Survey of Security Protocol in Wirless Sensor Networks

The sensing technology combined with processing power and wireless communication makes it profitable for being exploited in great quantity in future. Some researchers are fighting to develop improved WSN protocols, others are attempting to ameliorate node design; still others are working to resolve security issues including the focal WSN security risk of insecure radio links with snooping and information corruption possible. Most security mechanisms that exist today require exhaustive memory which is the limiting factor in wireless sensor networks. This paper discusses typical restrictions, security objectives, threat models and distinctive attacks on sensor networks and their protective techniques or countermeasures relevant to the sensor networks, including security approaches. The most risky area prone to attack is nearby the base station as the data is more aggregated, that should be kept secure using a number of defensive techniques

Diabetes Prediction: A Study of Various Classification based Data Mining Techniques

Data Mining is an integral part of KDD (Knowledge Discovery in Databases) process. It deals with discovering unknown patterns and knowledge hidden in data. Classification is a pivotal data mining technique with a very wide range of applications. Now a day’s diabetic has become a major disease which has almost crippled people across the globe. It is a medical condition that causes the metabolism to become dysfunctional and increases the blood sugar level in the body and it becomes a major concern for medical practitioner and people at large. An early diagnosis is the starting point for living well with diabetes. Classification Analysis on diabetic dataset is a part of this diagnosis process which can help to detect a diabetic patient from non-diabetic. In this paper classification algorithms are applied on the Pima Indian Diabetic Database which is collected from UCI Machine Learning Laboratory. Various classification algorithms which are Naïve Bayes Classifier, Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Support Vector Classifier and XGBoost Classifier are analyzed and compared based on the accuracy delivered by the models

Development of Robust Iris Localization and Impairment Pruning Schemes

Iris is the sphincter having flowery pattern around pupil in the eye region. The high randomness of the pattern makes iris unique for each individual and iris is identified by the scientists to be a candidate for automated machine recognition of identity of an individual. The morphogenesis of iris is completed while baby is in mother's womb; hence the iris pattern does not change throughout the span of life of a person. It makes iris one of the most reliable biometric traits. Localization of iris is the first step in iris biometric recognition system. The performance of matching is dependent on the accuracy of localization, because mislocalization would lead the next phases of biometric system to malfunction. The first part of the thesis investigates choke points of the existing localization approaches and proposes a method of devising an adaptive threshold of binarization for pupil detection. The thesis also contributes in modifying conventional integrodifferential operator based iris detection and proposes a modified version of it that uses canny detected edge map for iris detection. The other part of the thesis looks into pros and cons of the conventional global and local feature matching techniques for iris. The review of related research works on matching techniques leads to the observation that local features like Scale Invariant Feature Transform(SIFT) gives satisfactory recognition accuracy for good quality images. But the performance degrades when the images are occluded or taken non-cooperatively. As SIFT matches keypoints on the basis of 128-D local descriptors, hence it sometimes falsely pairs two keypoints which are from different portions of two iris images. Subsequently the need for filtering or pruning of faulty SIFT pairs is felt. The thesis proposes two methods of filtering impairments (faulty pairs) based on the knowledge of spatial information of the keypoints. The two proposed pruning algorithms (Angular Filtering and Scale Filtering) are applied separately and applied in union to have a complete comparative analysis of the result of matching

Procedural Building Generation

Tato práce se zabývá problematikou procedurálního generování budov. K implementaci nástroje vyvinutého vrámci této práce byla vybrána metoda generování prostřednictvím generativních gramatik. V práci jsou popsané některé vhodné metody pro řešení dané problematiky. Obsahuje popis návrhu, implementace aplikace procedurálně generující budovy a zhodnocení dosažených výsledků. Výsledná aplikace umožnuje exportování vygenerovaných modelů bodov do formátu  Wavefront .obj.This thesis deals with procedural building generation. The method which was chosen for implementation of the tool created during this study is based on generative grammars. In this document are introduced some of the advisable methods for solving given problem. It deals with predesign, implementation and evaluation of the results of the application for procedural building generation created in this work. The final tool also allows export of the generated models to Wavefront .obj format.

