Exploring Broadband Enabled Smart eEnvironment: Wireless Sensor (Mesh) Network

This paper explored the emergent importance of the use sensors as complementary or as alternative to environmental sensing and monitoring, industrial monitoring, and surface explorations. Advances in wireless broadband technology have enabled the use Wireless Sensor (Mesh) Network (WSN), a type mobile ad hoc network (MANET), in all facet of human endeavor. As a next-generation wireless communication, which centered on energy savings, communication reliability, and security, WSN has increased our processing, sensing, and communications capabilities. Hence, this paper is an exploration of recent reliance on sensors as result of broadband enabled smart environment for activities, such as environmental and habitat monitory, military surveillance, target tracking, search and rescue, and logistical tracking and supply-chain management

Generic Home Automation System Using IoT Gateway Based on Wi-Fi and ant+ Sensor Network

This research article explores the use of internet of things (IoT) technology in home automation, including cloud computing and sensor networks to improve quality of life, and the increasing affordability through mobile connectivity. In this proposed smart home system, our main objective is to build a home automation system for the common consumer, which can help him to use home appliances with confidence and control at a low cost. The paper describes the building of an IoT gateway using the ANT multi-hop wireless network protocol and the Wi-Fi protocol, specifically utilizing the nRF24L01 and Esp8266 chips. Various sensor nodes, such as a water tank level sensor, human presence sensor, smart LED door sensor, and smart switch, will be integrated into the system. The main goal of the research is to develop an affordable solution for smart home technology for everyday consumers

Proposal of architecture for IoT solution for monitoring and management of plantations

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production This work presents a systematic review of the existing literature on smart farming with IoT. The systematic review reveals an evolution in the way data are processed by IoT solutions in recent years. Traditional approaches mostly used data in a reactive manner. In contrast, recent approaches allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis. Based on the finds of the systematic review, this work proposes an architecture of an IoT solution that enables monitoring and management of crops in real time. The proposed architecture allows the usage of big data and machine learning to process the collected data. A prototype is implemented to validate the operation of the proposed architecture and a security risk assessment of the implemented prototype is carried out. The implemented prototype successfully validates the proposed architecture. The architecture presented in this work allows the implementation of IoT solutions in different scenarios of farming, such as indoor and outdoor

Wireless Sensor Networks for Fire Detection and Control

Due to current technological progress, the manufacturing of tiny and low price sensors became technically and economically feasible. Sensors can measure physical surroundings related to the environment and convert them into an electric signal. A huge quantity of these disposable sensors is networked to detect and monitor fire. This paper provides an analysis of utilisation of wireless sensor networks for fire detection and control

