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Über dieses Buch

The book is a collection of high-quality peer-reviewed research papers presented in the first International Conference on Signal, Networks, Computing, and Systems (ICSNCS 2016) held at Jawaharlal Nehru University, New Delhi, India during February 25–27, 2016. The book is organized in to two volumes and primarily focuses on theory and applications in the broad areas of communication technology, computer science and information security. The book aims to bring together the latest scientific research works of academic scientists, professors, research scholars and students in the areas of signal, networks, computing and systems detailing the practical challenges encountered and the solutions adopted.

Inhaltsverzeichnis

Frontmatter

Signal Processing Systems and Applications

Frontmatter

DFT-DCT Combination Based Novel Feature Extraction Method for Enhanced Iris Recognition

Iris Recognition (IR) using conventional methods is a challenging domain, and incorporating a combination of two transforms along with proposed novel extraction technique possesses the efficacy to address the problem at hand. This paper throws light upon the proposed unique Combined DFT-DCT feature extraction along with the inclusion of a disc shaped morphological structuring element in the preprocessing stage. Two novel methods, namely astroid and astroid ring shaped extraction techniques are proposed, and Binary Particle Swarm Optimization (BPSO) based algorithm for feature selection has been employed to procure the optimal subset of features from the feature space. Experimental results that have been obtained by implementing the proposed technique on two standard iris databases, IITD and MMU, lucidly outline the promising performance of the astroid shaped feature extraction resulting in a significant increase in rate of recognition accompanied by considerably lower number of features for iris recognition.

Anunita Raghu, Meghana Gundlapalli, K. Manikantan

Novel Digital Image Watermarking in SWT+SVD Domain

In this paper, Digital Watermarking is carried out in the frequency domain and the technique proposed uses single level Stationary Wavelet Transform (SWT) along with Singular Value Decomposition (SVD). SWT is used over other transformations because of its non-decimation and shift invariance property. The singular values of SWT transformed watermark image is embedded into the singularly decomposed HH sub-band (sub-image) of R, G or B channel of a Host color image. The experimental results of watermarked images shows increase in Peak Signal to Noise Ratio (PSNR) and the extracted watermark image is highly correlated with the original watermark for various types attacks.

Nikhil Purohit, M. Chennakrishna, K. Manikantan

An Improved Histogram Bin Shifting Based Reversible Data Hiding of Color Images

Reversible data hiding technique recovers the original image bit by bit after extracting the watermark bits from the watermarked image. In this paper, a novel reversible data hiding technique is proposed for color images. The proposed technique is based on histogram bin shifting technique, which is an efficient and widely used method for embedding the watermark in gray scale images in reversible manner. The proposed scheme extends the concept of histogram modification on RGB color images. However, as compared to basic histogram modification technique (used for reversible data hiding) which utilizes zero point and peak point, proposed scheme utilizes first peak point and second peak point so that the distortion generated due to the pixels shifting can be minimized. The successful application of proposed scheme on several standard color test images and experimental results in terms of higher PSNR values demonstrate the effectiveness of the proposed scheme.

Smita Agrawal, Manoj Kumar

Face Recognition Using Background Removal Based on Eccentricity and Area Using YCbCr and HSV Color Models

The process of Face Recognition is complicated due to the background, pose variations in the images. Using the Pre-processing techniques proposed in this paper the essential invariant features in an image have been made available for extraction. Background Removal based on Eccentricity is implemented by incorporating both YCbCr and HSV color models to eliminate unnecessary features in the background. Multi-scaled fusion is included for nullifying the variation in pose. Next, the images are subjected to feature extraction using two-dimensional Discrete Wavelet Transform (DWT) and feature selection algorithm. Experimental results show the effectiveness of the above mentioned techniques for face recognition on two benchmark face databases, namely, CMU-PIE and Caltech.

