An adaptive cluster based routing scheme for mobile wireless sensor networks

Clustering schemes improve energy efficiency of wireless sensor networks. The inclusion of mobility as a new criterion for the cluster creation and maintenance adds new challenges for these clustering schemes. Cluster formation and cluster head selection is done on a stochastic basis for most of the algorithms. In this paper we introduce a cluster formation and routing algorithm based on a mobility factor. The proposed algorithm is compared with LEACH-M protocol based on metrics viz. number of cluster head transitions, average residual energy, number of alive nodes and number of messages lost

Channel Adaptive MAC Protocol with Traffic-Aware Distributed Power Management in Wireless Sensor Networks-Some Performance Issues

In Wireless Sensor Networks (WSN), neglecting the effects of varying channel quality can lead to an unnecessary wastage of precious battery resources and in turn can result in the rapid depletion of sensor energy and the partitioning of the network. Fairness is a critical issue when accessing a shared wireless channel and fair scheduling must be employed to provide the proper flow of information in a WSN. In this paper, we develop a channel adaptive MAC protocol with a traffic-aware dynamic power management algorithm for efficient packet scheduling and queuing in a sensor network, with time varying characteristics of the wireless channel also taken into consideration. The proposed protocol calculates a combined weight value based on the channel state and link quality. Then transmission is allowed only for those nodes with weights greater than a minimum quality threshold and nodes attempting to access the wireless medium with a low weight will be allowed to transmit only when their weight becomes high. This results in many poor quality nodes being deprived of transmission for a considerable amount of time. To avoid the buffer overflow and to achieve fairness for the poor quality nodes, we design a Load prediction algorithm. We also design a traffic aware dynamic power management scheme to minimize the energy consumption by continuously turning off the radio interface of all the unnecessary nodes that are not included in the routing path. By Simulation results, we show that our proposed protocol achieves a higher throughput and fairness besides reducing the delay

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year

Image Denoising Using Sure-Based Adaptive Thresholding In Directionlet Domain

The standard separable two dimensional wavelet transform has achieved a great success in image denoising applications due to its sparse representation of images. However it fails to capture efficiently the anisotropic geometric structures like edges and contours in images as they intersect too many wavelet basis functions and lead to a non-sparse representation. In this paper a novel de-noising scheme based on multi directional and anisotropic wavelet transform called directionlet is presented. The image denoising in wavelet domain has been extended to the directionlet domain to make the image features to concentrate on fewer coefficients so that more effective thresholding is possible. The image is first segmented and the dominant direction of each segment is identified to make a directional map. Then according to the directional map, the directionlet transform is taken along the dominant direction of the selected segment. The decomposed images with directional energy are used for scale dependent subband adaptive optimal threshold computation based on SURE risk. This threshold is then applied to the sub-bands except the LLL subband. The threshold corrected sub-bands with the unprocessed first sub-band (LLL) are given as input to the inverse directionlet algorithm for getting the de-noised image. Experimental results show that the proposed method outperforms the standard wavelet-based denoising methods in terms of numeric and visual quality

Spatial Variability and Contamination of Heavy Metals in the Inter-tidal Systems of a Tropical Environment

Heavy metals in the surface sediments of the two coastal ecosystems of Cochin, southwest India were assessed. The study intends to evaluate the degree of anthropogenic influence on heavy metal concentration in the sediments of the mangrove and adjacent estuarine stations using enrichment factor and geoaccumulation index. The inverse relationship of Cd and Zn with texture in the mangrove sediments suggested the anthropogenic enrichment of these metals in the mangrove systems. In the estuarine sediments, the absence of any significant correlation of the heavy metals with other sedimentary parameters and their strong interdependence revealed the possibility that the input is not through the natural weathering processes. The analysis of enrichment factor indicated a minor enrichment for Pb and Zn in mangrove sediments. While, extremely severe enrichment for Cd, moderate enrichment for Zn and minor enrichment of Pb were observed in estuarine system. The geo accumulation index exhibited very low values for all metals except Zn, indicating the sediments of the mangrove ecosystem are unpolluted to moderately polluted by anthropogenic activities. However, very strongly polluted condition for Cd and a moderately polluted condition for Zn were evident in estuarine sediments

