As a key component of building management and security, occupancy inference through smart sensing has attracted a lot of research attentions for nearly two decades. Nevertheless, existing solutions mostly rely on either pre-deployed infrastructures or user device participation, thus hampering their wide adoption. This paper presents CeilingSee, a dedicated occupancy inference system free of heavy infrastructure deployments and user involvements. Building upon existing LED lighting systems, CeilingSee converts part of the ceiling-mounted LED luminaires to act as sensors, sensing the variances in diffuse reflection caused by occupants. In realizing CeilingSee, we first re-design the LED driver to leverage LED's photoelectric effect so as to transform a light emitter to a light sensor. In order to produce accurate occupancy inference, we then engineer efficient learning algorithms to fuse sensing information gathered by multiple LED luminaires. We build a testbed covering a 30m² office area; extensive experiments show that CeilingSee is able to achieve very high accuracy in occupancy inference.

Visible light positioning (VLP) has developed rapidly with the ubiquitous lighting infrastructure in recent years. Based on classic trilateration or triangulation, VLP usually requires at least three light sources which makes it infeasible in some scenarios where sparse LED luminaries are mounted. In this paper, we focus on VLP in such scenarios and propose a VLP method based on a single LED luminaire as the light transmitter and commercial off-the-shelf smartphone as the receiver. A critical problem that needs to be addressed is to derive the accurate orientation of the smartphone. Although the inertial measurement unit of the smartphone can be used to obtain the orientation, the measurement error severely degrades the positioning accuracy. To address the measurement error, this paper innovates in exploiting projective geometry to calibrate the orientation measurements. We build an experimental platform to evaluate the proposed VLP method. The experimental results show that the proposed VLP method can achieve a submeter accuracy and the indoor positioning errors are within 0.16 m by 90%.

This paper proposes a weight-aware deep reinforcement learning (WADRL) approach designed to address the multiobjective vehicle routing problem with time windows (MOVRPTW), aiming to use a single deep reinforcement learning (DRL) model to solve the entire multiobjective optimization problem. The Non-dominated sorting genetic algorithm-II (NSGA-II) method is then employed to optimize the outcomes produced by the WADRL, thereby mitigating the limitations of both approaches. Firstly, we design an MOVRPTW model to balance the minimization of travel cost and the maximization of customer satisfaction. Subsequently, we present a novel DRL framework that incorporates a transformer-based policy network. This network is composed of an encoder module, a weight embedding module where the weights of the objective functions are incorporated, and a decoder module. NSGA-II is then utilized to optimize the solutions generated by WADRL. Finally, extensive experimental results demonstrate that our method outperforms the existing and traditional methods. Due to the numerous constraints in VRPTW, generating initial solutions of the NSGA-II algorithm can be time-consuming. However, using solutions generated by the WADRL as initial solutions for NSGA-II significantly reduces the time required for generating initial solutions. Meanwhile, the NSGA-II algorithm can enhance the quality of solutions generated by WADRL, resulting in solutions with better scalability. Notably, the weight-aware strategy significantly reduces the training time of DRL while achieving better results, enabling a single DRL model to solve the entire multiobjective optimization problem.

As an advanced form of IoT technology, integrated sensing and communication (ISAC) deeply integrates communication and perception, enhancing the performance and application range of IoT. At the same time, the space-air-ground integrated network (SAGIN) provides a wider and more efficient connection and information processing support for both. However, the highly dynamic and time-varying characteristics of SAGIN lead to more frequent vertical handovers among heterogeneous wireless networks, which seriously affects the continuity and reliability of services. This motivates us to explore an effective vertical handover method in SAGIN to guarantee the quality of network service. The issue is a typical complex and high-dimensional problem with its online and dynamic characteristics, which provides a particularly favorable scenario for the adaptability of the diffusion model (DM). Accordingly, we propose a novel vertical handover decision algorithm with the aid of DM. First, we innovate a novel vertical handover analytical model that describes handover jitter, load difference, and handover robustness. Then we formulate it as a multiobjective optimization problem. Next, inspired by Generative AI (GAI), we propose a DM-based GAI-empowered handover decision (DGHD) algorithm to capture the time-varying and high-dimensional environments and generate optimal vertical handover decisions. Subsequently, the policy network of multiagent proximal policy optimization (MAPPO) is replaced with the proposed DGHD for addressing environmental uncertainty and enhancing efficiency. Finally, the simulations exhibit that our proposed algorithm outperforms existing algorithms.

In the 6G era, user demand for low-latency, cost-effective extreme services such as extended reality (XR) and holographic communications has significantly increased. Multi-domain networks, known for their vast capacity and coverage, are essential in fulfilling the growing demand for high-performance services. Despite their potential, these networks face challenges with domain isolation, requiring a software defined network (SDN) controller for inter-domain communication. Network function virtualization (NFV) enhances flexibility of service delivery with customizable service function chain (SFC), yet prior research falls short in delivering low-latency, cost-efficient services in multi-domain NFV networks alongside an unreasonable assumption that software on physical nodes can support the execution of all virtualization network functions (VNFs). In this paper, we study the problem of SFC deployment with VNF-dependent software migration (SD-VDSM) in multi-domain networks. Particularly, we first formulate the problem by setting an objective to minimize the end-to-end communication delay and the associated costs of service provisioning, while simultaneously ensuring load balancing across multi-domain networks. However, complexity of the issue escalates to an intractable level due to the intertwined nature of SFC deployment strategies and VNF-dependent software migration tactics, which mutually influence each other intricately. To tackle this issue, we propose an innovative heuristic algorithm, designated as the Joint SFC Deployment with VNF-Dependent Software Migration Algorithm (JSD-VDSMA). Comprising three fundamental steps, this algorithm is crafted to adeptly resolve the complexities of service provisioning across multi-domain networks. A suite of rigorous experimental assessments is detailed, demonstrating the capability of our proposed JSD-VDSMA. Through these comparative analyses, we demonstrate its effectiveness not only to increase the service acceptance rate but also to diminish both the end-to-end communication delay and resource utilization costs in comparison to its counterparts.