Amith Lawrence, N. V. Manoj Ashwin , K. Manikantan

An Efficient Multi-focus Image Fusion Approach Based on DWT

In this paper, a Discrete Wavelet Transform (DWT) based approach for multi-focus image fusion using a novel coefficients selection scheme is proposed. The proposed method enables the decomposition of source images into low and high frequency sub-bands. To combine the low and high frequency sub-band coefficients of the transformed images, pixel averaging method and gradient based fusion rule are applied to the various frequency sub-bands. Finally, we applied inverse DWT and obtained an enhanced fused image. The performance of the proposed scheme is evaluated on various multi-focus images and experimental results are also compared with existing methods. Experimental results demonstrate that the proposed scheme is better and effective in terms of Peak-Signal-to-Noise-Ratio (PSNR).

Sonam, Manoj Kumar

A Novel Fuzzy Filter for Mixed Impulse Gaussian Noise from Color Images

Removal of Mixed noise from digital color images is a challenging task because it requires processing of different types of noise. Also, noise need to be distinguished from the original image structures such as edges and details. Fuzzy theory is an effective solution to this problem. In this paper, a new fuzzy method is proposed to reduce impulse and Gaussian noise from color images. A weighted averaging filtering operation is used for this purpose. The weights in the averaging process are assigned using a fuzzy rule system based on a new certainty function, so as to reduce both noise types and to preserve image structures. Experimental results show that the method outperforms the state-of-the-art filters.

M. Jayasree, N. K. Narayanan

Face Recognition Using Snakes Algorithm and Skin Detection Based Face Localization

Skin detection is an important part of face localization since the most exposed part of human skin is the face. This paper proposes a novel algorithm for face localization via skin detection. The algorithm utilizes a kernel iterative procedure to check for the region of interest in an image where it is likely that the face exists. The algorithm utilizes a swarm of particles in a kernel that randomly check the fitness of the corresponding pixel BGR value which is determined from a skin BGR dataset. Consequently, the kernel which has the best fitness value is chosen as the region of the image where it is likely that the face exists. Following this, we employ an active contour model called Snakes algorithm which further converges on our region of interest and finally a contour of the face region is extracted from the original image.

Rakshit Ramesh, Anoop C. Kulkarni, N. R. Prasad, K. Manikantan

Quantifiable Image Nearness Approach Using Descriptive Neighbourhood

Similarity metrics plays an important role in Content-Based Image Retrieval (CBIR). This article introduces a new technique called Descriptive Proximal Coverage (DPC) to measure the quantifiable similarity index between images. This work is based on Near Neighbourhood approach in which perceptually relevant information is extracted from group of objects based on their description. Two images are considered as sets of perceptual objects and affinities between objects is defined by a tolerance relation. Two visual objects are similar if the difference between their descriptions is smaller than a tolerable level of error. Existing Notion of nearness stems from the observation that in nature it is rare to find exact objects but similar objects are often seen. It is imperative to provide a numeric value which will quantify similarity and nearness between images.

M. Sajwan, K. S. Patnaik

Robust Speaker Verification Using GFCC Based i-Vectors

This paper presents to ameliorate the performance of text-independent speaker recognition system in a noisy environment and cross-channel recordings of the utterances. In this paper presents the combination of Gammatone Frequency Cepstral Coefficients (GFCC) to handle noisy environment with i-vectors to handle the session variability. Experiments are evaluated on NIST-2003 database.

Medikonda Jeevan, Atul Dhingra, M. Hanmandlu, B. K. Panigrahi

Enhanced Automatic Speech Recognition with Non-acoustic Parameters

A novel method for improving the accuracy of automatic speech recognition system by adding non-acoustic parameters are discussed in this paper. The gestural features which are commonly co-expressive with speech is considered for improving the accuracy of ASR system in noisy environment. Both dynamic and static gestures are integrated with speech recognition system and tested in various environmental conditions, i.e., noise levels. The accuracy of continuous speech recognition system and isolated word recognition system are tested with and without gestures under various noise conditions. The addition of visual features provides stable recognition accuracy under different environmental noise conditions for acoustic signals.