Techniques to enhance the lifetime of mobile ad hoc networks

Devices in Mobile Ad Hoc Networks (MANETs) are mostly powered by battery. Since the battery capacity is fixed, some techniques to save energy at the device level or at the protocol stack should be applied to enhance the MANETs lifetime. In this thesis, we have proposed a few energy saving approaches at the network layer, and MAC layer. First, we proposed a routing technique, to which the following metrics are built into: (i) node lifetime, (ii) maximum limit on the number of connections to a destination, and (iii) variable transmission power. In this technique, we consider a new cost metric which takes into account the residual battery power and energy consumption rate in computing the lifetime of a node. To minimize the overutilization of a node, an upper bound is set on the number of connections that can be established to a destination. The proposed technique is compared with AODV [1] and LER [2]. It outperforms AODV and LER in terms of network lifetime. Next, a technique called Location Based Topology Control with Sleep Scheduling (LBTC) is proposed. It uses the feature of both topology control approach in which the transmission power of a node is reduced, and power management approach in which nodes are put to sleep state. In LBTC the transmission power of a node is determined from the neighborhood location information. A node goes to sleep state only when: (i) it has no traffic to participate, and (ii) its absence does not create a local partition. LBTC is compared with LFTC [3] and ANTC [4]. We observed that the network lifetime in LBTC is substantially enhanced. A framework for post-disaster communication using wireless ad hoc networks is proposed. This framework includes: (i) a multi-channel MAC protocol, (ii) a node-disjoint multipath routing, and (iii) a distributed topology aware scheme. Multi-channel MAC protocol minimizes the congestion in the network by transmitting data through multiple channels. Multipath routing overcomes the higher energy depletion rate at nodes associated with shortest path routing. Topology aware scheme minimizes the maximum power used at node level. Above proposals, taken together intend to increase the network throughput, reduce the end-to-end delay, and enhance the network lifetime of an ad hoc network deployed for disaster response

Feature Based Segmentation of Colour Textured Images using Markov Random Field Model

The problem of image segmentation has been investigated with a focus on colored textured image segmentation.Texture is a substantial feature for the analysis of different types of images. Texture segmentation has an assortment of important applications ranging from vision guided autonomous robotics and remote sensing to medical diagnosis and retrieval in large image databases. But the main problem with the textured images is that they contain texture elements of various sizes and in some cases each of which can itself be textured.Thus the texture image segmentation is widely discerned as a difficult and thought-provoking problem.In this thesis an attempt has been made to devise methodologies for automated color textured image segmentation scheme. This problem has been addressed in the literature, still many key open issues remain to be investigated. As an initial step in this direction, this thesis proposes two methods which address the problem of color texture image segmentation through feature extraction approach in partially supervised approach.The feature extraction approaches can be classified into feature based and model based techniques.In feature based technique features are assessed without any model in mind. But in case of model based approach an inherent mathematical model lets eatures to be measured by fitting the model to the texture.The inherent features of the texture are captured in a set of parameters in order to understand the properties generating the texture. Nevertheless, a clear distinction can not be made between the two approaches and hence a combination of approaches from different categories is frequently adopted. In textured image segmentation, image model assumes a significant role and is developed by capturing salient spatial properties of an image. Markov random field (MRF)theory provides a convenient and consistent way to model context dependent entities.In this context a new scheme is proposed using Gaussian MRF model where the segmentation problem is formulated as a pixel labeling problem.The a priori class labels are modeled as Markov random field model and the number of classes is known a priori in partially supervised framework.The image label estimation problem is cast in Bayesian framework using Maximum a Posteriori (MAP)criterion and the MAP estimates of the image labels are obtained using iterated conditional modes (ICM) algorithm. Though the MRF model takes into account the local spatial interactions, it has a limitation in modeling natural scenes of distinct regions. Hence in our formulation, the first scheme takes into account within and between color plane interactions to incorporate spectraland contextual features. Genetic algorithm is employed for the initialization of ICM algorithm to obtain MAP estimates of image labels. The faster convergence property of the ICM algorithm and global convergence property of genetic algorithm are hybridized to obtain segmentation with better accuracy as well as faster convergence

