2137839
Implementation of a Scalable Context-Aware Computing System
Context-aware computing involves the automatic tailoring of information and services based on the current location of the user. In this paper, we describe our experience in implementing Rover, a system that enables location-based services, as well as the traditional time-aware, user-aware and device-aware services. To achieve system scalability to very large client sets, Rover servers are implemented in an “action-based” concurrent software architecture that enables fine-grained application-specific scheduling of tasks. We have demonstrated its feasibility through implementations for both outdoor and indoor environments on multiple platforms.
14498501
A clustering scheme for hierarchical control in multi-hop wireless networks
In this paper we present a clustering scheme to create a hierarchical control structure for multi-hop wireless networks. A cluster is defined as a subset of vertices, whose induced graph is connected. In addition, a cluster is required to obey certain constraints that are useful for management and scalability of the hierarchy. All these constraints cannot be met simultaneously for general graphs, but we show how such a clustering can be obtained for wireless network topologies. Finally, we present an efficient distributed implementation of our clustering algorithm for a set of wireless nodes to create the set of desired clusters.
11368060
Packet classifiers in ternary CAMs can be smaller
Serving as the core component in many packet forwarding, differentiating and filtering schemes, packet classification continues to grow its importance in today's IP networks. Currently, most vendors use Ternary CAMs (TCAMs) for packet classification. TCAMs usually use brute-force parallel hardware to simultaneously check for all rules. One of the fundamental problems of TCAMs is that TCAMs suffer from range specifications because rules with range specifications need to be translated into multiple TCAM entries. Hence, the cost of packet classification will increase substantially as the number of TCAM entries grows. As a result, network operators hesitate to configure packet classifiers using range specifications. In this paper, we optimize packet classifier configurations by identifying semantically equivalent rule sets that lead to reduced number of TCAM entries when represented in hardware. In particular, we develop a number of effective techniques, which include: trimming rules, expanding rules, merging rules, and adding rules. Compared with previously proposed techniques which typically require modifications to the packet processor hardware, our scheme does not require any hardware modification, which is highly preferred by ISPs. Moreover, our scheme is complementary to previous techniques in that those techniques can be applied on the rule sets optimized by our scheme. We evaluate the effectiveness and potential of the proposed techniques using extensive experiments based on both real packet classifiers managed by a large tier-1 ISP and synthetic data generated randomly. We observe significant reduction on the number of TCAM entries that are needed to represent the optimized packet classifier configurations.
8584874
Throughput Optimization and Fair Bandwidth Allocation in Multi-Hop Wireless LANs
There is an inherent well-known conflict between fairness and throughput that arises in many networking scenarios. A number of researchers have studied this problem in the context of (single-hop) wireless local area networks (WLANs), where clients directly exchange traffic with access points (APs). More recently, researchers have proposed multi-hop extensions to WLANs where client traffic is forwarded via a series of client-client links. In this paper, we show that the objective of improving throughput without sacrificing fairness can be much better met in multi-hop WLANs. We decouple this objective into two separate but related problems. First, we need an algorithm to organize clients into a multi-hop structure such that fair bandwidth allocation within this structure leads to improved throughput. Second, we need algorithms for performing fair bandwidth allocation within the determined multi-hop structure. In this paper, we first design optimal fair bandwidth allocation algorithms for both max-min throughput fairness and max-min time fairness in multi-hop WLANs. Subsequently, we design an efficient algorithm to find desirable multi-hop structures. With slight modifications, our results in this paper can be generalized to other multi-hop wireless networks as well. Our proposed solutions seamlessly integrate with legacy devices and hence are incrementally deployable. Simulation results demonstrate that our solutions can effectively improve throughput (by up to 114% or more) as well as network coverage while preserving fairness.
14357613
Construction of an efficient overlay multicast infrastructure for real-time applications
This paper presents an overlay architecture where service providers deploy a set of service nodes (called MSNs) in the network to efficiently implement media-streaming applications. These MSNs are organized into an overlay and act as application-layer multicast forwarding entities for a set of clients. We present a decentralized scheme that organizes the MSNs into an appropriate overlay structure that is particularly beneficial for real-time applications. We formulate our optimization criterion as a "degree-constrained minimum average-latency problem" which is known to be NP-hard. A key feature of this formulation is that it gives a dynamic priority to different MSNs based on the size of its service set. Our proposed approach iteratively modifies the overlay tree using localized transformations to adapt with changing distribution of MSNs, clients, as well as network conditions. We show that a centralized greedy approach to this problem does not perform quite as well, while our distributed iterative scheme efficiently converges to near-optimal solutions.
