Abstract

—Rural connectivity has been a widely researched topic for several years. In India, around 50% of the population have poor or no connectivity to access digital services. Numerous technological solutions are being tested around the world, as well as in India. The key driving factor for reducing the digital divide is to lower the cost of network deployments and improve service adoption rate by exploring different technological and economical solutions. This survey aims to study rural connectivity and create awareness about the use-cases, state of the art projects and initiatives, challenges, and technologies to improve digital connectivity in rural parts of India. The strengths and weaknesses of different technologies tested for rural connectivity are analysed. The study includes a brief discussion of rural connectivity trials performed in India and around the world. We also explore the rural use-case of the 6G communication system, which would suit the rural Indian scenario

Abstract

Around 40% of the world’s population is currently without access to the Internet. The digital divide is due to the high cost of provisioning these services and the low return on investment for network operators. We propose using 5G network slicing with multi-tenancy (also known as neutral host networks (NHN)) for macro-cells and small cells in rural areas to reduce the costs. This paper investigates the techno-economic feasibility of using rural 5G NHN to minimise the digital divide. A generic model is developed to analyse the techno-economic analysis of 5G NHN deployment around the world, with a special focus on rural areas where no MNO is interested in providing services. To understand the application, it is applied to the Indian scenario. First, a discussion on existing infrastructure, competition and statistics for Indian telecommunications is presented. Next, the technical requirements are analysed using the key performance indicators (KPI) required for the rural 5G NHN for the Indian scenario. The study also analyses the relationship between coverage, investment in the network, the number of subscribers, investment time, demand, the investment per user and sensitivity analysis to understand the feasibility of the proposed solution for Indian villages with different input conditions. Later, a case study is carried out based on the proposed approach, along with coverage modelling for a few Indian villages having different topologies. The results show that 5G NHN using network slicing can significantly reduce the total investment required for providing 5G services in rural areas. Furthermore, the study shows that rural 5G NHN is a

Abstract

For understanding an environmental variable in a given geographical space, finding the optimal number of nodes is a tedious task. For this purpose, a framework is proposed in this paper based on hierarchical agglomerative clustering along with geographical distance based cluster representation. The proposed framework helps remove the redundant nodes in a practical IoT network by choosing the optimal nodes based on the target reconstruction error in the spatially interpolated map. The approach is employed on the data collected by an IoT network of ten particulate matter (PM) nodes on the campus of IIIT Hyderabad, India. The performance of the proposed approach is also compared with that of the brute force approach, which provides the lower bound on the reconstruction error. The results show that the proposed approach performs very closely to the brute force approach in terms of the reconstruction error with much fewer computations.

Abstract

In this paper, we propose beamformed energy detection (BFED) spectrum sensing schemes for a single secondary user (SU) or a cognitive radio to detect a primary user (PU) transmission in the presence of an interferer. In the millimeter wave (mmWave) band, due to high attenuation, there are fewer multipaths, and the channel is sparse, giving rise to fewer directions of arrivals (DoAs). Sensing in only these paths instead of blind energy detection can reap significant benefits. An analog beamforming weight vector is designed such that the beamforming gain in the true DoAs of the PU signal is maximized while minimizing interference from the interferer. To demonstrate the bound on the system performance, the proposed sensing scheme is designed under the knowledge of full channel state information (CSI) at the SU for the PU-SU and Interferer-SU channels. However, as the CSI may not be available at the SU, another BFED sensing scheme is proposed, which only utilizes the estimate the DoAs. To model the estimates of DoAs, perturbations are added to the true DoAs. The distribution of the test statistic for BFED with full CSI schemes is derived under the null hypothesis so that the threshold of the NeymanPearson detector can be found analytically. The performance of both schemes is also compared with the traditional energy detector for multi-antenna systems. Index Terms—Beamforming, direction of arrival (DoA), energy detection, mmWave, spectrum sensing.

