# Distributed Optimization for Energy-efficient Fog Computing in the Tactile Internet

arXiv: https://arxiv.org/abs/2001.10199 · HTML: https://mlsanity.com/paper/2001.10199/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 14 of 14)

- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach](https://mlsanity.com/paper/2503.15764/) (2025)
- [Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative Reasoning](https://mlsanity.com/paper/2210.16118/) (2022)
- [Time-sensitive Learning for Heterogeneous Federated Edge Intelligence](https://mlsanity.com/paper/2301.10977/) (2023)
- [SANet: A Semantic-aware Agentic AI Networking Framework for Cross-layer Optimization in 6G](https://mlsanity.com/paper/2512.22579/) (2025)
- [Learning Optimal Fronthauling and Decentralized Edge Computation in Fog Radio Access Networks](https://mlsanity.com/paper/2103.11284/) (2021)
- [Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach](https://mlsanity.com/paper/2302.00207/) (2023)
- [Towards Net-Zero Carbon Emissions in Network AI for 6G and Beyond](https://mlsanity.com/paper/2401.01007/) (2023)
- [Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks](https://mlsanity.com/paper/2103.07636/) (2021)
- [Federated Traffic Synthesizing and Classification Using Generative Adversarial Networks](https://mlsanity.com/paper/2104.10400/) (2021)
- [Distributed Task Management in Fog Computing: A Socially Concave Bandit Game](https://mlsanity.com/paper/2203.14572/) (2022)
- [Learning-based decentralized offloading decision making in an adversarial environment](https://mlsanity.com/paper/2104.12827/) (2021)
- [Adversarial Learning for Implicit Semantic-Aware Communications](https://mlsanity.com/paper/2301.11589/) (2023)
- [Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing](https://mlsanity.com/paper/2312.05043/) (2023)

## Abstract (arXiv)

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 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.