This research proposes a low-inclined orbit concept and design for the Internet-of-Things (IoT) using lean satellite standards in near-equatorial regions. The study aims to evaluate the coverage of various inclination angles at various latitudes and inclination angles in order to determine the most suitable satellite design for providing IoT coverage in these regions. The main methods applied in the study included analyzing the coverage performance of different inclination angles, the link budget analysis using simulations and the definition of the mission criteria. The results of the study show that the overall coverage performance decreases with an increase in the inclination angle. Satellites with lower inclination angles have ground tracks that are more closely aligned with the equator, while satellites with higher inclination angles have ground tracks that are inclined further toward the poles. In addition, the results show that the fraction of orbits with coverage (expressed as a percentage) declines with increasing latitude. Based on these findings, a low-inclined orbit of 24° provides the best coverage for IoT in near-equatorial regions within ±20 and 26° latitude, with a peak coverage of 27% at 24° latitude and a minimum coverage of 10% in the region spanning from 0° to ±27° latitude. This design offers more coverage time and a shorter revisit time to the selected regions for communication missions.

Combining Internet of Things (IoT) with Long Range (LoRa) networks opens a whole new world of possibilities. In an ever-evolving world with lots of data, the need for global coverage has rapidly increased. This paper looks at the design of a novel IoT device capable of directly communicating with any satellite as well as evaluation of the uplink/downlink tests. Using orbital simulations, the region coverage of single satellite is compared with a multi-vendor satellite constellation as well as the daily access to evaluate the revisit time for continuous data transmission round-the-clock. Directly communicating with the constellation of satellites eliminates the need of the multiple IoT gateways for establishing connectivity to every satellite with an average regular pass at 10-minute intervals.

The Internet of Things (IoT) refers to the interconnected network of physical devices, vehicles, buildings, and other objects that are embedded with sensors, software, and network connectivity, enabling them to collect and exchange data. As the number of connected devices and applications has increased significantly in recent years, the IoT has become a key feature of the modern technological ecosystem. One major challenge facing society today is the issue of energy scarcity, and there has been a growing focus on finding ways to increase energy efficiency. One approach to addressing this challenge is to use the IoT to monitor and optimize energy usage in various settings. This paper presents the design and implementation of an IoT-enabled laboratory that aims to increase energy efficiency over the long term through the use of IoT monitoring and artificial intelligence (AI). The paper describes a practical approach and provides results from the sensors employed in the IoT lab. This research contributes to the growing body of knowledge on how the IoT can be used to improve energy efficiency and address the issue of energy scarcity.

Recycling is a crucial process in decreasing pollution and waste materials. Unfortunately, many people find it a time-consuming task and are not incentivized enough to do it, thus the need for a system to simplify the process and make it more enjoyable. The system uses a Convolutional Neural Network to detect and classify the waste material and then a hardware mechanism to segregate the waste. The model utilizes a custom dataset and achieves a precision of 89%. The system is also equipped with a sensor network which connects to the Internet and views the results on an Internet-of-Things platform.