Wi-Fi Direct is a promising technology for the support of device-to-device
communications (D2D) on commercial mobile devices. However, the standard
as-it-is is not sufficient to support the real deployment of networking
solutions entirely based on D2D such as opportunistic networks. In fact, WiFi
Direct presents some characteristics that could limit the autonomous creation
of D2D connections among users' personal devices. Specifically, the standard
explicitly requires the user's authorization to establish a connection between
two or more devices, and it provides a limited support for inter-group
communication. In some cases, this might lead to the creation of isolated
groups of nodes which cannot communicate among each other. In this paper, we
propose a novel middleware-layer protocol for the efficient configuration and
management of WiFi Direct groups (WiFi Direct Group Manager, WFD-GM) to enable
autonomous connections and inter-group communication. This enables
opportunistic networks in real conditions (e.g., variable mobility and network
size). WFD-GM defines a context function that takes into account heterogeneous
parameters for the creation of the best group configuration in a specific time
window, including an index of nodes' stability and power levels. We evaluate
the protocol performances by simulating three reference scenarios including
different mobility models, geographical areas and number of nodes. Simulations
are also supported by experimental results related to the evaluation in a real
testbed of the involved context parameters. We compare WFD-GM with the
state-of-the-art solutions and we show that it performs significantly better
than a Baseline approach in scenarios with medium/low mobility, and it is
comparable with it in case of high mobility, without introducing additional
overhead.

提出了一种新的中间件层协议（WiFi Direct Group Manager，WFD-GM）来实现 WiFi Direct 组的高效配置和管理，从而使得在实际条件下（如可变移动性和网络规模），可以实现自主连接和组间通信，以启用机遇网络。

基于上下文的真实机会网络中 WiFi Direct 群组的配置和管理

Context-Aware Configuration and Management of WiFi Direct Groups for  Real Opportunistic Networks

Recommender Systems (RS) currently represent a fundamental tool in online
services, especially with the advent of Online Social Networks (OSN). In this
case, users generate huge amounts of contents and they can be quickly
overloaded by useless information. At the same time, social media represent an
important source of information to characterize contents and users' interests.
RS can exploit this information to further personalize suggestions and improve
the recommendation process. In this paper we present a survey of Recommender
Systems designed and implemented for Online and Mobile Social Networks,
highlighting how the use of social context information improves the
recommendation task, and how standard algorithms must be enhanced and optimized
to run in a fully distributed environment, as opportunistic networks. We
describe advantages and drawbacks of these systems in terms of algorithms,
target domains, evaluation metrics and performance evaluations. Eventually, we
present some open research challenges in this area.

该研究论文是一篇关于在在线和移动社交网络中设计和实现推荐系统的综述，重点介绍了如何利用社交上下文信息来改善推荐任务，以及标准算法如何在完全分布式环境中进行增强和优化，并讨论了这些系统的优缺点和性能评估。

在线和移动社交网络的推荐系统：一项调查

Recommender Systems for Online and Mobile Social Networks: A survey

Privacy-sensitive data is stored in autonomous vehicles, smart devices, or
sensor nodes that can move around with making opportunistic contact with each
other. Federation among such nodes was mainly discussed in the context of
federated learning with a centralized mechanism in many works. However, because
of multi-vendor issues, those nodes do not want to rely on a specific server
operated by a third party for this purpose. In this paper, we propose a
wireless ad hoc federated learning (WAFL) -- a fully distributed cooperative
machine learning organized by the nodes physically nearby. WAFL can develop
generalized models from Non-IID datasets stored in distributed nodes locally by
exchanging and aggregating them with each other over opportunistic node-to-node
contacts. In our benchmark-based evaluation with various opportunistic
networks, WAFL has achieved higher accuracy of 94.8-96.3% than the
self-training case of 84.7%. All our evaluation results show that WAFL can
train and converge the model parameters from highly-partitioned Non-IID
datasets over opportunistic networks without any centralized mechanisms.

本文提出了一种分布式的合作式机器学习方法，即 WAFL，它能够通过无线自组网络在非 IID 分散节点之间交换和聚合数据，从而构建广义模型，并在多种机会式网络中实现高达 94.8-96.3% 的准确率。