Reefer container monitoring system based on WSN and cloud technology

Reefer containers are the main transportation method for the import and export of food and medicine. For high-quality products is necessary to monitor the condition of the reefer containers in order to avoid affecting goods quality due to environmental variations. Monitoring the reefer containers which are used to transport fruits, vegetables, and dairy products is one of the examples. In this context appears the necessity to develop this work expressed by a distributed sensor system for monitoring reefer containers. With the support of the WSN (wireless sensor network) including a set of sensors, it is possible to obtain the information about the temperature, humidity and location data of the reefer container and upload those data to a cloud platform expressed in the case of the purposed system by The Things Network platform. Based on LEACH (Low Energy Adaptive Clustering Hierarchy) routing algorithm, the embedded software was developed to guarantee a well-balanced distribution of the energy load among WSN end-nodes. A web application and a mobile application has been developed to display the data coming from the WSN node. To check if the reefer container working in a good condition, an alarm software module has been developed to highlight abnormal data coming for the system. The routing algorithm has been simulated and the effectiveness of the algorithm is verified by simulation results. The effectiveness of the proposed system was experimentally tested, and several results are included in this dissertation.Os contentores frigoríficos são o principal método de transporte para a importação e exportação de alimentos e medicamentos. Em produtos de alta qualidade, é necessário monitorizar as condições dos contentores frigoríficos, a fim de evitar a perda da qualidade das merca dorias devido a variações térmicas. Por exemplo, monitorarizando os contentores frigoríficos usados para transportar frutas, vegetais e laticínios. Neste contexto, aparece a necessidade do desenvolvimento deste projeto descrito por um sistema de sensores distribuídos para monitorizar contentores frigoríficos. Com o suporte da rede de sensores sem fios, incluindo um conjunto de sensores, é possível obter informações sobre os dados da temperatura, humidade e localização do contentor refrigerado e fazer uplo ad desses dados numa plataforma em cloud expressa no caso do sistema proposto por plataforma de rede de coisas. Com base no algoritmo de roteamento LEACH, o software incorporado foi desenvolvido para garantir uma distribuição equilibrada da carga de energi a entre os nós de WSN. Uma aplicação Web e uma aplicação móvel foram desenvolvidas para mostrar os dados provenientes do nó WSN. Para verificar a qualidade dos dados, um módulo de software de alarme foi também desenvolvido para destacar dados anormais que chegam ao sistema. O algoritmo de roteamento foi simulado e a eficiência do algoritmo é verificada pelos resultados da simulação. A eficiência do sistema proposto foi testada experimentalmente e os vários resultados estão incluídos nesta dissertação

Communication solution for IoT devices using the Toit programming language

Toit is a new object-oriented programming language for microcontrollers. The Toit virtual machine enables multiple independent apps to run side-by-side through software-based fault isolation. Toit is being developed as open source by the Danish company Toitware ApS, which collaborates with DTU Compute in the EU project TRANSACT. Although there are a plethora of programming solutions for IoT devices, they typically either involve low-level programming or their high-level programming requires too many resources. The objective of the thesis is to develop a communication solution for IoT devices using the Toit language. The solution proposed in this thesis is a tree-based network that allows devices to exchange data over Bluetooth Low Energy data channels without involving cloud connectivity.Toit is a new object-oriented programming language for microcontrollers. The Toit virtual machine enables multiple independent apps to run side-by-side through software-based fault isolation. Toit is being developed as open source by the Danish company Toitware ApS, which collaborates with DTU Compute in the EU project TRANSACT. Although there are a plethora of programming solutions for IoT devices, they typically either involve low-level programming or their high-level programming requires too many resources. The objective of the thesis is to develop a communication solution for IoT devices using the Toit language. The solution proposed in this thesis is a tree-based network that allows devices to exchange data over Bluetooth Low Energy data channels without involving cloud connectivity

Wearable Devices in Health Monitoring from the Environmental towards Multiple Domains: A Survey

