Keeping key air pollutants below the World Health Organization recommended limits is important for combating the ever-increasing deaths resulting from the associated health problems. This is especially true for indoor environments where poor ventilation can magnify the effects of air pollution. Having Knowledge about the level of pollutants in the air would serve as a stepping stone to take mitigation measures. In this work, a domesticated air pollution monitoring system over the LoRa enabled Internet of Things framework is proposed. Two sensors for CO 2 and PM 2.5 that are important for air quality monitoring with compensated weather monitoring capabilities were deployed in the cafeteria kitchen and laboratory room of the University of Rwanda, College of Science and Technology. The sensed parameter readings are then sent to the cloud via LoRaWAN protocol supported gateway that interfaces the sensors and the cloud part of the network. The end users can query the system and access the data together with the analytic information via the developed Web-based user interface dashboard. An analysis of the data over a period of eleven (11) months is carried out and results show high parts per million of CO 2 of over 800 ppm and PM 2 . 5 concentration of over 100 ppm in the kitchen environment. Whilst a concentration of 500 ppm for CO 2 and zero ppm for PM 2 . 5 were observed in the laboratory room. Baseline algorithms that facilitate setting of triggers for each sensing node and pushing of notifications for when a measured parameter exceeds a certain threshold value are proposed and implemented.

A mismatch between staffing ratios and service demand leads to overcrowding of patients in waiting rooms of health centers. Overcrowding consequently leads to excessive patient waiting times, incomplete preventive service delivery and disgruntled medical staff. Worse, due to the limited patient load that a health center can handle, patients may leave the clinic before the medical examination is complete. It is true that as one health center may be struggling with an excessive patient load, another facility in the vicinity may have a low patient turn out. A centralized hospital management system, where hospitals are able to timely exchange patient load information would allow excess patient load from an overcrowded health center to be re-assigned in a timely way to the nearest health centers. In this paper, a machine learning-based patient load prediction model for forecasting future patient loads is proposed. Given current and historical patient load data as inputs, the model outputs future predicted patient loads. Furthermore, we propose re-assigning excess patient loads to nearby facilities that have minimal load as a way to control overcrowding and reduce the number of patients that leave health facilities without receiving medical care as a result of overcrowding. The re-assigning of patients will imply a need for transportation for the patient to move from one facility to another. To avoid putting a further strain on the already fragmented ambulatory services, we assume the existence of a scheduled bus system and propose an Internet of Things (IoT) integrated smart bus system. The developed IoT system can be tagged on buses and can be queried by patients through representation state transfer application program interfaces (APIs) to provide them with the position of the buses through web app or SMS relative to their origin and destination stop. The back end of the proposed system is based on message queue telemetry transport, which is lightweight, data efficient and scalable, unlike the traditionally used hypertext transfer protocol.

Accurate staff scheduling is crucial in overcoming the problem of mismatch between staffing ratios and demand for health services which can impede smooth patient flow. Patient flow is an important process towards provision of improved quality of service and also improved utilization of hospital resources. However, extensive waiting times remains a key source of dissatisfaction with the quality of health care service among patients. With rarely scheduled hospital visits, the in-balance between hospital staffing and health service demand remains a constant challenge in Sub-Saharan Africa. Accurate workload predictions help anticipate financial needs and also aids in strategic planning for the health facility. Using a local health facility for a case study, we investigate problems faced by hospital management in staff scheduling. We apply queuing theory techniques to assess and evaluate the relationship between staffing ratios and waiting times at the facility. Specifically, using patient flow data for a rural clinic in Malawi, we model queue parameters and also approximate recommended staffing ratios to achieve steady state leading to reduced waiting times and consequently, improved service delivery at the clinic.

Bees, like other insects, indirectly contribute to job creation, food security, and poverty reduction. However, across many parts of the world, bee populations are in decline, affecting crop yields due to reduced pollination and ultimately impacting human nutrition. Technology holds promise for countering the impacts of human activities and climatic change on bees’ survival and honey production. However, considering that smart beekeeping activities mostly operate in remote areas where the use of grid power is inaccessible and the use of batteries to power is not feasible, there is thus a need for such systems to be energy efficient. This work explores the integration of device-to-device communication with 5G technology as a solution to overcome the energy and throughput concerns in smart beekeeping technology. Mobile-based device-to-device communication facilitates devices to communicate directly without the need of immediate infrastructure. This type of communication offers advantages in terms of delay reduction, increased throughput, and reduced energy consumption. The faster data transmission capabilities and low-power modes of 5G networks would significantly enhance the energy efficiency during the system’s idle or standby states. Additionally, the paper analyzes the application of both the discovery and communication services offered by 5G in device-to-device-based smart bee farming. A novel, energy-efficient algorithm for smart beekeeping was developed using data integration and data scheduling and its performance was compared to existing algorithms. The simulation results demonstrated that the proposed smart beekeeping device-to-device communication with data integration guarantees a good quality of service while enhancing energy efficiency.

The impact of COVID-19 has been felt across all sectors, from transportation, education, and public works to the daily operations of businesses like selling, retailing, and so forth. The business sector is among those badly affected, especially micro, small, and medium enterprises. The understanding of ground prevailing conditions is key in driving informed policies that would have meaningful impact on society with regard to overcoming the effects of the virus. Hence, this work is an attempt to report the real ground statistics and necessity of technological support with the goal of submitting a report of recommended policies to the concerned authorities. In this direction, this work presents the outcome of a survey conducted to assess the impact of COVID-19 on operations of micro, small, and medium enterprises and also to find out the interventions put in place around business environments so as to enforce adherence to COVID-19 health safety measures. The survey was part of a study to develop automated IoT-powered technological solutions that would help to enforce proper mask wearing in indoor environments and also observance of social distance requirements within business premises. A customized questionnaire was designed to capture data on various aspects central to the focus of the study. The study was carried out in the month of May 2021, in the Huye district of Rwanda. According to the survey findings, the major challenges faced by businesses due to COVID-19 include failure by clients to settle bills, reduced ability to expand investment, difficulty in accessing inputs domestically, lower domestic sales to consumers, and lower domestic sales to businesses. The results also reveal some positive points that most businesses were found to have: hand washing points, hand sanitizer dispensers, and mechanisms to enforce social distance between customer and customer and also customer and front desk worker. In a nutshell, this work is unique in terms of (1) the customized questionnaire about Rwanda’s needs, (2) field visit-based data collection for accurate data, and (3) including an assessment of the importance of technological intervention for better handling of public safety, especially in the MSME business sector.