IoT Sensor

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H S Alraweshidy - One of the best experts on this subject based on the ideXlab platform.

  • sdq 6wi software defined quadcopter six wheeled IoT Sensor architecture for future wind turbine placement
    IEEE Access, 2018
    Co-Authors: Ammar Al K Mhdawi, H S Alraweshidy
    Abstract:

    Although wind-generated power was estimated to be 4% of the entire world electricity usage, wind turbines are considered to be a growing technology with many experts considering new approaches to wind turbine design and farmland selection to increase the wind turbine output efficiency. When implementing a new wind turbine install on a farm, many concerns are taken into consideration such as environmental challenges and cost. Thus, the power of the wind turbine can be increased at least 10 times when the most efficient wind power location is selected before the wind turbine is placed. In this paper, we propose a novel software-defined quadcopter–6 wheeled industrial IoT (SDQ-6WI) architecture that is based on a developed quadcopter system to collect wind speed data from a mobile IoT vehicle base station that is based on a developed open flow (DOV) protocol operation. The mobile ground vehicle act as a wind speed measurement system that travels on a given set of waypoints to measure the best optimal wind speed quality and send the collected information to the quadcopter-based SDN controller then to the cloud for further processing. Our proposed system can handle a heterogeneous environment that lacks Wi-Fi and cellular coverage and uses the minimum total transmission power when sending data. The experiential results showed that the measured wind speed data could be collected in a time-efficient manner compared to a traditional process which is considered to be costly, time wasting, and non-effective. Our extensive testing showed that about 23.19% in power was reduced in the wind measurement process in comparison with the fixed Sensor nodes. In essence, the proposed architecture help reduce the high cost of relocating wind turbines to an efficient location and increase the generated power by selecting the best optimal windy location.

Ammar Al K Mhdawi - One of the best experts on this subject based on the ideXlab platform.

  • sdq 6wi software defined quadcopter six wheeled IoT Sensor architecture for future wind turbine placement
    IEEE Access, 2018
    Co-Authors: Ammar Al K Mhdawi, H S Alraweshidy
    Abstract:

    Although wind-generated power was estimated to be 4% of the entire world electricity usage, wind turbines are considered to be a growing technology with many experts considering new approaches to wind turbine design and farmland selection to increase the wind turbine output efficiency. When implementing a new wind turbine install on a farm, many concerns are taken into consideration such as environmental challenges and cost. Thus, the power of the wind turbine can be increased at least 10 times when the most efficient wind power location is selected before the wind turbine is placed. In this paper, we propose a novel software-defined quadcopter–6 wheeled industrial IoT (SDQ-6WI) architecture that is based on a developed quadcopter system to collect wind speed data from a mobile IoT vehicle base station that is based on a developed open flow (DOV) protocol operation. The mobile ground vehicle act as a wind speed measurement system that travels on a given set of waypoints to measure the best optimal wind speed quality and send the collected information to the quadcopter-based SDN controller then to the cloud for further processing. Our proposed system can handle a heterogeneous environment that lacks Wi-Fi and cellular coverage and uses the minimum total transmission power when sending data. The experiential results showed that the measured wind speed data could be collected in a time-efficient manner compared to a traditional process which is considered to be costly, time wasting, and non-effective. Our extensive testing showed that about 23.19% in power was reduced in the wind measurement process in comparison with the fixed Sensor nodes. In essence, the proposed architecture help reduce the high cost of relocating wind turbines to an efficient location and increase the generated power by selecting the best optimal windy location.

Sangjo Yoo - One of the best experts on this subject based on the ideXlab platform.

