For large-scale LoRa-based Internet-of-Things (IoT) systems, using UAVs to collect data is a promising method, which not only leverages the long-range communication advantages of LoRa but also avoids the high costs associated with deploying fixed-location LoRa gateways. In this paper, we take into account the concurrent data reception capability inherent to LoRa, and propose an innovative algorithm that provides a bi-criteria approximation solution for UAV-assisted LoRa data collection, maximizing the sensor coverage under the UAV's energy budget. Our algorithm is twofold: first, we formulate the problem within a constrained submodular maximization framework incorporating sensor allocation and a Traveling Salesman Problem. Second, proving the NP-hardness of this sensor allocation problem, we develop a constant-factor approximation algorithm for energy-minimum sensor allocation. We present a bi-criteria approximation algorithm that employs a greedy strategy to maximize sensor coverage while adhering to the UAV's energy budget. We evaluate our designs through extensive numerical experiments, demonstrating their efficiency and effectiveness.
As wireless communication and Internet of Things (IoT) technologies advance, edge computing brings computing and storage capabilities closer to users, providing low-latency and high-quality services. However, the limited resources of edge servers and the diverse resource demands of heterogeneous tasks may result in high latency and poor energy efficiency. To address this challenge, we investigate a Docker-based resource management framework for edge servers, involving the allocation of heterogeneous tasks and the configuration of the Docker container clusters. Then, we formulate the problem as one of minimizing energy consumption and task latency, concerning task allocation and server resource management, subject to server resource constraints. To solve this problem, we propose an effective task assignment and resource management strategy, which is developed based on convex optimization theory, aiming to achieve an approximate optimal solution. Simulation results demonstrate that, compared to other algorithms, the proposed algorithm significantly reduces server operating power consumption and task response latency.
The surface mount technology (SMT) reflow soldering process for electronic modules with 'multi-variety, smallbatch, variable states' faces significant challenges in achieving agility, low cost, and high quality. Traditional physical validation methods, such as using thermocouple test boards, are costly and time-consuming, making it difficult to meet the high agility requirements and rapid quality convergence needed for simultaneous development, deployment, and improvement. There is an inherent need for production lines to achieve 'selfdetection, self-diagnosis, self-optimization, and self-correction' through intelligent manufacturing. Through the establishment of reflow soldering digital twin model, and reflow soldering 'physical entity' to establish sensing and control interaction, to achieve 'data perception - real - time analysis - intelligent decisionmaking - accurate implementation' of real-time intelligent closed loop. Through multi-scenario comparison test on physical test data and twin data, the result has good consistency, high repeatability, and the expenditure of time and funds is greatly reduced. Experimental results show that the reflow soldering digital twin can realize the design and optimization of reflow soldering curves, and real-time monitoring and adjustment of process parameters.
As widely deployed WiFi sensors in indoor scenarios, e.g., WiFi access points and WiFi monitors, WiFi signal-based indoor localization has attracted increasing attention from research communities in the past decade. Among various WiFi-based localization techniques, received signal strength (RSS) fingerprinting based on multiple sensors reveals its superiority and effectiveness in complex indoor environments. Existing multi-sensor-based techniques mainly focus on designing efficient algorithms to improve localization performance. However, the limitations of 1) densely pre-deployed WiFi sensors and, 2) sensitivity to changing sensors are not considered appropriately. In this paper, we propose a novel technique called Single Mobile Sensor (SMS) localization, which leverages a single mobile sensor for indoor localization. The SMS localization technique employs a fingerprinting technique with a custom-designed model, named SMS Fingerprint Matching (SMSFM) model, which is responsible for matching fingerprints constructed by a single mobile sensor to estimate targets' location. Numerous experiments conducted on a practical testbed have revealed that the SMSFM model surpasses conventional models, leading to SMS localization delivering competitive localization accuracy compared to previous multi-sensor-based technologies, despite relying solely on a single sensor.
