With the improvement of edge-based autonomous systems such as mobile Industrial IoT (IIoT) networks, edge devices can capture and upload videos with increasing bitrates. Massive edge-computing end nodes are eager for adequate multimedia data to satisfy the requirements of real-time video services. However, existing encoding standards for video services in Web 2.0 are specifically designed for something other than IoT video streaming. We have improved our Adaptive Compression-Reconstruction (ACORN) framework to obtain ACORN+, based on compressed sensing and recent advances in deep learning. At end nodes, we compress multiple sequential video frames into a single frame to reduce video volume. Given that multiple kinds of intelligent tasks are expected to be finished on the device side, we also designed a device-cloud collaboration scheme where deep learning-based algorithms can be executed on both the device and server sides. Experiments reveal that video analytics can be conducted on compressed frames. Taking action recognition as a device-cloud collaboration use case, we find ACORN \(+\) obtains more than 3 \(\times\) speedup on compressed frames. The reconstruction algorithm in ACORN \(+\) is with 1– 4 dB improvements. Moreover, the encoding time cost and the encoded video volume are reduced by more than 4 \(\times\) under the ACORN \(+\) framework. 1

The Internet of Things (IoT) has expanded its reach through applications like environmental monitoring and smart agriculture. Its growth is often limited by the need for extensive IoT gateways and base stations. Satellite-based IoT, using low Earth orbit (LEO) satellites, offers a solution by enabling global sensing without terrestrial infrastructure. This technology allows IoT devices to transmit data to satellite gateways, which is crucial for dynamic environments like the ocean where nodes drift with currents. In such applications, position information is valuable for data analysis and tracing. Traditional GPS modules, while affordable and low-energy, can be inefficient due to the large payload space required for precise positioning data. The payload used to report the position might be as long as the sensed data itself. This paper proposes a novel approach to terrestrial IoT device positioning using LoRa or FSK modulation, where positioning is performed on the satellite, saving GPS bytes and reducing packet size. By utilizing Doppler effects and satellite motion, the method calculates device positions, forming a positioning equation set without interfering with communication tasks. Challenges in Doppler effect measurement and positioning are addressed with a fine-grained frequency shift measurement method using ZoomFFT and zero-padding FFT, and a non-linear equation set is solved to determine the positions of terrestrial nodes, with constraints added to ensure practical solutions. The framework is validated through simulations, demonstrating a significant reduction in payload length and positioning accuracy comparable to GPS. In ideal cases, the framework can locate nodes with errors less than 60 meters without any GPS information from data packets.

IoT devices are enabled to capture and upload videos with increasing bitrates. Massive IIoT is eager for effective video processing techniques to satisfy the requirements of real-time video services. With the emergence of 5G-unlicensed (5G-U), ultra-low latency video applications become possible. However, existing encoding standards for video services in Web 2.0, such as H.265, are not naturally designed for IIoT video streaming, leading to bandwidth pressure where 5G-U coexists with various other wireless signals. To tackle this problem and to support low-latency video utilization by IIoT video sources, we propose an Adaptive Compression-Reconstruction framework named ACORN, which is based on compressed sensing and recent advances in deep learning. At end nodes, we compress multiple sequential video frames into a single frame to reduce video volume. We design a QoE-aware parameter selection mechanism to deal with volatile network environments during compression. With learnable gated convolution layers and channel-wise soft-thresholding operators, ACORN also builds a real-time reconstruction module. Experimental results reveal that video analytics can be conducted on compressed frames. The reconstruction algorithm in ACORN is with $1-4 \mathrm{~dB}$ improvements. Moreover, both the encoding time cost and the encoded video volume are reduced by more than $4 \times$ under the ACORN framework.

