in VANET.
Arjun Arora, Nitin Rakesh, Krishn K. Mishra
Broadcast Storm Problem—A Hidden Consequence of Content Distribution in Content Delivery Networks
Abstract
Content caching at the Internet edge is becoming very popular because of flash crowd problem. Content Delivery Networks (CDN) was evolved for content caching with a complete solution for network reliability, scalability and performance enhancement. A number of researches have been focused on CDN issues like replica server placement, content caching, request routing and accounting management. But still some more issues are yet to be solved. This paper focuses on the concept of Broadcast Storm Problem (BSP) in CDN due to content distribution and request routing. Several approaches are available for content distribution and content caching. When there is any update in any surrogate, the same has to be communicated to all other servers over the network to avoid data inconsistency. Simple flooding or gossiping is generally used for the same, but these approaches are accompanied with BSP. Numerous BSP algorithms have been evolved, but the main concern is the wireless sensor networks. In this paper a comparison among several BSP algorithms has been shown which reveals that counter-based approach is much simpler and can be applied to any network. In addition to the comparative analysis, the counter-based scheme gets modified for CDN.
Darothi Sarkar, Nitin Rakesh, Krishn K. Mishra

Artificial Intelligence and Evolutionary Algorithms

Frontmatter
Attribute Reduction Method Using the Combination of Entropy and Fuzzy Entropy
Abstract
The enormous size datasets are being used in various fields such as administration, engineering, management and so on. For information retreival from these datasets more time is being consumed. Fewer attribute datasets takes lesser time for computation, and are more understandable and intelligible. Attribute reduction is a tool for feature selection as it transforms data into knowledge. A new method using the combination of entropy and fuzzy entropy is proposed for removal of redundancy and irrelevant attributes which results in reducing the dataset size. The functioning of the proposed method is examined on standard datasets such as Sonar, Spambase and Tick-tack-Toe. Experimental results performed on various datasets show that proposed method gives significant improvement in attribute reduction. In this work, nearest neighbor classifier is used to examine the classification accuracy on original and reduced dataset.
Rashmi, Udayan Ghose, Rajesh Mehta
Evaluation of Factors Responsible for Road Accidents in India by Fuzzy AHP
Abstract
India is a developing country fighting against its population growth. There has been an exponential growth in the number of automobiles in India to meet the needs of growing population. Road safety is one of the major concern in our country. Although the Indian government has been trying to tackle this issue for several years, yet there is a substantial increase in the number of road crashes which has become the major cause of death. The Methodology adopted for identifying the most crucial factor of road accidents is based upon Fuzzy-AHP technique. A multi-criteria decision making (MCDM) model is constructed which takes seven different criteria as inputs from different literature reviews and practical investigations to assign different priorities/weights to the seven criteria. The weights of criteria are usually characterized by fuzzy numbers. In this paper data for 35 States was collected from National Crime Records Bureau, Ministry of Road Transport and Highway, Global status report on road safety 2013. Finally, the decision is made by the computational process and effectiveness of Fuzzy AHP.
Shivam Nanda, Shalini Singh
Development of Knowledge Capsules for Custom-Tailored Dissemination of Knowledge of Seismic Data Interpretation
Abstract
Knowledge management system is a repository of factual information. Seismic data interpretation is a field of exploration geophysics, which deals with interpretation of seismic images, to infer subsurface geology and provide information regarding hydrocarbon accumulation. This knowledge of interpretation is rare, expensive and largely individualistic. Lack of formal interpretation rules, causes seismic experts to use their own expertise gained over years of experience, leading to uncertainty. In current work a knowledge management framework is proposed, which initiates with the knowledge engineer gathering tacit knowledge from seismic experts, followed by a knowledge manager, synchronizing, sequencing, formalizing and organizing it in explicit form to develop a knowledge capsule, to facilitate its sharing through tutoring. Knowledge capsules have been refined to effectively suit different levels and knowledge grasping preferences of novice seismologists.