With advancements in hardware and communication technologies, the demand for 360-degree video applications has surged. However, the overall spectral efficiency is compromised due to the inability of current multicast schemes to accurately identify 360-degree video users with similar characteristics, resulting in improper user grouping. To address these issues, we propose Hcast360, an adaptive user-correlation-based 360-degree video multicast strategy, in which we designed a user correlation metric based on the throughput difference between multicast and unicast to determine the feasibility of multicast among users. This strategy accurately assigns users to appropriate multicast groups, significantly improving the utilization efficiency of wireless resources. We decompose the 360- degree video multicast problem into three sub-problems: user grouping, resource allocation, and bitrate adaptation. To solve these problems, we propose an adaptive grouping algorithm that assigns users to suitable multicast groups without predefining the number of multicast groups. Finally, we employ a genetic algorithm to determine the bitrate for 360-degree video tiles. Experimental results show that our algorithm can improve users' Quality of Experience (QoE) significantly, achieving noticeable gains without any increase in bandwidth usage.
Low-Earth orbit (LEO) satellite communications play a vital role in global and emergency communications and the movement of LEO satellites around the Earth necessitates frequent handovers for terrestrial users. Efficient handovers are complicated due to the limited coverage and rapid movement of LEO satellites, as well as the high mobility of user equipment (UE), leading to significant handover overhead. To address these challenges, this paper introduces an efficient area-division based handover scheme, in which, the Earth's surface is divided into areas. To reduce the impact of the UE's speed on the handover decision and reduce the handover overhead, handover decisions are made for areas instead of individual UEs based on the satellite trajectories and area characteristics when UEs move within the same area. The graph-based approach is used to compute the handover sequence, which takes multiple handover factors into account to ensure handover performance. We analyse the effects of area size and UE's speed on the overhead and handover failure rate. The experimental results verify that the proposed handover scheme can reduce the handover overhead and improve the handover success rate and data transmission efficiency.
Federated learning (FL) enables privacy-preserving model training across dispersed devices. Meanwhile, it faces non-IID data and backdoor attacks, which still has issues with consistency, efficiency, and security. In this work, we propose HBIpFL, a hypernetwork-based personalization and poisoning-free federated learning scheme to improve security and personalization in FL. HBIpFL dramatically reduces communication overhead without sacrificing performance. To achieve this, we leverage a hypernetwork-based parameter classifier to dynamically analyze and only upload important model parameters. Furthermore, we adopt local gradient ascent mechanism to track training loss trends and identify possible backdoor intrusions, guaranteeing the resilience and dependability of the global model. We compare HBIpFL with existing state-of-the-art schemes in the context of accuracy, communication overhead, and security. Comprehensive analysis indicate that HBIpFL provides a secure and efficient FL framework for diverse data distributions and privacy concerns.
Handover (HO) is a critical component of mobility management in the 5th generation (5G) of communication networks, which ensures seamless connectivity and optimal communication performance for user equipment (UE) in motion across different cells. In previous studies, the deep reinforcement learning (DRL) techniques were employed to solve the HO problem. However, for most of these methods, the growing complexity in action space was not considered as the number of UEs increases, leading to inefficient model convergence and HO failures. To address this issue, this paper proposes a novel PPO-MH (Proximal Policy Optimization with Masking for Handover) model for multi-cell selection handover problem. This model calculates the action mask for each UE before each handover using the UE's measurement report, providing prior information for the decision-making process. By dynamically masking base stations (BSs) that do not meet the handover conditions, the model avoids invalid hand overs and improves sampling efficiency. Experimental results demonstrate that the PPO- MH outperforms the traditional PPO across various scenarios, ensuring Quality of Service (QoS) for UEs and reducing the handover frequency. Additionally, PPO- MH converges significantly faster than PPO, which validates the effectiveness of the action mask strategy. Benefiting from these advantages, our methods have broad potential applications, especially in scenarios requiring efficient resource management and low-latency handovers.
