Lifting stripped and highly optimized binaries to the canonical compiler intermediate representation (IR) enables program analysis when source code is unavailable.However, compiler optimizations severely distort controlflow and data-flow structure, making existing rule-based and LLM-based decompilation approaches brittle.We present BRIDGE, a system that reliably lifts optimized binaries to analysis-friendly compiler IR.BRIDGE combines control-flow-aware retrieval-augmented generation with feedback-driven verification.It uses pseudo-probe instrumentation to align optimized binary fragments with normalized IR semantics, and then employs an iterative refinement loop guided by static analysis and runtime feedback to improve executability and semantic consistency.We evaluate BRIDGE on HumanEval-Decompile and MBPP, lifting x86-64 and ARM64 binaries to LLVM IR.BRIDGE outperforms seven baselines, achieving an average of over 30% higher re-executability than the strongest general-purpose LLM baseline.Void func (){ PROBE(1); If else branch …… PROBE(2); PROBE(3); for Loop … PROBE(4);}

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) networks encounter significant challenges in achieving balanced workload distribution, primarily due to the limited coverage areas of UAVs and their diverse computational capabilities. This paper proposes a UAV-enabled MEC framework that jointly optimizes three-dimensional (3D) trajectory planning and dynamic computation offloading. We formulate a mixed-integer programming (MIP) problem to minimize system latency by simultaneously optimizing UAV trajectory design and task offloading strategies, where UAV mobility and offloading decisions are tightly coupled.Unlike existing approaches that either optimize 3D trajectories without inter-UAV cooperation or implement cooperative computing under fixed altitudes with predetermined relay hops, our framework uniquely integrates adaptive multi-hop collaborative offloading with continuous 3D trajectory planning. The model complexity arises from its hybrid decision structure that simultaneously handles continuous trajectory parameters and discrete offloading variables. Our approach decomposes the problem into two tightly coupled subproblems: (1) 3D UAV trajectory optimization and (2) task offloading scheduling. We then propose a Decoupled Deep Reinforcement Learning for Parallelized Planning and Offloading (DDP3O) algorithm that systematically addresses these interconnected components. Experimental results demonstrate that DDP3O achieves fast convergence and superior performance compared to state-of-the-art methods including block coordinate descent optimization, DQN-based approaches, and fixed-hop cooperative schemes across multiple operational scenarios.

User devices (UDs) with non-independent and identically distributed (non-IID) data will worsen accuracy performance of the global model in federated learning (FL). Therefore, the implementation of diffusion strategies in machine learning (ML) models can enhance the effectiveness of federated learning with non-IID data. However, in a device-to-device (D2D) wireless federated learning (WFL) system, limited wireless resources and severe wireless channel interference become the important bottleneck to restrict the diffusion performance and model aggregation so as the global model of WFL with non-IID suffers from the weight divergence challenge. Thus, we propose a novel joint over-the-air computation (OAC) aggregation and diffusion framework by using a graph neural network (GNN) for WFL, termed an OAC-GNN-Dif framework. By integrating the OAC with message passing neural network (MPNN) of GNN, we further develop the OAC-MPNN-Dif algorithm based on the OAC-GNN-Dif framework. To further reduce communication costs, we designed an OAC message recurrent neural network (OAC-MPRNN-Dif) algorithm, where each UD propagates local models via D2D communications to refresh the graph embedding in the current frame based on the graph feature extraction and localization state of the previous frame to reduce communication costs. Additionally, we introduce dynamic time-varying MPNN for federated diffusion within evolving D2D network topologies. The experimental results indicate that our approach significantly performs well in communication overhead, with a 30%-60% decreasing in wireless resources overhead and 1.2-3.5 times decreasing in the number of model transfers compared to the FedDif methods. Moreover, our approach also improves the global model test accuracy, which is about 2.7% higher than the existing communication diffusion FL with non-IID characteristics.