N. S. Sreekanth, N. K. Narayanan

Dynamic Gesture Recognition—A Machine Vision Based Approach

Computationally simple method for dynamic hand gesture recognition is presented in this paper. The segmentation of hand which take parts in gesture production is being addressed tow different ways using color band based segmentation algorithms. The first one uses the special color stickers as part of finger and the second one does segmentation based on normal skin color. The movement of hand is tracked and Freeman’s eight directional code is generated corresponds to each gestures. A dynamic time wrapping based Levenshtein minimum edit distance algorithm is used for classification. The results of dynamic hand gestures with special color approach and without special color are discussed separately. The accuracy of the system is found to be more for special colour based segmentation than skin colour based segmentation techniques.

N. S. Sreekanth, N. K. Narayanan

Medical Image Security with Cheater Identification Using Secret Sharing Scheme

Security of medical images and relevant patient information is a matter of important concern while using public networks for transfer of medical images between patients and clinicians. Clinicians require to confirm the legitimacy of patient medical images for applications such as telediagnosis and teleconsultation. Furthermore, medical image should not be perceivable to unauthorized parties with malicious intentions on the patient’s health. As a result, medical images must be protected with suitable primitives while transferring them over public channel. In this paper, we present a scheme for protecting medical images using a threshold secret sharing scheme. The proposed scheme protects images from unauthorized access and intermediate tampering, thus, ensuring confidentiality and integrity of the shared images and associated patient records. The scheme takes into consideration the possibility of malevolence from any of the participating clinicians and detects and identifies cheating among the clinicians, if any. The proposed scheme is analyzed and simulated with electronic patient records and the experimental results satisfy all the properties of the scheme.

Arun Krishnan, Manik Lal Das

The Role of Fractal Dimension, Lacunarity and Multifractal Dimension for Texture Analysis in SAR Image—A Comparison Based Analysis

In present paper, a fractal approach to study the texture in SAR images has been explored and the utility and problems of fractals for texture analysis are discussed. Since satellite images are rich in texture, they have to be studied in details for texture analysis. In present study, an ERS2 SAR image has been used for estimation of fractal dimension, lacunarity and multifractal dimension where the texture has been studied on the basis of these parameters and compared. A conclusion regarding the applicability of these three parameters has been drawn in the study.

Triloki Pant

Efficient Storage and Processing of Video Data for Moving Object Detection Using Hadoop/MapReduce

Technological advances and easy availability of low cost video camera has further encouraged users for deploying network of surveillance systems. These systems generate massive data. Thus, storage and processing of the huge video data for application such as video forensics, post event investigation etc., has emerged as a challenging problem to the research community. In this paper we propose a powerful approach that makes use of Hadoop Distributed File System (HDFS) for efficient storage of video data and programming model called MapReduce for data intensive computing. The proposed approach detects moving objects and provides their coordinates which can be used for localizing post event investigation. We have analyzed the storage and processing of video data of varying resolution and size to assess the performance of proposed approach.

Jyoti Parsola, Durgaprasad Gangodkar, Ankush Mittal

Performance Evaluation of Digital Color Image Watermarking Using Column Walsh Wavelet Transform

This paper proposes a robust watermarking technique using wavelet transform generated from well-known orthogonal transform Walsh. Watermark embedding is done in middle frequency band of column wavelet transformed host image. Performance of proposed technique is evaluated against image processing attacks like compression, cropping, addition of run length noise with binary and Gaussian distribution and image resizing. Comparison of performance of these transforms is done on the basis of robustness to attacks using Mean Absolute Error (MAE) as a metric of robustness. Column wavelet is found preferable over full wavelet. Also column Walsh wavelet is preferable over column DCT wavelet for robustness.