Location awareness in mobile devices, particularly the detection of indoor and outdoor transitions, empowers devices to ascertain their own or user's position and offer pertinent intelligent services accordingly. In this paper, we present InOut, a robust and realistic indoor-outdoor detection system characterized by high precision, low latency, and cross-device transferability. Regarding effectiveness, considering that a singular sensor signal is inadequate in providing comprehensive environmental information for detection, we employ a multimodal fusion approach. Concerning efficiency, we optimize the model by pruning non-essential features through the calculation of Shapley values for importance assessment. Furthermore, given the heterogeneity of data from different devices, we implement an unsupervised domain adaptation method that enables effective model transfer across devices with limited unlabeled target domain data. Experimental results demonstrate that our InOut system achieves over 96% accuracy on the test dataset, with detection latency consistently maintained to be within 3.1 seconds (including 3 seconds of interface latency and less than 0.1 seconds of inference latency). Moreover, utilizing unlabeled data from a disparate mobile phone model, amounting to one-sixth the size of the original dataset, we enhance the model's accuracy from 83% before transfer to over 91%.
The Internet of Things (IoT) is gathering paces in the new era of Industry 4.0, and the Digital Twin (DT) technology bridges the gap between the bursting amounts of data generated by IoT devices and the user requirements for real-time data processing. DT services maintain living digital models of physical objects, and a DT network enables comprehensive service provisioning with the global knowledge of a group of DTs. On the other hand, exposing serverless computing in network edges, the recent advances in Serverless Edge Computing (SEC) introduce new inspirations to the DT landscape that ensure fine-grained resource management and low network-wide delay of DT services. However, social relationships among IoT devices and DT data privacy impact the orchestration of DTs. In this paper, we design a differential privacy-based federated learning framework to build a DT network for DT services in response to user DT service requests in SEC, thereby enhancing the Quality of Services (QoS). To this end, we first formulate a novel social-aware problem for placing DTs in an SEC network, and show its NP-hardness. We then provide an Integer Linear Program (ILP) solution to the problem when the problem size is small; otherwise, we design an approximation algorithm with a provable approximation ratio. We finally evaluate the algorithm performance through simulations. Simulation results demonstrate the proposed algorithm is promising, which improves by no less than 21.1 % of the performance of benchmarks.
Linear code, a foundational construct extensively employed in communication, data transmission, and error correction, has been the subject of rigorous study for decades. Despite significant academic successes and widespread adoption, recent studies show that decoding methods for general linear codes, such as syndrome decoding, require the storage and search of large decoding tables, leading to inefficiencies. To mitigate this, Debris-Alazard et al. first proposed adapting Babai's algorithm and the LLL algorithm from lattice theory to binary codes, achieving considerable performance gains. Inspired by this, in this paper, we aim to explore the design of more general linear codes to overcome the limitations of baseline binary codes and enhance their applicability in more advanced applications such as DNA storage and 5G communication systems. To address this gap, we extend the foundational domain and decoding algorithms from lattices to q-ary codes. Specifically, we define a new fundamental domain and propose a polynomial-time decoding algorithm, RedtoFun. To validate our findings, we conduct a series of experiments to evaluate its real-world performance. The results demonstrate that our optimized RedtoFun algorithm surpasses the syndrome decoding scheme in terms of memory overhead and runtime while maintaining performance on par with the SizeRed decoding scheme.
Accurate trajectory prediction for all agents within complex environments is a crucial step toward realizing autonomous driving navigation. However, this task poses significant challenges due to the uncertainty surrounding the agent's intentions and the intricate road topology. Existing trajectory prediction methods struggle to strike a balance between accuracy and efficiency. To address this challenge, we propose the graph-based trajectory prediction network (DGATP). The model utilizes a two-layer graph representation to capture both the geometric and topological features of the driving environment information and encodes the static and dynamic driving environments hierarchically. An inter-layer network employing an attention mechanism is employed for feature aggregation, leading to improved local-global feature fusion. Furthermore, we introduce a joint prediction framework for all agents in the scenario, which utilizes dynamic weight learning. This adaptive head enhances the model's capacity without increasing its size, thereby maintaining the efficiency of the inference process and leading to accurate and efficient trajectory predictions.
