Abstract

Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad out-of this, 43 devices were developed as part of this work and 6 devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor. A thorough analysis of data collected for seven months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM. In addition, event-driven spatiotemporal analysis is done for PM values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization.

Abstract

Due to the increasing number of Internet of Things (IoT) devices, a large amount of data is being generated. However, factors such as hardware malfunctions, network failures, or cyber-attacks affect data quality and result in inaccurate data generation. Therefore, to facilitate the data usage, we propose a novel 5D-IoT framework for heterogeneous IoT systems that provides uniform data quality assessment with meaningful data descriptions. Based on the quality assessment result, a data consumer can directly access data from any IoT source, which ultimately speeds up the analysis process and helps gain important insights in less time. The framework relies on semantic descriptions of sensor observations and SHACL shapes assessing the quality of such data. Evaluations carried out on real-time data show the added value of such a framework.

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.