Efficient energy management for the internet of things in smart cities

The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy-efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities

Rural Credit and Rural Development: Some Issues

Credit plays an important role in acquiring command over the use of working capital, fixed capital, and consumption goods that leads to growth and development of a country. The paper investigates that the role of non-institutional sources is quite clearly borne out. The price paid for institutional credit in Pakistan Kept low by the government. Institutional credit reforms implemented has not been effective. There is a great need to have the land reforms prior to credit reforms. The importance of social and economic infrastructure needs to be addressed. Appropriate use of new credit should be assured. Proper credit policy be designed and implemented.

Economic Value Added or Earnings per Share? An Incremental Content Analysis

The primary objective of the study is to determine the relative and incremental information content of Economic Value Added (EVA) as compared to the traditional accounting measure of Earnings per Share (EPS). The study employs the methodology derived from Easton and Harris (1991). The study sample comprises 30 largest listed non-financial firms on Pakistan Stock Exchange (PSX) and covers the period from 2005-2014. The findings indicate that EPS outperforms EVA in capturing the market trends of stock return performance. The results of the research negate the common notion of EVA as a superior measure of firm performance. Although, evidence obtained from empirical tests illustrates that EVA provides marginal incremental information combined with EPS, but it is low. The study offers academicians, practitioners and investors a more accurate measure by which to assess performance in the markets.&nbsp

Rural Credit and Rural Development: Some Issues

Credit is an important instrument of acquiring command over the use of working capital, fixed capital and consumption goods. In the wake of Green Revolution, land and labour have receded into the background as predominant factors of growth. Use of capital and adoption of modern techniques of production which have become major sources of growth of agricultural output necessitate access to credit markets for financing their use. Institutional sources of credit have become quite significant during the last few years. The rapid expansion of credit from institutional sources can be seen from various indicators. The total disbursement of agricultural loans has gone up from Rs. 306.75 million in 1972-73 to Rs. 5,102.14 million in 1981-82. On a per acre basis, the loans increased from Rs. 7.33 in 1972-73 to Rs. 106.83 in 1981-82. In this perspective, the disparities in income and wealth in rural areas would crucially depend on the distribution of capital among farms of different sizes and occupational groups. Neglecting equitable distribution of credit as a policy instrument for rural income redistribution may be a serious omission by the policy makers interested in an improvement of rural equity

A survey and taxonomy on nonorthogonal multiple-access schemes for 5G networks

Copyright © 2017 John Wiley & Sons, Ltd. The intensity in the requirements of Internet of Things and mobile internet makes the efficiency of fifth-generation (5G) wireless communications very challenging to achieve. Accomplishing the drastically increasing demand of massive connectivity and high spectral efficiency is a strenuous task. Because of the very large number of devices, 5G wireless communication systems are inevitable to satisfy the traffic requirements. Recently, nonorthogonal multiple-access (NOMA) schemes are immensely being explored to address the challenges in 5G, which include effective bandwidth utilization, support for a massive number of devices, and low latency. This paper provides the reader with a holistic view of multiple-access schemes, methods, and strategies for optimization in NOMA. First, we discuss the taxonomy of multiple-access schemes in the literature; then, we provide a detailed discussion of objectives, constraints, problem types, and solution approaches for NOMA. This paper also discusses the decoding methods and key performance indicators used in NOMA. Finally, we outline future research directions

Charging infrastructure placement for electric vehicles: An optimization prospective

© 2017 IEEE. Electric Vehicles (EVs) can be considered as a step forward towards the green environment and economical transportation. Moreover, EVs offer fuel economy, clean environment, and less cost of vehicle charging as compared to gasoline refilling. These are the main motivations towards the adaptation of EVs by the users. In order to increase the penetration of EVs into the transportation system, the EV charging stations become necessary to fulfill the charging needs. The charging stations can be placed considering different scenarios and objectives. Placement of charging stations in the service area requires a huge amount of budget and their locations are critical to select. In this paper, we formulate an optimization problem with an objective to minimize the overall cost of the charging infrastructure placement subject to the constraint on charging requirements in the service area. The proposed problem is solved using the branch and bound algorithm. Simulations results show the effectiveness of proposed placement strategy to minimize overall placement cost

Learning paradigms for communication and computing technologies in IoT systems

© 2020 Elsevier B.V. Wireless communication and computation technologies are becoming increasingly complex and dynamic due to the sophisticated and ubiquitous Internet of things (IoT) applications. Therefore, future wireless networks and computation solutions must be able to handle these challenges and dynamic user requirements for the success of IoT systems. Recently, learning strategies (particularly deep learning and reinforcement learning) are explored immensely to deal with the complexity and dynamic nature of communication and computation technologies for IoT systems, mainly because of their power to predict and efficient data analysis. Learning strategies can significantly enhance the performance of IoT systems at different stages, including at IoT node level, local communication, long-range communication, edge gateway, cloud platform, and corporate data centers. This paper presents a comprehensive overview of learning strategies for IoT systems. We categorize learning paradigms for communication and computing technologies in IoT systems into reinforcement learning, Bayesian algorithms, stochastic learning, and miscellaneous. We then present research in IoT with the integration of learning strategies from the optimization perspective where the optimization objectives are categorized into maximization and minimization along with corresponding applications. Learning strategies are discussed to illustrate how these strategies can enhance the performance of IoT applications. We also identify the key performance indicators (KPIs) used to evaluate the performance of IoT systems and discuss learning algorithms for these KPIs. Lastly, we provide future research directions to further enhance IoT systems using learning strategie

Energy efficient resource allocation for NOMA in cellular IoT with energy harvesting

© 2017 IEEE. The Internet of Things (IoT) offers connectivity of massive low-power devices and sensors through the Internet which requires spectrum and energy efficient solutions. Recently, non-orthogonal multiple access (NOMA) has been investigated to address the challenges associated with spectral efficiency and dense deployment of a large number of devices in 5G cellular networks. Further, energy harvesting can enhance the energy efficiency of IoT devices. In this paper, we propose an energy-efficient resource allocation scheme for NOMA (EERA-NOMA) in cellular IoT with RF energy harvesting to address the above mentioned challenges. We model a framework to optimize user grouping of IoT devices in most appropriate resource blocks, power allocation, and time allocation for information transfer and energy harvesting. The objective is to maximize energy efficiency while satisfying constraints on the minimum data rate requirement of each user and transmit power. We adopted mesh adaptive direct search (MADS) algorithm to solve the formulated problem. Simulation results are presented to show the performance of proposed framework in comparison with existing work