806 research outputs found
Design and analysis of adaptive hierarchical low-power long-range networks
Get PDFA new phase of evolution of Machine-to-Machine (M2M) communication has started where vertical Internet of Things (IoT) deployments dedicated to a single application domain gradually change to multi-purpose IoT infrastructures that service different applications across multiple industries. New networking technologies are being deployed operating over sub-GHz frequency bands that enable multi-tenant connectivity over long distances and increase network capacity by enforcing low transmission rates to increase network capacity. Such networking technologies allow cloud-based platforms to be connected with large numbers of IoT devices deployed several kilometres from the edges of the network. Despite the rapid uptake of Long-power Wide-area Networks (LPWANs), it remains unclear how to organize the wireless sensor network in a scaleable and adaptive way. This paper introduces a hierarchical communication scheme that utilizes the new capabilities of Long-Range Wireless Sensor Networking technologies by combining them with broadly used 802.11.4-based low-range low-power technologies. The design of the hierarchical scheme is presented in detail along with the technical details on the implementation in real-world hardware platforms. A platform-agnostic software firmware is produced that is evaluated in real-world large-scale testbeds. The performance of the networking scheme is evaluated through a series of experimental scenarios that generate environments with varying channel quality, failing nodes, and mobile nodes. The performance is evaluated in terms of the overall time required to organize the network and setup a hierarchy, the energy consumption and the overall lifetime of the network, as well as the ability to adapt to channel failures. The experimental analysis indicate that the combination of long-range and short-range networking technologies can lead to scalable solutions that can service concurrently multiple applications
Enabling Sustainability and Energy Awareness in Schools Based on IoT and Real-World Data
Get PDFFew IoT systems monitoring energy consumption in buildings have focused on the educational community. IoT in the educational domain can jump-start a process of sustainability awareness and behavioral change toward energy savings, as well as provide tangible financial savings. We present a real-world multisite IoT deployment, comprising 19 school buildings, aiming at enabling IoT-based energy awareness and sustainability lectures, promoting energy-saving behaviors supported by IoT data. We discuss scenarios where IoT-enabled applications are integrated into school life, providing an engaging and hands-on approach, based on real data, generating value in terms of educational and energy savings outcomes. We also present a set of first results, based on the analysis of school-building data, which highlight potential ways to identify irregularities and inefficiencies
Competition: An Adaptive Protocol Stack for High-Dependability based on the Population Protocols Paradigm
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Olive Leaf Infection Detection Using the Cloud-Edge Continuum
No full textThe use of computer vision, deep learning, and drones has revolutionized agriculture by enabling efficient crop monitoring and disease detection. Still, many challenges need to be overcome due to the vast diversity of plant species and their unique regional characteristics. Olive trees, which have been cultivated for thousands of years, present a particularly complex case for leaf-based disease diagnosis as disease symptoms can vary widely, both between different plant variations and even within individual leaves on the same plant. This complexity, coupled with the susceptibility of olive groves to various pathogens, including bacterial blight, olive knot, aculus olearius, and olive peacock spot, has hindered the development of effective disease detection algorithms. To address this challenge, we have devised a novel approach that combines deep learning techniques, leveraging convolutional neural networks, vision transformers, and cloud computing-based models. Aiming to detect and classify olive tree diseases the experimental results of our study have been highly promising, demonstrating the effectiveness of the combined transformer and cloud-based machine learning models, achieving an impressive accuracy of approximately 99.6% for multiclass classification cases including healthy, aculus olearius, and peacock spot infected leaves. These results highlight the potential of deep learning models in tackling the complexities of olive leaf disease detection and the need for further research in the field
Adaptive neighbor discovery for mobile and low power wireless sensor networks
No full textWireless Sensor Networks are by nature highly dynamic and communication between sensors is completely ad hoc, especially when mobile devices are part of the setup. Numerous protocols and applications proposed for such networks operate on the assumption that knowledge of the neighborhood is a priori available to all nodes. As a result, WSN deployments need to use or implement from scratch a neighborhood discovery mechanism. In this work we present a new protocol based on adaptive periodic beacon exchanges. We totally avoid continuous beaconing by adjusting the rate of broadcasts using the concept of consistency over the understanding of neighborhood that nearby devices share. We propose, implement and evaluate our adaptive neighborhood discovery protocol over our experimental testbed and using large scale simulations. Our results indicate that the new protocol operates more eficiently than existing reference implementations while it provides valid information to applications that use it. Extensive performance evaluation indicates that it successfully reduces generated network traffic by 90% and increases network lifetime by 20% compared to existing mechanisms that rely on continuous beaconing
A collective awareness platform for energy efficient smart buildings
No full textBuilding Energy Management Systems (BEMS) are mature computer-based systems that manage, control and monitor different building technical services (such as heating, lighting etc.) and the energy consumption of devices used by the building. In the recent years, significant efforts have been made towards the integration of sensor devices and embedded computing systems with the Internet, thus transforming BEMS into a new era of Internet Buildings. Smart buildings can learn and even anticipate the needs of a buildings' occupants, including their preferences for light, temperature and other services, resulting in energy savings through targeted supply. In this work we argue that in such future buildings, it will be simply infeasible to expect individuals to be aware of the full range of potentially relevant possibilities and be able to pull them together manually. We thus propose a system that proactively guides users' interactions based on their preferences and constraints. We develop a collective awareness platform where the control of the smart building is balanced between the people and machines. We present the basic design principles, the implementation details and our experimental findings after evaluating our system in a real-world testbed
Using IoT-based big data generated inside school buildings
Get PDFThe utilization of Internet of Things (IoT) in the educational domain so far has trailed other more commercial application domains. In this chapter, we study a number of aspects that are based on big data produced by a large-scale infrastructure deployed inside a fleet of educational buildings in Europe. We discuss how this infrastructure essentially enables a set of different applications, complemented by a detailed discussion regarding both performance aspects of the implementation of this IoT platform as well as results that provide insights to its actual application in real life, both from educational and business standpoints
Symmetric Coherent Link Degree, Adaptive Throughput-Transmission Power for Wireless Sensor Networks
No full textTopology Control Protocols configure transmission power of nodes in order to achieve specific properties to a given topology. These properties include the creation and maintenance of neighborhoods or other topological entities (like trees or clusters), load balancing in terms of connectivity degrees and provision of link symmetry. We see topology control as a two-fold problem where topological properties can also be affected by local network throughput. We propose SCLD-A2TP, a protocol that operates in a two phase adaptive scheme. First transmission power is adaptively adjusted with low throughput settings and nodes achieve a sufficient degree of symmetric and coherent links. Secondly throughput is maximized insofar as the degree is maintained. We assess various distributed heuristics for SCLD-A2TP via test bed experiments and show that up to an extend, link quality and symmetry as well as degree conformity of links can be regulated successfully by transmission power and adaptive throughput control
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