Heart rate (HR) and respiratory rate (RR) are essential physiological indicators of people's physical function and exercise performance. Advancement in sensor technology has rendered earable devices with in-ear microphones feasible for vital sign monitoring. However, it is rather challenging to monitor heart rate and respiration simultaneously with a single earable device especially when a person is doing exercises. This is because intense physical activities can lead to significant noise interference which can easily obscure physiological signals. To address this challenge, this paper presents VitalEar, an exercise physiological monitoring system based on in-ear microphones, designed to estimate HR and RR while addressing complex motion interference and variability in users and activities. VitalEar employs Empirical Wavelet Transform (EWT) to decompose heartbeats into periodic and harmonic coefficients, enhancing noise reduction in the ECG spectrogram reconstruction model. Additionally, VitalEar incorporates a DCN-LSTM-based breathing curve reconstruction model to mitigate background noise and variability in user and activity. The experiments show that VitalEar achieves an average MAE of 5.61 BPM and 2.31 RPM, MAPE of 4.16% and 10.58% for HR and RR estimation, respectively. Compared to related work, our approach offers significant advantages in robustness against intense physical activities

The Low Level Virtual Machine Intermediate Representation (LLVM IR) is a key component of modern compilers, valued for its universality and cross-platform adaptability. However, identifying optimal optimization passes across platforms remains challenging, as current autotuning frameworks struggle on mobile devices due to resource constraints and network variability. This paper introduces a novel LLVM IR performance optimization framework, LL VMTUNER. At its core is a deep neural network-based predictive model specifically designed to forecast the execution time of input passes on various platforms by analyzing IR features. By integrating this predictive model with the advanced autotuning framework, we enable a rapid and precise search for optimal pass list, removing the need for real-time latency measurements on target platforms and reducing data transmission between devices and cloud-based autotuning frameworks. LLVMTuner significantly enhances the performance of LLVM IR, providing a robust solution for efficient compiler optimizations across a spectrum of computing environments, from high-performance laptops to resource-constrained mobile devices. We evaluate LL VMTuNER on three heterogeneous platforms using over 600 LLVM IR benchmarks. The results show that LLVMTuner achieves an average speedup of 2.41x over the -03 optimization configuration. Additionally, LL VMTUNER reduces search overhead by 83.68% compared to OpenTuner.

Time-series data are vital for preserving earthen ruins and evaluating wind erosion effects. Harsh conditions at these sites often lead to sensor degradation and significant data gaps. To tackle wind speed data imputation for such environments, we introduce a Multi-Scale Conditional Generative Adversarial Network (MSC-GAN) with a Transformer-based generator. This model integrates features across hourly, daily, and weekly scales, combined with real-time wind direction data and random noise. Utilizing the Transformer’s ability to model long-range dependencies and multi-scale information, MSC-GAN adeptly manages complex missing data patterns. We validate MSC-GAN using nearly two years of near-surface wind speed data from the Suoyang City earthen ruins. Our experimental results reveal that MSC-GAN substantially improves imputation accuracy—by 40.2% for short gaps and 6.7% for long gaps—over traditional methods. Code is available at https://github.com/zizhou001/msc-gan.

The demand for 4 K live streaming has grown rapidly, driven by the desire for ultra-high-definition viewing experiences. However, delivering seamless 4 K live streams on mobile devices presents significant challenges due to mobile network limitations, including restricted bandwidth and upload capacity, which can lead to increased latency in broadcasting high-resolution video. Additionally, encoding 4 K video on mobile devices requires substantial computational resources, straining their capabilities. This paper presents StrongLive, a novel computation offloading framework designed to optimize$\mathbf{4 K}$live video streaming on mobile platforms. Specifically, StrongLive leverages advanced SR models to process the selected frames and utilizes decoding techniques to restore non-key frames by combining residual information with previously SR-processed frames. Additionally, StrongLive harnesses a GPU-accelerated video processing pipeline that seamlessly integrates decoding, upscaling, and encoding tasks, significantly enhancing processing speed without compromising 4 K quality. To adeptly handle scene changes, the StrongLive incorporates an online learning mechanism that continuously refines the SR model in real time, ensuring consistent perceptual quality across diverse broadcasting scenarios. We evaluate StrongLive on over 30004 K video clips under three typical network environments. Results show that StrongLive outperforms state-of-the-art methods, achieving an average reduction of 89.5 % in FPS violations and delivering high-quality$\mathbf{4 K}$live video streaming that meets user perceptual expectations.