Neelu Jyothi Ahuja, Ninni Singh, Amit Kumar
Design of Low Cost Blood Glucose Sensing System Using Diffused Reflectance Near-Infrared Light
Abstract
The present work proposes a low cost and portable Non-Invasive Blood Glucose Measurement (NIGM) system based on Near-infrared (NIR) light. In vitro system using single LED (940 nm) with an array of photodetectors is fabricated. Regression analysis is carried out to study the relationship between detector output voltage and actual glucose concentration. Low RMSEC (reflectance: 12.87 mg/dl, transmittance: 15 mg/dl) of in vitro measurement motivated to design a sensor patch for non-invasive in vivo glucose measurement. The accuracy of our indigenous device was tested by comparing non-invasively estimated blood glucose with invasively measured blood glucose. To estimate the glucose concentration from the detector voltage signal an ADAptive Linear NEuron (ADALINE) based Neural Network structure is used. The calibration model is prepared using data of 10 non-diabetic subjects. The observed RMSEP was 14.92 mg/dl with correlation coefficient (0.87) in the case of In vivo experiment. The prediction of glucose concentration is in the clinically acceptable region of the standard Clark Error Grid (CEG). The proposed design of NIR light based glucose measurement can be used to develop an NIGM system.
Jyoti Yadav, Asha Rani, Vijander Singh, Bhaskar Mohan Murari
Hybrid Chunker for Gujarati Language
Abstract
Gujarati is a first language of Indian state of Gujarat and furthermore for union territories of Daman and Diu and Dadra and Nagar Haveli. Chunking is the basic technique for any language processing work. It is a method of identifying and splitting the text into correlated small pieces (or chunks). The objective for presented research work is to build Hybrid Chunker for Gujarati. Initially baseline system using Hidden Markov Model is developed and then correction rules are applied on it. The annotated corpus of one lakh sentences developed by TDIL, MoCIT, GoI in association with Department of Gujarati, Gujarat University, Ahmedabad is used. The system is trained on 80,000 sentences of chunked Gujarati text and tested on 20,000 sentences. Afterwards, Post-processing is done with rules on output of baseline system. By applying these rules, the system achieved improvement in overall accuracy and it is 95.31%.
Parneet Kaur, Vishal Goyal, Kritida Shrenik Shah, Umrinderpal Singh
Sound Localization in 3-D Space Using Kalman Filter and Neural Network for Human like Robotics
Abstract
Sound Localization is the process of identifying direction (with distance) and location of the source from which the sound is detected. It is one of the important functions of human brain. In brain sound localization is done through the neurons present in it. The sound signals from the outside world are come inside the brain through the ear. In this paper, the process of Sound Localization activity performed by human brain that incorporates realistic neuron models is discussed and the accurate position of the sound sources by using the Kalman filter and neural network is examined. The results demonstrate that finding position in 3D is more accurate as compared in 2D as its average error gets reduced. This work can be used to detect the location of the sound sources in three dimensions and can be also implemented in robots and cochlear implants for treating hearing loss.
Aakanksha Tyagi, Sanjeev Kumar, Munesh Trivedi
Analysis of Stock Prices and Its Associated Volume of Social Network Activity
Abstract
Stock market prediction has been a convenient testing ground and a highly cited example for applying machine learning techniques to real-life scenarios. However, most of these problems using twitter feeds to analyze stock market prices make use of techniques such as sentimental analysis, mood scoring, financial behavioral analysis, and other such similar methods. In this paper, we propose to discover a correlation between the stock market prices and their associated twitter activity. It is always observed that whenever there are spikes in the stock market prices, there is a preceding twitter activity indicating the imminent spike in the aforementioned stock price. Our objective is to discover this existence of a correlation between the volumes of tweets observed when a market indices’ stock price spikes and the amount by which the stock price changes, with the help of machine learning techniques. If this correlation does exist, then an attempt is made to figure out a mathematical relationship between the two factors.
Krishna Kinnal, K. V. Sanjeev, K. Chandrasekaran
On Solutions to Capacitated Vehicle Routing Problem Using an Enhanced Ant Colony Optimization Technique
Abstract
This paper presents an enhanced ant colony optimization (ACO) algorithm for solving the capacitated vehicle routing problem (CVRP). CVRP is the most elementary version of VRP, but also a difficult combinatorial problem which contains both the TSP (routing) and BPP (packing) problems as special cases. In the CVRP a number of vehicles having uniform capacity starts and terminates at a common depot, services a set of customers with certain demands at minimum transit cost. In this paper, an enhanced version of ACO algorithm is implemented on Fisher and Christofides benchmark problems. Computational results compared with the performance of different algorithms and relaxations are presented. These results indicate that the proposed algorithm is a comparative approach to solve CVRP. The article is concluded by examining the possible future research directions in this field.