Since 2002, hundreds of thousands of vessels have equipped the Automatic Identification System (AIS), which continuously broadcasts its identity and location information for vessel collision avoidance. To utilize these scattered AIS data for further analysis, there are multiple AIS data platforms collecting AIS data from vessels around the world through their satellites and land-based stations. Thus, users can obtain AIS data of vessels from these platforms without dedicated devices. However, existing platforms work in silos, and AIS data is distributed across different platforms, resulting in reduced data availability. In addition, AIS is vulnerable to jamming and spoofing attacks, which can undermine the authenticity of AIS data. In this paper, we propose AISChain, a secure and fast blockchain-based AIS data platform. AISChain adopts consortium blockchain, which only permits those authorized parties (i.e., AIS data providers) to participate in the consensus protocol, and is compatible with current commodity AIS hardware. Since the whole system is co-maintained by multiple authorized parties, AISChain can integrate AIS data resources in a secure way. For avoiding repeated recording of AIS data on the chain, we design the Dynamic Bloom Filter Tree (DBFT) to realize efficient duplication detection in the transaction verification phase. We also propose the dual signature scheme to clarify the AIS data ownership. Moreover, we leverage the geographical location-based blockchain sharding approach to further improve the scalability of AISChain. We implement a prototype of AISChain, and conduct extensive experiments to evaluate the performance of AISChain. Evaluation results show that the search time of DBFT is negligible (4.3 ms) with an extreme low error ratio (0.4%). Meanwhile, AISChain can achieve more than 730 tx/s throughput even when nodes scale to 36. To the best of our knowledge, AISChain is the first work to apply the blockchain technology to secure the AIS data platform.

With the proliferation of laser communication and space networks, the increasing communication requests, tasks, and traffic loads introduce new challenges to data routing to laser space networks (LSNs). Existing routing strategies perform in a distributed manner on single-layer networks. But they suffer from the delay of information update in highly dynamic LSNs, which encounter unstable laser links and uneven traffic distribution problems. In this paper, we first propose a multi-layer LSN architecture. It is composed of a routing layer with high-orbit satellites for routing planning and a forwarding layer with low-orbit satellites for data transmitting. A centralized multi-layer optical routing strategy is further designed, namely CMOR. It is expected to provide the data forwarding plan depending on the global view of the routing layer on real-time laser link status and traffic loads of the forwarding layer. Compared with single-layer distributed routing, CMOR is proven to have a lower packet loss rate and transmission latency with large-scale satellite simulations.

With the fast evolution in the cellular communication, the unlicensed spectrum is exploited to resolve the shortage of band resources. The sharing of the unlicensed spectrum extends the applications of LTE and 5G NR techniques, especially in the industrial Internet of Things (IIoT). However, the coexistence problem among various communication technologies in the unlicensed spectrum arises great concerns due to the different communication mechanisms. The existing solutions are either not compatible with the LTE/NR standards or not flexible enough for complex and dynamic IIoT environments. In this paper, we propose a policy learning based unlicensed communication (PLUC) framework to directly learn coexistence policies from the spectrogram of frequency channels. A recurrent neural network (RNN) is built to deal with the observations from time-variant spectrogram and extract deep learning features. We further verify this framework under the duty cycle mechanism and the listen before talk mechanism in 3GPP standards, respectively. The experiments reveal the effectiveness of the proposed framework in the dynamic environment.

Indoor vehicle localization is an underlying technology for realizing Autonomous Valet Parking (AVP), which demands high accuracy and reliability. However, existing localization technologies, such as GPS, WiFi, Bluetooth, suffer from either low availability or high cost, which are not practical in the real world. In order to put AVP into practice, We desperately need an efficient and reliable indoor vehicle localization technology. In this paper, we propose aDeep learning andFM fingerprint map based indoor vehicleLocalization method, namely DeFLoc, which leverages FM signals to achieve accurate and practical indoor localization. In order to reduce the workload of the FM fingerprints collecting process, DeFLoc uses partially uniform sampling to decrease sample data volume and reconstructs the FM fingerprint map from collected incomplete fingerprints precisely using a dedicated deep Convolutional Neural Network (CNN). To alleviate the influence of signal distortions in some FM frequencies, we further design smooth layers in the neural network for improving the accuracy of map reconstruction. Moreover, we devise a continuous vehicle localization algorithm by considering the preferences of vehicle movements to assist us to calibrate localization. We implemented a prototype of DeFLoc and conducted extensive experiments both in simulation and practice. Evaluation results show that our proposed reconstruction model improves accuracy by 40% over conventional matrix completion methods even under the 60% data missing rate. With the precisely reconstructed fingerprint map, DeFLoc achieves over 90% localization accuracy, which indicates DeFLoc can realize accurate and practical indoor vehicle localization.