Hemant B. Kekre, Shachi Natu, Tanuja Sarode

Structural (Shape) Feature Extraction for Ear Biometric System

The Ear Biometrics is an emerging modern human identification system, which is a passive biometrics system where the recognition of an individual is done without the knowledge of the human subject. This kind of passive biometrics system can help the image analyst to extract useful information feature for public surveillance system. The proposed article extracts structure (shape) features such as Area, Perimeter, Eccentricity, Elongation, Compactness, Horizontal Height, Vertical Height, Major Axis, Minor Axis, Circularity and Rectangularity of the human ear to construct an effective ear biometrics system.

P. Ramesh Kumar, S. S. Dhenakaran

Networking Theory and Distributed Systems

Frontmatter

DHAC Based Routing in Wireless Sensor Network with Asymmetric Links

In Wireless Sensor Network (WSN), various routing strategies have been adopted to prolong the network lifetime. Clustering is the important technique in comparison to all the other existing routing techniques. Proposed algorithm adopts the hierarchical structure for cluster formation and selecting cluster head in distributed approach. In this paper, clustering of nodes is carried out by considering the asymmetric communication links between nodes. Energy consumption in proposed technique is reduced and hence lifetime of the network is improved. The Simulation is carried out in MATLAB7.9 and the obtained result verifies that proposed algorithm increased network as compared to the existing routing protocols.

Laxita Vyas, C. P. Gupta, Md Arquam

Automatization of AAOCC to Find Trust Score of Websites

Today World Wide Web has emerged as a second world. Everything we can think of is now available on this digital world. In the real world there exists many kinds of people. Some are good, some are bad, and some are trustworthy while the rest are liars. Similarly, in the digital world, there exits many websites out of which some are good while the rest are useless. So the big question is how can one know which content on www is trustworthy as every information can be changed with a few keystrokes. A lot of algorithms have been developed to identify the trust rank of a website, but none of them are up to the mark. So through this paper, we propose a simple mechanism by which we can test a website and automatically calculate the trust score of the website on the basis of not a single parameter but a cumulative combination of five parameters. Various test results have been included in this paper which prove that the method proposed in this paper is much better than the conventional methods which test websites manually.

Manish Kumar Verma, Sarowar Kumar, Kumar Abhishek, M. P. Singh

A Multi-level Weight Based Routing Algorithm for Prolonging Network Lifetime in Cluster Based Sensor Networks

Energy efficiency in routing is an important design issue in wireless sensor networks where nodes are battery operated which may or may not be rechargeable in many cases. Due to relaying high volume data packets, nodes closer to the base station consume more energy than other nodes in the network. In this paper, we propose a Multi-level Weight based Routing Algorithm (abbreviated shortly as MWRA) for cluster based sensor networks with an aim to minimize inter-cluster energy consumption and to balance the energy dissipation at nodes. In MWRA, clusterheads are assigned levels based on the distance between the clusterheads and the base station and then a 2-connected backbone network is formed to find energy efficient next hop nodes. Moreover, a weight function based routing technique is used based on which clusterhead chooses its relay node for forwarding the packets. In MWRA, the network reconfiguration is performed periodically to achieve balanced energy consumption among clusterheads. The experimental results show that our algorithm significantly improves the network lifetime and the energy efficiency of the network than the existing clustering algorithm.

Priyanka Pukhrambam, Sanghita Bhattacharjee, Himanish Shekhar Das

An Approach to Optimize Unnecessary Energy Consumption During Dynamic Routing in Wireless Sensor Networks

The paper proposes an approach to optimize unbalanced energy consumption during dynamic routing in WSN (wireless sensor network). The concept of intermediate nodes are generally used to route or transmit overloaded data. In a normal scenario, when gathered data is not overloaded and are to be transmitted to the required destination, presence of intermediate nodes might pose a communication delay causing the overall energy to reduce by the certain amount. We have analyzed the given problem to ensure optimality and thus reduce the delay due to intermediate nodes in WSN.