Seasonal Variability Of Dissolved Nutrients In Mangrove Ecosystems Along South West Coast Of Kerala, India

The mangroves of Kerala are fast disappearing due to developmental activities.There are very few studies conducted in the chemical aspects of these ecosystems.The main objective of this study is to assess the spatial and seasonal variation of hydrographical as well as nutrients in mangrove ecosystems along Kerala coast. Five sampling sites least intervened by industries were selected for the study. Sampling was done for a period of six months in monthly intervals. A monsoonal hike of dissolved nutrients was observed in all ecosystems except in the constructed mangrove wetland. The constructed wetland exhibited a different hydrography and nutrient level in all seasons. The mangrove forest in this area consists of the species Bruguiera gymnorrhiza which has been planted since forty years.

Development of Directionally Adaptive Techniques for Single Image Super-Resolution

Super Resolution problem is an inverse problem and refers to the process of producing a High resolution (HR) image, making use of one or more Low Resolution (LR) observations. It includes up sampling the image, thereby, increasing the maximum spatial frequency and removing degradations that arise during the image capture namely aliasing and blurring. The work presented in this thesis is based on learning based single image super-resolution. In learning based super-resolution algorithms, a training set or database of available HR images are used to construct the HR image of an image captured using a LR camera. In the training set, images are stored as patches or coefficients of feature representations like wavelet transform, DCT, etc. Single frame image super-resolution can be used in applications where database of HR images are available. The advantage of this method is that by skilfully creating a database of suitable training images, one can improve the quality of the super-resolved image. A new super resolution method based on wavelet transform is developed and it is better than conventional wavelet transform based methods and standard interpolation methods. Super-resolution techniques based on skewed anisotropic transform called directionlet transform are developed to convert a low resolution image which is of small size into a high resolution image of large size. Super-resolution algorithm not only increases the size, but also reduces the degradations occurred during the process of capturing image. This method outperforms the standard interpolation methods and the wavelet methods, both visually and in terms of SNR values. Artifacts like aliasing and ringing effects are also eliminated in this method. The super-resolution methods are implemented using, both critically sampled and over sampled directionlets. The conventional directionlet transform is computationally complex. Hence lifting scheme is used for implementation of directionlets. The new single image super-resolution method based on lifting scheme reduces computational complexity and thereby reduces computation time. The quality of the super resolved image depends on the type of wavelet basis used. A study is conducted to find the effect of different wavelets on the single image super-resolution method. Finally this new method implemented on grey images is extended to colour images and noisy images

Spatially Adaptive Image Denoising Techniques Using Directionlets

The aim of the thesis was to design and develop spatially adaptive denoising techniques with edge and feature preservation, for images corrupted with additive white Gaussian noise and SAR images affected with speckle noise. Image denoising is a well researched topic. It has found multifaceted applications in our day to day life. Image denoising based on multi resolution analysis using wavelet transform has received considerable attention in recent years. The directionlet based denoising schemes presented in this thesis are effective in preserving the image specific features like edges and contours in denoising. Scope of this research is still open in areas like further optimization in terms of speed and extension of the techniques to other related areas like colour and video image denoising. Such studies would further augment the practical use of these techniques.

Anisotropic adaptive resolution of boundary layers for heat conduction problems

We deal with the numerical solution of heat conduction problems featuring steep gradients. In order to solve the associated partial differential equation a finite volume technique is used and unstructured grids are employed. A discrete maximum principle for triangulations of a Delaunay type is developed. To capture thin boundary layers incorporating steep gradients an anisotropic mesh adaptation technique is implemented. Computational tests are performed for an academic problem where the exact solution is known as well as for a real world problem of a computer simulation of the thermoregulation of premature infants.

