LoRa Networks is one of the fast-growing and promising technologies to enable communications for the Internet of Things (IoT) devices on a large scale or long-range communication. Spreading Factors (SF) plays a significant role in enabling multiple long-range receptions of packets with every packet assigned a different spreading factor. Therefore, a change in SF is necessary for improving the data rate for transmission where the link is better and allow LoRa networks to adapt the range trade-off. This work uses FLoRa open source framework for carrying out end-to-end LoRa simulations network in the OMNET++ simulator. In this paper, we investigated the Adaptive Data Rate (ADR) and provided the behaviour of SF and data rate in LoRa wide Are network (LoRaWAN). Some of the findings includes the ability of transmitting data very fast possessed by the low SF no matter the size of the network and high amount of energy consumed by the high SF.

Wireless sensor networks (WSN) consist of a collection of independent sensor nodes that monitor certain conditions in different locations and transfer data through a network to an endpoint called base station. Because WSN nodes are often deployed outdoors where a direct power outlet may not be available, energy efficiency becomes a key factor in the design of WSNs. This work proposes an antenna transmission power optimisation algorithm (TPOA) which dynamically adjusts the power level of the transmission according to the last received signal strength indicator (RSSI). The mechanism was implemented and tested on an Xbee WSN that uses a modified Mixed Hop and signal Received routing for mobile Wireless Sensor Networks (MHRWSN) protocol. The energy consumption results have shown that the proposed system can save as much as 76% of power in operations while maintaining a packet reception rate of above 85%.

The 5G mobile telephony has become one of the worldwide most anticipated technology, which is followed by strong controversy regarding potentially dangerous health effects. This technology relies on electromagnetic field (EMF) emission from its network base stations, increasing the level of existing EMF in the environment. Consequently, this fact has initiated deep concerns of the public, who demanded overall investigation and monitoring of the inevitable 5G EMF exposure. In the last decade, the wireless sensors networks for EMF monitoring emerged as an innovative approach for effective analysis of EMF in the environment. The latest one is the Serbian EMF RATEL network, which offers a sophisticated approach of telecommunication service-based EMF monitoring. This network performs wideband monitoring, counting the EMF contribution of all active EMF sources in a predetermined frequency sub-band. In this paper, the preliminary EMF monitoring of 5G is presented, explaining technical details on the used Narda AMS 8061 sensor, the acquisition process, as well as the analysis and dissemination of the measurement results. The EMF RATEL is envisioned to be a support for the control and management of EMFs in upcoming smart-city ecosystems, in which is expected that will display intensive EMF radiation in living surrounding, regarding various telecommunication services.

Nowadays Location-Based Services (LBS) applications are proposed and presented in literature because of high growth in wireless sensor networks. The LBS provide useful information about the person or object’s current position. Among these applications, Global Positioning System (GPS)-based positioning and navigation services have been deployed in an outdoor environment. However, GPS requires being in sight with satellites due to line of sight challenge. The GPS fails in an indoor environment because of multipath effects caused by walls or indoor setup in general. Meanwhile, Wi-Fi-based positioning system are being proposed in literature and most of them utilizes the fingerprinting algorithm. Whereby, measurements of Received Signal Strength Indicator (RSSI) are collected during the offline phase for radio map and positioning is performed during the online phase. Similarly, this approach is faced with signal attenuation challenge caused by walls, desks, moving people, or just the indoor setup in general. The collection of the signal strengths without correction can affect positioning accuracy. In this paper, we present a model that utilizes Mean and Standard Deviation to identify and correct unstable low RSSI outliers during offline phase of Fingerprinting algorithm. We conducted comparative performance of Machine learning classification algorithms based on corrected RSSI and measured RSSI. The simulation results performed in MATLAB, indicates that correcting the RSSI improves the accuracy, precision, and recall of Fine Tree, Coarse Decision Tree, and Kernel Naïve Bayes to 95.1%, 94.8% and 94.5% respectively.

With the rapid growth of the Internet of Things (), the deployment, management, and identification of devices that are connected to networks become a big concern. Consequently, they emerge as a prominent challenge either for mobile network operators who try to offer cost-effective services tailored to market, or for network administrators who aim to identify as well reduce costs processing and optimize traffic management of connected environments. In order to achieve high accuracy in terms of reliability, loss and response time, new devices real time discovery techniques based on traffic characteristics are mandatory in favor of the identification of connected devices.