Narasimha Kamath, U. K. Anirudh

Game Theoretic Modeling of Gray Hole Attacks in Wireless Ad Hoc Networks

Wireless ad hoc networks rely on the cooperation of participating nodes to undertake activities such as routing. Malicious nodes participating in the network may refuse to forward packets and instead discard them to mount a denial-of-service attack called a packet drop or blackhole attack. Blackhole attacks can however be easily detected using common networking tools like trace route as all packets passing through the malicious node is dropped. A gray hole attack on the other hand accomplishes denial of service by selectively dropping packets thus escaping detection. In this paper, a novel two player incomplete information extensive form game is used to model the defender and the attacker both of whom are considered rational agents in an effort to determine their optimal (equilibrium) strategies under different values for the parameters true detection rate, false alarm rate, packet value, packets forwarded per unit time, probability of the node being a gray hole, cost of exposure of the attacker and cost of not using a node for the defender. The respective equilibrium strategies if followed guarantee maximum possible protection for the defender and maximal possible damage potential for the attacker.

Chintan Ketankumar Doshi, Sreecharan Sankaranarayanan, Vidyashankar B. Lakshman, K. Chandrasekaran

Chi-Square Based Mobile Radio Propagation Model Analysis and Validation

In urban and semi-urban areas, the ever increasing population is creating high raised structures and increasing tele-density. It is becoming difficult for the mobile network providers to offer quality service to the mobile user. One of the main reasons causing degradation in signal quality is multipath propagation. Because of this, the Received Signal Strength (RSS) may be either reduced or completely attenuated at the receiver. So modelling and characterisation of the channel is necessary. If there is no line-of-sight signal component from transmitting station to the receiver, then the envelop of the received signal can be statistically described by Rayleigh distribution. In this paper, real time mobile RSS in terms of power (in dBm) is recorded, analysed and its quality is tested using theoretical Rayleigh distribution and also validated using Chi-square fitness-of good test.

Lavanya Vadda, G. Sasibhushana Rao, L. Ganesh

Collision Theory Based Sentiment Detection of Twitter Using Discourse Relations

Social networking sites such as Twitter are contributing to a large increase in the growth of data today, and are a rich source for sentiment detection or mining. This research employs collision theory to achieve a query based sentiment detection of Twitter data with discourse analysis. Hadoop has been exploited for speed. Our experiments show effective results.

Anuta Mukherjee, Saswati Mukherjee

Malicious Account Detection Based on Short URLs in Twitter

The popularity of Social Networks during the last several years have attracted attention of cyber-criminals for spreading of spam and malicious contents. In order to send spam messages to lured users, spammers creating fake profiles, leading to fraud and/or malware campaigns. Sometimes to send malicious messages, cyber-criminals use stolen accounts of legitimate users. Nowadays they are creating short URLs by the short URL service provider and post it on friend’s board. Lured users unknowingly clicking on these links, are redirected to malicious websites. To control such type of activities over Twitter we have calculated a trust score for each user. Based on the trust score, one can decide whether a user is trustable or not. With usage of trust score, we have achieved accuracy of 92.6 % and F-measure of 81 % with our proposed approach.

Rasula Venkatesh, Jitendra Kumar Rout, S. K. Jena

Distance, Energy and Link Quality Based Routing Protocol for Internet of Things

In future communication networks with IoT, each of the things will be able to communicate with other things ubiquitously throughout the time clock. Multipath distortion, noise and interference create problems for low power communication devices. These smart devices contain limited amount of battery, energy and processing power. In this context this dissertation proposes Distance, Energy and Link quality based Routing protocol (DELR) to enhance routing success probability and to minimize route setup delay. Finally, the performance of the proposed algorithm is evaluated with respect to the protocol: REL considering the metric such as routing success probability and route setup delay in the various rounds. The results demonstrate that the performance of proposed algorithm is better than the compared algorithm: REL in terms of routing success probability and route setup delay on the simulated network in IoT applications.