The role of ligneous vegetation for livestock nutrition in the sub-Sahelian and Sudanian zones of West Africa: Potential effects of climate change

Climate change and variability in sub-Saharan West Africa is expected to have negative consequences for crop and livestock farming due to the strong dependence of these sectors on rainfall and natural resources, and the low adaptive capacity of crops farmers, agro-pastoralist and pastoralists in the region. The objective of this PhD research was to investigate the anticipated impacts of expected future climate change and variability on nutrition and grazing management of livestock in the prevailing extensive agro-pastoral and pastoral systems of the Sahelian and Sudanian zones of Burkina Faso. To achieve this, three studies were undertaken in selected village territories (100 km² each) in the southern Sahelian (Taffogo), northern Sudanian (Nobere, Safane) and southern Sudanian (Sokouraba) zone of the country during 2009 and 2010. The choice of two villages in the northern Sudanian zone was guided by the dichotomy between intense agricultural land use and high population density near Safane, and lower agricultural land use in the tampon zone between the village of Nobere and the National Park Kaboré Tambi of Pô. Using global positioning and geographical information systems tools, the spatio-temporal variation in the use of grazing areas by cattle, sheep and goats, and in their foraging behaviour in the four villages was assessed by monitoring three herds each per species during a one-year cycle (Chapter 2). Maximum itinerary lengths (km/d) were observed in the hot dry season (March-May); they were longer for sheep (18.8) and cattle (17.4) than for goats (10.5, p<0.05). Daily total grazing time spent on pasture ranged from 6 - 11 h with cattle staying longer on pasture than small ruminants (p<0.05). Feeding time accounted for 52% - 72% of daily time on pasture, irrespective of species. Herds spent longer time on pasture and walked farther distances in the southern Sahelian than the two Sudanian zones (p<0.01), while daily feeding time was longer in the southern Sudanian than in the other two zones (p>0.05). Proportional time spent resting decreased from the rainy (June - October) to the cool (November - February) and hot dry season (p<0.05), while in parallel the proportion of walking time increased. Feeding time of all species was to a significantly high proportion spent on wooded land (tree crown cover 5-10%, or shrub cover >10%) in the southern Sahelian zone, and on forest land (tree crown cover >10%) in the two Sudanian zones, irrespective of season. It is concluded that with the expansion of cropland in the whole region, remaining islands of wooded land, including also fields fallowed for three or more years with their considerable shrub cover, are particularly valuable pasturing areas for ruminant stock. Measures must be taken that counteract the shrinking of wooded land and forests across the whole region, including also active protection and (re)establishment of drought-tolerant fodder trees. Observation of the selection behaviour of the above herds of cattle and small ruminant as far as browse species were concerned, and interviews with 75 of Fulani livestock keepers on use of browse as feed by their ruminant stock and as remedies for animal disease treatment was undertaken (Chapter 3) in order to evaluate the consequence of climate change for the contribution of browse to livestock nutrition and animal health in the extensive grazing-based livestock systems. The results indicated that grazing cattle and small ruminants do make considerable use of browse species on pasture across the studied agro-ecological zones. Goats spent more time (p<0.01) feeding on browse species than sheep and cattle, which spent a low to moderate proportion of their feeding time on browsing in any of the study sites. As far as the agro-ecological zones were concerned, the contribution of browse species to livestock nutrition was more important in the southern Sahelian and northern Sudanian zone than the southern Sudanian zone, and this contribution is higher during the cold and hot dry season than during the rainy season. A total of 75 browse species were selected on pasture year around, whereby cattle strongly preferred Afzelia africana, Pterocarpus erinaceus and Piliostigma sp., while sheep and goats primarily fed on Balanites aegyptiaca, Ziziphus mauritiana and Acacia sp. Crude protein concentration (in DM) of pods or fruits of the most important browse species selected by goats, sheep and cattle ranged from 7% to 13% for pods, and from 10% to 18% for foliage. The concentration of digestible organic matter of preferred browse species mostly ranged from 40% to 60%, and the concentrations of total phenols, condensed tannins and acid detergent lignin were low. Linear regression analyses showed that browse preference on pasture is strongly related to its contents (% of DM) of CP, ADF, NDF and OM digestibility. Interviewed livestock keepers reported that browse species are increasingly use by their grazing animals, while for animal health care use of tree- and shrub-based remedies decreased over the last two decades. It is concluded that due to climate change with expected negative impact on the productivity of the herbaceous layer of communal pastures browse fodder will gain in importance for animal nutrition. Therefore re-establishment and dissemination of locally adapted browse species preferred by ruminants is needed to increase the nutritional situation of ruminant stock in the region and contribute to species diversity and soil fertility restoration in degraded pasture areas. In Chapter 4 a combination of household surveys and participatory research approaches was used in the four villages, and additionally in the village of Zogoré (southern Sahelian zone) and of Karangasso Vigué (northern Sudanian zone) to investigate pastoralists’ (n= 76) and agro-pastoralists’ (n= 83) perception of climate change, and their adaptation strategies in crop and livestock production at farm level. Across the three agro-ecological zones, the majority of the interviewees perceived an increase in maximum day temperatures and decrease of total annual rainfall over the last two decades. Perceptions of change in climate patterns were in line with meteorological data for increased temperatures while for total rainfall farmers’ views contrasted the rainfall records which showed a slight increase of precipitation. According to all interviewees climate change and variability have negative impacts on their crop and animal husbandry, and most of them already adopted some coping and adaptation strategies at farm level to secure their livelihoods and reduce negative impacts on their farming system. Although these strategies are valuable and can help crop and livestock farmers to cope with the recurrent droughts and climate variability, they are not effective against expected extreme climate events. Governmental and non-governmental organisations should develop effective policies and strategies at local, regional and national level to support farmers in their endeavours to cope with climate change phenomena; measures should be site-specific and take into account farmers’ experiences and strategies already in place.

