Architecture for Cooperative Prefetching in P2P Video-on- Demand System

Most P2P VoD schemes focused on service architectures and overlays optimization without considering segments rarity and the performance of prefetching strategies. As a result, they cannot better support VCRoriented service in heterogeneous environment having clients using free VCR controls. Despite the remarkable popularity in VoD systems, there exist no prior work that studies the performance gap between different prefetching strategies. In this paper, we analyze and understand the performance of different prefetching strategies. Our analytical characterization brings us not only a better understanding of several fundamental tradeoffs in prefetching strategies, but also important insights on the design of P2P VoD system. On the basis of this analysis, we finally proposed a cooperative prefetching strategy called "cooching". In this strategy, the requested segments in VCR interactivities are prefetched into session beforehand using the information collected through gossips. We evaluate our strategy through extensive simulations. The results indicate that the proposed strategy outperforms the existing prefetching mechanisms.Comment: 13 Pages, IJCN

Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning

The vehicular edge computing (VEC) can cache contents in different RSUs at the network edge to support the real-time vehicular applications. In VEC, owing to the high-mobility characteristics of vehicles, it is necessary to cache the user data in advance and learn the most popular and interesting contents for vehicular users. Since user data usually contains privacy information, users are reluctant to share their data with others. To solve this problem, traditional federated learning (FL) needs to update the global model synchronously through aggregating all users' local models to protect users' privacy. However, vehicles may frequently drive out of the coverage area of the VEC before they achieve their local model trainings and thus the local models cannot be uploaded as expected, which would reduce the accuracy of the global model. In addition, the caching capacity of the local RSU is limited and the popular contents are diverse, thus the size of the predicted popular contents usually exceeds the cache capacity of the local RSU. Hence, the VEC should cache the predicted popular contents in different RSUs while considering the content transmission delay. In this paper, we consider the mobility of vehicles and propose a cooperative Caching scheme in the VEC based on Asynchronous Federated and deep Reinforcement learning (CAFR). We first consider the mobility of vehicles and propose an asynchronous FL algorithm to obtain an accurate global model, and then propose an algorithm to predict the popular contents based on the global model. In addition, we consider the mobility of vehicles and propose a deep reinforcement learning algorithm to obtain the optimal cooperative caching location for the predicted popular contents in order to optimize the content transmission delay. Extensive experimental results have demonstrated that the CAFR scheme outperforms other baseline caching schemes.Comment: This paper has been submitted to IEEE Journal of Selected Topics in Signal Processin

Improving forwarding mechanisms for mobile personal area networks

This thesis presents novel methods for improving forwarding mechanisms for personal area networks. Personal area networks are formed by interconnecting personal devices such as personal digital assistants, portable multimedia devices, digital cameras and laptop computers, in an ad hoc fashion. These devices are typically characterised by low complexity hardware, low memory and are usually batterypowered. Protocols and mechanisms developed for general ad hoc networking cannot be directly applied to personal area networks as they are not optimised to suit their specific constraints. The work presented herein proposes solutions for improving error control and routing over personal area networks, which are very important ingredients to the good functioning of the network. The proposed Packet Error Correction (PEC) technique resends only a subset of the transmitted packets, thereby reducing the overhead, while ensuring improved error rates. PEC adapts the number of re-transmissible packets to the conditions of the channel so that unnecessary retransmissions are avoided. It is shown by means of computer simulation that PEC behaves better, in terms of error reduction and overhead, than traditional error control mechanisms, which means that it is adequate for low-power personal devices. The proposed C2HR routing protocol, on the other hand, is designed such that the network lifetime is maximised. This is achieved by forwarding packets through the most energy efficient paths. C2HR is a hybrid routing protocol in the sense that it employs table-driven (proactive) as well as on-demand (reactive) components. Proactive routes are the primary routes, i.e., packets are forwarded through those paths when the network is stable; however, in case of failures, the protocol searches for alternative routes on-demand, through which data is routed temporarily. The advantage of C2HR is that data can still be forwarded even when routing is re-converging, thereby increasing the throughput. Simulation results show that the proposed routing method is more energy efficient than traditional least hops routing, and results in higher data throughput. C2HR relies on a network leader for collecting and distributing topology information, which in turn requires an estimate of the underlying topology. Thus, this thesis also proposes a new cooperative leader election algorithm and techniques for estimating network characteristics in mobile environments. The proposed solutions are simulated under various conditions and demonstrate appreciable behaviour