The World Health Organization (WHO) recognizes the environmental, behavioral, physiological, and psychological domains that impact adversely human health, well-being, and quality of life (QoL) in general. The environmental domain has significant interaction with the others. With respect to proactive and personalized medicine and the Internet of medical things (IoMT), wearables are most important for continuous health monitoring. In this work, we analyze wearables in healthcare from a perspective of innovation by categorizing them according to the four domains. Furthermore, we consider the mode of wearability, costs, and prolonged monitoring. We identify features and investigate the wearable devices in the terms of sampling rate, resolution, data usage (propagation), and data transmission. We also investigate applications of wearable devices. Web of Science, Scopus, PubMed, IEEE Xplore, and ACM Library delivered wearables that we require to monitor at least one environmental parameter, e.g., a pollutant. According to the number of domains, from which the wearables record data, we identify groups: G1, environmental parameters only; G2, environmental and behavioral parameters; G3, environmental, behavioral, and physiological parameters; and G4 parameters from all domains. In total, we included 53 devices of which 35, 9, 9, and 0 belong to G1, G2, G3, and G4, respectively. Furthermore, 32, 11, 7, and 5 wearables are applied in general health and well-being monitoring, specific diagnostics, disease management, and non-medical. We further propose customized and quantified output for future wearables from both, the perspectives of users, as well as physicians. Our study shows a shift of wearable devices towards disease management and particular applications. It also indicates the significant role of wearables in proactive healthcare, having capability of creating big data and linking to external healthcare systems for real-time monitoring and care delivery at the point of perception

Implementing Efficient and Multi-Hop Image Acquisition In Remote Monitoring IoT systems using LoRa Technology

Remote sensing or monitoring through the deployment of wireless sensor networks (WSNs) is considered an economical and convenient manner in which to collect information without cumbersome human intervention. Unfortunately, due to challenging deployment conditions, such as large geographic area, and lack of electricity and network infrastructure, designing such wireless sensor networks for large-scale farms or forests is difficult and expensive. Many WSN-appropriate wireless technologies, such as Wi-Fi, Bluetooth, Zigbee and 6LoWPAN, have been widely adopted in remote sensing. The performance of these technologies, however, is not sufficient for use across large areas. Generally, as the geographical scope expands, more devices need to be employed to expand network coverage, so the number and cost of devices in wireless sensor networks will increase dramatically. Besides, this type of deployment usually not only has a high probability of failure and high transmission costs, but also imposes additional overhead on system management and maintenance. LoRa is an emerging physical layer standard for long range wireless communication. By utilizing chirp spread spectrum modulation, LoRa features a long communication range and broad signal coverage. At the same time, LoRa also has low power consumption. Thus, LoRa outperforms similar technologies in terms of hardware cost, power consumption and radio coverage. It is also considered to be one of the promising solutions for the future of the Internet of Things (IoT). As the research and development of LoRa are still in its early stages, it lacks sufficient support for multi-packet transport and complex deployment topologies. Therefore, LoRa is not able to further expand its network coverage and efficiently support big data transfers like other conventional technologies. Besides, due to the smaller payload and data rate in LoRa physical design, it is more challenging to implement these features in LoRa. These shortcomings limit the potential for LoRa to be used in more productive application scenarios. This thesis addresses the problem of multi-packet and multi-hop transmission using LoRa by proposing two novel protocols, namely Multi-Packet LoRa (MPLR) and Multi-Hop LoRa (MHLR). LoRa's ability to transmit large messages is first evaluated in this thesis, and then the protocols are well designed and implemented to enrich LoRa's possibilities in image transmission applications and multi-hop topologies. MPLR introduces a reliable transport mechanism for multi-packet sensory data, making its network not limited to the transmission of small sensor data only. In collaboration with a data channel reservation technique, MPLR is able to greatly mitigate data collisions caused by the increased transmission time in laboratory experiments. MHLR realizes efficient routing in LoRa multi-hop transmission by utilizing the power of machine learning. The results of both indoor and outdoor experiments show that the machine learning based routing is effective in wireless sensor networks

New Waves of IoT Technologies Research – Transcending Intelligence and Senses at the Edge to Create Multi Experience Environments

The next wave of Internet of Things (IoT) and Industrial Internet of Things (IIoT) brings new technological developments that incorporate radical advances in Artificial Intelligence (AI), edge computing processing, new sensing capabilities, more security protection and autonomous functions accelerating progress towards the ability for IoT systems to self-develop, self-maintain and self-optimise. The emergence of hyper autonomous IoT applications with enhanced sensing, distributed intelligence, edge processing and connectivity, combined with human augmentation, has the potential to power the transformation and optimisation of industrial sectors and to change the innovation landscape. This chapter is reviewing the most recent advances in the next wave of the IoT by looking not only at the technology enabling the IoT but also at the platforms and smart data aspects that will bring intelligence, sustainability, dependability, autonomy, and will support human-centric solutions.acceptedVersio

