Cross-Technology Communications for Heterogeneous IoT Devices Through Artificial Doppler Shifts

Wang, Wei; He, Shiyue; Sun, Liang; Jiang, Tao; Zhang, Qian

doi:10.1109/twc.2018.2883443

Cited by 58 publications

(30 citation statements)

References 27 publications

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“…Recent years have seen numerous research works on CTC between heterogenous devices, such as CTC between WiFi and ZigBee [1], [2], [8], [10], [14]- [20], between WiFi and Bluetooth [21], [22], between ZigBee and Bluetooth [23], [24], between LTE and ZigBee [25], between LoRa and ZigBee [26], between LoRa and Bluetooth [27], and between RFID and WiFi [28]. Some researchers have further analyzed other problems of the CTC system, such as attack [29], [30] and network throughput improvement [31], [32].…”

Section: Related Workmentioning

confidence: 99%

Cross-Technology Communication for Heterogeneous Wireless Devices Through Symbol-Level Energy Modulation

Yao

Zheng

Xie

et al. 2022

IEEE Trans. on Mobile Comput.

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The coexistence of heterogeneous devices in wireless networks brings a new topic on cross-technology communication (CTC) to improve the coexistence efficiency and boost collaboration among these devices. Current advances on CTC mainly fall into two categories, physical-layer CTC and packet-level energy modulation (PLEM). The physical-layer CTC achieves a high CTC data rate, but with channel incompatible to commercial devices, making it hard to be deployed in current wireless networks. PLEM is channel and physical layer compatible, but with two main drawbacks of the low CTC data rate and MAC incompatibility, which will induce severe interference to the other devices' normal data transmissions. In this paper, we propose symbol-level energy modulation (SLEM), the first CTC method that is fully compatible with current devices in both channel and the physical/MAC layer processes, having the ability to be deployed in commercial wireless networks smoothly. SLEM inserts extra bits to WiFi data bits to generate the transmitting bits, so as to adjust the energy levels of WiFi symbols to deliver CTC information. We make theoretical analysis to figure out the performance of both CTC and WiFi transmissions. We also conduct experiments to demonstrate the feasibility of SLEM and its performance under different network situations.

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Section: Related Workmentioning

confidence: 99%

Cross-Technology Communication for Heterogeneous Wireless Devices Through Symbol-Level Energy Modulation

Yao

Zheng

Xie

et al. 2022

IEEE Trans. on Mobile Comput.

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“…Application of sensor data. The sensor data brings great convenience for IoT device communication [44], especially in mobile scenarios. The sensor data is widely used in mobile devices.…”

Section: Sensor Assistance Vs No Sensor Assistancementioning

confidence: 99%

Sensor-Augmented Neural Adaptive Bitrate Video Streaming on UAVs

Xiao

Wang

Chen

et al. 2020

IEEE Trans. Multimedia

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Recent advances in unmanned aerial vehicle (UAV) technology have revolutionized a broad class of civil and military applications. However, the designs of wireless technologies that enable real-time streaming of high-definition video between UAVs and ground clients present a conundrum. Most existing adaptive bitrate (ABR) algorithms are not optimized for the air-to-ground links, which usually fluctuate dramatically due to the dynamic flight states of the UAV. In this paper, we present SA-ABR, a new sensor-augmented system that generates ABR video streaming algorithms with the assistance of various kinds of inherent sensor data that are used to pilot UAVs. By incorporating the inherent sensor data with network observations, SA-ABR trains a deep reinforcement learning (DRL) model to extract salient features from the flight state information and automatically learn an ABR algorithm to adapt to the varying UAV channel capacity through the training process. SA-ABR does not rely on any assumptions or models about UAV's flight states or the environment, but instead, it makes decisions by exploiting temporal properties of past throughput through the long short-term memory (LSTM) to adapt itself to a wide range of highly dynamic environments. We have implemented SA-ABR in a commercial UAV and evaluated it in the wild. We compare SA-ABR with a variety of existing stateof-the-art ABR algorithms, and the results show that our system outperforms the best known existing ABR algorithm by 21.4% in terms of the average quality of experience (QoE) reward.Index Terms-Unmanned aerial vehicle, adaptive bitrate algorithm, video streaming, sensor-augmented system, deep reinforcement learning.

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“…The authors have also illustrated the challenges and opportunities of machine learning for the management of autonomic networks. In Wang et al, DopplerFi has been proposed that enables the two‐way CTC between Bluetooth low energy (BLE) and Wi‐Fi by minute shifting of carrier frequency of a sender and thus achieves minimal disturbance in a legacy network.…”

Section: Literature Surveymentioning

confidence: 99%

ORuML: Optimized Routing in wireless networks using Machine Learning

Chaudhary

Johari

2020

Int J Communication

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Summary Routing is a process of selecting a path in a network for delivering a packet from source node to destination node. Successful delivery of a message is a challenge, and therefore, this paper proposes an algorithm for a wireless network called Optimized Routing in wireless networks using Machine Learning (ORuML), which uses machine learning algorithm namely, K‐nearest neighbor (KNN), Support Vector Machine (SVM), and Multinomial Logistic Regression (MLR), to predict the network type of the source and destination nodes. The ML model is trained by using characteristic features of a node collected in real time such as battery power utilization, available internal storage, IP address, and range of a node. Intuitively, the MLR should outperform KNN and SVM in terms of accuracy and Area under ROC Curve (AUC). The proposed algorithm determines whether the source and destination nodes are co‐located and also, determines the best neighboring hop for efficient routing.

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Cross-Technology Communications for Heterogeneous IoT Devices Through Artificial Doppler Shifts

Cited by 58 publications

References 27 publications

Cross-Technology Communication for Heterogeneous Wireless Devices Through Symbol-Level Energy Modulation

Cross-Technology Communication for Heterogeneous Wireless Devices Through Symbol-Level Energy Modulation

Sensor-Augmented Neural Adaptive Bitrate Video Streaming on UAVs

ORuML: Optimized Routing in wireless networks using Machine Learning

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