Assessing adaptation – Climate change and indigenous livelihood in the Andes of Bolivia

Based on a case study of Charazani – Bolivia, this article outlines the understanding of adaptive strategies to cope with climate change and its impact on environmental and socioeconomic conditions that are affecting rural livelihoods. Mainly qualitative methods were used to collect and analyze data following the framework for vulnerability assessments of a socio-ecological system. Climate data reveals an increase of precipitation and temperature during the last decades. Furthermore the occurrence of extreme weather events, particularly drought, frost, hailstorms and consequently landslides and fire are increasing. Local testimonies highlight these events as the principle reasons for agricultural losses. This climatic variability and simultaneous social changes were identified as the drivers of vulnerability. Yet, several adaptive measures were identified at household, community and external levels in order to cope with such vulnerability; e.g. traditional techniques in agriculture and risk management. Gradually, farmers complement these activities with contemporary practices in agriculture, like intensification of land use, diversification of irrigation system and use of artificial fertilizers. As part of a recent trend community members are forced to search for new off-farm alternatives beyond agriculture for subsistence. Despite there is a correspondingly large array of possible adaptation measures that families are implementing, local testimonies point out, that farmers often do not have the capacity and neither the economical resources to mitigate the risk in agricultural production. Although several actions are already considered to promote further adaptive capacity, the current target is to improve existing livelihood strategies by reducing vulnerability to hazards induced by climate change.

