Quality of Service based Retrieval Strategy for Distributed Video on Demand on Multiple Servers

The recent advances and development of inexpensive computers and high speed networking technology have enabled the Video on Demand (VoD) application to connect to shared-computing servers, replacing the traditional computing environments where each application was having its own dedicated computing hardware. The VoD application enables the viewer to select, from a list of video files, his favorite video file and watch its reproduction at will. Early video on demand applications were based on single video server where video streams are initiated from a single server, then with the increase in the number of the clients who became interested in VoD services, the focus became on Distributed VoD architectures (DVoD) where the context of distribution may be distributed system components, distributed streaming servers, distributed media content etc.The VoD server must handle several issues in order to be able to present a successful service. It has to receive the clients’ requests and analyze them, calculate the necessary resources for each request, and decide whether a request can be admitted or not. Once the request is admitted, the server must schedule the request, retrieve the required video data and send the video data in a timely manner so that the client does not suffer data starvation in his buffer during the video reproduction. So, the overall objective of a VoD service provider is to provide a better Quality of Service (QoS). Some issues related to QoS are-efficient use of bandwidth, providing better throughput etc.One of the important issues is to retrieve the video data from the servers in minimum time and to start the playback of the video at client side with a minimum waiting time. The overall time elapsed in retrieving the video data and starting the playback is known as access time. The thesis presents an efficient retrieval strategy for a distributed VoD environment where the basic objective is to minimize the access time by maintaining the presentation continuity at the client side. We have neglected some of the network parameters which may affect the access time, by assuming a high speed network between the servers and the client. The performance of the strategy has been analyzed and is compared with the referred PAR (Play After Retrieval) strategy. Further, the strategy is also analyzed under availability condition which is a more realistic approach

Consensus-based Time Synchronization Algorithms for Wireless Sensor Networks with Topological Optimization Strategies for Performance Improvement

Wireless Sensor Networks(WSNs)have received considerable attention in recent years because of its broad area of applications.In the same breadth,it also faces many challenges.Time synchronization is one of those fundamental challenges faced by WSN being a distributed system.It is a service by which all nodes in the network will share a common notion of time.It is a prerequisite for correctness of other protocols and services like security,localization and tracking protocols.Several approaches have been proposed in the last decade for time synchronization in WSNs.The well-known methods are based on synchronizing to a reference(root)node's time by considering a hierarchical backbone for the network.However,this approach seems to be not purely distributed,higher accumulated synchronization error for the farthest node from the root and subjected to the root node failure problem.Recently,consensus based approaches are gaining popularity due its computational lightness,robustness, and distributed nature.In this thesis,average consensus-based time synchronization algorithms are proposed,aiming to improve the performance metrics like number of iterations for convergence,total synchronization error,local synchronization error,message complexity,and scalability.Further,to cope up with energy constraint environment, Genetic algorithm based topological optimization strategies are proposed to minimize energy consumption and to accelerate the consensus convergence of the existing consensus-based time synchronization algorithms.All algorithms are analyzed mathematically and validated through simulation in MATLAB based PROWLER simulator.Firstly,a distributed Selective Average Time Synchronization (SATS) algorithm is proposed based on average consensus theory.The algorithm is purely distributed(runs at each node),and each node exploits a selective averaging with the neighboring node having maximum clock difference. To identify the neighboring node with maximum clock difference,every node broadcasts a synchronization initiation message to the neighboring nodes at its local oscillation period and waits for a random interval to get the synchronization acknowledgment messages.After receiving acknowledgment messages,a node estimates relative clock value and sends an averaging message to the selected node.The iteration continues until all nodes reach an acceptable synchronization error bound. The optimal convergence of the proposed SATS algorithm is analyzed and validated through simulation and compared with some state-of-the-art,average consensus based time synchronization algorithms. Furthermore, it is observed that most of the consensus-based time synchronization algorithms are one-hop in nature, i.e., the algorithms iterate by averaging with one-hop neighbors' clock value. In a sparse network with a lower average degree of connectivity, these algorithms show poor performance. In order to have better convergence on the sparse network, a multi-hop SATS algorithm is proposed. The basic principle of multi-hop SATS algorithm remains same as that of SATS algorithm, i.e., performing selective averaging with the neighboring node, having maximum clock difference. But, in this case, the search for neighboring node goes beyond one hop. The major challenge lies in multi-hop search is the end-to-end delay which increases with the increase in hop count. So, to search a multi-hop neighboring node with maximum clock difference and with minimum and bounded end-to-end delay, a distributed, constraint-based dynamic programming approach is proposed for multi-hop SATS algorithm. The performance of the proposed multi-hop SATS algorithm is compared with some one-hop consensus time synchronization algorithms. Simulation results show notable improvement in terms of convergence speed, total synchronization error within a restricted hop count. The trade-off with the increase in number of hops is also studied. The well-known consensus-based time synchronization algorithms are ``all node based'', i.e., every node iterates the algorithm to reach the synchronized state. This increases the overall message complexity and consumption of energy. Further, congestion in the network increases due to extensive synchronization message exchanges and induces the delay in the network. The delay induced in the message exchange is the main source of synchronization error and slows down the convergence speed to the synchronized (consensus) state. Hence, it is desirable that a subset of sensors along with a reasonable number of neighboring sensors should be selected in such a way that the resultant logical topology will accelerate the consensus algorithm with optimal message complexity and minimizes energy consumption. This problem is formulated as topological optimization problem which is claimed to be NP-complete in nature. Therefore, Genetic Algorithm (GA) based approaches are used to tackle this problem. Considering dense network topology, a single objective GA-based approach is proposed and considering sparse topology, a multi-objective Random Weighted GA based approach is proposed. Using the proposed topological optimization strategy, significant improvements are observed for consensus-based time synchronization algorithms in terms of average number of messages exchanged, energy consumption, and average mean square synchronization error