Abstract

During the COVID-19 pandemic, India’s complete lockdown was implemented from March 24 to May 3 2020, to minimize the effects of community transfer and control the rapidly growing rate of the virus spread. In this paper, we focus on quantifying the change in air pollution due to Hyderabad’s lockdown, the capital of Telangana State. For this, two datasets are employed. The first dataset is from the Central Pollution Control Board (CPCB) stations in the city. In contrast, the second dataset is the dense IoT network of PM monitors deployed in the educational campus of IIITH in Gachibowli, Hyderabad. An analysis is done on the collected data to understand the effect of lockdown on PM values while considering the yearly and seasonal variations. It has been shown that while there has been a significant drop in PM values. However, through correlation analysis between the temperature and the PM values during the regular times, not all PM values decrease because of the lockdown. Index Terms

Abstract

Around 40% of the world’s population is currently without access to the Internet. The digital divide is due to the high cost of provisioning these services and the low return on investment for network operators. We propose using 5G network slicing with multi-tenancy (also known as neutral host networks (NHN)) for macro-cells and small cells in rural areas to reduce the costs. This paper investigates the techno-economic feasibility of using rural 5G NHN to minimise the digital divide. A generic model is developed to analyse the techno-economic analysis of 5G NHN deployment around the world, with a special focus on rural areas where no MNO is interested in providing services. To understand the application, it is applied to the Indian scenario. First, a discussion on existing infrastructure, competition and statistics for Indian telecommunications is presented. Next, the technical requirements are analysed using the key performance indicators (KPI) required for the rural 5G NHN for the Indian scenario. The study also analyses the relationship between coverage, investment in the network, the number of subscribers, investment time, demand, the investment per user and sensitivity analysis to understand the feasibility of the proposed solution for Indian villages with different input conditions. Later, a case study is carried out based on the proposed approach, along with coverage modelling for a few Indian villages having different topologies. The results show that 5G NHN using network slicing can significantly reduce the total investment required for providing 5G services in rural areas. Furthermore, the study shows that rural 5G NHN is a viable investment and a key enabler for Internet connectivity for villages with 10-year investment, having a subscribers’ base as low as 100 with a customer growth rate of 7%.

Abstract

In recent times, a few new low-cost sensors have been introduced to the global market for monitoring particulate matter (PM). In this paper, the performance of three such low-cost PM sensors, namely SDS011, Prana Air, and SPS30, for measuring PM2.5 and PM10 levels is evaluated against a standard reference Aeroqual Series-500. The test setup was exposed to PM concentrations ranging from 30 µg/cm3 to 600 µg/cm3 . The results were based on 1 min, 15 min, 30 min, and 1 hr average readings. The experiments were carried out in indoor as well as outdoor environments. The comparative evaluation was performed before and after calibration. The performance of these sensors is evaluated in terms of coefficient of determination (R 2 ), coefficient of variation (Cv) and root mean square error (RMSE). Evaluation results show that these low-cost sensors have good performance after calibration with a reference sensor.

Abstract

During the COVID-19 pandemic, India’s complete lockdown was implemented from March 24 to May 3 2020, to minimize the effects of community transfer and control the rapidly growing rate of the virus spread. In this paper, we focus on quantifying the change in air pollution due to Hyderabad’s lockdown, the capital of Telangana State. For this, two datasets are employed. The first dataset is from the Central Pollution Control Board (CPCB) stations in the city. In contrast, the second dataset is the dense IoT network of PM monitors deployed in the educational campus of IIITH in Gachibowli, Hyderabad. An analysis is done on the collected data to understand the effect of lockdown on PM values while considering the yearly and seasonal variations. It has been shown that while there has been a significant drop in PM values. However, through correlation analysis between the temperature and the PM values during the regular times, not all PM values decrease because of the lockdown

Abstract

—This paper introduces an internet-of-things (IoT) based economic retrofitting setup for digitising the analog water meters to make them smart. The setup contains a Raspberry-Pi microcontroller and a Pi-camera mounted on top of the analog water meter to take its images. The captured images are then preprocessed to estimate readings using a machine learning (ML) model. The employed ML algorithm is trained on a rich dataset that includes digits from the images of water meters captured by the hardware setup for ten days. The readings are posted on a cloud server in real-time using Raspberry-Pi. High temporal resolution plots of flow rate and volume are generated to derive inferences. The collected data can be used for deriving water consumption patterns and fault detection for efficient water management.

Abstract

For understanding an environmental variable in a given geographical space, finding the optimal number of nodes is a tedious task. For this purpose, a framework is proposed in this paper based on hierarchical agglomerative clustering along with geographical distance based cluster representation. The proposed framework helps remove the redundant nodes in a practical IoT network by choosing the optimal nodes based on the target reconstruction error in the spatially interpolated map. The approach is employed on the data collected by an IoT network of ten particulate matter (PM) nodes on the campus of IIIT Hyderabad, India. The performance of the proposed approach is also compared with that of the brute force approach, which provides the lower bound on the reconstruction error. The results show that the proposed approach performs very closely to the brute force approach in terms of the reconstruction error with much fewer computations.