Mobile edge computing (MEC) enabled artificial intelligence-generated content (AIGC) has garnered considerable attention. To support AIGC metaverse applications within MEC in Internet of Things (IoT) networks, it is effective to offload computation tasks, particularly those involving neural networks generative in AIGC, from mobile devices to edge clouds. Existing solutions typically assume the availability of a dedicated and powerful edge server for each user with single modal data, which can handle the entire AIGC service offloading. However, the practical availability of such dedicated and powerful servers may be limited, necessitating the utilization of less capable alternatives. Thus, we propose the multimodal parallel offloading AIGC framework which partitions multimodal content and offloads partial diffusion tasks to multiple servers. Our proposed scheme accelerates mobile deep vision multimodal metaverse applications through parallel offloading provided by multiple servers. We further utilize the generative AI scheme to solve offloading problems to adapt the dynamic and available communication and computing resource in wireless IoT network. Our framework proposed a multimodal parallel diffusion offloading scheme with integrating the recurrent region proposal prediction algorithm to optimize communication and computing resources while minimizing delay. Simulation results show that our approach can significantly reduce delay compared to conventional algorithms.

Using the wireless waveform superposition property, over-the-air computation (OAC) enables federated learning (FL) to achieve fast model aggregation. However, this computing paradigm is vulnerable to poisoning attacks due to the openness of a wireless channel over time, where malicious mobile devices can introduce cumulative errors for the global FL model in a time-varying wireless environment for each communication round. This article presents a trust online OAC (TO-OAC) scheme to minimize impacts on the global model introduced by malicious devices adjusting to dynamic attack and wireless channel fluctuations over time. TO-OAC achieves this by utilizing trustworthy security quantification of OAC for each FL training round. To optimize the cumulative training loss at the aggregation node with the long-term power and trust constraints of mobile devices, we propose a joint trust, power, and channel-aware algorithm to flexibly update local and global models in response to the dynamic changes in the wireless and secure environment. We analyze the performance limits for the aggregation of trust models, considering metrics for computation and communication through time. We then propose another trust online regularization over-the-air computation (TOR-OAC) as an improved version of the TO-OAC scheme to decrease convergence time while ensuring long-term trust and power limitation. Experimental results performed on real-life datasets show that the two proposed schemes (TO-OAC and TOR-OAC) outperform prior works, especially in noisy, time-varying wireless channels and malicious attacks.

Lightweight training and distributed tiny data storage in local model will lead to the severe challenge of convergence for tiny federated learning (FL). Achieving fast convergence in tiny FL is crucial for many emerging applications in Internet of Unmanned Aerial Vehicles (IUAVs) networks. Excessive information exchange between UAVs and IoT devices could lead to security risks and data breaches, while insufficient information can slow down the learning process and negatively system performance experience due to significant computational and communication constraints in tiny FL hardware system. This paper proposes a trusting, low latency, and energy-efficient tiny wireless FL framework with blockchain (TBWFL) for IUAV systems. We develop a quantifiable model to determine the trustworthiness of IoT devices in IUAV networks. This model incorporates the time spent in communication, computation, and block production with a decay function in each round of FL at the UAVs. Then it combines the trust information from different UAVs, considering their credibility of trust recommendation. We formulate the TBWFL as an optimization problem that balances trustworthiness, learning speed, and energy consumption for IoT devices with diverse computing and energy capabilities. We decompose the complex optimization problem into three sub-problems for improved local accuracy, fast learning, trust verification, and energy efficiency of IoT devices. Our extensive experiments show that TBWFL offers higher trustworthiness, faster convergence, and lower energy consumption than the existing state-of-the-art FL scheme.

The integration of semantic communication with Internet of Things (IoT) technologies has advanced the development of Semantic IoT (SIoT), with edge mobile networks playing an increasingly vital role. This paper presents a framework for SIoT-based image retrieval services, focusing on the application in automotive market analysis. Here, semantic information in the form of textual representations is transmitted to users, such as automotive companies, and stored as knowledge graphs, instead of raw imagery. This approach reduces the amount of data transmitted, thereby lowering communication resource usage, and ensures user privacy. We explore potential adversarial attacks that could disrupt image transmission in SIoT and propose a defense mechanism utilizing Generative Artificial Intelligence (GAI), specifically the Generative Diffusion Models (GDMs). Unlike methods that necessitate adversarial training with specifically crafted adversarial example samples, GDMs adopt a strategy of adding and removing noise to negate adversarial perturbations embedded in images, offering a more universally applicable defense strategy. The GDM-based defense aims to protect image transmission in SIoT. Furthermore, considering mobile devices' resource constraints, we employ GDM to devise resource allocation strategies, optimizing energy use and balancing between image transmission and defense-related energy consumption. Our numerical analysis reveals the efficacy of GDM in reducing energy consumption during adversarial attacks. For instance, in a scenario, GDM-based defense lowers energy consumption by 5.64%, decreasing the number of image retransmissions from 18 to 6, thus underscoring GDM's role in bolstering network security.

