Mobility-Aware Resource Assignment to IoT Applications in Long-Range Wide Area Networks

Self Cite

A long-range wide area network (LoRaWAN) is one of the leading communication technologies for Internet of Things (IoT) applications. In order to fulfill the IoT-enabled application requirements, LoRaWAN employs an adaptive data rate (ADR) mechanism at both the end device (ED) and the network server (NS). NS-managed ADR aims to offer a reliable and battery-efficient resource to EDs by managing the spreading factor (SF) and transmit power (TP). However, such management is severely affected by the lack of agility in adapting to the variable channel conditions. Thus, several hours or even days may be required to converge at a level of stable and energy-efficient communication. Therefore, we propose two NS-managed ADRs, a Gaussian filter-based ADR (G-ADR) and an exponential moving average-based ADR (EMA-ADR). Both of the proposed schemes operate as a low-pass filter to resist rapid changes in the signal-to-noise ratio of received packets at the NS. The proposed methods aim to allocate the best SF and TP to both static and mobile EDs by seeking to reduce the convergence period in the confirmed mode of LoRaWAN. Based on the simulation results, we show that the G-ADR and EMA-ADR schemes reduce the convergence period in a static scenario by 16% and 68%, and in a mobility scenario by 17% and 81%, respectively, as compared to typical ADR. Moreover, we show that the proposed schemes are successful in reducing the energy consumption and enhancing the packet success ratio.

packets/day during four days of the simulation time, where the results are generated using an average of 10 simulations with different seeds.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…However, after a short period of stability,

decreases, and both typical ADR and ADR+ suffer from a convergence period. This convergence period is caused when a mobile ED receives a

MAC command containing SF and/or TP from the NS; the propagation environment might have been drastically altered, resulting in massive packet loss [ 16 , 32 ].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Overall, the energy consumptions of the proposed G-ADR and EMA-ADR (with and without I-SFA) are lower because of the higher

Section: Experimental Results and Analysismentioning

confidence: 99%

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Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices

Farhad

Kim

Subedi

et al. 2020

Self Cite

“…Kevin Ashton initially coined the IoT concept in 1999 [ 1 , 3 ]. Sensor-based IoT devices can provide various types of services, such as health, traffic congestion control, robotics, and data analysis, which play a significant role in daily life assistance [ 4 ]. Target tracking is another critical area where the sensors can be utilized to collect the target real-time position and report it to a server with its relevant information.…”

Section: Introductionmentioning

confidence: 99%

Energy Conservation for Internet of Things Tracking Applications Using Deep Reinforcement Learning

Sultan

Waleed

Pyun

et al. 2021

Self Cite

The Internet of Things (IoT)-based target tracking system is required for applications such as smart farm, smart factory, and smart city where many sensor devices are jointly connected to collect the moving target positions. Each sensor device continuously runs on battery-operated power, consuming energy while perceiving target information in a particular environment. To reduce sensor device energy consumption in real-time IoT tracking applications, many traditional methods such as clustering, information-driven, and other approaches have previously been utilized to select the best sensor. However, applying machine learning methods, particularly deep reinforcement learning (Deep RL), to address the problem of sensor selection in tracking applications is quite demanding because of the limited sensor node battery lifetime. In this study, we proposed a long short-term memory deep Q-network (DQN)-based Deep RL target tracking model to overcome the problem of energy consumption in IoT target applications. The proposed method is utilized to select the energy-efficient best sensor while tracking the target. The best sensor is defined by the minimum distance function (i.e., derived as the state), which leads to lower energy consumption. The simulation results show favorable features in terms of the best sensor selection and energy consumption.

“…Within the Internet of Things, 5G’s mMTC technology is being postulated as an unprecedented connectivity solution for the development of connected products and services [ 6 , 7 ]. The specification sets requirements around networking devices with up to 10 years of battery life, coverage penetration of 164 dB with a capacity of 160 bits per second, coverage density to support up to one million connected devices per square kilometer, communication latencies of less than 10 s for 20 bytes of data, and a crucial element for massive scaling: very inexpensive hardware [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

PSON: A Serialization Format for IoT Sensor Networks

Luis¹,

Casares²,

Cuadrado-Gallego

et al. 2021

In many Internet of Things (IoT) environments, the lifetime of a sensor is linked to its power supply. Sensor devices capture external information and transmit it. They also receive messages with control commands, which means that one of the largest computational overheads of sensor devices is spent on data serialization and deserialization tasks, as well as data transmission. The simpler the serialization/deserialization and the smaller the size of the information to be transmitted, the longer the lifetime of the sensor device and, consequently, the longer the service life. This paper presents a new serialization format (PSON) for these environments, which simplifies the serialization/deserialization tasks and minimizes the messages to be sent/received. The paper presents evaluation results with the most popular serialization formats, demonstrating the improvement obtained with the new PSON format.