We consider media streaming using application-level multicast (ALM) where packet loss has to be recovered via retransmission in a timely manner. Since packets may be lost due to congestion, node failures, and join and leave dynamics, traditional "vertical" recovery approach where upstream nodes retransmit the lost packets is no longer effective. We therefore propose lateral error recovery (LER). In LER, hosts are divided into a number of planes, each of which forms an independent ALM tree. Since error correlation across planes is low, a node effectively recovers its error by "laterally" requesting retransmission from nearby nodes in other planes.We present analysis on the complexity and recovery delay on LER. Using Internet-like topologies, we show via simulations that LER is an effective error recovery mechanism. It achieves low overhead in terms of delivery delay (i.e., relative delay penalty) and physical link stress. As compared with traditional recovery schemes, LER attains much lower residual loss rate (i.e., loss rate after retransmission) under a certain deadline constraint. The performance can be substantially improved in the presence of some reliable proxies. Index Terms-Application-level multicast (ALM), error recovery, retransmission deadline, streaming applications.
Abstract-In wireless sensor networks, estimating nodal positions is important for routing efficiency and location-based services. Traditional techniques based on precise measurements are often expensive and power-inefficient, while approaches based on landmarks often require bandwidth-inefficient flooding and hence are not scalable for large networks. In this paper, we propose and investigate a cost-effective and distributed algorithm to accurately estimate nodal positions for wireless sensor networks. In our algorithm, a node only needs to identify and exchange information with a certain number of neighbors (around 30) in its proximity in order to estimate its relative nodal position accurately. For location-identification, only a small number of nodes (around 10) are needed to have additional GPS capabilities to accurately estimate the absolute position of every node in the network. Our algorithm is shown to have fast convergence with low estimation error, even for large networks.Index Terms-position estimation, wireless sensors network, position-based routing, location identification I. INTRODUCTION In recent years, there has been an increasing interest in wireless sensor networks in academic, industrial, and commercial sectors. Unlike ad-hoc mobile networks, such networks are usually assumed to have a high density of nodes with lower computational power. It is often useful to know the relative or absolute nodal positions in order to improve the quality of position-related services provided. Two typical examples are: Position-based routing in which relative nodal positions are used for correct and efficient route estimation; Locationbased services in which absolute nodal positions are used to provide location-specific servicesIn this paper, we propose and investigate a cost-effective and distributed algorithm to estimate nodal positions in wireless sensor networks. In our algorithm, each node has a certain maximum transmission power. By controlling its power in a quantized manner, a node only needs to discover its onehop neighbors at discrete distances away 1 . Based on this information, each node computes its own position in the network in an autonomous manner, and only needs to exchange the information with a number of its neighbors. Our algorithm starts with a certain number (1 to 10) of "bootstrap" nodes and the position information propagates in the network like ripples until all nodes are able to estimate their own positions.
Abstract-Although peer-to-peer (P2P) streaming can efficiently deliver live video content to large user populations, existing applications often suffer from limited video quality, periodic hiccups, and high delays. To overcome some of the limitations of today's unstructured (mesh-based) designs, we have developed and deployed FastMesh-SIM, a novel P2P streaming system that leverages proxies, push-mechanism and IP multicast to achieve lower playback delay and better stream continuity. Having control over a real P2P streaming system also gives us a rare opportunity to conduct controlled experiments where we vary major design parameters (e.g., push vs. pull delivery, IP multicast support, streaming rate, and video segment size) under a range of operating conditions (e.g., dynamics of peer churn, and different network configurations), while collecting detailed, fine-granular measurements (e.g., the various components of endto-end delay). Analysis of the measurement data, consisting of seven trials of streaming several live TV channels for more than 100 hours to 140 peers, sheds light on how design decisions and the operating environment affect important performance metrics. Our experiments show that a push-based, proxy-P2P system can achieve low delay and good video quality, though network bottlenecks on long-haul connections can sometimes cause disruptions in a global deployment. Theory-practice gaps observed from the data are also discussed. Large-scale, global experiments are now being carried out.I. INTRODUCTION Unstructured (or mesh-based) peer-to-peer (P2P) systems, where peers asynchronously swap segments of the video, can substantially reduce server bandwidth and system cost [1], though often at the cost of high latency and periodic hiccups [2]- [4]. An attractive alternative for efficient, low-latency video streaming is to explicitly organize the peers into an application-level multicast tree, and use proxies and recovery protocols to enhance streaming quality. Still, many important questions remain about how to most effectively design and operate a tree-based P2P streaming system. In this paper, we perform an in-depth measurement study of a proxy-P2P network, FastMesh-SIM [5], [6], which broadcasts live TV channels and is deployed in Hong Kong and Princeton.
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