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IoT Sensor Applications

Age of Information-Aware Networks for Low-Power IoT Sensor Applications


The Internet of Things (IoT) is a fast-growing field that has found a variety of applications, such as smart agriculture and industrial processing. In these applications, it is important for nodes to maximize the amount of useful information transmitted over a limited channel. This work seeks to improve the performance of low-powered sensor networks by developing an architecture that leverages existing techniques such as lossy compression and different queuing strategies in order to minimize their drawbacks and meet the performance needs of backend applications.

The Age of Information (AoI) provides a useful metric for quantifying Quality of Service (QoS) in low-powered sensor networks and provides a method for measuring the freshness of data in the network. In this paper, we investigate QoS requirements and the effects of lossy compression and queue strategies on AoI. Furthermore, two important use cases for low-powered IoT sensor networks are studied, namely, real-time feedback control and image classification. The results highlight the relative importance of QoS metrics for applications with different needs.

To this end, we introduce a QoS-aware architecture to optimize network performance for the QoS requirements of the studied applications. The proposed network architecture was tested with a mixture of application traffic settings and was shown to greatly improve network QoS compared to commonly used transmission architectures such as Slotted ALOHA.

This work makes several contributions. It highlights the types of applications that low-power sensor networks must service along with the QoS requirements needed to properly service these applications. AoI proves to be an effective metric for quantifying the QoS requirements of different applications. Different queuing and compression strategies are used to minimize the amount of excessive information in order to achieve these requirements while reducing the amount of latency and wasted energy. Lossy compression is shown to be very effective for reducing the average AoI of the network, and our results show that the effects can be modeled by scaling the arrival rate with the compression ratio. 

We developed two lossy compression algorithms, namely, a greedy algorithm and a model-based algorithm, with the former proving to be more effective at selecting the optimal compression settings.
Furthermore, this work demonstrates the different drawbacks of these methods. By using a packet scheduler that is aware of QoS needs, these disadvantages can be mitigated. To this end, we integrated a scheduling algorithm into the proposed architecture to ensure that the AoI requirements are met when servicing multiple applications with differing QoS requirements. 

These schedulers were tested in a simulation environment, verifying that they all meet the QoS requirements of the backend applications. Finally, we present an architecture that meets the strict QoS requirements of different types of packets for different backend applications. This architecture is capable of balancing the drawbacks of the different techniques to improve performance and meet the needs of backend applications. Overall, a QoS-aware architecture is able to ensure that data requirements can be met. Our results show that using adaptive compression can greatly reduce the AoI of data while minimizing the amount of distortion added to the data. 

Furthermore, round-robin scheduling methods, although more complex and requiring synchronization between nodes, enable real-time applications to receive data in a timely manner even in high-traffic channels.

In future work, the architecture presented in this work will be further refined and geared specifically towards high-speed IoT applications where data freshness and data quality are important. One specific topic worthy of further effort involves quantifying QoS requirements in terms of AoI and distortion, for instance through Value of Information (VoI), a fast-growing and successful offshoot of AoI which has shown promise for use in safety-critical applications. The use of VoI would allow for a more generalized system model, enabling networks to service a wider range of applications.

temperature sensor, pressure sensor, motion sensor, proximity sensor, light sensor, gas sensor, humidity sensor, infrared sensor, touch sensor, accelerometer, gyroscope, ultrasonic sensor, biosensor, chemical sensor, optical sensor, magnetic sensor, RFID sensor, flow sensor, vibration sensor, IoT sensor

#TemperatureSensor, #PressureSensor, #MotionSensor, #ProximitySensor, #LightSensor, #GasSensor, #HumiditySensor, #InfraredSensor, #TouchSensor, #AccelerometerSensor, #GyroscopeSensor, #UltrasonicSensor, #Biosensor, #ChemicalSensor, #OpticalSensor, #MagneticSensor, #RFIDSensor, #FlowSensor, #VibrationSensor, #IoTSensor

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