International Journal of Agricultural and Environmental Information Systems, 2021
With the enormous use of internet of things-based devices for enabling smart agriculture, there i... more With the enormous use of internet of things-based devices for enabling smart agriculture, there is a significant need for efficient systems in order to improve agricultural practices. It can help efficiently to develop optimal web-based information system using the data of field monitoring. But, the collection of such data in the presence of connectivity disruptions poses new challenges for users. This paper targets to determine such offloaders with less infrastructural costs to enable smart agriculture based on network heuristics. Although, few works contribute to the trust established, most of them are applicable only for static networks. This paper explores a trust-based solution for mobile data offloading. This paper identifies the need and impact of trust determination using the trust model algorithm. The proposed algorithm outperforms the hybrid trust-based mobility aware clustering algorithm for trust-based offloaders with up to 13% better offloading potential saving a minimum of 8 pJ energy per user with just 25% contributors with 50% lesser time delay.
International Journal of Data and Network Science, 2021
Data offloading offers a significant solution to the problem of explosive rise in mobile data tra... more Data offloading offers a significant solution to the problem of explosive rise in mobile data traffic. A naive approach would be to utilize the infrastructure (cellular tower, WiFi, femtocell) or other mobile devices to offload data. However, increasing the number of a cellular towers, WiFi, or femtocells is costlier deal for data delivery. Recently, device-to-device (D2D) paradigm of data communication has emerged out as one of the most promising solutions to deal with cost effective cellular traffic offloading. D2D communication provides a direct communication link between closely located mobile users. Another significant feature of D2D is its content centric nature, which makes it useful in data offloading. In this paper, we have addressed the issue of data offloading in mobile devices and proposed a hybrid model of D2D communication with ad-hoc nature. The paper also considers the issues like memory constraints of the devices, pruning of replicated messages and energy efficiency...
The rapid rate of dependence over internet usage using digital devices also results in enormous d... more The rapid rate of dependence over internet usage using digital devices also results in enormous data traffic. The conventional way to handle these services is to increase the infrastructure. However, it results in high cost of implementation. Therefore, to overcome the data burden, researchers have come up with data offloading schemes using solutions for NP-hard Target Set Selection (TSS) problem. Our work focuses on TSS optimization and respective data offloading scheme. We propose a heuristics-based optimal TSS algorithm, a distinctive community identification algorithm, and an opportunistic data offloading algorithm. The proposed scheme has an overall polynomial time complexity of the order O(k3), where k is the number of nodes in the primary target set for convergence. However we have obtained its realization to linear order for practical reasons. To validate our results, we have used state-of-the-art datasets and compared it with literature-based approaches. Our analysis shows ...
Agriculture remains the backbone of several economies in the world, especially in underdeveloped ... more Agriculture remains the backbone of several economies in the world, especially in underdeveloped countries. With the rapid growth of the population and the increasing demand in food, farmers need to maximize the productivity and one possibility is the reduction of losses. Weeds are one of the major dangers in farming.Indeed, they competevigorously with the crop for nutrients and water. As result, they can cause the loss of 10% to 100% of the total harvest. This work aimed at developing a new model tailored to classify crops and weeds images. Using a pubic dataset of 5339 plant images from Aarhus University Signal Processing group in collaboration with University of Southern Denmark, we proposed a methodology based on transfer learning technique to classify 12 species of crops and weeds. Firstly, we converted images to jpeg format in order to accelerate the convergence and data augmentation techniques such as resizing, rotating, flipping, scaling were employed to reduce the chances of overfitting. Then, a model trained on ImageNet dataset with Residual Network 101 architecture was used for performing transfer learning. Finally, the network's parameters were adjusted through various techniques involving progressive resizing, cyclical learning rate and focal loss function for improving the performance. Our model achieved an overall accuracy of 98,47% during validation and of 96,04% on the test set. We already deployed the model over Internet through a web application and our next step will be to integrate this solution within a mobile application and embedded devices.Future works concerns with the use of more features and descriptors to accurately distinguish two specificclasses of weeds: Black-grass and Loose Silky-Bent, and the possibility to extend our approach to other kinds of plants.