Summary As an important technology in computer vision, video‐based human action recognition has a great commercial value, which has attracted extensive attention in the field of computer vision and pattern recognition in both academia and industry. To date, there are a wide variety of applications of human action recognition, such as surveillance, robotics, health care, video searching, and human–computer interaction. However, there are many challenges involved in human action recognition in videos, such as cluttered backgrounds, occlusions, viewpoint variation, execution rate, and camera motion. However, data redundancy and single feature were largely limited the accuracy of human action recognition. In this article, adopting the key frame extraction and multifeature fusion techniques, a novel action recognition method was proposed, which can improve the recognition accuracy. The main works are as follows: 1) in order to solve the problem of data redundancy, a key frame extraction method based on node contribution weighting is proposed to extract video key frames; 2) different kinds of information flows are extracted from the obtained key frame sequences, and different convolutional neural networks are used to obtain corresponding classification results and merge, so as to better complement the information in different flows. Lastly, the experimental results show that our method improves the accuracy of action recognition.

Over-the-air computation (OAC) is a promising technology that can achieve rapid model aggregation by utilizing the wireless waveform superposition feature to harness the interference of multiple-access channel for wireless federated learning (FL). However, OAC-based aggregation for OAC faces critical security challenges due to unfavorable and wireless broadcast properties, such as privacy leaks and eavesdropping attacks. In this paper, we propose to utilize an intelligent reflecting surface (IRS) to support covert OAC-based FL. We first derive the optimal condition for covertness in OAC with IRS and formulate a joint optimization problem to select the maximum covert devices participating in the model aggregation while satisfying the mean squared error (MSE) requirement. We then design a covert difference-of-convex-functions program (CDC) to efficiently determine the transmission power of the device, aggregation beamforming of base station (BS), phase shifts, and reflection amplitudes at the IRS. Simulation results demonstrate that our proposed approach can achieve significant performance gain compared to the baseline algorithms by deploying IRS into covert OAC-based FL.

On-device deep learning enables mobile devices to perform complex tasks, such as object detection and voice translation, regardless of the network condition. The advanced deep learning model gives an excellent performance, also leads to a heavy burden on resource-limited devices (i.e., mobile devices). To speed up the on-device deep learning. Prior studies focus on developing lightweight network architecture for real-time inference by sacrificing model accuracy. This paper presents ATO-EDGE: adaptive task offloading for deep learning based on edge computing. Considering three optimization goals, energy consumption, accuracy, and latency, ATO-EDGE leverages an offline pre-trained model to select a suitable deep learning model on a specific device to process the given task. We apply our approach to object detection and evaluate it on Jetson TX2, Xilinx ZYNQ 7020, and Raspberry 3B+. The deep learning model candidates contain ten typical object detection models trained on Microsoft COCO 2017 dataset. We obtain, on average, 28.25%, 35.44%, and 0.9 improvements respectively for latency, energy consumption, and mAP (mean average precision) when compared to the SOTA DETR model on the Raspberry Pi.

With the popularity and development of Location-Based Services (LBS), location privacy-preservation has become a hot research topic in recent years, especially research on k-anonymity. Although previous studies have done a lot of work on privacy protection, they ignore the negative impact on the security of the knowledge of user-related semantic information of locations that attacker has. To solve this issue, we proposed a User-related Semantic Location Privacy Protection Mechanism (USPPM) based on k-anonymity. First, the anonymity set generation method that combines user-related mobile semantic feature of locations and semantic diversity entropy is proposed to improve the location semantic privacy safety. Second, we design an anonymity set optimization method which enhances sensitive semantic location privacy, through stackberg game model between attacker and protector. Finally, compared with other solutions, experiment on the real dataset shows that our algorithms can provide location privacy efficiently.

Interference management and power transfer can provide a significant improvement over the 5th generation mobile networks (5G) dense Internet of Things (IoT) heterogeneous networks (HetNets). In this paper, we present a novel approach to simultaneously manage inferences at the downlink (DL) and uplink (UL), and to identify opportunities for power transfer and additional UL transmissions integrated with existing protocols and infrastructures for enhanced inter-cell interference coordination (eICIC) protocol in dense IoT HetNets, while considering practical non-linear energy harvesting (EH) model. The design is formulated as the joint optimization of interference aware UL/DL decoupling, airtime resource allocation and energy transfer. The key insight of our algorithm is to translate the original, intractable joint-optimization problem into a problem space where a good approximate solution can be quickly found. We evaluate our scheme through theoretical analysis and simulation. The evaluation shows that our approach improves the system utility by over 20% compared to start-of-the-art in dense IoT HetNets. Compared to alternative schemes, our approach maintains the best user fairness and rate experience and can solve the problem in a fast and scalable way.