In future 6G vehicular networks, precise positioning is essential for improving communication quality and efficiency. This paper proposes a novel TDOA/FDOA-based cooperative localization method among multiple vehicles. By selecting anchor vehicles within the range of the base station and utilizing their received echo information, this method enables efficient and lowlatency positioning. First, based on a defined variable selection criterion, a subset of vehicles with optimal locations is chosen. Using the echo signals generated by inter-vehicle communication, the method jointly predicts the motion parameters of target vehicles. A time-delay Doppler approach based on matched filtering is employed to estimate the reflected echo information for dynamic vehicle cooperation, ultimately assisting the base station in achieving directional communication with the target vehicles. Results show that under specified noise conditions, the proposed V2V cooperative localization achieves performance close to the CRLB lower bound, with deviations between 0 and 0.8. This method offers a new approach to directional communication in vehicular networks, particularly suited for high-dynamic, highconcurrency large-scale vehicle communication in complex traffic environments.
With the rapid development of cutting-edge technologies such as software-defined networking, edge computing, and deep learning (DL), the application of the field of Industrial Internet of Things (IIoT) has been deepening, especially in the areas of fault diagnosis, defect detection, and production management, which has shown great potential. Federated learning (FL) is a collaborative model training approach that allows multiple clients to work together while maintaining data privacy. This method is particularly useful for DL methods in industrial surface defect classification, which often require a large amount of training data that can be hard to gather due to its distributed nature across various sources. However, the aggregated model in federated learning may not perform well when there is a discrepancy between the training dataset (source domain) and the testing dataset (target domain), as well as when individual users face data scarcity. To counter these challenges, we propose a novel federated transfer learning framework with multi-scale aggregation (FTL-MSA) for surface defect classification in the IIoT system. A dynamic central loss function, which takes into account both intra-instance and inter-instance contrasting, is proposed to enhance the model's accuracy. Furthermore, we introduce a multi-scale model aggregation technique for FL. This technique considers the distances between the source domain and the target domain at multiple scales, which utilizes the Jensen-Shannon distance for statistical consistency, and the cosine distance for directional consistency, thereby effectively mitigating the impacts of domain differences. Empirical validation on two public steel defect datasets shows that our FTL-MSA framework outperforms state-of-the-art methods, achieving accuracy improvements of 3.12%-12.51%.
Open-world attribute extraction is one of the most important tasks of information extraction aiming to mine all the valuable attributes of entities and their corresponding values from unstructured texts, usually in the form of (entity, attribute, value) triplets. However, existing methods have difficulty extracting attribute triplets from open-world sparse corpora where the attribute names are not previously given, especially in the few- shot scenario with only few manual annotations available. To solve the above problems, we propose a two-stage Meta-pattern-Enhanced Generative Few-shot Attribute Extraction (MEGFAE) framework which can be used to discover utmost valuable attribute triplets from open-world sparse corpora in a generative manner. For evaluation on open-world sparse corpora, we introduce a benchmark dataset called OSN-515($$(11) over bar) The dataset is available in https://github.com/sunshower-liu/OSN-515.. Experimental results verifies the effectiveness of our framework and inspires future explorations on the text mining on sparse corpora.
With the rapid development of 6G networks and artificial intelligence of things(AIoT), the volume of generated data has surged exponentially. Deep Compressed sensing(DCS) achieve accurate data reconstruction at Sub-Nyquist rates, minimizing data transmission volume and optimizing performance in unimodal vision tasks. However, given the substantial memory and computational resources required, deploying these DCS models on resource-constrained edge devices and executing dynamic training poses formidable challenges. Additionally, cloud-based processing introduces privacy vulnerabilities and potential performance degradation. This paper introduces a ConvMixed-ViT architecture based on Low-Rank Adaptation (LoRA) and employs an end-to-end methodology integrating learnable CS to facilitate compression and precise reconstruction on edge environments. Specifically, the LoRA freezes pre-trained model weights and injects them into the ConvMixed-ViT Architecture's Transformer variant framework through trainable rank decomposition matrices, greatly reducing the number of trainable parameters for downstream tasks and controlling the model size. The experimental results demonstrate that our proposed architecture achieves excellent reconfiguration performance on standard benchmark datasets. In resource-limited environments, CTLCS offers efficient adaptability and optimizes performance, proving its suitability for future edge AI applications.