The evolution of technologies and the emergence of high-performance computing have created new op... more The evolution of technologies and the emergence of high-performance computing have created new opportunities for data science in agricultural domain. Precision Agriculture is the process of utilizing new technologies in order to enhance traditional practice of farming. Its main principle is to allocate the right doses of input at the right place and at the right time. For doing that, right tools must be provided to the farmers to assist them in making decisions. Machine Learning consisting of learning computers without being explicitly programmed, plays a crucial role in the development of new generation intelligent farmers' tools. In this paper, we present a systematic review of researches employing machine learning for weeds detection. Our paper focuses on fresh relevant works conducted during the recent years. By applying machine learning, the manner of doing agriculture has achieved a new level and henceforth, farmers have better tools to increase agricultural productivity.
In this paper we have analyzed the comparison of radix sort algorithm on sequential and parallel ... more In this paper we have analyzed the comparison of radix sort algorithm on sequential and parallel procedures across three programming language platforms namely C, OpenMP based C++ and CUDA programming fixtures. The importance of radixsort to be a non comparison based sorting algorithm has been truncated. The algorithmic flow of data from the unsorted input to the desired output has also been shown using flowchart. Next to it, the theoretical complexity for radix sort is being illustrated and experimented for varied datasets of non-fixed sizes with varied bucket size ranges. Towards the end, the algorithmic complexities for radix sort have also been evaluated for the three algorithmic procedures used. The comparative efficiencies of radix sort for sorting a list of integers of different sizes and ranges have been evaluated. On the basis of analysis we have concluded the CUDA program to be approximately 100 times faster than the sequential C program and the multi-threaded parallel program enabled by OpenMP in C++ to be approximately 10 times faster than the sequential C program to sort a varied sized text file according to radix sort in an increasing order. This paper is a review of the serial and parallel algorithm implementation for sorting anonymously large dataset which can be used for graph explorations implemented over network analysis, which is also the future proposed work for our analytic study. Categories and Subject Descriptors F.2.2 [ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY]: Nonnumerical Algorithms and Problems – Complexity of proof procedures, Sorting and searching.
— There has been a rapid growth in demand for computational power which has led to the creation o... more — There has been a rapid growth in demand for computational power which has led to the creation of large data centers. These consume enormous amounts of electrical power resulting in high cost of operation and carbon dioxide emissions. The computing model of cloud usage is also increasing the power consumption by the ICT equipments involved in between a service provider and the user across the network. The paper proposes a few new strategies and solutions to reduce the energy usage in the network involving data center, network and client system.
International Journal of Agricultural and Environmental Information Systems, 2021
With the enormous use of internet of things-based devices for enabling smart agriculture, there i... more With the enormous use of internet of things-based devices for enabling smart agriculture, there is a significant need for efficient systems in order to improve agricultural practices. It can help efficiently to develop optimal web-based information system using the data of field monitoring. But, the collection of such data in the presence of connectivity disruptions poses new challenges for users. This paper targets to determine such offloaders with less infrastructural costs to enable smart agriculture based on network heuristics. Although, few works contribute to the trust established, most of them are applicable only for static networks. This paper explores a trust-based solution for mobile data offloading. This paper identifies the need and impact of trust determination using the trust model algorithm. The proposed algorithm outperforms the hybrid trust-based mobility aware clustering algorithm for trust-based offloaders with up to 13% better offloading potential saving a minimum of 8 pJ energy per user with just 25% contributors with 50% lesser time delay.
International Journal of Data and Network Science, 2021
Data offloading offers a significant solution to the problem of explosive rise in mobile data tra... more Data offloading offers a significant solution to the problem of explosive rise in mobile data traffic. A naive approach would be to utilize the infrastructure (cellular tower, WiFi, femtocell) or other mobile devices to offload data. However, increasing the number of a cellular towers, WiFi, or femtocells is costlier deal for data delivery. Recently, device-to-device (D2D) paradigm of data communication has emerged out as one of the most promising solutions to deal with cost effective cellular traffic offloading. D2D communication provides a direct communication link between closely located mobile users. Another significant feature of D2D is its content centric nature, which makes it useful in data offloading. In this paper, we have addressed the issue of data offloading in mobile devices and proposed a hybrid model of D2D communication with ad-hoc nature. The paper also considers the issues like memory constraints of the devices, pruning of replicated messages and energy efficiency...