  • optimization of cognitive radio secondary information gathering station positioning and operating channel selection for IoT Sensor networks
    Mobile Information Systems, 2018
    Co-Authors: Jinyi Wen, Qin Yang, Sangjo Yoo
    Abstract:

    The Internet of Things (IoT) is the interconnection of different objects through the internet using different communication technologies. The objects are equipped with Sensors and communications modules. The cognitive radio network is a key technique for the IoT and can effectively address spectrum-related issues for IoT applications. In our paper, a novel method for IoT Sensor networks is proposed to obtain the optimal positions of secondary information gathering stations (SIGSs) and to select the optimal operating channel. Our objective is to maximize secondary system capacity while protecting the primary system. In addition, we propose an appearance probability matrix for secondary IoT devices (SIDs) to maximize the supportable number of SIDs that can be installed in a car, in wearable devices, or for other monitoring devices, based on optimal deployment and probability. We derive fitness functions based on the above objectives and also consider signal to interference-plus-noise ratio (SINR) and position constraints. The particle swarm optimization (PSO) technique is used to find the best position and operating channel for the SIGSs. In a simulation study, the performance of the proposed method is evaluated and compared with a random resources allocation algorithm (parts of this paper were presented at the ICTC2017 conference (Wen et al., 2017)).

  • optimal uav path planning sensing data acquisition over IoT Sensor networks using multi objective bio inspired algorithms
    IEEE Access, 2018
    Co-Authors: Qin Yang, Sangjo Yoo
    Abstract:

    The use of unmanned aerial vehicles (UAVs) has been considered to be an efficient platform for monitoring critical infrastructures spanning over geographical areas. UAVs have also demonstrated exceptional feasibility when collecting data due to the wide wireless Sensor networks in which they operate. Based on environmental information such as prohibited airspace, geo-locational conditions, flight risk, and Sensor deployment statistics, we developed an optimal flight path planning mechanism by using multi-objective bio-inspired algorithms. In this paper, we first acquire data sensing points from the entire Sensor field, in which UAV communicates with Sensors to obtain Sensor data, then we determine the best flight path between neighboring acquisition points. Using the proposed joint genetic algorithm and ant colony optimization from possible UAV flight paths, an optimal one is selected in accordance with sensing, energy, time, and risk utilities. The simulation results show that our method can obtain dynamic environmental adaptivity and high utility in various practical situations.

  • optimization of cognitive radio secondary base station positioning and operating channel selection for IoT Sensor networks
    International Conference on Information and Communication Technology Convergence, 2017
    Co-Authors: Wen Jinyi, Yang Qin, Anish Prasad Shrestha, Sangjo Yoo
    Abstract:

    In our paper, a novel method is proposed to obtain the optimal positions of secondary base stations (SBSs) for cognitive radio Internet of Things (IoT) Sensor networks and to select the optimal operating channel in order to maximize the secondary capacity, while also protecting the primary systems. We proposed an appearance probability matrix for secondary IoT Sensor devices in order to maximize the supportable number of Sensor devices based on the optimal deployment case and probability. We derived fitness functions based on the above objectives and also considered the constraint. The particle swarm optimization (PSO) technique is used to find the best position and operating channel of SBSs.

  • flying path optimization in uav assisted IoT Sensor networks
    ICT Express, 2016
    Co-Authors: Sangjo Yoo, Jaehyun Park, Suhee Kim, Anish Prasad Shrestha
    Abstract:

    Abstract In this paper, we present an optimal flying path for unmanned aerial vehicle-assisted internet of things Sensor networks using a location aware multi-layer information map considering different utility functions based on the Sensor density, energy consumption, flight time, and flying risk level. The overall weighted sum of multi-objective utility functions is maximized using the genetic algorithm. The simulation results verify that the optimum solution points can be obtained by adjusting the weights while satisfying the required constraints.

Mehmet Rasit Yuce - One of the best experts on this subject based on the ideXlab platform.