The Human in the Loop: User Participation in Self-Adaptive Software

Self-adaptive software provides a profound solution for adapting applications to changing contexts in dynamic and heterogeneous environments. Having emerged from Autonomic Computing, it incorporates fully autonomous decision making based on predefined structural and behavioural models. The most common approach for architectural runtime adaptation is the MAPE-K adaptation loop implementing an external adaptation manager without manual user control. However, it has turned out that adaptation behaviour lacks acceptance if it does not correspond to a user’s expectations – particularly for Ubiquitous Computing scenarios with user interaction. Adaptations can be irritating and distracting if they are not appropriate for a certain situation. In general, uncertainty during development and at run-time causes problems with users being outside the adaptation loop. In a literature study, we analyse publications about self-adaptive software research. The results show a discrepancy between the motivated application domains, the maturity of examples, and the quality of evaluations on the one hand and the provided solutions on the other hand. Only few publications analysed the impact of their work on the user, but many employ user-oriented examples for motivation and demonstration. To incorporate the user within the adaptation loop and to deal with uncertainty, our proposed solutions enable user participation for interactive selfadaptive software while at the same time maintaining the benefits of intelligent autonomous behaviour. We define three dimensions of user participation, namely temporal, behavioural, and structural user participation. This dissertation contributes solutions for user participation in the temporal and behavioural dimension. The temporal dimension addresses the moment of adaptation which is classically determined by the self-adaptive system. We provide mechanisms allowing users to influence or to define the moment of adaptation. With our solution, users can have full control over the moment of adaptation or the self-adaptive software considers the user’s situation more appropriately. The behavioural dimension addresses the actual adaptation logic and the resulting run-time behaviour. Application behaviour is established during development and does not necessarily match the run-time expectations. Our contributions are three distinct solutions which allow users to make changes to the application’s runtime behaviour: dynamic utility functions, fuzzy-based reasoning, and learning-based reasoning. The foundation of our work is a notification and feedback solution that improves intelligibility and controllability of self-adaptive applications by implementing a bi-directional communication between self-adaptive software and the user. The different mechanisms from the temporal and behavioural participation dimension require the notification and feedback solution to inform users on adaptation actions and to provide a mechanism to influence adaptations. Case studies show the feasibility of the developed solutions. Moreover, an extensive user study with 62 participants was conducted to evaluate the impact of notifications before and after adaptations. Although the study revealed that there is no preference for a particular notification design, participants clearly appreciated intelligibility and controllability over autonomous adaptations.

Adaptive Real-time Anomaly-based Intrusion Detection using Data Mining and Machine Learning Techniques

