Cooperation Strategies for Enhanced Connectivity at Home

WHILE AT HOME , USERS MAY EXPERIENCE A POOR I NTERNET SERVICE while being connected to their 802.11 Access Points (APs). The AP is just one component of the Internet Gateway (GW) that generally includes a backhaul connection (ADSL, fiber,etc..) and a router providing a LAN. The root cause of performance degradation may be poor/congested wireless channel between the user and the GW or congested/bandwidth limited backhaul connection. The latter is a serious issue for DSL users that are located far from the central office because the greater the distance the lesser the achievable physical datarate. Furthermore, the GW is one of the few devices in the home that is left always on, resulting in energy waste and electromagnetic pollution increase. This thesis proposes two strategies to enhance Internet connectivity at home by (i) creating a wireless resource sharing scheme through the federation and the coordination of neighboring GWs in order to achieve energy efficiency while avoiding congestion, (ii) exploiting different king of connectivities, i.e., the wired plus the cellular (3G/4G) connections, through the aggregation of the available bandwidth across multiple access technologies. In order to achieve the aforementioned strategies we study and develop: • A viable interference estimation technique for 802.11 BSSes that can be implemented on commodity hardware at the MAC layer, without requiring active measurements, changes in the 802.11 standard, cooperation from the wireless stations (WSs). We extend previous theoretical results on the saturation throughput in order to quantify the impact in term of throughput loss of any kind of interferer. We im- plement and extensively evaluate our estimation technique with a real testbed and with different kind of interferer, achieving always good accuracy. • Two available bandwidth estimation algorithms for 802.11 BSSes that rely only on passive measurements and that account for different kind of interferers on the ISM band. This algorithms can be implemented on commodity hardware, as they require only software modifications. The first algorithm applies to intra-GW while the second one applies to inter-GW available bandwidth estimation. Indeed, we use the first algorithm to compute the metric for assessing the Wi-Fi load of a GW and the second one to compute the metric to decide whether accept incoming WSs from neighboring GWs or not. Note that in the latter case it is assumed that one or more WSs with known traffic profile are requested to relocate from one GW to another one. We evaluate both algorithms with simulation as well as with a real test-bed for different traffic patterns, achieving high precision. • A fully distributed and decentralized inter-access point protocol for federated GWs that allows to dynamically manage the associations of the wireless stations (WSs) in the federated network in order to achieve energy efficiency and offloading con- gested GWs, i.e, we keep a minimum number of GWs ON while avoiding to create congestion and real-time throughput loss. We evaluate this protocol in a federated scenario, using both simulation and a real test-bed, achieving up to 65% of energy saving in the simulated setting. We compare the energy saving achieved by our protocol against a centralized optimal scheme, obtaining close to optimal results. • An application level solution that accelerates slow ADSL connections with the parallel use of cellular (3G/4G) connections. We study the feasibility and the potential performance of this scheme at scale using both extensive throughput measurement of the cellular network and trace driven analysis. We validate our solution by implementing a real test bed and evaluating it “in the wild, at several residential locations of a major European city. We test two applications: Video-on-Demand (VoD) and picture upload, obtaining remarkable throughput increase for both applications at all locations. Our implementation features a multipath scheduler which we compare to other scheduling policies as well as to transport level solution like MTCP, obtaining always better results

Advanced Wireless LAN

The past two decades have witnessed starling advances in wireless LAN technologies that were stimulated by its increasing popularity in the home due to ease of installation, and in commercial complexes offering wireless access to their customers. This book presents some of the latest development status of wireless LAN, covering the topics on physical layer, MAC layer, QoS and systems. It provides an opportunity for both practitioners and researchers to explore the problems that arise in the rapidly developed technologies in wireless LAN

An Adaptive Packet Aggregation Algorithm (AAM) for Wireless Networks

Packet aggregation algorithms are used to improve the throughput performance by combining a number of packets into a single transmission unit in order to reduce the overhead associated with each transmission within a packet-based communications network. However, the throughput improvement is also accompanied by a delay increase. The biggest drawback of a significant number of the proposed packet aggregation algorithms is that they tend to only optimize a single metric, i.e. either to maximize throughput or to minimize delay. They do not permit an optimal trade-off between maximizing throughput and minimizing delay. Therefore, these algorithms cannot achieve the optimal network performance for mixed traffic loads containing a number of different types of applications which may have very different network performance requirements. In this thesis an adaptive packet aggregation algorithm called the Adaptive Aggregation Mechanism (AAM) is proposed which achieves an aggregation trade-off in terms of realizing the largest average throughput with the smallest average delay compared to a number of other popular aggregation algorithms under saturation conditions in wireless networks. The AAM algorithm is the first packet aggregation algorithm that employs an adaptive selection window mechanism where the selection window size is adaptively adjusted in order to respond to the varying nature of both the packet size and packet rate. This algorithm is essentially a feedback control system incorporating a hybrid selection strategy for selecting the packets. Simulation results demonstrate that the proposed algorithm can (a) achieve a large number of sub-packets per aggregate packet for a given delay and (b) significantly improve the performance in terms of the aggregation trade-off for different traffic loads. Furthermore, the AAM algorithm is a robust algorithm as it can significantly improve the performance in terms of the average throughput in error-prone wireless networks

Learning Wi-Fi Performance

Accurate prediction of wireless network performance is important when performing link adaptation or resource allocation. However, the complexity of interference interactions at MAC and PHY layers, as well as the vast variety of possible wireless configurations make it notoriously hard to design explicit performance models. In this paper, we advocate an approach of “learning by observation” that can remove the need for designing explicit and complex performance models. We use machine-learning techniques to learn implicit performance models, from a limited number of real-world measurements. These models do not require to know the internal mechanics of interfering Wi-Fi links. Yet, our results show that they improve accuracy by at least 49% compared to measurement-seeded models based on SINR. To demonstrate that learned models can be useful in practice, we build a new algorithm that uses such a model as an oracle to jointly allocate spectrum and transmit power. Our algorithm is utility-optimal, distributed, and it produces efficient allocations that significantly improve performance and fairness