  • WE-Safe: A Self-Powered Wearable IoT Sensor Network for Safety Applications Based on LoRa
    IEEE Access, 2018
    Co-Authors: Fan Wu, Jean-michel Redouté, Mehmet Rasit Yuce
    Abstract:

    Poor environmental conditions can lead to severe health problems. It is essential to develop effective, reliable, and fast response systems for people working in hazardous environments. This paper presents a wearable Internet of Things Sensor network aimed at monitoring harmful environmental conditions for safety applications via a Lora wireless network. The proposed Sensor node, called the WE-Safe node, is based on a customized Sensor node, which is self-powered, low-power, and supports multiple environmental Sensors. Environmental data is monitored by the Sensor node in real-time and transmitted to a remote cloud server. The data can be displayed to users through a web-based application located on the cloud server and the device will alert the user via a mobile application when an emergency condition is detected. The experimental results indicate that the presented safety monitoring network works reliably using energy harvesting.

  • design and field test of an autonomous IoT wsn platform for environmental monitoring
    2017 27th International Telecommunication Networks and Applications Conference (ITNAC), 2017
    Co-Authors: Fan Wu, Christoph Rüdiger, Mehmet Rasit Yuce
    Abstract:

    An autonomous Internet of Things (IoT) wireless Sensor network (WSN) powered by a solar energy harvester with low power electronics is presented in this paper. The power supply is a critical challenge for IoT Sensors as many of them normally have limited lifetime due to the battery with limited capacity used. The WSN system, Sensor node electronics, and the energy harvesting techniques are all configured to achieve a continuous energy source and low power consumption. The IoT network system has been deployed on Monash University Clayton campus, Melbourne, for monitoring temperature, relative humidity, carbon dioxide and carbon monoxide data. The Sensor network uses multiple XBee wireless modules and successfully monitors the useful data for six months. This work demonstrates that energy harvesting enables an IoT Sensor platform to become always active and reliable for long-term, providing many opportunities and applications.

  • Real-time performance of a self-powered environmental IoT Sensor network system
    Sensors (Switzerland), 2017
    Co-Authors: Fan Wu, Christoph Rüdiger, Mehmet Rasit Yuce
    Abstract:

    Wireless Sensor networks (WSNs) play an increasingly important role in monitoring applications in many areas. With the emergence of the Internet-of-Things (IoT), many more lowpower Sensors will need to be deployed in various environments to collect and monitor data about environmental factors in real time. Providing power supply to these Sensor nodes becomes a critical challenge for realizations of IoT applications as Sensor nodes are normally battery-powered and have a limited lifetime. This paper proposes a wireless Sensor network that is powered by solar energy harvesting. The Sensor network monitors the environmental data with low-power Sensor electronics and forms a network using multiple XBee wireless modules. A detailed performance analysis of the network system under solar energy harvesting has been presented. The Sensor network system and the proposed energy-harvesting techniques are configured to achieve a continuous energy source for the Sensor network. The proposed energy-harvesting system has been successfully designed to enable an energy solution in order to keep Sensor nodes active and reliable for a whole day. The paper also outlines some of our experiences in real-time implementation of a Sensor network system with energy harvesting.

Alessandro Pozzebon - One of the best experts on this subject based on the ideXlab platform.

  • a low power IoT Sensor node architecture for waste management within smart cities context
    Sensors, 2018
    Co-Authors: Matteo Cerchecci, Francesco Luti, Alessandro Mecocci, Stefano Parrino, Giacomo Peruzzi, Alessandro Pozzebon
    Abstract:

    This paper focuses on the realization of an Internet of Things (IoT) architecture to optimize waste management in the context of Smart Cities. In particular, a novel typology of Sensor node based on the use of low cost and low power components is described. This node is provided with a single-chip microcontroller, a Sensor able to measure the filling level of trash bins using ultrasounds and a data transmission module based on the LoRa LPWAN (Low Power Wide Area Network) technology. Together with the node, a minimal network architecture was designed, based on a LoRa gateway, with the purpose of testing the IoT node performances. Especially, the paper analyzes in detail the node architecture, focusing on the energy saving technologies and policies, with the purpose of extending the batteries lifetime by reducing power consumption, through hardware and software optimization. Tests on Sensor and radio module effectiveness are also presented.