The rapid rate of dependence over internet usage using digital devices also results in enormous d... more The rapid rate of dependence over internet usage using digital devices also results in enormous data traffic. The conventional way to handle these services is to increase the infrastructure. However, it results in high cost of implementation. Therefore, to overcome the data burden, researchers have come up with data offloading schemes using solutions for NP-hard Target Set Selection (TSS) problem. Our work focuses on TSS optimization and respective data offloading scheme. We propose a heuristics-based optimal TSS algorithm, a distinctive community identification algorithm, and an opportunistic data offloading algorithm. The proposed scheme has an overall polynomial time complexity of the order O(k3), where k is the number of nodes in the primary target set for convergence. However we have obtained its realization to linear order for practical reasons. To validate our results, we have used state-of-the-art datasets and compared it with literature-based approaches. Our analysis shows ...
Agriculture remains the backbone of several economies in the world, especially in underdeveloped ... more Agriculture remains the backbone of several economies in the world, especially in underdeveloped countries. With the rapid growth of the population and the increasing demand in food, farmers need to maximize the productivity and one possibility is the reduction of losses. Weeds are one of the major dangers in farming.Indeed, they competevigorously with the crop for nutrients and water. As result, they can cause the loss of 10% to 100% of the total harvest. This work aimed at developing a new model tailored to classify crops and weeds images. Using a pubic dataset of 5339 plant images from Aarhus University Signal Processing group in collaboration with University of Southern Denmark, we proposed a methodology based on transfer learning technique to classify 12 species of crops and weeds. Firstly, we converted images to jpeg format in order to accelerate the convergence and data augmentation techniques such as resizing, rotating, flipping, scaling were employed to reduce the chances of overfitting. Then, a model trained on ImageNet dataset with Residual Network 101 architecture was used for performing transfer learning. Finally, the network's parameters were adjusted through various techniques involving progressive resizing, cyclical learning rate and focal loss function for improving the performance. Our model achieved an overall accuracy of 98,47% during validation and of 96,04% on the test set. We already deployed the model over Internet through a web application and our next step will be to integrate this solution within a mobile application and embedded devices.Future works concerns with the use of more features and descriptors to accurately distinguish two specificclasses of weeds: Black-grass and Loose Silky-Bent, and the possibility to extend our approach to other kinds of plants.
The evolution of technologies and the emergence of high-performance computing have created new op... more The evolution of technologies and the emergence of high-performance computing have created new opportunities for data science in agricultural domain. Precision Agriculture is the process of utilizing new technologies in order to enhance traditional practice of farming. Its main principle is to allocate the right doses of input at the right place and at the right time. For doing that, right tools must be provided to the farmers to assist them in making decisions. Machine Learning consisting of learning computers without being explicitly programmed, plays a crucial role in the development of new generation intelligent farmers' tools. In this paper, we present a systematic review of researches employing machine learning for weeds detection. Our paper focuses on fresh relevant works conducted during the recent years. By applying machine learning, the manner of doing agriculture has achieved a new level and henceforth, farmers have better tools to increase agricultural productivity.
In this paper we have analyzed the comparison of radix sort algorithm on sequential and parallel ... more In this paper we have analyzed the comparison of radix sort algorithm on sequential and parallel procedures across three programming language platforms namely C, OpenMP based C++ and CUDA programming fixtures. The importance of radixsort to be a non comparison based sorting algorithm has been truncated. The algorithmic flow of data from the unsorted input to the desired output has also been shown using flowchart. Next to it, the theoretical complexity for radix sort is being illustrated and experimented for varied datasets of non-fixed sizes with varied bucket size ranges. Towards the end, the algorithmic complexities for radix sort have also been evaluated for the three algorithmic procedures used. The comparative efficiencies of radix sort for sorting a list of integers of different sizes and ranges have been evaluated. On the basis of analysis we have concluded the CUDA program to be approximately 100 times faster than the sequential C program and the multi-threaded parallel program enabled by OpenMP in C++ to be approximately 10 times faster than the sequential C program to sort a varied sized text file according to radix sort in an increasing order. This paper is a review of the serial and parallel algorithm implementation for sorting anonymously large dataset which can be used for graph explorations implemented over network analysis, which is also the future proposed work for our analytic study. Categories and Subject Descriptors F.2.2 [ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY]: Nonnumerical Algorithms and Problems – Complexity of proof procedures, Sorting and searching.
— There has been a rapid growth in demand for computational power which has led to the creation o... more — There has been a rapid growth in demand for computational power which has led to the creation of large data centers. These consume enormous amounts of electrical power resulting in high cost of operation and carbon dioxide emissions. The computing model of cloud usage is also increasing the power consumption by the ICT equipments involved in between a service provider and the user across the network. The paper proposes a few new strategies and solutions to reduce the energy usage in the network involving data center, network and client system.
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