A qualitative analysis of rural fishermen: Potential for blockchain-enabled framework for livelihood sustainability

Rural fishing communities face numerous challenges related to livelihood security, as they are engaged in a risky and labour-intensive occupation. They often receive only a small portion of the profits, due to the influence of self-serving local intermediaries, lack of transparency in the business processes, trust issues, and power differentials among stakeholders. Although still in its infancy, blockchain technology has been adopted in various urban settings to mitigate similar challenges and to build trust through its security attributes, data ledger transparency, and smart contract automation. Yet, few have explored the efficacy of blockchain technology in addressing the unique challenges faced by rural fishermen in marketing their catch and connecting them to a broader range of customers for improved livelihood resilience. This study aims to examine how the livelihood resilience of fishermen can be increased through the potential of a blockchain, in a fishing community in the coastal village of Alappad in Kerala, India. Thematic analysis of data acquired from 43 semi-structured qualitative interviews and participatory rural appraisal tools revealed five categories of challenges: business cost and profitability, government regulations, low education and digital illiteracy, socio-cultural limitations, and over-dependence on middlemen as inhibitors to fishermen's livelihoods. The study proposes a blockchain-based e-commerce framework to mitigate selected challenges that emerged due to a lack of trust and transparency in the local fish market. It contributes to rural development by exploring an innovative, solution aligned with five UN Sustainable Development Goals, in contrast to the Business-as-usual approach in offering technological solutions

NeoCommLight: A Visible Light Communication System for RF-Restricted NICUs

Visible Light Communication (VLC) is one of the emerging technologies of Optical Wireless Communications (OWCs) in finding sustainable solutions for the spectrum crunch of beyond 5G access technologies. This research work introduces the NeoCommLight system, a healthcare communication architecture for Neonatal Intensive Care Units (NICU) based on visible light communication, which offers a promising solution for RF-restricted areas and addresses the challenges posed by spectrum crunch and health concerns associated with traditional radio communication technologies. To demonstrate the feasibility and practicality of the proposed system, a prototype design is presented, accompanied by implementation details. Furthermore, the performance analysis of the NeoCommLight system is conducted, shedding light on crucial aspects such as communication delay, transmitter-to-receiver distance, variations in transmitter angle concerning the Line of Sight (LoS) axis, and the impact of diffraction caused by a knife edge obstacle placed between the transmitter and receiver. The performance analysis of the system showed that it could transmit data at a maximum data rate of 3 Mbps at a distance of 5 cm. The system could transmit data at a data rate 800 Kbps to a maximum distance of 2 meters

A QoS-Aware IoT Edge Network for Mobile Telemedicine Enabling In-Transit Monitoring of Emergency Patients

Addressing the inadequacy of medical facilities in rural communities and the high number of patients affected by ailments that need to be treated immediately is of prime importance for all countries. The various recent healthcare emergency situations bring out the importance of telemedicine and demand rapid transportation of patients to nearby hospitals with available resources to provide the required medical care. Many current healthcare facilities and ambulances are not equipped to provide real-time risk assessment for each patient and dynamically provide the required medical interventions. This work proposes an IoT-based mobile medical edge (IM2E) node to be integrated with wearable and portable devices for the continuous monitoring of emergency patients transported via ambulances and it delves deeper into the existing challenges, such as (a) a lack of a simplified patient risk scoring system, (b) the need for architecture that enables seamless communication for dynamically varying QoS requirements, and (c)the need for context-aware knowledge regarding the effect of end-to-end delay and the packet loss ratio (PLR) on the real-time monitoring of health risks in emergency patients. The proposed work builds a data path selection model to identify the most effective path through which to route the data packets in an effective manner. The signal-to-noise interference ratio and the fading in the path are chosen to analyze the suitable path for data transmission

Delay and Energy Efficient Offloading Strategies for an IoT Integrated Water Distribution System in Smart Cities

In the fast-moving world of information and communications technologies, one significant issue in metropolitan cities is water scarcity and the need for an intelligent water distribution system for sustainable water management. An IoT-based monitoring system can improve water distribution system management and mitigate challenges in the distribution network networks such as leakage, breakage, theft, overflow, dry running of pumps and so on. However, the increase in the number of communication and sensing devices within smart cities has evoked challenges to existing communication networks due to the increase in delay and energy consumption within the network. The work presents different strategies for efficient delay and energy offloading in IoT-integrated water distribution systems in smart cities. Different IoT-enabled communication network topology diagrams are proposed, considering the different water network design parameters, land cover patterns and wireless channels for communication. From these topologies and by considering all the relevant communication parameters, the optimum communication network architecture to continuously monitor a water distribution network in a metropolitan city in India is identified. As a case study, an IoT design and analysis model is studied for a secondary metropolitan city in India. The selected study area is in Kochi, India. Based on the site-specific model and land use and land cover pattern, delay and energy modeling of the IoT-based water distribution system is discussed. Algorithms for node categorisation and edge-to-fog allocation are discussed, and numerical analyses of delay and energy models are included. An approximation of the delay and energy of the network is calculated using these models. On the basis of these study results, and state transition diagrams, the optimum placement of fog nodes linked with edge nodes and a cloud server could be carried out. Also, by considering different scenarios, up to a 40% improvement in energy efficiency can be achieved by incorporating a greater number of states in the state transition diagram. These strategies could be utilized in implementing delay and energy-efficient IoT-enabled communication networks for site-specific applications

4D electrical resistivity to monitor unstable slopes in mountainous tropical regions: an example from Munnar, India

The number of large landslides in India has risen in the recent years, due to an increased occurrence of extreme monsoon rainfall events. There is an urgent need to improve our understanding of moisture-induced landslide dynamics, which vary both spatially and temporally. Geophysical methods provide integrated tools to monitor subsurface hydrological processes in unstable slopes at high spatial resolution. They are complementary to more conventional approaches using networks of point sensors, which can provide high temporal resolution information but are severely limited in terms of spatial resolution. Here, we present and discuss data from an electrical resistivity tomography monitoring system—called PRIME—deployed at the Amrita Landslide Early Warning System (Amrita-LEWS) site located in Munnar in the Western Ghats (Kerala, India). The system monitors changes in electrical resistivity in the subsurface of a landslide-prone slope that directly threatens a local community. The monitoring system provides a 4D resistivity model informing on the moisture dynamics in the subsurface of the slope. Results from a 10-month period spanning from pre-monsoon to the end of the monsoon season 2019 are presented and discussed with regard to the spatial variation of soil moisture. The temporal changes in resistivity within the slope are further investigated through the use of time-series clustering and compared to weather and subsurface pore water pressure data. This study sheds new light on the hydrological processes occurring in the shallow subsurface during the monsoon and potentially leading to slope failure. This geophysical approach aims at better understanding and forecasting slope failure to reduce the risk for the local community, thereby providing a powerful tool to be included in local landslide early warning systems