Reconfigurable Intelligent Surfaces (RISs) and Non-Orthogonal Multiple Access (NOMA) can enhance Visible Light Communication (VLC) systems by mitigating signal blockage and improving spectrum utilization. While boosting communication efficiency is crucial, maintaining high illumination quality is equally important. This paper investigates a novel approach to simultaneously improving the sum rate (SR) and illumination uniformity (IU) in a RIS-assisted NOMA-based VLC system. Communication and illumination optimization problems are formulated as a non-convex mixed-integer non-linear programming problem, considering RIS placement, LED-user association, and power allocation. To the best of our knowledge, this is the first work to jointly optimize SR and IU with explicit consideration of RIS placement. We propose a joint optimization approach that leverages a differential evolution algorithm to optimize RIS placement. During each iteration, the obtained solutions are further refined using a block coordinate descent algorithm, which iteratively solves the decomposed sub-problems of LED-user association and power allocation. Simulation results show that the approach outperforms existing methods in both SR and IU. Moreover, RIS placement optimization is shown to be crucial, as neglecting it significantly degrades performance. Finally, the impacts of noise power and total LED power are analyzed, offering practical insights for parameter selection in VLC systems.

Performing video analytics tasks based on deep neural networks (DNNs) on resource-constrained mobile devices is extremely challenging because of the huge volume of video data and the computationally intensive nature of DNN. One promising solution is to offload tasks to the edge servers for execution. However, due to explosive growth in the number of end devices, more and more mobile devices are connected to the edge servers. This makes it difficult for the edge server to meet the specific service level objective (SLO) of on-edge video analytics when facing concurrent computing requests, especially in the real-time scene. To address this issue, this article presents EHCI, an on-edge high-throughput collaborative inference framework for real-time video analytics. On the mobile device, EHCI crops the key regions from the current video frame based on the local detection cache and offloads these regions to the edge server, which can significantly reduce bandwidth consumption and computation costs. Besides, considering concurrent DNN inference requests from multiple mobile devices, EHCI uses a key region patching method to achieve high-throughput DNN inference on the edge server, along with a scheduling algorithm to meet the SLO for each mobile device. It has been validated with testing that the EHCI outperforms the state-of-the-art technology by 159% in achieved throughput, reduces the average end-to-end delay by 36%, and the application accuracy sacrifice is within a reasonable range.

To accomplish computationally intensive visual tasks on mobile devices with limited memory and computation capability, a common solution is to offload the tasks to the edge with more powerful computing resources. Nevertheless, due to the extra communication, even edge computing is challenged by the growing demands for real-time interactive tasks (e.g., VR and AR), which are generally performed by deep CNNs with heavy computation. In this paper, we design a novel system called HRCache to reduce the end-to-end latency by effectively recompressing video data and reusing cached inference results on the edge. As similar image regions have been indicated in the offloading video coding, the edge can approximately infer these regions with results stored on the edge to save the time for calculating these regions. Moreover, since the data in these regions do not require calculation, the transmitted data can be further simplified to reduce transmission latency. Furthermore, HRCache can quickly and continuously adjust the coding parameters to adapt the accuracy loss and overall latency with application requirements. Compared with the original offloading schemes, HRCache significantly reduces the average latency, about 13.60% to 18.83%, at little accuracy loss of 1.25% in Top-1 accuracy for classification and 0.135 in IoU for object detection.

In the Distributed Parallel Stream Processing Systems (DPSPS), elastic resource allocation allows applications to dynamically response to workload fluctuations. However, resource provisioning can be particularly challenging, due to the unpredictability of the workload. In addition, unlike CPU resources, bandwidth resources are often ignored in resource allocation. Moreover, resource allocation and resource placement are considered separately. In this paper, we investigate the proactive elastic resource scheduling problem for computation-intensive and communication-intensive applications, which aims at meeting the latency requirement with the minimal energy cost, and propose a dynamic collaborative strategy from the systemic perspective. Specifically, we first model a collaborative workload prediction pattern to accurately predict the upcoming workload, and construct a latency estimation model to estimate the latency of the application. Then, we design an energy-efficient resource pre-allocation method, in which the CPU frequency adjustment and the stability of resource reconfigurations are both considered. Finally, we present a communication-aware resource placement approach. Simulation results show that, compared with the reactive strategies, our strategy achieves an obviously better latency performance, and effectively avoids unnecessary resource adjustments. Meanwhile, the energy consumption is about saved by 50 percent on average, and the communication cost is maintained at a very low level of 4 percent.

With increasing real-time and resource-intensive requirements, approximate computing is widely adopted to improve the performance of query processing over data streams. However, existing works concentrate on simple queries with single-step operations, such as point or join queries. There are a large number of nested queries with selection or filtering operations before aggregation. In this poster, we focus on approximate nested stream queries. We first propose a novel approximate model, SCM-sketches, that makes two-stage approximation for nested query answering with guaranteed errors. In the first stage for nested filtering operations, we use the sampling method to compress the arriving data. Then in the second stage, a sketch is used for further aggregation or join operations. We also theoretically analyze the effect of error propagation on approximate errors. Compared with existing sketch-based methods, experiment results with real-life datasets verify the effectiveness of SCM-sketches.

In online approximate stream processing, customers generally submit their requests with some specific quality requirements (e.g. maximum error). This raises a critical problem that online quality control is necessary to meet customized requirements. Since continuous arriving data needs to be processed immediately, it brings the difficulty of acquiring knowledge which significantly affects the efficiency of sampling. Hence, it's more challenging to ensure a prescribed level of quality without knowledge about data. In this paper, we present an adaptive approximate processing framework for online stream applications to address the challenges mentioned above. Specially, we first design a new data knowledge learning scheme to stratify the arriving stream data. Then, based on the online learning results, we propose a dynamic sampling strategy with the consideration of the stream rate. Finally, we further present a double-check error control mechanism to manage the output quality. Experiments with real world datasets show that the proposed approximate framework is not only applicable to different data distributions, but also provides a customized error control.

In recent years, rapidly accumulating genomic data have posed a challenge to integrate multiple data sources and to analyze the integrated networks globally. In this paper we present a method to reverse engineer integrative gene networks. The main advantage of our method is the integration of different quantitative and qualitative data sets in order to reconstruct a multiplex network, without necessarily imposing data constraints, such as each genomic datum needs to have the same number of entities. The computation boils down to solving small quadratic programs based on local neighborhood of nodes. We applied the method to DREAM5 dataset, and compared the results with the community networks from the challenge. We further demonstrated our method through a case study using breast cancer data, integrating metastasis gene expression data with interactome data. Overall, our method can be applied in many settings of network system biology.