Building the Internet of Things platform for smart maternal healthcare services with wearable devices and cloud computing

The United Nations’ Sustainable Development Goals (SDGs) set out to improve the quality of life of people in developed, emerging, and developing countries by covering social and economic aspects, with a focus on environmental sustainability. At the same time, data-driven technologies influence our lives in all areas and have caused fundamental economical and societal changes. This study presents a comprehensive literature review on how data-driven approaches have enabled or inhibited the successful achievement of the 17 SDGs to date. Our findings show that data-driven analytics and tools contribute to achieving the 17 SDGs, e.g., by making information more reliable, supporting better-informed decision-making, implementing data-based policies, prioritizing actions, and optimizing the allocation of resources. Based on a qualitative content analysis, results were aggregated into a conceptual framework, including the following categories: (1) uses of data-driven methods (e.g., monitoring, measurement, mapping or modeling, forecasting, risk assessment, and planning purposes), (2) resulting positive effects, (3) arising challenges, and (4) recommendations for action to overcome these challenges. Despite positive effects and versatile applications, problems such as data gaps, data biases, high energy consumption of computational resources, ethical concerns, privacy, ownership, and security issues stand in the way of achieving the 17 SDGs.

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The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

Wireless rechargeable sensor networks (WRSNs) addressed the energy bottle-neck inherent in Wireless sensor Networks (WSN) by utilizing mobile chargers to travel and replenish the energy of nodes. Most WRSNs schemes operate by allowing simultaneous data gathering with energy recharging. This increases the storage and processing power requirements of the nodes and their overall design cost. A non-concurrent data gathering and energy replenishment in WRSNs named Spatio-Temporal Non-Concurrent Data gathering and energy Recharging (ST-NCDR) is hereby proposed to enable realistic deployment of WRSNs using low-cost sensors that can easily be implemented. Simulation results indicate that the developed scheme has a lower energy consumption of the mobile chargers (MCs) and also a higher average residual energy of nodes than a scheme that executes the two tasks simultaneously. 

A Spatio-Temporal Non-Concurrent Data Gathering and Energy Replenishment in Wireless Rechargeable Sensor Networks (ST-NCDR)

In recent years, the industry of wearable devices for pregnancies has been developing rapidly. The devices ranges from foetal monitors to multi-functional health examination instruments, which helped monitoring and management of maternal health indicators such as foetal heart rate, blood glucose, and blood pressure in home. Pregnant women and obstetricians are bounded together by wearable devices in an unprecedented way. With the universal use of the Internet of Things technology, the Smart Maternal construction started to raise people’s attention. This paper investigated the impact of the state-of-the-art monitoring techniques such as Internet of Things for smart maternal healthcare services. We have performed an experimental analysis based on a dataset of 315 samples of pregnant women using questionnaires. The results implies there are generally a large impact that pregnant women’s recognition and acceptance of using wearable IoT devices during pregnancy.

The Impact of Healthcare Monitoring Technologies for Better Pregnancy

As an essential part of IoTs, wireless sensor networks are inseparable from its future development. However, sensor nodes are usually deployed in some complex and harsh areas, and it is difficult to replacing batteries manually. Thus, energy is the most key factor affecting the life cycle of wireless sensor networks. The energy loss occurs during the process of recharging and the movement among the sensors. To maintain the normal operation of the networks, sensors need to be recharged by deploying chargers. Therefore, we aim to find the shortest route. This paper studies two optimal routes planning of chargers by developing models based on genetic algorithm. The first route planning is in the case of only one mobile charger in the network. The second is based on the network with four chargers. After performing millions of iterations, the optimal dispatching scheme of mobile chargers was obtained.

Xinqi Zhu

Papers

The Impact of Healthcare Monitoring Technologies for Better Pregnancy

Charging Route Planning for Wireless Rechargeable Sensor Networks