Many fire situations have represented the loss of lives and material costs due to the lack of early fire detection through smoke or gas sensing, which can become complex and critical Meanwhile, engineers worldwide develop and test multiple systems for smoke and gas detection, commonly based on sensor networks, digital image processing, or computer vision Furthermore, the detection system must work thoroughly with alarms and warnings that aware of a risk situation for prompt evacuation of the population in the surroundings based on a reliable data network topology with adequate device deployments that will let us know the moment a fire outbreak This paper presents a low-cost Internet of Things (IoT) prototype for fire detection in outdoor environments based on sensors and Low Power Wide Area Network (LPWAN), focused on the accuracy in the temperature and gas measurement at the moment a fire starts For its achievement, we integrated wireless components, development boards, and electronic devices, following the management of information updates through a database schema for the alarm settings based on the data gathered from the sensors

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LPWAN Based IoT Surveillance System for Outdoor Fire Detection

This paper develops an architecture for flying ad-hoc networks (FANETs) to enable monitoring of water quality in a shrimp farm. Firstly, the key monitoring parameters for the characterization of water quality are highlighted and their desired operational ranges are summarized. These parameters directly influence shrimp survival and healthy growth. Based on the considered sensing modality, a reference architecture for implementing a cost-effective FANET based mobile sensing platform is developed. The controlled mobility of the platform is harnessed to increase the spatial monitoring resolution without the need for extensive infrastructure deployment. The proposed solution will be offered to shrimp farmers in the Mexican state of Colima once the laboratory trials are concluded.

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Monitoring of water quality in a shrimp farm using a FANET

In critical Internet of Things (IoT) application domains, such as the Defense Industry and Healthcare, false alerts have many negative effects, such as fear, disruption of emergency services, and waste of resources. Therefore, an alert must only be sent if triggered by a correct event. Nevertheless, IoT networks are exposed to intrusions, which affects event detection accuracy. In this paper, an Anomaly Detection System (ADS) is proposed in a smart hospital IoT system for detecting events of interest about patients’ health and environment and, at the same time, for network intrusions. Providing a single system for network infrastructure supervision and e-health monitoring has been shown to optimize resources and enforce the system reliability. Consequently, decisions regarding patients’ care and their environments’ adaptation are more accurate. The low latency is ensured, thanks to a deployment on the edge to allow for a processing close to data sources. The proposed ADS is implemented and evaluated while using Contiki Cooja simulator and the e-health event detection is based on a realistic data-set analysis. The results show a high detection accuracy for both e-health related events and IoT network intrusions.

Efficient Anomaly Detection for Smart Hospital IoT Systems.

Fire detection has been an issue of interest to researchers due to its significant damage to lives and property within a very short time. One of the recent solutions developed to detect fire is to use Internet-of-Things (IoT) devices equipped with cameras for surveillance. The captured videos of surroundings may be processed by the IoT devices themselves or at the cloud. The latter case is required if the detection algorithm is computationally demanding. However, the use of cloud has a flaw. In fact, using the cloud could pose the threat of having the privacy of a place violated, either through hacking or unauthorized access to the footage of the place where the cloud is installed. In this paper, a fire detection system that preserves the privacy of surroundings, while maintaining a high level of accuracy for fire detection is proposed. The proposed system makes use of the cloud for fire detection; and that is achieved by sending to the cloud features extracted from the video captured by the IoT device, instead of sending the actual footage. Binary video descriptors and Convolutional Neural Network (CNN) have been used to develop the fire detection algorithm. The video descriptors are used to extract features, while the CNN is used for classification. Videos with real fire and non-fire scenes have been used in this development. Results show that the performance of proposed fire detection algorithm can achieve 97.5% classification accuracy, that outperforms the state-of-the art algorithms which make direct use of raw videos. Therefore, the proposed fire detector is as reliable as other available systems, with the advantage of having a privacy-preserving capability. It is also demonstrated that the proposed video descriptors can be implemented for real-time processing using an IoT device, Raspberry Pi 4 platform, with an average processing speed of 100ms per frame, which well satisfies practical needs.