Ashima Gupta, Sanjay Saini
Music Recommendation System with User-Based and Item-Based Collaborative Filtering Technique
Abstract
Internet and E-commerce are the generators of abundant of data, causing information Overloading. The problem of information overloading is addressed by Recommendation Systems (RS). RS can provide suggestions about a new product, movie or music etc. This paper is about Music Recommendation System, which will recommend songs to users based on their past history i.e. taste. In this paper we proposed a collaborative filtering technique based on users and items. First user-item rating matrix is used to form user clusters and item clusters. Next these clusters are used to find the most similar user cluster or most similar item cluster to a target user. Finally songs are recommended from the most similar user and item clusters. The proposed algorithm is implemented on the benchmark dataset Last.fm. Results show that the performance of proposed method is better than the most popular baseline method.
M. Sunitha, T. Adilakshmi
GLCM Feature Extraction for Insect Bites Pattern Recognition
Abstract
This paper describes the elements that are vital for feature extraction process from a Grey Level Co-occurrence Matrix. Every pattern recognition model consists of a primary phase where Feature Extraction is implemented, that focuses on determining distinct parameters from a given data. With respect to data set of images, which are a complex form of data, it is very difficult to analyze it’s features due to its nature. Image processing community is inundated with research on classification processes, none has been done on classification of insect bites ever before. This paper will propose a model to extract features from images of insect bites which can further be used so as to classify insect bites based on their vectors. Computer aided diagnosis can be achieved with successful detection of insect bites, that can aid at remote locations, such as Forests. Textural analysis of insect bite can help in classification of insect bites. The search for image point correspondences involves finding the interest points, neighborhood of those interest points are represented using vectors and finally the vectors are matched with the targeted image.
Abdul Rehman Khan, Nitin Rakesh, Rakesh Matam, Shailesh Tiwari
A Novel Hybrid PSO-DA Algorithm for Global Numerical Optimization
Abstract
Recent trend of research is to hybridize two and more algorithms to obtain superior solution in the field of optimization problems. In this context, a new technique hybrid Particle Swarm Optimization (PSO)—Dragonfly Algorithm (DA) is exercised on some unconstraint benchmark test functions and overcurrent relay co-ordination optimization problems in contrast to test results on constrained/complex design problem. Hybrid PSO-DA is combination of PSO used for exploitation phase and DA for exploration phase in uncertain environment. Position and Velocity of particle is updated according to position of dragonflies in each iteration. Analysis of competitive results obtained from PSO-DA validates its effectiveness compare to standard PSO and DA algorithm separately.
Indrajit N. Trivedi, Pradeep Jangir, Arvind Kumar, Narottam Jangir, R. H. Bhesdadiya, Rahul Totlani
Short-Term Electricity Price Forecasting Using Hybrid SARIMA and GJR-GARCH Model
Abstract
The liberalization of the power markets gained a remarkable momentum in the context of trading electricity as a commodity. With the upsurge in restructuring of the power markets, electricity price plays a dominant role in the current deregulated market scenario which is majorly influenced by the economics being governed. Electricity price has got great affect on the market and is used as a basic information device to evaluate the future markets. However, highly volatile nature of the electricity price makes it even more difficult to forecast. In order to achieve better forecast from any model, the volatility of the electricity price need to be considered. This paper proposes a price forecasting approach combining wavelet, SARIMA and GJR-GARCH models. The input price series is transformed using wavelet transform and the obtained approximate and detail components are predicted separately using SARIMA and GJR-GARCH model respectively. The case study of New South Wales electricity market is considered to check the performance of the proposed model.
Vipin Kumar, Nitin Singh, Deepak Kumar Singh, S. R. Mohanty
Backmatter
Metadaten
Titel
Networking Communication and Data Knowledge Engineering
herausgegeben von
Prof. Gregorio Martinez Perez
Dr. Krishn K. Mishra
Prof. Shailesh Tiwari
Dr. Munesh C. Trivedi
Copyright-Jahr
2018
Verlag
Springer Singapore
Electronic ISBN
978-981-10-4585-1
Print ISBN
978-981-10-4584-4
DOI
https://doi.org/10.1007/978-981-10-4585-1

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