We consider a network of smart sensors for edge computing application that
sample a signal of interest and send updates to a base station for remote
global monitoring. Sensors are equipped with sensing and compute, and can
either send raw data or process them on-board before transmission. Limited
hardware resources at the edge generate a fundamental latency-accuracy
trade-off: raw measurements are inaccurate but timely, whereas accurate
processed updates are available after computational delay. Also, if sensor
on-board processing entails data compression, latency caused by wireless
communication might be higher for raw measurements. Hence, one needs to decide
when sensors should transmit raw measurements or rely on local processing to
maximize overall network performance. To tackle this sensing design problem, we
model an estimation-theoretic optimization framework that embeds computation
and communication delays, and propose a Reinforcement Learning-based approach
to dynamically allocate computational resources at each sensor. Effectiveness
of our proposed approach is validated through numerical simulations with case
studies motivated by the Internet of Drones and self-driving vehicles.

通过支持感知和计算，智能传感器在边缘计算应用中采样并发送感兴趣的信号更新，从而形成一个网络，可远程全局监控传感器的性能。为了解决原始测量的不准确和及时性问题，提出了一个强化学习算法来动态分配计算资源。数值仿真结果表示，提议的方法在无人机网络和自动驾驶汽车等应用场景中比传统方法更有效。

如何计算？—— 资源受限的边缘计算中的自适应智能感知

To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing

Unmanned aerial vehicle (UAV) has a broad application prospect in the future,
especially in the Industry 4.0. The development of Internet of Drones (IoD)
makes UAV operation more autonomous. Network virtualization technology is a
promising technology to support IoD, so the allocation of virtual resources
becomes a crucial issue in IoD. How to rationally allocate potential material
resources has become an urgent problem to be solved. The main work of this
paper is presented as follows: (1) In order to improve the optimization
performance and reduce the computation time, we propose a multi-domain virtual
network embedding algorithm (MP-VNE) adopting the centralized hierarchical
multi-domain architecture. The proposed algorithm can avoid the local optimum
through incorporating the genetic variation factor into the traditional
particle swarm optimization process. (2) In order to simplify the
multi-objective optimization problem, we transform the multi-objective problem
into a single-objective problem through weighted summation method. The results
prove that the proposed algorithm can rapidly converge to the optimal solution.
(3) In order to reduce the mapping cost, we propose an algorithm for selecting
candidate nodes based on the estimated mapping cost. Each physical domain
calculates the estimated mapping cost of all nodes according to the formula of
the estimated mapping cost, and chooses the node with the lowest estimated
mapping cost as the candidate node. The simulation results show that the
proposed MP-VNE algorithm has better performance than MC-VNM, LID-VNE and other
algorithms in terms of delay, cost and comprehensive indicators.

该论文提出一种基于多域架构和遗传变异因素优化的多域虚拟网络嵌入算法，通过加权累加法将多目标问题转化为单目标问题，选择相应物理域上的成本最低点作为备选节点，此算法在延迟、成本、综合性指标等方面都优于 MC-VNM、LID-VNE 和其他算法。

基于多目标优化的 IoD 架构下的工业 4.0 多域 VNE 算法

A multi-domain VNE algorithm based on multi-objective optimization for IoD architecture in Industry 4.0

Air access networks have been recognized as a significant driver of various
Internet of Things (IoT) services and applications. In particular, the aerial
computing network infrastructure centered on the Internet of Drones has set off
a new revolution in automatic image recognition. This emerging technology
relies on sharing ground truth labeled data between Unmanned Aerial Vehicle
(UAV) swarms to train a high-quality automatic image recognition model.
However, such an approach will bring data privacy and data availability
challenges. To address these issues, we first present a Semi-supervised
Federated Learning (SSFL) framework for privacy-preserving UAV image
recognition. Specifically, we propose model parameters mixing strategy to
improve the naive combination of FL and semi-supervised learning methods under
two realistic scenarios (labels-at-client and labels-at-server), which is
referred to as Federated Mixing (FedMix). Furthermore, there are significant
differences in the number, features, and distribution of local data collected
by UAVs using different camera modules in different environments, i.e.,
statistical heterogeneity. To alleviate the statistical heterogeneity problem,
we propose an aggregation rule based on the frequency of the client's
participation in training, namely the FedFreq aggregation rule, which can
adjust the weight of the corresponding local model according to its frequency.
Numerical results demonstrate that the performance of our proposed method is
significantly better than those of the current baseline and is robust to
different non-IID levels of client data.

利用半监督联邦学习框架和模型参数混合策略来保护数据隐私实现无人机图像识别；根据不同相机模块、地域环境等数据的统计异质性问题，提出基于客户端频率的聚合规则来调整对应本地模型的权重。