No Arabic abstract
With the increasing interest in connected vehicles, it is useful to evaluate the capability of delivering large content over a WiFi infrastructure to vehicles. The throughput achieved over WiFi channels can be highly variable and also rapidly degrades as the distance from the access point increases. While this behavior is well understood at the data link layer, the interactions across the various protocol layers (data link and up through the transport layer) and the effect of mobility may reduce the amount of content transferred to the vehicle, as it travels along the roadway. This paper examines the throughput achieved at the TCP layer over a carefully designed outdoor WiFi environment and the interactions across the layers that impact the performance achieved, as a function of the receiver mobility. The experimental studies conducted reveal that impairments over the WiFi link (frame loss, ARQ and increased delay) and the residual loss seen by TCP causes a cascade of duplicate ACKs to be generated. This triggers large congestion window reductions at the sender, leading to a drastic degradation of throughput to the vehicular client. To ensure outdoor WiFi infrastructures have the potential to sustain reasonable downlink throughput for drive-by vehicles, we speculate that there is a need to adapt how WiFi and TCP (as well as mobility protocols) function for such vehicular applications.
We address the problem of content replication in large distributed content delivery networks, composed of a data center assisted by many small servers with limited capabilities and located at the edge of the network. The objective is to optimize the placement of contents on the servers to offload as much as possible the data center. We model the system constituted by the small servers as a loss network, each loss corresponding to a request to the data center. Based on large system / storage behavior, we obtain an asymptotic formula for the optimal replication of contents and propose adaptive schemes related to those encountered in cache networks but reacting here to loss events, and faster algorithms generating virtual events at higher rate while keeping the same target replication. We show through simulations that our adaptive schemes outperform significantly standard replication strategies both in terms of loss rates and adaptation speed.
With the proliferation of mobile computing devices, the demand for continuous network connectivity regardless of physical location has spurred interest in the use of mobile ad hoc networks. Since Transmission Control Protocol (TCP) is the standard network protocol for communication in the internet, any wireless network with Internet service need to be compatible with TCP. TCP is tuned to perform well in traditional wired networks, where packet losses occur mostly because of congestion. However, TCP connections in Ad-hoc mobile networks are plagued by problems such as high bit error rates, frequent route changes, multipath routing and temporary network partitions. The throughput of TCP over such connection is not satisfactory, because TCP misinterprets the packet loss or delay as congestion and invokes congestion control and avoidance algorithm. In this research, the performance of TCP in Adhoc mobile network with high Bit Error rate (BER) and mobility is studied and investigated. Simulation model is implemented and experiments are performed using the Network Simulatior 2 (NS2).
Blockchain is a merging technology for decentralized management and data security, which was first introduced as the core technology of cryptocurrency, e.g., Bitcoin. Since the first success in financial sector, blockchain has shown great potentials in various domains, e.g., internet of things and mobile networks. In this paper, we propose a novel blockchain-based architecture for content delivery networks (B-CDN), which exploits the advances of the blockchain technology to provide a decentralized and secure platform to connect content providers (CPs) with users. On one hand, the proposed B-CDN will leverage the registration and subscription of the users to different CPs, while guaranteeing the user privacy thanks to virtual identity provided by the blockchain network. On the other hand, the B-CDN creates a public immutable database of the requested contents (from all CPs), based on which each CP can better evaluate the user preference on its contents. The benefits of B-CDN are demonstrated via an edge-caching application, in which a feature-based caching algorithm is proposed for all CPs. The proposed caching algorithm is verified with the realistic Movielens dataset. A win-win relation between the CPs and users is observed, where the B-CDN improves user quality of experience and reduces cost of delivering content for the CPs.
We consider a set of flows passing through a set of servers. The injection rate into each flow is governed by a flow control that increases the injection rate when all the servers on the flows path are empty and decreases the injection rate when some server is congested. We show that if each servers congestion is governed by the arriving traffic at the server then the system can *oscillate*. This is in contrast to previous work on flow control where congestion was modeled as a function of the flow injection rates and the system was shown to converge to a steady state that maximizes an overall network utility.
We present the DeepWiFi protocol, which hardens the baseline WiFi (IEEE 802.11ac) with deep learning and sustains high throughput by mitigating out-of-network interference. DeepWiFi is interoperable with baseline WiFi and builds upon the existing WiFis PHY transceiver chain without changing the MAC frame format. Users run DeepWiFi for i) RF front end processing; ii) spectrum sensing and signal classification; iii) signal authentication; iv) channel selection and access; v) power control; vi) modulation and coding scheme (MCS) adaptation; and vii) routing. DeepWiFi mitigates the effects of probabilistic, sensing-based, and adaptive jammers. RF front end processing applies a deep learning-based autoencoder to extract spectrum-representative features. Then a deep neural network is trained to classify waveforms reliably as idle, WiFi, or jammer. Utilizing channel labels, users effectively access idle or jammed channels, while avoiding interference with legitimate WiFi transmissions (authenticated by machine learning-based RF fingerprinting) resulting in higher throughput. Users optimize their transmit power for low probability of intercept/detection and their MCS to maximize link rates used by backpressure algorithm for routing. Supported by embedded platform implementation, DeepWiFi provides major throughput gains compared to baseline WiFi and another jamming-resistant protocol, especially when channels are likely to be jammed and the signal-to-interference-plus-noise-ratio is low.