Evaluation of regression algorithms for estimating leaf area index and canopy water content from water stressed rice canopy reflectance

Optical remote sensing (RS) with robust algorithms is needed for accurate assessment of crop canopy features. Despite intensive studies on algorithms, their performance using RS needs to be improved. We evaluated five different algorithms (partial-least-squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), locally-weighted-PLSR (PLSRLW) and PLSR with feature selection (PLSRFS)) for rapid assessment of leaf area index (LAI) and canopy water content (CWC) for rice canopies using canopy reflectance spectra over visible to short-wave infrared region. Two pooled datasets of LAI (600) and CWC (480) were collected from two replicated field experiments during 2014–15 and 2015–16 rice growing season. The performance of each algorithm was evaluated using coefficient of determination (R2). Results showed that PLSRLW performed more accurately than other algorithms with R2 values 0.77 and 0.66 for LAI and CWC, respectively. We also used a bootstrapping approach to generate a kernel density estimator of root mean squared error values for each model. The results suggested that the improvement in prediction accuracy of LAI and CWC can be achieved if a suitable algorithm is selected by assigning higher weights to calibration samples, which has similar canopy structure as the test sample. Subsetting of the canopy spectral data results large error values in test dataset, therefore the use of entire season canopy spectral data should be used for model calibration

Identifying opportunities to improve management of water stress in banana production

