Tactile Internet is an emerging concept that focuses on supporting high-fidelity, ultra-responsive, and widely available human-to-machine interactions. To reduce the transmission latency and alleviate Internet congestion, fog computing has been advocated as an important component of the Tactile Internet. In this paper, we focus on energy-efficient design of fog computing networks that support low-latency Tactile Internet applications. We investigate two performance metrics: Service response time of end-users and power usage efficiency of fog nodes. We quantify the fundamental tradeoff between these two metrics and then extend our analysis to fog computing networks involving cooperation between fog nodes. We introduce a novel cooperative fog computing concept, referred to as offload forwarding, in which a set of fog nodes with different computing and energy resources can cooperate with each other. The objective of this cooperation is to balance the workload processed by different fog nodes, further reduce the service response time, and improve the efficiency of power usage. We develop a distributed optimization framework based on dual decomposition to achieve the optimal tradeoff. Our framework does not require fog nodes to disclose their private information nor conduct back-and-forth negotiations with each other. Two distributed optimization algorithms are proposed. One is based on the subgradient method with dual decomposition and the other is based on distributed alternating direction method of multipliers via variable splitting (ADMM-VS). We prove that both algorithms can achieve the optimal workload allocation that minimizes the response time under the given power efficiency constraints of fog nodes. Finally, to evaluate the performance of our proposed concept, we simulate a possible implementation of a city-wide self-driving bus system supported by fog computing in the city of Dublin. The fog computing network topology is set based on a real cellular network infrastructure involving 200 base stations deployed by a major cellular operator in Ireland. Numerical results show that our proposed framework can balance the power usage efficiency among fog nodes and reduce the service latency for users by around 50% in urban scenarios.

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions.

Semantic communication has witnessed a great progress with the development of natural language processing (NLP) and deep learning (DL). Although existing semantic communication technologies can effectively reduce errors in semantic interpretation, most of these solutions adopt a fixed bit length structure, along with a rigid transmission scheme, which is inefficient and lacks scalability faced with different meanings and signal-to-noise ratio (SNR) conditions. In this paper, we explore the impact of adaptive bit lengths on semantic coding (SC) under various channel conditions. First, we propose progressive semantic hybrid automatic repeat request (HARQ) schemes that utilize incremental knowledge (IK) to simultaneously reduce the communication cost and semantic error. On top of this, we design a novel semantic encoding solution with multi-bit length selection. In this fashion, the transmitter employs a policy network to decide the appropriate coding rate, so as to secure the correct information delivery at the cost of minimal bits. Moreover, a specific denoiser is further introduced to reduce the semantic errors encountered in the transmission process according to the semantic characteristics of context. Extensive simulation results have been conducted to verify the effectiveness of the proposed solution.

Wireless radio frequency (RF) energy transfer is a promising technology to provide a reliability-guaranteed power supply for wireless sensor networks. In this paper, we consider a special wireless-powered sensor network consisting of a mobile energy station that can travel through a preplanned path to charge wireless-powered sensors located in the considered area. We develop a hardware platform using off-the-shelf RF energy transfer hardware equipment to evaluate the practical performance of wireless sensor networks powered by RF energy transfer. We establish an empirical model based on our developed platform and use the empirical model to jointly optimize path planning and mobile charge scheduling for wireless-powered sensor networks. We derive the optimal policy for the mobile energy station to optimize its decisions about the path that it will travel and the subset of sensors to charge during each time period. Numerical results show that our derived policy significantly improves the performance of wireless sensor networks in different practical scenarios.

Semantic communication has recently attracted significant interest from both industry and academia due to its potential to transform the existing data-focused communication architecture towards a more generally intelligent and goal-oriented semantic-aware networking system. Despite its promising potential, semantic communications and semantic-aware networking are still in their infancy. Most existing works focus on transporting and delivering the explicit semantic information, e.g., labels or features of objects, that can be directly identified from the source signal. The original definition of semantics as well as recent results in cognitive neuroscience suggest that it is the implicit semantic information, in particular the hidden relations connecting different concepts and feature items that play the fundamental role in recognizing, communicating, and delivering the real semantic meanings of messages. Motivated by this observation, we propose a novel reasoning-based implicit semantic-aware communication network architecture that allows destination users to directly learn a reasoning mechanism that can automatically generate complex implicit semantic information based on a limited clue information sent by the source users. Our proposed architecture can be implemented in a multi-tier cloud/edge computing networks in which multiple tiers of cloud data center (CDC) and edge servers can collaborate and support efficient semantic encoding, decoding, and implicit semantic interpretation for multiple end-users. We introduce a new multi-layer representation of semantic information taking into consideration both the hierarchical structure of implicit semantics as well as the personalized inference preference of individual users. We model the semantic reasoning process as a reinforcement learning process and then propose an imitation-based semantic reasoning mechanism learning (iRML) solution to learning a reasoning policy that imitates the inference behavior of the source user. A federated graph convolutional network (GCN)-based collaborative reasoning solution is proposed to allow multiple edge servers to jointly construct a shared semantic interpretation model based on decentralized semantic message samples. Extensive experiments have been conducted based on real-world datasets to evaluate the performance of our proposed architecture. Numerical results confirm that iRML offers up to 25.8 dB improvement on the semantic symbol error rate, compared to the semantic-irrelevant communication solutions.

Edge computing is a promising technology that offers data analysis and computing for Internet of Things (IoT) services at the network edge. It has the potential to significantly reduce the latency and improve the reliability of IoT services by allowing computation workloads and local data generated by IoT devices to be offloaded to edge nodes. This paper aims to develop algorithms for efficient provision of both job assignment and resource allocation for edge computing networks. The main objective is to minimize the long-term average of the response time delay subject to constraints on computation resources and power consumption. We apply a drift-plus-penalty based Lyapunov optimization approach to convert the original problem into an upper bound optimization problem. We then relax the latter to a convex optimization problem. Finally, a distributed algorithm based on branch-and-bound approach is provided and the gap between the distributed algorithm solution and the optimal solution of the original problem is theoretically analyzed. Numerical results based on extensive experiments have demonstrated that our distributed algorithm can achieve the required performance of edge computing that supports IoT systems, under static traffic conditions as well as under dynamic environments with time-varying traffic.