3-D language Gaussian splatting has recently shown strong potential for open-vocabulary scene understanding and robotic manipulation. However, most existing methods require dense multiview observations to achieve accurate geometry reconstruction and reliable semantic alignment, which limits their applicability in scenarios where only sparse-view observations are available. In this work, we propose SparseGrasper, a framework for language-guided zero-shot robotic grasping under sparse-view conditions. SparseGrasper constructs a 3-D Gaussian language field from as few as three RGB images, enabling joint reasoning over geometry and semantics without the need for dense observations. To improve representation learning under sparse observations, we introduce a dual feature distillation module that fuses local object features with global contextual cues. We further design a language-guided grasp pose generation strategy that incorporates semantic grounding into grasp candidate selection, encouraging grasps that are both semantically relevant and geometrically feasible. Real-world experiments on a 7-DoF robotic manipulator validate that SparseGrasper effectively performs language-guided grasping of diverse, previously unseen objects from sparse observations.

Deep learning shows great promise in providing more intelligence to the cross-platform web. However, insufficient infrastructure, heavy models, and intensive computation limit the use of deep learning with low-performing web browsers. We propose DeepAdapter, an integrated cloud-edge-device framework that ties the edge, the remote cloud, with the device by cross-platform web technology for adaptive deep learning services towards lower latency, lower mobile energy, and higher system throughput. DeepAdapter consists of context-aware pruning, service updating, and online scheduling. First, the offline pruning module provides a context-aware pruning algorithm that incorporates the latency, the network condition, and the device's computing capability to fit various contexts. Second, the service updating module optimizes branch model cache on the edge for massive mobile users and updates the new model pruning requirements. Third, the online scheduling module matches optimal branch models for mobile users. Also, a two-stage DRL-based online scheduling method named DeepScheduler can handle high concurrent requests between edge centers and remote cloud by designing the reward prediction model. Extensive experiments show that DeepAdapter can decrease average latency by 1.33x, reduce average mobile energy by 1.4x, and improve system throughput by 2.1x with considerable accuracy.

The emergence of edge computing and 5G networks has fueled the growth of mobile Web AR. Although efforts have been made to improve the edge system efficiency for Web AR applications, efficient edge-assisted mobile Web AR services remain technically challenging. This paper presents EARNet, a distributed edge system orchestration approach for mobile Web AR in 5G networks. The design of EARNet makes three novel contributions. First, EARNet manages the edge network dynamics with respect to user mobility and their Web AR service requests by employing landmarks and grid index based edge node localization mechanisms. Second, EARNet takes into account both request serving performance and offloading cost in managing workload balance and quality of service and leverages dynamic hash and max heap mechanisms for efficient Web AR service lookup and AR computations. Third, EARNet designs the service migration schemes by optimizing several performance factors, such as message efficiency, scheduling latency, request density and locality of mobile users and edge nodes, and accuracy of Web AR services after migration. Experimental evaluations are conducted using the real base station deployment data in the Melbourne Central Business District (CBD) area. The results shows the effectiveness of the EARNet edge orchestration approach compared to several baseline approaches.

Intelligent systems are technologically advanced machines that can sense and respond to the surrounding environment. They have been widely used in medicine, military, transportation, automation, and other fields. However, when these systems deal with their environments, problems such as leakage of identities may occur. The adversary can damage the system communication and attack important nodes. To handle resource-constrained wireless sensor network environments, we propose a secure and anonymous data aggregation scheme. First, based on the bilinear mapping operation and onion routing concepts, we propose a key negotiation and secure information transmission scheme, which conducts confidential transmission and anonymous forwarding of messages in data aggregation. Second, an aggregation routing scheme based on link direction and residual energy is proposed to pledge messages that can arrive the base station without passing through many nodes, which saves network resources to a certain extent. Third, on the basis of the first two contributions, we propose an identity-privacy-aware secure and anonymous data aggregation scheme that protects the identity's privacy. This scheme can conceal the real identity of important nodes and protect the anonymity of messages and link relationships. In addition, an anonymous identity update and synchronization scheme is also proposed to ensure the reliability and security of communication. Meanwhile, our performance evaluations and simulations show that the proposed framework is more effective than several standard schemes with respect to the ability against various attacks, security, and overhead.

Smart TV can perform interactive computing while also providing video content services. However, this leads to a high delay during interactive computing because of the lack of computing capability, thus smart TVs are unable to undertake large scenes and complex interactive computing tasks. This article proposes a collaborative task offloading framework (CTOF) for interactive computing of smart TV video applications. The main contributions of this article are as follows: 1) a computing offloading mechanism is proposed for interactive computing with video content. A part of the interactive computing task is offloaded to the user’s high-computing mobile device in a household video service environment via a Wi-Fi Direct channel and 2) a collaborative computing offloading algorithm is proposed for complex interactive computing tasks. According to the computing complexity and the computing expansion coefficient, a parallel and serial collaborative smart offloading framework is established to minimize the delay. We conduct extensive experiments to indicate that in the existing experimental network environment, the video-based complex interactive service operation efficiency is improved by 20%. With a smart offloading framework, we can further achieve a satisfactory experience in terms of the interactive computing delay. Consequently, the smart TV can quickly respond to the complex video interactive service and improve the business interaction capability of the smart TV.