Banana (Musa spp.) is one of the most valuable global agricultural commodities, with commercial plantations responsible for supplying nearly 15 % of total global banana production. These plantations are underpinned by major infrastructural investments and a high dependence on fertilizer, pesticide and irrigation inputs. In contrast, smallholders and subsistence farmers often cultivate bananas for local markets with minimal inputs. Water stress due to increasing rainfall variability and competition for water resources are emerging as major production constraints for both commercial and smallholder production. Water stress-induced yield losses of up to 65 % have been reported due to loss in bunch weight even in moderate to low rainfall areas. Thus, investments in more efficient irrigation systems and water-saving technologies are being widely promoted to increase water productivity through improved scheduling to reduce drainage and runoff losses. This paper synthesises scientific and industry evidence on crop growth and development including root and shoot development, plant water relations, and yield response to water. It also critiques the importance of irrigation scheduling for maximising irrigation efficiency. New evidence to support the synchronization of irrigation with crop water demand to reduce environmental impacts is provided. High variability in crop water demand (1200–2690 mm per year) was found to be linked to cultivar choice, crop development cycle, and fluctuating conditions in environmental and edaphic factors. The findings confirm that irrigation should be scheduled at moderate levels of soil water deficit sufficient to promote deep and extensive rooting while maintaining banana quality. Management practices are recommended to mitigate water stress without compromising yield under limited rainfall and irrigation conditions. The ratooning cycle of banana also affects rooting activity and crop coefficients (Kc) compared to other annual crops. These aspects need to be considered when improving irrigation and crop modelling for banana. The findings provide valuable new insights and evidence for scientists and practitioners involved in banana research and management

Modelling water fluxes to improve banana irrigation scheduling and management in Magdalena, Colombia

In this paper, an irrigation scheduling model for banana (Musa sp.) was developed to simulate crop growth and water fluxes under typical commercial plantation conditions. Whilst generic models exist for scheduling irrigation for many crops, their suitability for bananas are limited because of the asynchronous nature of crop growth. Individual fields on banana plantations typically contain trees at varying stages in their development cycle, so it is important for scheduling to account for this heterogeneity in simulating crop production. A crop modelling approach was developed using field data from Magdalena, an economically important region of banana production in Colombia. Following model development and calibration, irrigation water demand was estimated and weekly irrigation scheduling advice then transmitted by SMS to individual farmers in the region. The model also takes into account farmer feedback on actual irrigation practices to compare against estimated irrigation demands and to train model performance. Despite good model calibration, analysis of irrigation practices from farmer feedback showed only moderate to poor correlation between actual irrigation applications and the scheduling guidance. This implies a reluctance of farmers to change long-established traditional irrigation management practices, despite awareness of the impacts of systematic over-irrigation on yields and increased nutrient leaching risks. Significant ongoing research efforts will be needed to support improved knowledge and practical water management for key plantation crops.Innovate UK: TS/S011986/1

Scaling up indigenous rainwater harvesting: a preliminary assessment in Rajasthan, India

Rainwater harvesting (RWH) has the potential to enhance the sustainability of ground and surface water to meet increasing water demands and constrained supplies, even under a changing climate. Since arid and semi-arid regions frequently experience highly variable spatiotemporal rainfall patterns, rural communities have developed indigenous RWH techniques to capture and store rainwater for multiple uses. However, selecting appropriate sites for RWH, especially across large regions, remains challenging since the data required to evaluate suitability using critical criteria are often lacking. This study aimed to identify the essential criteria and develop a methodology to select potential RWH sites in Rajasthan (India). We combined GIS modeling (multicriteria decision analysis) with applied remote sensing techniques as it has the potential to assess land suitability for RWH. As assessment criteria, spatial datasets relating to land use/cover, rainfall, slope, soil texture, NDVI, and drainage density were considered. Later, weights were assigned to each criterion based on their relative importance to the RWH system, evidence from published literature, local expert advice, and field visits. GIS analyses were used to create RWH suitability maps (high, moderate, and unsuited maps). The sensitivity analysis was also carried out for identified weights to check the inadequacy and inconsistency among preferences. It was estimated that 3.6%, 8.2%, and 27.3% of the study area were highly, moderately, and unsuitable, respectively, for Chauka implementation. Further, sensitivity analysis results show that LULC is highly sensitive and NDVI is the least sensitive parameter in the selected study region, which suggests that changing the weight of these parameters is more likely to decide the outcome. Overall, this study shows the applicability of the GIS-based MCDA approach for up-scaling the traditional RWH systems and its suitability in other regions with similar field conditions, where RWH offers the potential to increase water resource availability and reliability to support rural communities and livelihoods

