Research on Smart Environment Monitoring Systems based on Secure Internet of Things (IoT)

Significant environmental threats include poor air quality, water contamination, and radiation pollution. A healthy society must be maintained for the planet to experience sustained growth. Environmental monitoring has transformed into smart environment monitoring (SEM) systems in recent years due to the growth of an internet of things (IoT). The Internet of Things (IoT) concept has developed into technology for creating smart environments and also has its disadvantage. To collect, evaluate, and recommend specific actions in smart environments for various purposes, a secure IoT-based platform is proposed. The proposed method follows the flow outlined here: data collection, normalization technique is used for data preprocessing, Linear Discriminant Analysis (LDA) is used for feature extraction, then data stored in IoT, Advanced Twofish encryption algorithm is proposed for securing the data, then user decryption, and finally performance is analyzed for smart environment monitoring using secure IoT. The proposed work aims to complete a critical evaluation of significant contributions to SEM that focus on the monitoring of water quality, air quality, radiation contamination, and agricultural systems. Secure IoT is based on the optimal integration and use of data gathered from several sources. This algorithm provides smart environment monitoring and also exhibits optimal integration

Wastewater-based epidemiology in hazard forecasting and early-warning systems for global health risks

With the advent of the SARS-CoV-2 pandemic, Wastewater-Based Epidemiology (WBE) has been applied to track community infection in cities worldwide and has proven succesful as an early warning system for identification of hotspots and changingprevalence of infections (both symptomatic and asymptomatic) at a city or sub-city level. Wastewater is only one of environmental compartments that requires consideration. In this manuscript, we have critically evaluated the knowledge-base and preparedness for building early warning systems in a rapidly urbanising world, with particular attention to Africa, which experiences rapid population growth and urbanisation. We have proposed a Digital Urban Environment Fingerprinting Platform (DUEF) – a new approach in hazard forecasting and early-warning systems for global health risks and an extension to the existing concept of smart cities. The urban environment (especially wastewater) contains a complex mixture of substances including toxic chemicals, infectious biological agents and human excretion products. DUEF assumes that these specific endo- and exogenous residues, anonymously pooled by communities’ wastewater, are indicative of community-wide exposure and the resulting effects. DUEF postulates that the measurement of the substances continuously and anonymously pooled by the receiving environment (sewage, surface water, soils and air), can provide near real-time dynamic information about the quantity and type of physical, biological or chemical stressors to which the surveyed systems are exposed, and can create a risk profile on the potential effects of these exposures. Successful development and utilisation of a DUEF globally requires a tiered approach including: Stage I: network building, capacity building, stakeholder engagement as well as a conceptual model, followed by Stage II: DUEF development, Stage III: implementation, and Stage IV: management and utilization. We have identified four key pillars required for the establishment of a DUEF framework: (1) Environmental fingerprints, (2) Socioeconomic fingerprints, (3) Statistics and modelling and (4) Information systems. This manuscript critically evaluates the current knowledge base within each pillar and provides recommendations for further developments with an aim of laying grounds for successful development of global DUEF platforms

Design and Implementation of SEMAR IoT Server Platform with Applications

Nowadays, rapid developments of Internet of Things (IoT) technologies have increased possibilities of realizing smart cities where collaborations and integrations of various IoT application systems are essential. However, IoT application systems have often been designed and deployed independently without considering the standards of devices, logics, and data communications. In this paper, we present the design and implementation of the IoT server platform called Smart Environmental Monitoring and Analytical in Real-Time (SEMAR) for integrating IoT application systems using standards. SEMAR offers Big Data environments with built-in functions for data aggregations, synchronizations, and classifications with machine learning. Moreover, plug-in functions can be easily implemented. Data from devices for different sensors can be accepted directly and through network connections, which will be used in real-time for user interfaces, text files, and access to other systems through Representational State Transfer Application Programming Interface (REST API) services. For evaluations of SEMAR, we implemented the platform and integrated five IoT application systems, namely, the air-conditioning guidance system, the fingerprint-based indoor localization system, the water quality monitoring system, the environment monitoring system, and the air quality monitoring system. When compared with existing research on IoT platforms, the proposed SEMAR IoT application server platform offers higher flexibility and interoperability with the functions for IoT device managements, data communications, decision making, synchronizations, and filters that can be easily integrated with external programs or IoT applications without changing the codes. The results confirm the effectiveness and efficiency of the proposal

IoT-based control and monitoring system of a solar-powered brushless dc motor for agro-machines – the case of a Tanzanian-made oil press machine

A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Science in Embedded and Mobile Systems of the Nelson Mandela African Institution of Science and TechnologyThe impulse in designing local agricultural machinery for curbing post-harvest losses in most African countries particularly Tanzania is unmatched. Locally made agricultural machines have proven to elevate the life of many small-scale farmers, which has increased the need to incorporate machine drives and controls to ease the process and operations. With potentials in Solar Energy, powering machine drive systems that operate in off-grid areas has been the best solution. Using the principles of Internet of Things (IoT) together with advancement in motor designs and readily available off the shelf microcontrollers such as the Raspberry Pi and Arduino UNO in the market, we achieve machinery that caters for our needs and the local content. Mobile apps play a huge role in industrialization where monitoring and even controls of machines can be performed by the mobile phones. This project incorporated Agile-Scrum methods to develop a control and monitoring system for a locally made avocado oil extraction machine that is powered by a solar system with 1600W panel arrays and 800Ah battery pack, and uses a Brushless Direct Current Motor coupled with electric solenoid valve, relay modules and a controller unit assisting on the control process and collecting crucial motor operation data such as voltage and current. The designed Mobile app ‘Blue’ acquire motor operation data from the Raspberry Pi via Bluetooth technology, delivering data to cloud server for later analysis. Easing data acquisition in off grid areas when engineers, technicians or operators have a physical access to the stations. It was concluded that this novel design would provide an effective control and monitoring mechanism with an acceptance on reliability, usability and effectiveness of up to 85.65% for a plethora of locally-made machinery that available in the market which still uses the manual means of operation emphasizing ease of use and productivity, thence joining hands with the global world on attaining some of the Sustainable Development Goals