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Integrative Assessment and Modelling of the Non Timber Forest Products Potential in Nuba Mountains of Sudan by Field Methods, Remote Sensing and GIS

Pressure imposed at any one place or point in time results in a complexity of spatial and temporal interactions within topographical ecosystems. It can be propagated through the system and may have implications for future ecosystem functions over a wide array of various spatial and temporal scales. Under conditions of wars and other socio-economic conflicts, these processes are most forceful in developing countries amidst declining economic growth, lack of awareness, deterioration of ecosystem services, loss of existing traditional knowledge bases and weak governance structures. Forests are an essential part of ecosystem services, not only as a resource but as a contributor to biological systems as well. They represent one of the most important sectors in the context of Environmental Change (EC), both from the point of mitigation as well as adaptation. While forests are projected to be adversely impacted under EC, they are also providing opportunities to mitigate these changes. Yet this is one of the least understood sectors, especially at the regional level - many of its fundamental metrics such as mitigation potential, vulnerability and the likely impacts of EC are still not well understood until now. Thus, there is a need for research and field investigations into the synergy of mitigation and adaptation so that the cost of addressing EC impacts can be reduced and the co-benefits can be increased. The aim of this study is to focus particularly on forest-based ecosystem services and to use forests as a strategy for inducing environmental change within the Nuba Mountains in Sudan, specifically for systems in poor condition under EC, and furthermore to explore forests as an entry point for investigating the relationship between urban and rural development and ecosystem services. In addition, the aim is also to raise understanding of the relations between patterns of local-level economic and demographic changes, the nature and value of local ecosystem services, and the role of such services in increasingly interlinked urban and rural livelihood systems. The methodology applied in the current research is three-pronged: a formal literature review, a socio–economic survey (based on semi-structured interviews of household heads via Rapid Rural Appraisal (RRA), with a focus on group discussions, informal meetings, free listening and key informant techniques), and multitemporal optical satellite data analysis (i.e. Landsat and RapidEye). Landsat imagery was utilized to gather the spatial characteristics of the region and to study the Land Use/Land Cover (LU/LC) changes during the period from 1984 to 2014. Meanwhile, RapidEye imagery was used to generate the tree species distribution map. Qualitative and quantitative techniques were applied to analyze socio-economic data. Moreover, Food Consumption Score (FCS) was used to gauge both diversity and frequency of food consumption in surveyed areas. Geographic object-based image analysis (i.e. K-Nearest Neighbour classifier and knowledge-based classifiers) based on a developed model of integrated features (such as vegetation indices, DEM, thematic layers and meteorological information) was applied. Post Classification Analysis (PCA) as well as Post Change Detection (PCD) techniques were used. Hotspot analysis was conducted to detect the areas affected by deforestation. Furthermore, Ordinary Least Squares regression (OLS), Autocorrelation (Moran's) analysis, and Geographically Weighted Regression analyses (GWR) were applied to address the interaction of the different socioeconomic/ecological factors on Non Timber Forest Products (NTFPs) collection and to simulate the dependency scenarios of NTFPs along with their impact on poverty alleviation. Additionally, simulation was performed to estimate the future forest density and predict the dependency on forest services. An increasing impact of intensive interactions between the rural and urban areas has long been acknowledged. However, recent changes in the global political economy and environmental systems, as well as local dynamics of the study area driven by war, drought and deforestation, have led to an increasing rapidity and depth in rural transformation, as well as to a significant impact on urban areas. Like most environmental problems, the effects of these drivers are complex and are stressed diversely across different geographic regions by the socio-political processes that underlie recent economic and cultural globalization. These interactions and processes have increasingly brought rapid changes in land cover, social, institutional and livelihood transformation across broad areas of South Kordofan. Moreover, the study unveils new dynamics such as high rates of migration and mobility by the indigenous population and the increasing domination of market-centric livelihoods in many villages that were once dominated by rural agricultural and natural resourcesbased socio-economic systems. Furthermore, the research highlights the significant roles of NTFPs and trees in contributing to Nuba Mountains’ economic development, food security and environmental health, indicating which requirements need to be addressed in order to improve these potentials. The study proves that drawing on a wide range of these products for livelihood strengthens rural people’s ability to deal with and adapt to both EC and extreme events. Moreover, the results underline the importance of participatory approaches of rural women and their impact on NTFPs management with recommendations of more emphasis on potential roles and the ability of women to participate in public fora. Furthermore, the study shows that the use of high-resolution satellite imagery, integrated with model-based terrestrial information, provides a precise knowledge about the magnitude and distribution of LU/LC patterns. These methods can make an important contribution towards a better understanding of EC dynamics over time. The study reveals that more information exchange is needed to inform actors and decision makers regarding specific experiences, capacity gaps and knowledge to address EC. Subsequently, new policies and strategies are required to much more specifically focus on how to deal with consequences of longer-term EC rather than with the impacts of sudden natural disasters