Multi-user mobile Augmented Reality (AR) has been successfully used in various fields as a novel visual interaction technology. But current mainstream wearable device-based and app-based solutions are still facing cross-platform, real-time communication, and intensive computing requirements. Mobile Web technology is envisioned to be a promising supporting technology for cross-platform application of mobile AR especially in 5G networks, which provide pervasive communication and computing resources thereby forming a formidable framework for the practical application of multi-user mobile Web AR. However, the problem of how to use these new techniques properly to achieve efficient communication and computing collaboration is obviously paramount in order for multi-user mobile Web AR to be realized in 5G networks. In this article, we propose the first edge-assisted multi-user collaborative framework for mobile Web AR in the 5G era. First, we propose a heuristic mechanism BA-CPP for efficient communication planning, which allows multi-user interaction synchronization to be achieved. Second, we introduce a motion-aware key frame selection mechanism called Mo-KFP to optimize the computational efficiency of the edge system, and simultaneously alleviate the initialization problem by collaborating with nearby mobile devices using the Device-to-Device (D2D) communication technique. Experiments are conducted in a real-world 5G network, and the results demonstrate the superiority of our proposed collaborative framework.

Enabling deep learning technology on the mobile web can improve the user’s experience for achieving web artificial intelligence in various fields. However, heavy DNN models and limited computing resources of the mobile web are now unable to support executing computationally intensive DNNs when deploying in a cloud computing platform. With the help of promising edge computing, we propose a lightweight collaborative deep neural network for the mobile web, named LcDNN, which contributes to three aspects: (1) We design a composite collaborative DNN that reduces the model size, accelerates inference, and reduces mobile energy cost by executing a lightweight binary neural network (BNN) branch on the mobile web. (2) We provide a jointly training method for LcDNN and implement an energy-efficient inference library for executing the BNN branch on the mobile web. (3) To further promote the resource utilization of the edge cloud, we develop a DRL-based online scheduling scheme to obtain an optimal allocation for LcDNN. The experimental results show that LcDNN outperforms existing approaches for reducing the model size by about 16x to 29x. It also reduces the end-to-end latency and mobile energy cost with acceptable accuracy and improves the throughput and resource utilization of the edge cloud.

Web-based Deep Neural Networks (DNNs) enhance the ability of object recognition and has attracted considerable attention in mobile Web AR and other services. However, neither performing the DNN inference on mobile Web browsers locally nor offloading computations to the cloud can strike a balance between accuracy and efficiency; generally, rude methods are often accompanied by unsatisfactory accuracy. Collaborative approaches seem to fill this gap by coordinating the distributed hierarchical computing resources, especially in the 5G era, but it still faces challenges in the current solutions, such as the lack of (1) full use of 5G resources for the one point DNN computation partitioning schemes; (2) fine-grained branching mechanism; (3) efficient partitioning method; and (4) multi-objective optimization. To this end, we present the fine-grained elastic computation partitioning mechanism for distributed DNN in 5G networks. First, we elaborate two collaborative scenarios. Second, we study the DNN branching mechanism at layer granularity. Next, we propose a DNN computation partitioning algorithm based on deep reinforcement learning. Finally, we develop a mobile Web AR application as a proof of concept. The experiments were conducted in an actually deployed 5G trial network, and the results show the superiority of this collaborative approach. The common theme is, under the premise that Quality of Service (QoS) is satisfied, to balance multiple interests by orchestrating computations across heterogeneous computing platforms.

Combining image segmentation with Web technology lays a good foundation for lightweight, cross-platform, and pervasive Web artificial intelligence applications, and further improves the capability of Web-of-Things (WoT) applications. However, no matter whether we use a Web real-time communication media server for advanced processing that views camera inputs as a video stream, or transfer continuous camera frames to the remote cloud for processing, we are unable to obtain a satisfactory real-time experience due to high resource consumption and unacceptable latency. In this article, we present EdgeBooster, a computational-efficient architecture that leverages a common edge server to minimize the communication costs, accelerates the camera frame segmentation, and guarantees an acceptable segmentation accuracy with the prior knowledge. EdgeBooster provides real-time segmentation by developing parallel technology that enables segmentation on slices of a camera frame and using presegmentation based on superpixels to accelerate the graph-based segmentation. It also introduces recent DNN-based segmentation results as the prior knowledge to improve the performance of the graph-based segmentation, especially in nonideal scenes, such as dark light and weak contrast. Finally, it creates a pure frontend segmentation that can provide continuous and stable services for mobile users in unstable networks, such as a weak network or with an unstable edge server. The experimental results show that EdgeBooster is able to achieve a considerable accuracy for the mobile Web, running at no less than 30 frames per second in real scenes.

Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as data analytics, autonomous systems, and security diagnostics. As an important part of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in military, transportation, medical, and household fields. However, in the applications of wireless sensor networks, the adversary can infer the location of a source node and an event by backtracking attacks and traffic analysis. The location privacy leakage of a source node has become one of the most urgent problems to be solved in wireless sensor networks. To solve the problem of source location privacy leakage, in this paper, we first propose a proxy source node selection mechanism by constructing the candidate region. Secondly, based on the residual energy of the node, we propose a shortest routing algorithm to achieve better forwarding efficiency. Finally, by combining the proposed proxy source node selection mechanism with the proposed shortest routing algorithm based on the residual energy, we further propose a new, anonymous communication scheme. Meanwhile, the performance analysis indicates that the anonymous communication scheme can effectively protect the location privacy of the source nodes and reduce the network overhead.

Deep learning shows great promise in providing more intelligence to the mobile web, but insufficient infrastructure, heavy models, and intensive computation limit the use of deep learning in mobile web applications. In this paper, we present DeepAdapter, a collaborative framework that ties the mobile web with an edge server and a remote cloud server to allow executing deep learning on the mobile web with lower processing latency, lower mobile energy, and higher system throughput. DeepAdapter provides a context-aware pruning algorithm that incorporates the latency, the network condition and the computing capability of the mobile device to fit the resource constraints of the mobile web better. It also provides a model cache update mechanism improving the model request hit rate for mobile web users. At runtime, it matches an appropriate model with the mobile web user and provides a collaborative mechanism to ensure accuracy. Our results show that DeepAdapter decreases average latency by 1.33x, reduces average mobile energy consumption by 1.4x, and improves system throughput by 2.1x with a considerable accuracy. Its contextaware pruning algorithm also improves inference accuracy by up to 0.3% with a smaller and faster model.

Lightweight and precise recognition is a key component of web-based augmented reality (Web AR) applications. Although edge-based distributed deep learning approach is now possible to achieve satisfactory recognition for Web AR applications, it puts significant pressure on the computation and energy consumption of the mobile web browser, especially the app-based embedded browser. Thus, reducing the model size and accelerating the inference are regarded as the two fundamental challenges to enable this edge-based collaborative recognition system efficiently. In this paper, we propose a lightweight collaborative recognition system (LCRS) for Web AR applications. LCRS contributes to three aspects: (1) we design a composite deep neural network for reducing the model size and inference latency by introducing binary convolutional neural network; (2) we provide a joint training method to co-train the general branch and the binary branch; (3) we develop a JavaScript library for the mobile web browser to execute and accelerate inference of the binary branch, which also provides a collaborative mechanism between the mobile web browser and the edge server. We have conducted extensive experiments using several well-known networks and datasets. The experimental results have shown that the proposed system outperforms the existing approaches in terms of reducing the model size by about 16x to 29x, and it also reduces end-to-end latency and outpaces the existing state-of-the-art approaches by over 3x to 60x when applying it in practical Web AR cases.

Mobile augmented reality (Mobile AR) is gaining increasing attention from both academia and industry. Hardware-based Mobile AR and App-based Mobile AR are the two dominant platforms for Mobile AR applications. However, hardware-based Mobile AR implementation is known to be costly and lacks flexibility, while the App-based one requires additional downloading and installation in advance and is inconvenient for cross-platform deployment. In comparison, Web-based AR (Web AR) implementation can provide a pervasive Mobile AR experience to users thanks to the many successful deployments of the Web as a lightweight and cross-platform service provisioning platform. Furthermore, the emergence of 5G mobile communication networks has the potential to enhance the communication efficiency of Mobile AR dense computing in the Web-based approach. We conjecture that Web AR will deliver an innovative technology to enrich our ways of interacting with the physical (and cyber) world around us. This paper reviews the state-of-the-art technology and existing implementations of Mobile AR, as well as enabling technologies and challenges when AR meets the Web. Furthermore, we elaborate on the different potential Web AR provisioning approaches, especially the adaptive and scalable collaborative distributed solution which adopts the osmotic computing paradigm to provide Web AR services. We conclude this paper with the discussions of open challenges and research directions under current 3G/4G networks and the future 5G networks. We hope that this paper will help researchers and developers to gain a better understanding of the state of the research and development in Web AR and at the same time stimulate more research interest and effort on delivering life-enriching Web AR experiences to the fast-growing mobile and wireless business and consumer industry of the 21st century.