Utilization of Internet of Things and wireless sensor networks for sustainable smallholder agriculture

Agriculture is the economy’s backbone for most developing countries. Most of these countries suffer from insufficient agricultural production. The availability of real-time, reliable and farm-specific information may significantly contribute to more sufficient and sustained production. Typically, such information is usually fragmented and often does fit one-on-one with the farm or farm plot. Automated, precise and affordable data collection and dissemination tools are vital to bring such information to these levels. The tools must address details of spatial and temporal variability. The Internet of Things (IoT) and wireless sensor networks (WSNs) are useful technology in this respect. This paper investigates the usability of IoT and WSN for smallholder agriculture applications. An in-depth qualitative and quantitative analysis of relevant work over the past decade was conducted. We explore the type and purpose of agricultural parameters, study and describe available resources, needed skills and technological requirements that allow sustained deployment of IoT and WSN technology. Our findings reveal significant gaps in utilization of the technology in the context of smallholder farm practices caused by social, economic, infrastructural and technological barriers. We also identify a significant future opportunity to design and implement affordable and reliable data acquisition tools and frameworks, with a possible integration of citizen science

Drone Base Station Trajectory Management for Optimal Scheduling in LTE-Based Sparse Delay-Sensitive M2M Networks

Providing connectivity in areas out of reach of the cellular infrastructure is a very active area of research. This connectivity is particularly needed in case of the deployment of machine type communication devices (MTCDs) for critical purposes such as homeland security. In such applications, MTCDs are deployed in areas that are hard to reach using regular communications infrastructure while the collected data is timely critical. Drone-supported communications constitute a new trend in complementing the reach of the terrestrial communication infrastructure. In this study, drones are used as base stations to provide real-time communication services to gather critical data out of a group of MTCDs that are sparsely deployed in a marine environment. Studying different communication technologies as LTE, WiFi, LPWAN and Free-Space Optical communication (FSOC) incorporated with the drone communications was important in the first phase of this research to identify the best candidate for addressing this need. We have determined the cellular technology, and particularly LTE, to be the most suitable candidate to support such applications. In this case, an LTE base station would be mounted on the drone which will help communicate with the different MTCDs to transmit their data to the network backhaul. We then formulate the problem model mathematically and devise the trajectory planning and scheduling algorithm that decides the drone path and the resulting scheduling. Based on this formulation, we decided to compare between an Ant Colony Optimization (ACO) based technique that optimizes the drone movement among the sparsely-deployed MTCDs and a Genetic Algorithm (GA) based solution that achieves the same purpose. This optimization is based on minimizing the energy cost of the drone movement while ensuring the data transmission deadline missing is minimized. We present the results of several simulation experiments that validate the different performance aspects of the technique

LoRa-based solution for smart city sensing

Creating a network of IoT devices using LoRa to allow the exchange data obtained from sensor in a smart cit

Secure Communication in Disaster Scenarios

Während Naturkatastrophen oder terroristischer Anschläge ist die bestehende Kommunikationsinfrastruktur häufig überlastet oder fällt komplett aus. In diesen Situationen können mobile Geräte mithilfe von drahtloser ad-hoc- und unterbrechungstoleranter Vernetzung miteinander verbunden werden, um ein Notfall-Kommunikationssystem für Zivilisten und Rettungsdienste einzurichten. Falls verfügbar, kann eine Verbindung zu Cloud-Diensten im Internet eine wertvolle Hilfe im Krisen- und Katastrophenmanagement sein. Solche Kommunikationssysteme bergen jedoch ernsthafte Sicherheitsrisiken, da Angreifer versuchen könnten, vertrauliche Daten zu stehlen, gefälschte Benachrichtigungen von Notfalldiensten einzuspeisen oder Denial-of-Service (DoS) Angriffe durchzuführen. Diese Dissertation schlägt neue Ansätze zur Kommunikation in Notfallnetzen von mobilen Geräten vor, die von der Kommunikation zwischen Mobilfunkgeräten bis zu Cloud-Diensten auf Servern im Internet reichen. Durch die Nutzung dieser Ansätze werden die Sicherheit der Geräte-zu-Geräte-Kommunikation, die Sicherheit von Notfall-Apps auf mobilen Geräten und die Sicherheit von Server-Systemen für Cloud-Dienste verbessert