Automated manufacturing of smart tunnel segments

Tunnels, essential infrastructures, require regular inspections and maintenance to ensure their prolonged service life. While conventional methods heavily rely on expert human manpower, modern tunnel structural monitoring techniques, such as sensor-based Structural Health Monitoring (SHM), are increasingly utilized in both existing and newly constructed tunnels. Despite providing valuable insights into post-construction structural behaviour, these methods often overlook the behaviour of individual precast elements, such as tunnel segments, before their installation. This thesis explores the concept of smart tunnel segments instrumented by robotic means to address this gap. In this project lab-scale tunnel segments were instrumented using a 6-axis robotic arm making them smart enabling their properties to be tracked from manufacturing through the operational phase of the tunnel. The research involves a comprehensive review of current tunnel instrumentation practices, identifying structural strains as the most monitored parameters. Vibrating Wire Strain Gauges (VWSGs) were identified as the most suitable sensors for this application due to their compatibility with a modular system and superior long-term properties, especially when embedded in concrete. Furthermore, the study identifies untapped potential in fully automated precast factories and proposes repurposing certain features of industrial robots to deploy VWSGs nodes via robotic pick-and-place. Through a novel evaluation framework, the research demonstrates the effectiveness of automated sensor deployment by robots. This includes the robotic installation of a pair of embedded VWSGs in lab-scale tunnel segments, thereby rendering them "smart," and subjecting them to repetitive flexural loadings to evaluate their performance and accuracy. The calculated strain transfer exhibits consistent and repeatable behaviour across segments. Finally, the thesis outlines the economic justification for smart segments, which outperform traditional on-site wired and wireless alternatives, thereby contributing to a more comprehensive and cost-effective tunnel maintenance strategyTunnels, essential infrastructures, require regular inspections and maintenance to ensure their prolonged service life. While conventional methods heavily rely on expert human manpower, modern tunnel structural monitoring techniques, such as sensor-based Structural Health Monitoring (SHM), are increasingly utilized in both existing and newly constructed tunnels. Despite providing valuable insights into post-construction structural behaviour, these methods often overlook the behaviour of individual precast elements, such as tunnel segments, before their installation. This thesis explores the concept of smart tunnel segments instrumented by robotic means to address this gap. In this project lab-scale tunnel segments were instrumented using a 6-axis robotic arm making them smart enabling their properties to be tracked from manufacturing through the operational phase of the tunnel. The research involves a comprehensive review of current tunnel instrumentation practices, identifying structural strains as the most monitored parameters. Vibrating Wire Strain Gauges (VWSGs) were identified as the most suitable sensors for this application due to their compatibility with a modular system and superior long-term properties, especially when embedded in concrete. Furthermore, the study identifies untapped potential in fully automated precast factories and proposes repurposing certain features of industrial robots to deploy VWSGs nodes via robotic pick-and-place. Through a novel evaluation framework, the research demonstrates the effectiveness of automated sensor deployment by robots. This includes the robotic installation of a pair of embedded VWSGs in lab-scale tunnel segments, thereby rendering them "smart," and subjecting them to repetitive flexural loadings to evaluate their performance and accuracy. The calculated strain transfer exhibits consistent and repeatable behaviour across segments. Finally, the thesis outlines the economic justification for smart segments, which outperform traditional on-site wired and wireless alternatives, thereby contributing to a more comprehensive and cost-effective tunnel maintenance strateg

Scalable IoT Architecture for Monitoring IEQ Conditions in Public and Private Buildings

This paper presents a scalable IoT architecture based on the edge–fog–cloud paradigm for monitoring the Indoor Environmental Quality (IEQ) parameters in public buildings. Nowadays, IEQ monitoring systems are becoming important for several reasons: (1) to ensure that temperature and humidity conditions are adequate, improving the comfort and productivity of the occupants; (2) to introduce actions to reduce energy consumption, contributing to achieving the Sustainable Development Goals (SDG); and (3) to guarantee the quality of the air—a key concern due to the COVID-19 worldwide pandemic. Two kinds of nodes compose the proposed architecture; these are the so-called: (1) smart IEQ sensor nodes, responsible for acquiring indoor environmental measures locally, and (2) the IEQ concentrators, responsible for collecting the data from smart sensor nodes distributed along the facilities. The IEQ concentrators are also responsible for configuring the acquisition system locally, logging the acquired local data, analyzing the information, and connecting to cloud applications. The presented architecture has been designed using low-cost open-source hardware and software—specifically, single board computers and microcontrollers such as Raspberry Pis and Arduino boards. WiFi and TCP/IP communication technologies were selected, since they are typically available in corporative buildings, benefiting from already available communication infrastructures. The application layer was implemented with MQTT. A prototype was built and deployed at the Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), using the existing network infrastructure. This prototype allowed for collecting data within different academic scenarios. Finally, a smart sensor node was designed including low-cost sensors to measure temperature, humidity, eCO2, and VOC.The authors wish to express their gratitude, for supporting this work, to the Fundación Vital through project VITAL21/05 and the University of the Basque Country (UPV/EHU), through the Campus Bizia Lab (CBL) program. Partial support has been also received from the Basque Government, through project EKOHEGAZ (ELKARTEK KK-2021/00092), the Diputación Foral de Álava (DFA) through the project CONAVANTER, and the UPV/EHU through the GIU20/063 grant