Sécurisation par dynamiques chaotiques des réseaux locaux sans fil au niveau de la couche MAC

The security of wireless sensor network is a growing field of research hampered by limited battery life time and computing constraints. The originality of this thesis is to provide Low Power chaotic cryptosystems for sensor networks more suitable than conventional algorithms and achieve an implementation on a real platform.. We present first a state of the art of wireless networks, threats and constraints of the security process as well as conventional cryptographic techniques. We give an overview of the chaos theory and we validate the randomness of several chaotic maps by the NIST statistical tests. Then, we propose new methods of chaotic S-Box construction, while demonstrating their robustness against traditional attacks. Finally, we propose a new image encryption algorithm dedicated to wireless sensor network. Validation of our contributions is performed by simulation and experimental measurements on a platform of real sensor networks (SensLab).Les travaux de recherche de cette thèse s’inscrivent dans le cadre de la sécurité par chaos des réseaux locaux sans fil, en particulier les réseaux de capteurs sans fil. L’originalité de cette thèse consiste à proposer des cryptosystèmes à base de chaos plus adaptés aux réseaux de capteurs, en termes de consommation d’énergie, que les algorithmes conventionnels et à réaliser une implémentation sur une plateforme réelle. Nous présentons en premier lieu un état de l’art des réseaux, les menaces, les contraintes limitant le processus de sécurité des informations ainsi que les principales techniques de cryptographie. Nous donnons un aperçu sur la théorie de chaos et nous validons l’aspect aléatoire de plusieurs suites chaotiques par les tests statistiques du NIST. Nous proposons ensuite des nouvelles méthodes de construction de S-Box chaotiques tout en prouvant leur robustesse contre les attaques traditionnelles. Nous proposons enfin un nouvel algorithme de cryptage d’image dédié au réseau de capteurs sans fil. La validation de nos contributions est effectuée par simulation et par des mesures expérimentales sur une plateforme de réseaux de capteurs réels (SensLab)

Usability evaluation of an e-learning tutorial using two evaluation methods

The use of interactive e-learning tutorials is an effective form of teaching and learning. It is therefore important that attention is paid to their usability. This research relates to the evaluation of a CD-based e-learning tutorial for learning Business English, with the aims of investigating its usability and identifying problems. Particular attention is paid to aspects that hinder the learner from achieving the learning objectives. The study uses two usability evaluation methods (UEMs), namely controlled usability testing in an HCI laboratory and a user questionnaire survey. The main aim of the study is to compare the findings and determine the impact of using two methods in combination. The first outcome of the research was a synthesized framework of evaluation criteria that was applied in the two UEMs. Secondly, findings of the evaluations indicated that two UEMs identified similar problems, thus confirming their reliability in usability evaluation. Another finding was instances where one method produced results not obtained by the other, which shows the complementary value of two different UEMs. A third benefit of the study was that it identified usability problems in the target system.Information SystemsMA (Information Systems