We present a real-time indoor visible light positioning system based on optical camera communication, where the coordinate data in the on and off keying format is transmitted via light emitting diode (LED) based lights and captured using a smartphone camera. The position of camera is determined using a novel Perspective-n-Point problem algorithm, which determines the geometric perspective of the LED coordinates. The experimental results show that the proposed system offers mean position errors of 4.81 and 6.58 cm for the heights of 50 cm and 80 cm, respectively.

Vehicular fog computing (VFC) is a promising approach to provide ultra-low-latency service to vehicles and end users by extending the fog computing to conventional vehicular networks. Parked vehicle assistance (PVA), as a critical technique in VFC, can be integrated with smart parking in order to exploit its full potentials. In this paper, we propose a smart VFC system by combining both PVA and smart parking. A VFC- aware parking reservation auction is proposed to guide the on- the-move vehicles to the available parking places with less effort and meanwhile exploit the fog capability of parked vehicles to assist the delay-sensitive computing services by monetary rewards to compensate for their service cost. The proposed allocation rule maximizes the aggregate utility of the smart vehicles and the proposed payment rule guarantees incentive compatibility, individual rationality, and budget balance. We further provide an observation stage with dynamic offload pricing update to improve the offload efficiency and the profit of fog system. The simulation results confirmed the win-win performance enhancement to the fog node controller (FNC), the smart vehicles and the parking places from the proposed design.

Mounting reconfigurable intelligent surface (RIS) on unmanned aerial vehicle (UAV), called UAV-RIS, combines the benefits of these two techniques, which can further improve the communication performance. However, high-quality air-ground channel links are more vulnerable to both the adversarial eavesdropping and the malicious jamming. Therefore, this paper proposes a reliable and secure communication approach assisted by the UAV-RIS to maximize the secrecy rate, while ensuring the quality of service (QoS) requirement of the legitimate user against both the eavesdroppers and the jammer. Specifically, with the imperfect channel state information and behaviors of mixed attacks, we try to maximize the achievable worst-case secrecy rate by jointly designing the transmit beamforming, artificial noise, UAV-RIS placement, and RIS’s passive beamforming. As the optimization problem is non-convex and the environment is highly dynamic, a post-decision state deep Q-network combined with Fourier feature mapping algorithm (called PDS-DQN-FFM) is further designed to effectively achieve the robust anti-attack transmission strategy. Simulation results demonstrate that our proposed learning based reliable and secure transmission approach significantly enhances both the secrecy rate and QoS satisfaction level as compared with existing approaches.

With the development of telecommunication technologies and the proliferation of network applications in the past decades, the traditional cloud network architecture becomes unable to accommodate such demands due to the heavy burden on the backhaul links and long latency. Therefore, edge computing, which brings network functions close to end-users by providing caching, computing and communication resources at network edges, turns into a promising paradigm. Benefit from its nature, edge computing enables emerging scenarios and use cases, such as augmented reality (AR) and Internet of Things (IowT). However, it also creates complexities to efficiently orchestrate heterogeneous services and manage distributed resources in the edge network. In this survey, we make a comprehensive review of the research efforts on service orchestration and resource management for edge computing. We first give an overview of edge computing, including architectures, advantages, enabling technologies and standardization. Next, a comprehensive survey of state-of-the-art techniques in the management and orchestration of edge computing is presented. Subsequently, the state-of-the-art research on the infrastructure of edge computing is discussed in various aspects. Finally, open research challenges and future directions are presented as well.

Collaborative inference in mobile edge computing (MEC) enables mobile devices to offload the computation tasks for the computation-intensive perception services, and the inference policy determines the inference latency and energy consumption. The optimal inference policy depends on the inference performance model of deep learning, the data generation model and the network model that are rarely known by mobile devices in time. In this paper, we propose a multi-agent reinforcement learning (RL) based energy-efficient MEC collaborative inference scheme, which enables each mobile device to choose both the partition point of deep learning and the collaborative edge of each mobile device based on the image quantity, the channel conditions and the previous inference performance. A learning experience exchange mechanism exploits the Q-values of the neighboring mobile devices to accelerate the inference policy optimization with less energy consumption. We also provide a deep multi-agent RL based inference scheme to accelerate learning for large-scale MEC networks, in which an actor network yields the collaborative inference policy probability distribution and a critic network guides the weight update of the actor network to enhance sample efficiency. We provide the inference performance bound and analyze the computational complexity. Both simulation and experimental results show that our proposed schemes reduce the inference latency and save the MEC energy consumption.

With the rapid development of deep learning technology, the modern internet-of-things (IoT) cameras have very high demands on communication, computing, and memory resources so as to achieve low latency and high accuracy live video analytics. Thanks to mobile edge computing (MEC), intelligent offloading to the MEC nodes can bring a lot of benefits, especially when the decomposable pipeline is adopted in the cloud-edge architecture. In this paper, we provide Decomposable Intelligence on a Cloud-Edge IoT (DICE-IoT) framework to support joint latency-and accuracy-aware live video analytic services. Specifically, the intelligent framework enables the pipeline sharing mechanism to reduce MEC resource usage. A Nash bargaining is proposed to incentivize cooperative computing provision between the MEC and the cloud, and a Generalized Benders Decomposition (GBD) based approach is utilized to optimize the social welfare. The results show that the proposed DICE-IoT framework can achieve a win-win-win solution to the IoT device, the MEC, and the cloud stratums.