Fundamental understanding and modeling of spin coating process : A review

A mathematical model is derived to elucidate the dominant mechanism governing film formation. It leads to a relation between film thickness and film radius spreading with time. Inclusion of evaporation and shear stress was made with extension to non-Newtonian fluid. The advantages and disadvantages of this process with applications are reviewed.Niranjan Sahu*, B Parija and S Panigrahi Department of Physics, National Institute of Technology, Rourkela-769 008, Orissa, India E-mail : [email protected] of Physics, National Institute of Technology, Rourkela-769 008, Orissa, Indi

Data from: Canopy spectral reflectance as a predictor of soil water potential in rice

Soil water potential (SWP) is a key parameter for characterizing water stress. Typically, a tensiometer is used to measure SWP. However, the measurement range for commercially available tensiometers is limited to -90 kPa and a tensiometer can only provide estimate of SWP at a single location. In this study, a new approach was developed for estimating SWP from spectral reflectance data of a standing rice crop over the visible to shortwave-infrared region (wavelength: 350 nm to 2500 nm). Five water stress treatments corresponding to targeted SWP of – 30 kPa, - 50 kPa, - 70 kPa, -120 kPa and - 140 kPa were examined by withholding irrigation during the vegetative growth stage of three rice varieties. Tensiometers and mechanistic water flow model were used for monitoring SWP. Spectral models for SWP was developed using partial-least-squares regression (PLSR), support vector regression (SVR), and coupled PLSR and feature selection (PLSRFS) approaches. Results showed that the SVR approach was the best model for estimating SWP from spectral reflectance data with the coefficient of determination values of 0.71 and 0.55 for the calibration and validation datasets, respectively. Observed root-mean-squared residuals for the predicted SWPs were in the range of -7 to -19 kPa. A new spectral water stress index was also developed using the reflectance values at 745 nm and 2002 nm, which showed strong correlation with relative water contents and electrolyte leakage. This new approach is rapid and non-invasive and may be used for estimating SWP over large areas

Rice canopy spectral data under water stress

The data file contains whole 780 canopy spectra and their corresponding soil water potential (kPa) and 72 canopy spectra and their corresponding terminal soil water potential (kPa), relative water content (%) and electrolyte leakage(%)

Identifying opportunities to improve digital soil mapping in India: a systematic review

Soaring food demand, population pressure, land degradation, small size of agricultural land holdings, and diversified soil types in India require advanced digital soil mapping (DSM) for sustainable land management. This paper systematically reviews the common trends of SCORPAN based DSM in India to identify the important research gaps and opportunities to improve in future. A systematic literature search from 2000 to October 2021 has yielded 35 numbers of peer reviewed articles, which have performed DSM in India following the SCORPAN approach. The increased number of published articles from 2017 onwards suggests that there is a growing interest for DSM in India over the past few years. However, only two articles have prepared digital soil maps at the national extent. Moreover, the local and regional extent DSM are being limited to only a few parts of the country. There still remains 50% of the states and Union Territories of the country where no DSM studies have been performed so far except the national and global level interventions. Among the target variables, soil carbon related attributes have been predicted most frequently, whereas soil classes have been rarely predicted. Environmental covariates representing organism (O) and relief (R) have been widely included for DSM, whereas the use of other covariates has been limited. Among different machine learning (ML) algorithms, regression kriging has been adopted most frequently followed by random forest and quantile regression forest. Most articles have adopted data splitting (76%) as the model and map evaluation approach, whereas independent validation has been limited to only 5% of the articles. Only 34% of the articles have presented the uncertainty maps. Major research gaps identified by this review include lack of standardized digital soil databases, poor sampling density, coarse resolution, limited use of environmental covariates, insufficient comparative studies among ML algorithms, inadequate independent validation, and undersupply of uncertainty maps. Key evidences from this review will be helpful for improving future DSM activities by scientists and practitioners involved with DSM in India and abroad