Multi-access Edge Cloud (MEC) networks are powerful for providing emerging computation-intensive and latency-sensitive applications with low latency leveraging ubiquitous edge devices. These networks enable complex applications to be split into multiple components/subtasks and deployed among multiple edge servers with limited computation and communication resources. However, multiple subtasks within an application are dependent on each other. They cannot be executed in parallel, resulting in non-trivial resource waste when allocating resources to every subtask throughout the lifetime of the application. This paper investigates the multi-component task offloading problem in MEC networks that addresses the dependencies among components and three-dimensional (3D) resource allocation, i.e., computation, communication, and time slots. The problem is NP-hard and challenging to solve due to the complex task dependencies, including triangular dependencies among multiple subtasks and the routing of edges between dependent subtasks. To address the challenge, we first propose a non-destructive task reconfiguration algorithm that transforms a task call graph into multiple sequential layers, breaking out the triangular dependency. Then, we develop a de P endency-awa R e task offlo A ding algorithm w I th ta S k r E configuration ( PRAISE ) algorithm to maximize the total offloading benefit. PRAISE decouples the original problem into task offloading and 3D convex resource optimization. Simulation results show that PRAISE outperforms baselines with higher system benefits and lower resource costs.

Internet of Things (IoT) plays as the key infrastructure for various applications, e.g., smart city, smart grid, and smart manufacturing. Wireless communication is greatly limited for IoT devices due to the severe electromagnetic interference in industrial radio-hostile environments. Visible light communication (VLC) has been considered as a promising communication technique that could complement the wireless communication. Due to complex industrial environments, it is not practical to wire such scale of VLC-enabled IoT devices into the power line or replace batteries for them. To cope with this challenge, we propose to harvest the energy from electromagnetic interference signals to power the VLC for IoT devices. We achieve this goal by proposing advanced signal processing designs, new energy-efficient schemes, and seamless integration. Finally, we prototype the system on real devices, and our extensive experiments show that our system can achieve promising results.

Free Space Optical (FSO) communication is promising to provide a wireless connection of tens to hundreds of gigabit capacity. Despite of many advantages, FSO link is sensitive to weather conditions and atmospheric turbulence. To offset these limitations, using Radio Frequency (RF) link as a backup link is a wise choice and thus hybrid FSO/RF system gains wider attention. Research on link switching is of great importance as a key technology in hybrid FSO/RF system. It is well known that link switching is closely related to the Received Signal Strength Indication (RSSI). In this letter, by combining power control and link switching using Recurrent Neural Networks (RNN) to predict RSSI, we propose an intelligent power control and link switching scheme to enhance transmission effectiveness in hybrid FSO/RF system. We set up a testbed of hybrid FSO/RF system and experimentally validate the proposed scheme. Experimental results demonstrate that the proposed scheme can forecast the trend of RSSI accurately and the power control strategy can effectively improve transmission quality of FSO link by reducing the frequency of switching.

Edge computing that utilizes ubiquitous edge devices locating in close proximity to users is powerful for providing Quality of Service guaranteed computation offloading services. Toward the limited resources of edge servers and wireless links, large services can be split into multiple interconnected components to be served by multiple edge servers cooperatively. The current works on service placement either assume unsplittable services or ignore the geographically isolated property of edge servers. They also ignore the interference among online services that share the same physical nodes/links in terms of executing delay. Namely, every service adds load to the placed nodes/links and every increment on load of nodes/links risks delay violation of existing services. To overcome above challenges, this article emphasizes on the interference-aware (IA) online multicomponent service placement in edge cloud networks. First, the delay of tree-like services is analyzed considering the dependency among components, based on which the IA residual capacities of physical nodes, links, and paths are defined and formulated theoretically. Furthermore, we reduce the problem of multicomponent service placement to be NP-hard and transform it into an ant colony optimization (ACO) problem to obtain the near-optimal solution. More importantly, a level traversal component ranking method and an IA dynamic pruning method are proposed for ACO to achieve faster convergence, interference awareness, and higher acceptance ratio of services. Simulation results are presented to validate the effectiveness of proposed methods. In addition, the classic artificial intelligence application of image classification is experimented to further strength the motivation of IA investigation in practical.

We propose a chaotic region-optimized probabilistic constellation shaping (CRPCS) scheme to enhance the security and the resistance to turbulence for free-space optical (FSO) communications. For this approach, a four-dimensional hyperchaotic system generates a pseudorandom sequence to rotate and encrypt the constellation. Constellation distribution of short pseudorandom sequences behaves as the law of a non-uniform character. Grouping long pseudorandom sequences and counting the characteristics of constellation distribution can realize probabilistic constellation shaping with low and fixed redundant information. We demonstrate a 56 Gbyte/s coherent FSO communication system based on log-normal and Gamma–Gamma turbulence models with a key space of 10⁷⁵. The results show that the optical receiver sensitivity is improved by 0.3–1.1 dB, and the transmission distance is also improved by 3.2%–7.0% in different shaping cases.

Free space optical (FSO) communication has attracted significant attention due to its high transmission rate and information security. Nevertheless, the FSO link is sensitive to various weather conditions which limit its application range. Thus, research on the FSO channel plays an important role for combatting channel fading. In the paper, we first establish a FSO transmission testbed to obtain received signal strength indication (RSSI) information of optical signal. Then we utilize an environmental chamber for indoor experiments to simulate weather changes in the real world. Finally, based on the true meteorological dataset from the official department and the RSSI dataset from the experiments, we employ expanded gated recurrent unit (GRU) neural networks to implement the FSO channel prediction. The results demonstrate that the proposed scheme can achieve the prediction of FSO channel fading with a high precision, where the absolute percentage error (APE) values lower than 6.9% account for up to ninety% of results.