To assist intelligent traffic management, traffic flow prediction, which plays a crucial role in intelligent transportation system, involves forecasting future traffic flow based on road characteristics and historical traffic data. Due to the inherent complexity of traffic systems, achieving high accuracy in long-term traffic flow prediction poses significant challenges. Therefore, we propose a novel neural network model, which is able to capture both temporal and spatial dependencies in the traffic flow data using the combination of GCN layer and LSTM layer. Experiments have demonstrated that our model is effective and accurate when used to predict the short-term traffic flow.
Collaborative analysis on graph data from diverse sources has shown great promise in finance, social networking, and predictive modeling. However, efficiently collaborative graphdata computations involving different parties while ensuring data privacy pose a significant challenge. To address this issue, we introduce a novel secure undirected-graph message passing (SUMP) protocol, which is specially optimized for secure analysis of undirected graph data. Our SUMP algorithm adopts a newly designed encoding paradigm to reduce the processing overhead in existing algorithms. The data scale in processing is reduced by around 2x in our SUMP algorithm compared to existing works. We apply SUMP to implement a graph-analysis framework, Topk common neighbors (TKCN), which facilitates analyzing the relationships related to entities in graphs. To further optimize the efficiency and scalability, we adopt fewer and more efficient secure binary operations, differently from numerous expensive secure comparison operations in existing implementations. Our evaluations on real-world datasets demonstrate that, our SUMP algorithm accelerates the state-of-the-art by 1.99x. Our secure TKCN implementation achieves a speed up by 700x over the state-of-the-art on large-scale datasets.
The proliferation of Internet of Things applications has driven rapid Mobile Edge Computing (MEC) systems development by various Edge Service Providers (ESPs), creating a competitive computing market. Handling all received tasks from each ESP individually can significantly degrade the service performance of the MEC system. To enhance flexibility in network workload and service coverage, unmanned aerial vehicles (UAVs) have been employed in the MEC system. This paper proposes a potential game based trustful task offloading scheme for the multiple EPSs scenario in UAV-assisted MEC. Specifically, we formulate the Multiple ESPs Task Offloading (METO) problem into a potential game and prove the existence of Nash Equilibrium (NE). To guarantee the security of resource trading among different EPSs, we propose a blockchain-based sharing mechanism that converges to NE. Additionally, a reputation smart contract assesses ESPs' Quality of Service (QoS), influencing task allocation. Extensive simulations show our approach outperforms traditional baselines in maximizing each ESP's utility.
With the rise of edge devices, video analytics has become a key application in edge computing. Single-camera systems are limited by their Field of View (FoV), making them inadequate for complex environments such as traffic intersections. In this paper, we propose Adaptive Cross-Camera Video Analytics (ACCVA), a novel system for intelligent traffic monitoring. ACCVA introduces a novel camera selection algorithm that adaptively chooses the best camera based on vehicle location and historical data, as well as an adaptive retention algorithm to prevent occlusion and recover lost objects. ACCVA establishes dynamic segmentation of camera regions and cross-camera correlation, managing real-time video inference and result sharing. Implemented on the NVIDIA Jetson Orin NX and evaluated with a real-world traffic surveillance dataset, ACCVA significantly reduces end-to-end latency and enhances accuracy compared to existing state-of-the-art systems. ACCVA excels in complex scenarios by balancing low latency and high accuracy, providing an effective solution for intelligent traffic monitoring.