Die zunehmende Vernetzung der Informations- und Kommunikationssysteme führt zu einer weiteren Erhöhung der Komplexität und damit auch zu einer weiteren Zunahme von Sicherheitslücken. Klassische Schutzmechanismen wie Firewall-Systeme und Anti-Malware-Lösungen bieten schon lange keinen Schutz mehr vor Eindringversuchen in IT-Infrastrukturen. Als ein sehr wirkungsvolles Instrument zum Schutz gegenüber Cyber-Attacken haben sich hierbei die Intrusion Detection Systeme (IDS) etabliert. Solche Systeme sammeln und analysieren Informationen von Netzwerkkomponenten und Rechnern, um ungewöhnliches Verhalten und Sicherheitsverletzungen automatisiert festzustellen. Während signatur-basierte Ansätze nur bereits bekannte Angriffsmuster detektieren können, sind anomalie-basierte IDS auch in der Lage, neue bisher unbekannte Angriffe (Zero-Day-Attacks) frühzeitig zu erkennen. Das Kernproblem von Intrusion Detection Systeme besteht jedoch in der optimalen Verarbeitung der gewaltigen Netzdaten und der Entwicklung eines in Echtzeit arbeitenden adaptiven Erkennungsmodells. Um diese Herausforderungen lösen zu können, stellt diese Dissertation ein Framework bereit, das aus zwei Hauptteilen besteht. Der erste Teil, OptiFilter genannt, verwendet ein dynamisches "Queuing Concept", um die zahlreich anfallenden Netzdaten weiter zu verarbeiten, baut fortlaufend Netzverbindungen auf, und exportiert strukturierte Input-Daten für das IDS. Den zweiten Teil stellt ein adaptiver Klassifikator dar, der ein Klassifikator-Modell basierend auf "Enhanced Growing Hierarchical Self Organizing Map" (EGHSOM), ein Modell für Netzwerk Normalzustand (NNB) und ein "Update Model" umfasst. In dem OptiFilter werden Tcpdump und SNMP traps benutzt, um die Netzwerkpakete und Hostereignisse fortlaufend zu aggregieren. Diese aggregierten Netzwerkpackete und Hostereignisse werden weiter analysiert und in Verbindungsvektoren umgewandelt. Zur Verbesserung der Erkennungsrate des adaptiven Klassifikators wird das künstliche neuronale Netz GHSOM intensiv untersucht und wesentlich weiterentwickelt. In dieser Dissertation werden unterschiedliche Ansätze vorgeschlagen und diskutiert. So wird eine classification-confidence margin threshold definiert, um die unbekannten bösartigen Verbindungen aufzudecken, die Stabilität der Wachstumstopologie durch neuartige Ansätze für die Initialisierung der Gewichtvektoren und durch die Stärkung der Winner Neuronen erhöht, und ein selbst-adaptives Verfahren eingeführt, um das Modell ständig aktualisieren zu können. Darüber hinaus besteht die Hauptaufgabe des NNB-Modells in der weiteren Untersuchung der erkannten unbekannten Verbindungen von der EGHSOM und der Überprüfung, ob sie normal sind. Jedoch, ändern sich die Netzverkehrsdaten wegen des Concept drif Phänomens ständig, was in Echtzeit zur Erzeugung nicht stationärer Netzdaten führt. Dieses Phänomen wird von dem Update-Modell besser kontrolliert. Das EGHSOM-Modell kann die neuen Anomalien effektiv erkennen und das NNB-Model passt die Änderungen in Netzdaten optimal an. Bei den experimentellen Untersuchungen hat das Framework erfolgversprechende Ergebnisse gezeigt. Im ersten Experiment wurde das Framework in Offline-Betriebsmodus evaluiert. Der OptiFilter wurde mit offline-, synthetischen- und realistischen Daten ausgewertet. Der adaptive Klassifikator wurde mit dem 10-Fold Cross Validation Verfahren evaluiert, um dessen Genauigkeit abzuschätzen. Im zweiten Experiment wurde das Framework auf einer 1 bis 10 GB Netzwerkstrecke installiert und im Online-Betriebsmodus in Echtzeit ausgewertet. Der OptiFilter hat erfolgreich die gewaltige Menge von Netzdaten in die strukturierten Verbindungsvektoren umgewandelt und der adaptive Klassifikator hat sie präzise klassifiziert. Die Vergleichsstudie zwischen dem entwickelten Framework und anderen bekannten IDS-Ansätzen zeigt, dass der vorgeschlagene IDSFramework alle anderen Ansätze übertrifft. Dies lässt sich auf folgende Kernpunkte zurückführen: Bearbeitung der gesammelten Netzdaten, Erreichung der besten Performanz (wie die Gesamtgenauigkeit), Detektieren unbekannter Verbindungen und Entwicklung des in Echtzeit arbeitenden Erkennungsmodells von Eindringversuchen.

A pilot investigation of the relationship between climate variability and milk compounds under the bootstrap technique

This study analyzes the linear relationship between climate variables and milk components in Iran by applying bootstrapping to include and assess the uncertainty. The climate parameters, Temperature Humidity Index (THI) and Equivalent Temperature Index (ETI) are computed from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis (2002–2010). Milk data for fat, protein (measured on fresh matter bases), and milk yield are taken from 936,227 milk records for the same period, using cows fed by natural pasture from April to September. Confidence intervals for the regression model are calculated using the bootstrap technique. This method is applied to the original times series, generating statistically equivalent surrogate samples. As a result, despite the short time data and the related uncertainties, an interesting behavior of the relationships between milk compound and the climate parameters is visible. During spring only, a weak dependency of milk yield and climate variations is obvious, while fat and protein concentrations show reasonable correlations. In summer, milk yield shows a similar level of relationship with ETI, but not with temperature and THI. We suggest this methodology for studies in the field of the impacts of climate change and agriculture, also environment and food with short-term data.