Informatics Publishing Limited

Puneet Sharma ¹, Arun Kaushal ¹

Affiliations
1 Department of Soil and Water Engineering, Punjab Agricultural University, Ludhiana - 141004, Punjab, IN

Abstract

Objective: To study the economic viability of Okra crop grown under different drip fertigation levels and different drip irrigation subsidy percentages. Methods: The field experiment was done at the research farm of department of Soil and Water Engineering, PAU, Ludhiana, India during 2014-15. The Nine drip fertigation treatments consisted of T1 - 60% fertilizer Nitrogen (N) with irrigation applied at 0.60 IW/CPE (irrigation water/cumulative pan evaporation) ratio, T2 - 60% fertilizer (N) with 0.80 IW/CPE ratio, T3 - 60% N with 1.00 IW/CPE ratio, T4 - 80% N with 0.60 IW/CPE ratio, T5 - 80% N with 0.80 IW/CPE ratio, T6 - 80% N with 1.00 IW/CPE ratio, T7 - 100% N with 0.60 IW/CPE ratio, T8 - 100% N with 0.80 IW/CPE ratio and T9 - 100% N with 1.00 IW/CPE ratio. Economical viability of drip fertigation was evaluated by computing Benefit-Cost ratio (B/C ratio) for each of drip fertigation treatment obtained by dividing gross returns by total seasonal cost. Economic analysis was done as per the cost involved in drip irrigation system components and requirement of fertilizer for one hectare area. The seasonal cost of growing okra under drip fertigation was calculated by considering depreciation, life of components, interest, fertilizers, insecticide, labors and cost of cultivation of growing Okra. Findings: In case of no drip irrigation subsidy, 60% drip irrigation subsidy and 75% drip irrigation subsidy, maximum B/C ratio was obtained in T5 treatment (2.25), (2.82) and (3.01) respectively; while minimum B/C ratio was obtained in T1 treatment (1.52), (1.9) and (2.03), respectively. The statistical analysis revealed that combination of fertilizers and irrigation levels had significant effect on B/C ratio of Okra production. Conclusion: It is economically viable to grow okra under drip fertigation with 80% fertilizer (N) along with irrigation applied at 0.80 IW/CPE ratio, only if drip irrigation subsidy provided by the government is higher than 30%.

Keywords

Benefit-Cost Ratio, Drip Fertigation, Economic Viability, Okra, Subsidy

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C. B. Anuradha ¹, Puneet Sharma ¹

Affiliations
1 Ericsson, IN

Abstract

The next telecommunications standard, 5G, envisions that the future networks will support advanced use cases, such as Internet of things while supporting voluminous simultaneous connections with high bandwidth as well as low latency. Further, these 5G deployments will not be static in nature, with new use cases and service requirements evolving in future. Such requirements pose many deployment and operational challenges to MNOs. These use cases would not only require the networks to be aware of connectivity related parameters, but also adapt intelligently based on parameters beyond the network. This requires the 5G networks to be capable of addressing conditions which are not foreseen at the time of designing them. Such capability requirements can be adequately addressed by advances in the field of AI and machine learning. The objective of this paper is to explore ways to leverage AI and machine learning for enhancing the 5G network deployments and operations. This paper attempts to decipher future demands from the 5G networks analyzing specific requirements in the areas of network planning, network operations and network optimization. This paper also discusses the strategic perspective for MNOs to benefit from applications of AI in 5G networks.

Keywords

5G, AI, Machine Learning, 5G Challenges, Operations, Network Automation.

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Puneet Sharma ¹

Affiliations
1 Ericsson, IN

Abstract

The topic of smart buildings is often discussed interchangeably with sustainable buildings which have been in focus for quite some time. The key selling point for such buildings is addressing the green environment agenda as well as reducing operational costs via efficient energy usage. A significant aspect however often left out while discussing smart buildings is related to the potential benefits that businesses can accrue by leveraging the power of ICT. These benefits mainly encompass enhanced employee productivity, employee engagement and improved space utilization of building premises.

The objective of this paper is to highlight the advances in ICT which can be utilized to create smarter buildings. Although there are several benefits of smarter buildings, the paper focuses on employing ICT to create better working environment for the occupants while enhancing the space utilization. This paper explores how smart workspaces can benefit organizations and identifies the ICT architecture for smart buildings. This paper also discusses stakeholders in the context of smart building and their perspective on benefits and opportunities in this space. The paper concludes with highlighting a few case studies of smart buildings across the world. The methodology includes analyzing and proposing advanced use cases possible by integrating the enterprise IT systems with IoT/M2M sensors and building management systems.

Keywords

Smart Building, Smart Workspace, Employee Productivity, Space Utilization, Digital Transformation.

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Puneet Sharma ¹, T. Sakthivel ², Deepak Arora ¹

Affiliations
1 Department of Computer Science and Engineering, Amity University, Uttar Pradesh, IN
2 Firstsoft Technologies Private Ltd., Chennai, Tamil Nadu, IN

Abstract

Mobile Cloud Computing (MCC) enables computation offloading procedures and has become popular in resolving the resource limitations of mobile devices. To accomplish effective offloading in the mobile cloud, modeling the application execution environment with Quality of Service (QoS) is crucial. Hence, optimization of resource allocation and management plays a major role in ensuring the seamless execution of mobile applications. Recently cloud computing research has adopted the reinforcement learning models to optimize resource allocation and offloading. In addition, several optimization mechanisms have considered the network transmission rate while selecting the network resources. However, mitigating the response time becomes critical among the dynamically varying mobile cloud resources. Thus, this paper proposes a joint resource optimization methodology for the processing and network resources in the integrated mobile-network-cloud environment. The proposed approach presents the Energy and Network-Aware Optimization solution with the assistance of the Deep Q-learning Network (ENAO-DQN). Designing an energy and network-aware resource optimization strategy recognizes the quality factors that preserve the device energy while allocating the resources and executing the compute-intensive mobile applications. With the potential advantage of the Deep Q-learning model in decision-making, the ENAO-DQN approach optimally selects the network resources with the enrichment of the maximized rewards. Initially, the optimization algorithm prefetches the quality factors based on the mobile and application characteristics, wireless network parameters, and cloud resource characteristics. Secondly, it generates the allocation plan for the application-network resource pair based on the prefetched quality factors with the assistance of the enhanced deep reinforcement learning model. Thus, the experimental results demonstrate that the ENAO-DQN model outperforms the baseline mobile execution and cloud offloading models.

Keywords

Mobile Cloud Computing, Resource Allocation, Optimization, Energy Consumption, QoS, Deep Reinforcement Learning, Q-learning, Wireless Network Resource.

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