Web technology underpins many interactive mobile applications. However, energy-efficient mobile web interactions is an outstanding challenge. Given the increasing diversity and complexity of mobile hardware, any practical optimization scheme must work for a wide range of users, mobile platforms and web workloads. This paper presents CAMEL, a novel energy optimization system for mobile web interactions. CAMEL leverages machine learning techniques to develop a smart, adaptive scheme to judiciously trade performance for reduced power consumption. Unlike prior work, CAMEL directly models how a given web content affects the user expectation and uses this to guide energy optimization. It goes further by employing transfer learning and conformal predictions to tune a previously learned model in the end-user environment and improve it over time. We apply CAMEL to Chromium and evaluate it on four distinct mobile systems involving 1,000 testing webpages and 30 users. Compared to four state-of-the-art web-event optimizers, CAMEL delivers 22% more energy savings, but with 49% fewer violations on the quality of user experience, and exhibits orders of magnitudes less overhead when targeting a new computing environment.

We present Proteus, a novel network-aware approach for optimizing web browsing on heterogeneous multi-core mobile systems. It employs machine learning techniques to predict which of the heterogeneous cores to use to render a given webpage and the operating frequencies of the processors. It achieves this by first learning offline a set of predictive models for a range of typical networking environments. A learnt model is then chosen at runtime to predict the optimal processor configuration, based on the web content, the network status and the optimization goal. We evaluate Proteus by implementing it into the open-source Chromium browser and testing it on two representative ARM big.LITTLE mobile multi-core platforms. We apply Proteus to the top 1,000 popular websites across seven typical network environments. Proteus achieves over 80% of best available performance. It obtains, on average, over 17% (up to 63%), 31% (up to 88%), and 30% (up to 91%) improvement respectively for load time, energy consumption and the energy delay product, when compared to two state-of-the-art approaches.

The adaptive enhanced inter-cell interference coordination (eICIC) configuration is critical for interference management. This problem is challenging especially from energy efficiency perspective and taking individual user fairness into account. Therefore, we formulate a max-min energy efficiency eICIC configuration problem, i.e., determining the number of almost blank subframes (ABS) and user associates with macro or pico while considering fairness jointly. Since the mixed combinatorial and non-smooth features of the problem, an iterative- distributed algorithm is proposed with using fractional programming and Lagrangian dual theory. Numerical results demonstrate the effectiveness of the proposed algorithm and verify fairness achieved among users, and validate the tradeoff between energy efficiency and fairness for eICIC in HetNets comparing with the existing algorithms.

Three-dimensional (3D) beamforming and small cells are two effective techniques to meet the demand of explosive data rate in 5G wireless networks nowadays. The cooperation of these two schemes can help small cell involved heterogeneous networks (HetNets) to achieve high performance. In this paper, we investigate the performance of small cell in-band wireless backhaul in a downlink HetNet considering 3D beamforming. We first analyze the received signals of small cells and users for in-band small cell backhauling and then derive the closed-form achievable data rates for users and the overall system based on gamma distribution. Finally, we formulate the problem based on the derived achievable data rate, followed by analysis of the problem. Simulation results demonstrate that combining 3D beamforming with HetNets can significantly improve the system performance.

The densification and expansion of heterogeneous cellular networks (HetNets) pose new challenges on interference management and reduction of energy consumption. The 3GPP has proposed enhanced intercell interference coordination (eICIC) by making a macrocell silent in almost blank subframes (ABSs) to mitigate interference for low power base stations (BSs) in HetNets. However, energy efficiency (EE) is very crucial for the deployment of a large number of low power nodes as they consume a lot of energy. In this work, we develop a novel EE-eICIC algorithm to determine the amount of ABSs and user equipment (UE) that should associate with picocells or macrocells from energy efficiency perspective. Due to the nonsmooth and mixed combinatorial features of this formulation, we focus on a suboptimal algorithm design. Using generalized fractional programming and the convex programming theory, we propose an iterative and relaxed-rounding algorithm to solve the problem. Numerical results illustrate that the proposed EE-eICIC algorithm achieves superior performance in comparison with state-of-the-art methods in terms of energy efficiency of both system and user.