Technical network challenges in 5G relates to handover authentication, user privacy protection and resource management. Due to interoperability requirements among the heterogeneous networks (Hetnets), the security requirements for 5G are high compared to 2G, 3G and 4G. The current 5G handover protocols are based on either fuzzy logic (FL), artificial neural networks (ANN), blockchain, software defined network (SDN), or Multi-layer Feed Forward Network (MFNN). These protocols have either long latencies or focus on either security or quality of services parameters such as user satisfaction. The usage of these inefficient authentication schemes during 5G handovers lead to performance degradation in heterogeneous cells and increases the delay. In addition, 5G networks experience frequent handover failures and increased handover delays. Consequently, the provision of strong security, privacy and low latency handovers is required for the successful deployment of 5G networks such as 5G wireless local area networks (5G-WLAN) heterogeneous networks. These new requirements, coupled with demands for higher scalability, reliability, security, data rates, quality of service (QoS), and support for internet of everything (IoE) have seen the shift from 5G to beyond 5G (B5G). However, 5G and B5G are incapable of providing the complete requirements of IoE such as enhanced security and QoS. This paper sought to develop an ANN-FL protocol that addressed both security and QoS in 5G and B5G networks. The simulation results showed that the developed protocol was robust against attacks such de-synchronization and tracing attacks and yielded a 27.1% increase in handover success rate, a 27.3% reduction in handover failure rate, and a 24.1% reduction in ping pong handovers.

Small-scale farming has an important role in agriculture. Driven by the popularization of the Internet of things (IoT), this paper presents an IoT sensing box prototype coupled with soil analysis through computer vision to help small-scale farmer improve their yields. The idea of combining image-based soil classification with regular soil sensors is to improve the reliability of the sensed data, by minimizing the occurrence of incorrect estimates for specific soil types. The prototype follows a Do-It-Yourself (DIY) approach, and is based on commercial off-the-shelf (COTS) hardware and open source software. Additionally, the prototype includes a casing to house all the hardware which was designed considering standard 3D printing to be easily replicated. As the presented solution is currently on a prototype stage, the validations carried out in a controlled, in-lab environment include guaranteeing the proper process for image acquisition and data quality of the computer vision component (images collected from sensor camera, and their quality), suitable data exchange over the sockets, and the sensing box’s ability to gather data.

Remote teaching and learning (RTL) is a system of education, wherein teachers and learners are not in the same location but separated by time and space. Global pandemics such as the COVID-19, necessitate social distance, thereby rendering traditional “contact” based classroom learning unfeasible. e-Learning which is a viable alternative often depends on reliable Internet. Unfortunately, Internet penetration in many areas of the world is still abysmally low. RTL in these under-served regions of the world is thus a major challenge. In this paper, we review the state of RTL and consider feasible options for under-served communities. Requirement for RTL, including data and associated cost of attending classes online are also considered. Finally, recommendations for achieving least cost RTL for under-served communities are given.

Faced with the need of overall cleaner production processes, the Government of Mexico, in coalition with international cooperation agencies, has made efforts to spark to a cleaner transition by the conferment of financing under preferential conditions for small and medium-sized enterprises (SMEs), which are the backbone of the Mexican economy. This will help the implementation of sustainable projects that promote the reduction of energy consumption and Greenhouse Gas (GHG) emissions. However, these efforts have not yielded the expected results since the viability of the loans is determined by the Financial Institutions (FIs) based solely on profitability analysis of the SMEs. If this analysis is complemented with consideration of environmental and social aspects, a greater number of projects would be subject to financing. Real Options method allows the calculation of future cash flows incorporating the uncertainty from the volatility associated with the projects, allowing future adjustments to be made to them based on their performance. In addition, under the economic cost-effectiveness analysis, costs incurred in the implemented projects are compared against the results, assigning a monetary value to them to determine their overall impact. Derived from the limitations in the valuation of sustainable projects in SMEs, this article aims to establish a guide for comprehensive evaluation, using a cost-effectiveness approach and addressing the uncertainty of the project through Real Options. Considering the diversity of the SME during this study, the target companies and the type of projects that have the best opportunities for successful application of the proposed guide were segmented.

The aim of this paper is to analyze the feasibility of establishing a supply chain for castor oil biodiesel distribution in Central Mexico, based on the distribution of land with the highest castor oil seed yield potential. The supply chain analysis is focused on meeting the biodiesel demand for the region, assuming a B20 mix usage, from the proposed plantation sites to refineries and gas stations, using Network Optimization, and defining the required biodiesel production increase to achieve this.