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A Privacy-Preserving Iot-Based Fire Detector

Internet of Things (IoT) appliances, especially those realized through wireless sensor networks (WSNs), have been a dominant subject for heavy research in the environmental and agricultural sectors. To address the ever-increasing demands for real-time monitoring and sufficiently handle the growing volumes of raw data, the cloud/fog computing paradigm is deemed a highly promising solution. This paper presents a WSN-based IoT system that seamlessly integrates all aforementioned technologies, having at its core the cloud/fog hybrid network architecture. The system was intensively validated using a demo prototype in the Ionian University facilities, focusing on response time, an important metric of future smart applications. Further, the developed prototype is able to autonomously adjust its sensing behavior based on the criticality of the prevailing environmental conditions, regarding one of the most notable climate hazards, wildfires. Extensive experimentation verified its efficiency and reported on its alertness and highly conforming characteristics considering the use-case scenario of Corfu Island’s 2019 fire risk severity. In all presented cases, it is shown that through fog leveraging it is feasible to contrive significant delay reduction, with high precision and throughput, whilst controlling the energy consumption levels. Finally, a user-driven web interface is highlighted to accompany the system; it is capable of augmenting the data curation and visualization, and offering real-time wildfire risk forecasting based on Chandler’s burning index scoring.

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An Alertness-Adjustable Cloud/Fog IoT Solution for Timely Environmental Monitoring Based on Wildfire Risk Forecasting

Open-source technologies enable communication channels between web platforms and innovative architectures to provide reliable data distribution, in which healthcare applications can particularly benefit from them. This work presents a communication channel design to improve the user experience about telemedicine apps, especially when patients are in remote locations while assuring their information using an innovative approach. The general purpose is to avoid users having to physically go to medical facilities by the correct data management related to their appointments and medical history. By preventing the attendance to healthcare facilities, patients do not expose themselves unnecessarily to viruses and bacteria. Therefore, this research includes a data communications model based on the FIWARE platform and cloud technologies for reliable user medical information distribution. The prototype is developed based on open-source technologies and registered the evaluation of different performance metrics that included cases scenarios in which administrators of healthcare centers configured options according to the availability of assets and informatics resources. The results show the effectiveness of the communication model under realistic conditions for encouraging the acceptance of telemedicine alternatives, especially when patients and medical staff present limitations regarding mobility.

FIWARE-Based Telemedicine Apps Modeling for Patients’ Data Management

This paper presents a solution for different activities concerning shrimp farming, especially focused on the middle-class artisanal productive sector, using both open source hardware and open source software. The proposal aims to optimize monitoring during shrimp farming, measuring parameters, such as dissolved oxygen, water temperature and food levels inside reservoirs, in order to have a real-time reading of parameters. In case critical values appear, an alarm activates by control center, notifying by either electronic mail or web interface. The attractiveness of the proposal consists in the automation of the feeding system, which provides food supply to shrimps at certain daily periods, controlling by web interface the different options the breeder has available, a process that can be even managed remotely.

Development of a Prototype of an Automatic System for Shrimp Fusing Telecontrol as a Viable Proposal for Small Producers

Contingency plans administered in computer centers are necessary for business continuity since organizations not only store or process data to offer different service platforms. Organizations could experience different repercussions facing external or internal risks affecting their investments. The fact of having computer support for unwanted events that affect devices is important. Cyber-attacks in data networks lead to a serious problem due to economic damages that an unauthorized intrusion entails. A contingency plan for cyber-attacks can cover just technical issues. However, these are long term projects with continuous execution and improvement, since necessary tasks master for going from production to contingency, and then reversing the changes in the case whether an emergency or an unexpected event occurs. This paper aims to be a framework reference about how to cope with a situation of cyber-attack.

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A Contingency Plan Framework for Cyber-Attacks

Vehicular trafﬁc in metropolitan areas turns congested along either paths or periods. As a case study, we have considered a mass transport system with a bus ﬂeet that rides over exclusive lanes across streets and avenues in an urban area that does not allow
the circulation of lightweight vehicles, cargo, and motorcycles. This trafﬁc ﬂow becomes congested due to the absence of restriction policies based on criteria. Moreover, the exclusive lanes are at ground level, decreasing lanes for other vehicles. The main objective of this proposal consists of controlling the access to the exclusive lanes by a cyber-physical system following authorization conditions, verifying the permission status of a vehicle by the accurate recognition of license plates to reduce trafﬁc congestion. Therefore, in the case of invading an exclusive lane without permission, the vehicle owner gets a notiﬁcation of the ﬁne with the respective evidence.

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Vladimir Sanchez Padilla

Papers

LPWAN Based IoT Surveillance System for Outdoor Fire Detection

FIWARE-Based Telemedicine Apps Modeling for Patients’ Data Management

Development of a Prototype of an Automatic System for Shrimp Fusing Telecontrol as a Viable Proposal for Small Producers

A Contingency Plan Framework for Cyber-Attacks

Cyber-physical system based on image recognition to improve traffic flow: A case study