41447667
Load Balancing in Large-Scale RFID Systems
A radio frequency identifier (RFID) system consists of inexpensive, uniquely-identifiable tags that are mounted on physical objects, and readers that track these tags (and hence these physical objects) through RF communication. In this paper we, therefore, address this load balancing problem for readers - given a set of tags that are within range of each reader, which of these tags should each reader be responsible for such that the cost for monitoring tags across the different readers is balanced, while guaranteeing that each tag is monitored by at least one reader. We show that a generalized variant of the load balancing problem is NP-hard and hence present a 2-approximation centralized algorithm. We next present an optimal centralized solution for a specialized variant. Subsequently, we present a localized distributed algorithm that is probabilistic in nature and closely matches the performance of the centralized algorithms. Our results demonstrate that our schemes achieve very good performance even in highly dynamic large-scale RFID systems.
2934868
Performance of selective packet dropping policies in heterogeneous networks
The performance analyses of selective packet dropping policies have usually been performed using homogeneous traffic sources and under one node scenarios. In this paper we focus our attention not only on heterogeneous sources but also heterogeneous environments. We present simulation results of application level performance for heterogeneous traffic sources under three different packet dropping schemes: partial packet discard (PPD), early packet discard (EPD), and preemptive partial packet discard (pPPD). In addition, we also present simulation results for homogeneous sources over multi-hop networks with heterogeneous packet dropping policies.
6727358
Segmenting meetings into agenda items by extracting implicit supervision from human note-taking
Splitting a meeting into segments such that each segment contains discussions on exactly one agenda item is useful for tasks such as retrieval and summarization of agenda item discussions. However, accurate topic segmentation of meetings is a difficult task. In this paper, we investigate the idea of acquiring implicit supervision from human meeting participants to solve the segmentation problem. Specifically we have implemented and tested a note taking interface that gives value to users by helping them organize and retrieve their notes easily, but that also extracts a segmentation of the meeting based on note taking behavior. We show that the segmentation so obtained achieves a Pk value of 0.212 which improves upon an unsupervised baseline by 45% relative, and compares favorably with a current state-of-the-art algorithm. Most importantly, we achieve this performance without any features or algorithms in the classic sense.
12036050
A Dynamic Manifestation Approach for Providing Universal Access to Digital Library Objects
Digital libraries are concerned with the creation and management of information sources, the movement of information across global networks, and the effective use of this information by a wide range of users. A digital library is a vast collection of objects that are of multimedia nature, e.g. text, video, images, and audio. Users wishing to access the digital library objects may possess varying capabilities, preferences, domain expertise, and may use different information appliances. Facilitating access to complex multimedia digital library objects that suits the users' requirements is known as universal access. We present an object manifestation approach in which digital library objects automatically manifest themselves to cater to the users' capabilities and characteristics. We provide a formal framework, based on Petri nets, to represent the various components of the digital library objects, their modality and fidelity, and the playback synchronization relationships among them. We develop methodologies for object delivery without any deadtime under network delays.
40585807
Using Measures of Semantic Relatedness for Word Sense Disambiguation
This paper generalizes the Adapted Lesk Algorithm of Banerjee and Pedersen (2002) to a method of word sense disambiguation based on semantic relatedness. This is possible since Lesk's original algorithm (1986) is based on gloss overlaps which can be viewed as a measure of semantic relatedness. We evaluate a variety of measures of semantic relatedness when applied to word sense disambiguation by carrying out experiments using the English lexical sample data of SENSEVAL-2. We find that the gloss overlaps of Adapted Lesk and the semantic distance measure of Jiang and Conrath (1997) result in the highest accuracy.
2969941
SmartNotes: Implicit Labeling of Meeting Data through User Note-Taking and Browsing
We have implemented SmartNotes, a system that automatically acquires labeled meeting data as users take notes during meetings and browse the notes afterwards. Such data can enable meeting understanding components such as topic and action item detectors to automatically improve their performance over a sequence of meetings. The SmartNotes system consists of a laptop based note taking application, and a web based note retrieval system. We shall demonstrate the functionalities of this system, and will also demonstrate the labeled data obtained during typical meetings and browsing sessions.