1,721,014 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
Competition: An Adaptive Protocol Stack for High-Dependability based on the Population Protocols Paradigm
Get PDF[Abstract not available
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
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
Open Source IoT Meter Devices for Smart and Energy-Efficient School Buildings
Get PDFOne oft-cited strategy towards sustainability is improving energy efficiency inside public buildings. In this context, the educational buildings sector presents a very interesting and important case for the monitoring and management of buildings, since it addresses both energy and educational issues. In this work, we present and discuss the hardware IoT infrastructure substrate that provides real-time monitoring in multiple school buildings. We believe that such a system needs to follow an open design approach: rely on hardware-agnostic components that communicate over well-defined open interfaces. We present in detail the design of our hardware components, while also providing insights to the overall system design and a first set of results on their operation. The presented hardware components are utilized as the core hardware devices for GAIA, an EU research project aimed at the educational community. As our system has been deployed and tested in several public school buildings in Greece, we also report on its validation
An IoT-Based Solution for Monitoring a Fleet of Educational Buildings Focusing on Energy Efficiency
Get PDFRaising awareness among young people and changing their behaviour and habits concerning energy usage is key to achieving sustained energy saving. Additionally, young people are very sensitive to environmental protection so raising awareness among children is much easier than with any other group of citizens. This work examines ways to create an innovative Information & Communication Technologies (ICT) ecosystem (including web-based, mobile, social and sensing elements) tailored specifically for school environments, taking into account both the users (faculty, staff, students, parents) and school buildings, thus motivating and supporting young citizens’ behavioural change to achieve greater energy efficiency. A mixture of open-source IoT hardware and proprietary platforms on the infrastructure level, are currently being utilized for monitoring a fleet of 18 educational buildings across 3 countries, comprising over 700 IoT monitoring points. Hereon presented is the system’s high-level architecture, as well as several aspects of its implementation, related to the application domain of educational building monitoring and energy efficiency. The system is developed based on open-source technologies and services in order to make it capable of providing open IT-infrastructure and support from different commercial hardware/sensor vendors as well as open-source solutions. The system presented can be used to develop and offer new app-based solutions that can be used either for educational purposes or for managing the energy efficiency of the building. The system is replicable and adaptable to settings that may be different than the scenarios envisioned here (e.g., targeting different climate zones), different IT infrastructures and can be easily extended to accommodate integration with other systems. The overall performance of the system is evaluated in real-world environment in terms of scalability, responsiveness and simplicity
On the design of a fog computing-based, driving behaviour monitoring framework
No full textRecent technological improvements in vehicle manufacturing may greatly improve safety however, the individuals' driving behaviour still remains a factor of paramount importance with aggressiveness, lack of focus and carelessness being the main cause of the majority of traffic incidents. The imminent deployment of 5G networking infrastructure, paired with the advent of Fog computing and the establishment of the Internet of Things (IoT) as a reliable and cost-effective service delivery framework may provide the means for the deployment of an accurate driving monitoring solution which could be utilized to further understand the underlying reasons of peculiar road behaviour, as well as its correlation to the driver's physiological state, the vehicle condition and certain environmental parameters. This paper presents some of the fundamental attributes of Fog computing along with the functional requirements of a driving behaviour monitoring framework, followed by its high level architecture blueprint and the description of the prototype implementation process
Enabling stream processing for people-centric IoT based on the fog computing paradigm
Get PDFThe world of machine-to-machine (M2M) communication is gradually moving from vertical single purpose solutions to multi-purpose and collaborative applications interacting across industry verticals, organizations and people - A world of Internet of Things (IoT). The dominant approach for delivering IoT applications relies on the development of cloud-based IoT platforms that collect all the data generated by the sensing elements and centrally process the information to create real business value. In this paper, we present a system that follows the Fog Computing paradigm where the sensor resources, as well as the intermediate layers between embedded devices and cloud computing datacenters, participate by providing computational, storage, and control. We discuss the design aspects of our system and present a pilot deployment for the evaluating the performance in a real-world environment. Our findings indicate that Fog Computing can address the ever-increasing amount of data that is inherent in an IoT world by effective communication among all elements of the architecture
Design and evaluation of a person-centric heart monitoring system over fog computing infrastructure
Get PDFHeart disease and stroke are becoming the leading cause of death worldwide. Electrocardiography monitoring devices (ECG) are the only tool that helps physicians diagnose cardiac abnormalities. Although the design of ECGs has followed closely the electronics miniaturization evolution over the years, existing wearable ECG have limited accuracy and rely on external resources to analyze the signal and evaluate heart activity. In this paper, we work towards empowering the wearable device with processing capabilities to locally analyze the signal and identify abnormal behavior. The ability to differentiate between normal and abnormal heart activity significantly reduces (a) the need to store the signals, (b) the data transmitted to the cloud and (c) the overall power consumption. Based on this concept, the HEART platform is presented that combines wearable embedded devices, mobile edge devices, and cloud services to provide on-the-spot, reliable, accurate and instant monitoring of the heart. The performance of the system is evaluated concerning the accuracy of detecting abnormal events and the power consumption of the wearable device. Results indicate that a very high percentage of success can be achieved in terms of event detection ratio and the device being operative up to a several days without the need for a recharge
- …