Kirshna Kumar, Sushil Kumar, Omprakash Kaiwartya

Effect and Suppression of Noise in 2D PC/OOC Scheme for Optical CDMA Systems

The performance of 2D wavelength/time optical code division multiple access (OCDMA) system is adversely affected by the presence of noise at its physical layer. It is present in the form of Multiple Access Interference (MAI) and beat noise in OCDMA systems and degrades its performance. In this paper an attempt has been made to determine as well as to mitigate the effect of noise using either Optical Hard Limiter (OHL) or Coherent detection technique for a system employing prime sequence permutations over time spreading optical orthogonal codes (OOCs). Investigations reveal that performance of system is severely affected by noise and it can be improved by using coherent detection as well as hard limiter. Specifically for this PC/OOC coding technique, it has been reported in the results that coherent detection for reducing the noise outperforms the use of optical hard limiter.

Manisha Bharti, Ajay K. Sharma, Manoj Kumar

On-the-Fly Segment Density (OFSD) in Adaptive Beaconing System (ABS) Based Connectivity-Aware Geocast Routing (CAGR) in VANETs

Vehicular Ad-hoc Networks (VANETs) is a Critical Communication Infrastructure (CII), a part of Intelligent Transportation System (ITS) that enable the system to minimize vehicular accident by reducing the traffic congestion. The embedment of connectivity analysis in designing of geocast routing is the in-separable phase in VANETs for fulfillment of the said objective. High mobility, link disconnection and uneven distribution of vehicular nodes makes analysis of connectivity in VANETs imperative. This paper proposes On-the-Fly Segment Density (OFSD) in Adaptive Beaconing System (ABS) based Connectivity-Aware Geocast Routing (CAGR) protocol which enhances the data delivery ratio by guaranteeing assured connectivity. Simulation result shows that the proposed scheme works better when the no of nodes is higher, as compared to greedy routing.

Durga Prasada Dora, Sushil Kumar, Puspanjali Mallik

Investigation and Analysis of Energy Efficiency in Distributed Antenna System: Technology Towards Green Communications

Distributed Antenna Systems has the potential to get higher Spectral and Energy efficiencies. This paper focused on implementation of DAS system in moderate and low load modes. It compares DAS with CAS and proved that Energy efficiency is excellent in DAS. We considered different load scenarios and investigated power consumption parameter and proposed a novel DAS implementation to reduce the overall power consumption per day. The results revealed that the proposed system efficiency is far ahead than CAS. We achieved 27 % energy savings too with the guarantee of high speed and capacity.

Seetaiah Kilaru, S. Padmaja, K. Venugopal Reddy

A Novel Trust Based Access Control Model for Cloud Environment

Cloud computing is a service oriented technology which offers the services (IaaS, PaaS, and SaaS) as a utility over the Internet. Since cloud computing is one of the most popular form of Internet application, the resources and services in cloud environment is more vulnerable to security threats and attacks. In order to protect the cloud environment from malicious users, we proposed a novel trust based access control model. The proposed model authorize the user based on user trust value before entering to cloud environment. The user must be trusted before accessing the resources and the resources must be trusted before providing the services to the user. In this paper, we evaluate the trust value of both user and cloud resources. The user trust value is evaluated based on the user behaviour parameter and the resource trust value is evaluated based on the Service Level Agreement (SLA) parameter. If the trust value of both users and cloud resources are more than their threshold value then they are considered as trusted. We implement the proposed model using java and oracle as database server. The implementation result shows the trust value of different type of users and CSP and compare with the QoS model. The proposed model performs better than QoS model in terms of Rate of Successful Transaction (RST).

Pratap Kumar Behera, Pabitra Mohan Khilar

Live News Streams Extraction for Visualization of Stock Market Trends

The live news data is vital role in the movement of stock prices. The real time unstructured data is generated through electronic and online news sources. Text mining is used for preprocessing of news stories from web sources. The visualization of stock trends can be correlated with actual market prices. The proposed analysis on current news stories helps to predict stock trends. Other techniques like tagging of stock related terms can be added for improvement in results. Stock market trends can be captured with help of this technique.

