The emerging and vast adoption of the Internet of Things (IoT) has sprung a plethora of research works regarding the potential benefits in smart agriculture. A popular implementation involves the deployment of Wireless Sensor Networks (WSNs), which embed low energy consumption sensory nodes to capture the critical environmental parameters prevailing on the farms. However, to manage the ever-increasing volumes of raw data successfully, new approaches must be explored. Under this scope, current work reports on the design and development of an IoT system, having in mind the case of olive groves, which are considered the dominant sector for agricultural activity in the Mediterranean Basin. The system incorporates the cloud/fog computing paradigm to equip the olive growers with a low-cost solution for accurate, reliable, and almost real-time monitoring of their crops. Its core is based on a three-layered network architecture, capable of dynamically balancing the generated load, by pushing cloud-elastic resources to the underlying fog network. As such, the premise of the approach lies in the conforming character of the system that allows for targeted alterations to its operational functionality to meet stringent latency and traffic load environmental monitoring constraints. To evaluate the performance of the proposed architecture, a demo prototype is developed and deployed in the facilities of the Ionian University. Experimental results illustrate the efficiency, flexibility, and scalability of the approach in terms of latency, achieving response time reduction across all platforms, a subject of the utmost importance when it comes to precision agriculture of the future. Moreover, it is shown that the system is capable of dynamic functionality adaptation, to meet network traffic load constraints, achieving high throughput (on average 95%) and addressing potential environmental dangers to olive oil production.

The advent of Internet of Things has propelled the agricultural domain through the integration of sensory devices, capable of monitoring and wirelessly propagating information to producers; thus, they employ Wireless Sensor Networks (WSNs). These WSNs allow real time monitoring, enabling intelligent decision-making to maximize yields and minimize cost. Designing and deploying a WSN is a challenging and multivariate task, dependent on the considered environment. For example, a need for network synchronization arises in such networks to correlate acquired measurements. This work focuses on the design and installation of a WSN that is capable of facilitating the sensing aspects of smart and precision agriculture applications. A system is designed and implemented to address specific design requirements that are brought about by the considered environment. A simple synchronization scheme is described to provide time-correlated measurements using the sink node’s clock as reference. The proposed system was installed on an olive grove to assess its effectiveness in providing a low-cost system, capable of acquiring synchronized measurements. The obtained results indicate the system’s overall effectiveness, revealing a small but expected difference in the acquired measurements’ time correlation, caused mostly by serial transmission delays, while yielding a plethora of relevant environmental conditions.

Fog computing is an emerging and evolving technology, which bridges the cloud with the network edges, allowing computing to work in a decentralized manner. As such, it introduces a number of complex issues to the research community and the industry alike. Both of them have to deal with many open challenges including architecture standardization, resource management and placement, service management, Quality of Service (QoS), communication, participation, to name a few. In this work, we provide a comprehensive literature review along two axes—modeling with an emphasis in the proposed fog computing architectures and simulation which investigates the simulation tools which can be used to develop and evaluate novel fog-related ideas.

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.

Technological evolution and in particular the development of the Internet of Things (IoT) has paved the way for material prosperity and a better standard of living. A critical factor in the effectiveness of emerging IoT applications, which heavily rely on sensor information flow, is the development of a functional and efficient Wireless Sensor Network. Additionally, the levels of automation are conducive to usability and time efficiency by reducing the need for human intervention, as well as increasing the rate at which experiments can be carried out. In current work, an already installed infrastructure on the Ionian University campus is considered and enhanced, with the goal of elevating accessibility and user-friendliness, by designing a web platform. The presented platform enables the remote development, execution and monitoring of simple but necessary network-based algorithms using a custom language, without requiring code to be uploaded to remote nodes. As a proof of concept, three information dissemination algorithms are implemented and provided as example templates for users, promoting simultaneously ease of use.

In this work, a regression method is implemented on a low-cost digital temperature sensor to improve the sensor’s accuracy; thus, following the EN12830 European standard. This standard defines that the maximum acceptable error regarding temperature monitoring devices should not exceed 1 °C for the refrigeration and freezer areas. The purpose of the proposed method is to improve the accuracy of a low-cost digital temperature sensor by correcting its nonlinear response using simple linear regression (SLR). In the experimental part of this study, the proposed method’s outcome (in a custom created dataset containing values taken from a refrigerator) is compared against the values taken from a sensor complying with the EN12830 standard. The experimental results confirmed that the proposed method reduced the mean absolute error (MAE) by 82% for the refrigeration area and 69% for the freezer area—resulting in the accuracy improvement of the low-cost digital temperature sensor. Moreover, it managed to achieve a lower generalization error on the test set when compared to three other machine learning algorithms (SVM, B-ELM, and OS-ELM).

Structural health monitoring (SHM) in historical buildings can be achieved through systems that rely on Wireless Sensor Networks (WSNs). Such systems have the ability to acquire information fast and reliably, making them ideal for SHM applications. Historical buildings, having been exposed to the wear of time, are especially sensitive to any kind of intervention. As a result, keeping them as intact as possible is a critical factor for their integrity. In the current work, a low-cost WSN system and a synchronization algorithm are developed in order to be employed for SHM of historical buildings. The synchronization of WSN-based SHM systems is critical for the collection of accurate information describing the building’s condition. The effectiveness of the assumed hardware and the synchronization algorithm were evaluated with two experiments. The first took place under laboratory conditions and its results showcase the algorithm’s accuracy of synchronization. The second experiment was conducted in a real-world environment. For that purpose, the Ionian Campus Testbed was chosen since it comprises buildings that are more than 200 years old. These buildings are appropriate since they were built with thick stone walls which help determine the system’s effectiveness under such conditions. The results derived from the second experiment showcase the effects of the packet transmission collision avoidance mechanism utilized by the assumed hardware. Finally, an estimation of the average clock skew required for accurate information gathering is derived, based on the materials comprising a historical building.

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