ApproxNet: Content and Contention-Aware Video Object Classification System for Embedded Clients

Videos take a lot of time to transport over the network, hence running analytics on the live video on embedded or mobile devices has become an important system driver. Considering such devices, e.g., surveillance cameras or AR/VR gadgets, are resource constrained, although there has been significant work in creating lightweight deep neural networks (DNNs) for such clients, none of these can adapt to changing runtime conditions, e.g., changes in resource availability on the device, the content characteristics, or requirements from the user. In this article, we introduce ApproxNet, a video object classification system for embedded or mobile clients. It enables novel dynamic approximation techniques to achieve desired inference latency and accuracy trade-off under changing runtime conditions. It achieves this by enabling two approximation knobs within a single DNN model rather than creating and maintaining an ensemble of models, e.g., MCDNN [MobiSys-16]. We show that ApproxNet can adapt seamlessly at runtime to these changes, provides low and stable latency for the image and video frame classification problems, and shows the improvement in accuracy and latency over ResNet [CVPR-16], MCDNN [MobiSys-16], MobileNets [Google-17], NestDNN [MobiCom-18], and MSDNet [ICLR-18].

Modeling in the OMNET ++ environment of connection processes in Wi-Fi networks

Предложен подход к разработке в среде OMNeT++ INET простейшей имитационной модели инфраструктурного режима функционирования Wi-Fi сети, который позволяет проводить подробный анализ функционирования таких сетей, а также строить и анализировать временные диаграммы взаимодействия всех элементов сети. Разработанную модель можно использовать как базовую для формирования более сложных моделей с произвольным числом мобильных клиентов, позволяя определять необходимое количество точек доступа и мест их размещения для обеспечения полноценного покрытия зоны мониторинга лесной территории. An approach to the development in the OMNeT ++ INET environment of the simplest simulation model of the infrastructure mode of Wi-Fi network operation is proposed, which allows a detailed analysis of the functioning of such networks, as well as to build and analyze the time diagram of the interaction of all network elements. The developed model can be used as a base for the formation of more complex models with an arbitrary number of mobile clients, allowing you to determine the required number of access points and their locations to ensure full coverage of the monitoring area of the forest area.

DistFL

Federated learning (FL) has emerged as an effective solution to decentralized and privacy-preserving machine learning for mobile clients. While traditional FL has demonstrated its superiority, it ignores the non-iid (independently identically distributed) situation, which widely exists in mobile scenarios. Failing to handle non-iid situations could cause problems such as performance decreasing and possible attacks. Previous studies focus on the "symptoms" directly, as they try to improve the accuracy or detect possible attacks by adding extra steps to conventional FL models. However, previous techniques overlook the root causes for the "symptoms": blindly aggregating models with the non-iid distributions. In this paper, we try to fundamentally address the issue by decomposing the overall non-iid situation into several iid clusters and conducting aggregation in each cluster. Specifically, we propose DistFL, a novel framework to achieve automated and accurate Distribution-aware Federated Learning in a cost-efficient way. DistFL achieves clustering via extracting and comparing the distribution knowledge from the uploaded models. With this framework, we are able to generate multiple personalized models with distinctive distributions and assign them to the corresponding clients. Extensive experiments on mobile scenarios with popular model architectures have demonstrated the effectiveness of DistFL.

TAMEC: Trusted Augmented Mobile Execution on Cloud

Cloud computing has emerged as an attractive platform for individuals and businesses to augment their basic processing capabilities. Mobile devices with access to Internet are also turning towards clouds for resource-intensive tasks by working out a trade-off between resources required for performing computation on-device against those required for off-loading task to the cloud. However, as with desktop clients, mobile clients face significant concerns related to confidentiality and integrity of data and applications moved to and from the cloud. Cloud-related security solutions proposed for desktop clients could not be readily ported to mobile clients owing to the obvious limitation in their processing capabilities and restrained battery life. We address this problem by proposing architecture for secure exchange and trusted execution between mobile devices and cloud hosts. We establish a symmetric-key-based secure communication channel between mobile and cloud, backed by a trusted coordinator. We also employee a Trusted Platform Module- (TPM-) based attestation of the cloud nodes on which the data and applications of mobile device will be hosted. This gives a comprehensive solution for end-to-end secure and trusted interaction of the mobile device with cloud hosts.

Server-aided Public Key Signatures for Diverse Network Devices

One of the main challenges of securing effective computation in diverse network devices tends to be a limitation of their computational power. Server assisted signature scheme was recently presented as nonrepudiation service for mobile and constrained devices. They all tend to have a feature in common: limited computational capabilities and equally limited power (as most operate on batteries). The scheme suffered with high storage requirements and memory requirements for the mobile clients. This makes them ill-suited for public key signatures. This paper examines practical and conceptual implications of using Server-Aided Signatures (SAS) for these devices. SAS is a signature method that relies on partially-trusted servers for generating (normally expensive) public key signatures for regular users. Although the primary goal is to aid small, resource- limited devices in signature generation, SAS also fast certificate revocation, signature causality and with reliable timestamping. Keywords: Public key infrastructure; Digital signature; Certificate authority.