Vaishali Ingle, Sachin Deshmukh

Categorization of Cloud Workload Types with Clustering

The paper presents a new classification schema of IaaS cloud workloads types, based on the functional characteristics. We show the results of an experiment of automatic categorization performed with different benchmarks that represent particular workload types. Monitoring of resource utilization allowed us to construct workload models that can be processed with machine learning algorithms. The direct connection between the functional classes and the resource utilization was shown, using unsupervised categorization approach based on moving average for finding a class number, and k-means algorithm for clustering.

Piotr Orzechowski, Jerzy Proficz, Henryk Krawczyk, Julian Szymański

Development of a General Search Based Path Follower in Real Time Environment

The path planning problem of an Unmanned Ground Vehicle in a predefined structured environment is dealt in this paper. Here the environment chosen as the roadmap of NIT Rourkela obtained from Google maps as reference. An Unmanned Ground Vehicle (UGV) is developed and programmed so as to move autonomously from an indicated source location to the defined destination in the given map following the most optimal path. An algorithm based on linear search is implemented to the autonomous robot to generate shortest paths in the environment. The developed algorithm is verified with the simulations as well as in experimental environments.

B. B. V. L. Deepak, G. Raviteja, Upasana Behera, Ravi Prakash

SDN Architecture on Fog Devices for Realtime Traffic Management: A Case Study

Software Defined Network has become one of the most important technology to manage the large scale networks. The separation of the control plane from the data plane in networking devices is the main idea of SDN. Currently, Open Flow is the popular SDN standard, which has a set of functionalities. In the emerging cloud scenario smart devices plays an important role. But they are facing latency and intermittent connectivity. For this fog devices are placing in-between cloud and smart devices. Fog computing is currently applying on connected vehicles, sensor network etc. This article looks into the vehicular network area as a case study where SDN architecture can apply on fog devices for enhancement of the performance and betterment of traffic management and QoS on distribution of real time data.

Kshira Sagar Sahoo, Bibhudatta Sahoo

Maximizing Network Lifetime of Wireless Sensor Networks: An Energy Harvesting Approach

Energy preservation is very crucial in wireless sensor networks as they are operated in hostile and non-accessible areas. The use of renewable energy sources is an alternative technique for extending lifetime of a sensor network where the battery-driven sensor nodes run out of battery power faster. In this paper, we study and solve the problem of extending network lifetime by introduce energy-harvesting (EH) sensor nodes and propose a clustering algorithm to extend the network lifetime. In the proposed algorithm, we present an efficient scheme for cluster head selection by considering the locations of EH sensor nodes and all of these EH sensor nodes serve as relay nodes to the cluster heads. Simulation results and their theoretical analysis show that the proposed algorithm outperforms the existing algorithm.

Srikanth Jannu, Prasanta K. Jana

Hybrid Network Intrusion Detection Systems: A Decade’s Perspective

With the increasing deployment of network systems, network attacks are increasing in intensity as well as complexity. Along with these increasing network attacks, many network intrusion detection techniques have been proposed which are broadly classified as being signature-based, classification-based, or anomaly-based. A deployable network intrusion detection system (NIDS) should be capable of detecting of known and unknown attacks in near real time with very low false positive rate. Supervised approaches for intrusion detection provides good detection accuracy for known attacks, but they can not detect unknown attacks. Some of the existing NIDS emphasize on unknown attack detection by using unsupervised anomaly detection techniques, but they can not distinguish network data as accurately as supervised approaches. Moreover they do not consider some other important issues like real time detection or minimization of false alarm. To overcome these problems, in the recent years many hybrid NIDS have been proposed which are basically aimed at detecting both known and unknown attacks with high accuracy of detection. In this literature review on hybrid network intrusion detection systems, we will discuss a few of the notable hybrid NIDS proposed in the recent years and will try to provide a comparative study on them.

Asish Kumar Dalai, Sanjay Kumar Jena

Backmatter

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