2657410
General AIMD congestion control
Instead of the increase-by-one decrease-to-half strategy used in TCP for congestion window adjustment, we consider the general case such that the increase value and decrease ratio are parameters. That is, in the congestion avoidance state, the window size is increased by /spl alpha/ per window of packets acknowledged and it is decreased to /spl beta/ of the current value when there is congestion indication. We refer to this window adjustment strategy as general additive increase multiplicative decrease (GAIMD). We present the (mean) sending rate of a GAIMD flow as a function of /spl alpha/, /spl beta/, loss rate, mean round-trip time, mean timeout value, and the number of packets acknowledged by each ACK. We conducted extensive experiments to validate this sending rate formula. We found the formula to be quite accurate for a loss rate of up to 20%. We also present a simple relationship between /spl alpha/ and /spl beta/ for a GAIMD flow to be TCP-friendly, that is, for the GAIMD flow to have approximately the same sending rate as a TCP flow under the same path conditions. We present results from simulations in which TCP-friendly GAIMD flows (/spl alpha/=0.31, /spl beta/=7/8) compete for bandwidth with TCP Reno flows and with TCP SACK flows, on a DropTail link as well as on a RED link. We found that the GAIMD flows were highly, TCP-friendly. Furthermore, with /spl beta/ at 7/8 instead of 1/2, these GAIMD flows have reduced rate fluctuations compared to TCP flows.
3103973
Sprite: a simple, cheat-proof, credit-based system for mobile ad-hoc networks
Mobile ad hoc networking has been an active research area for several years. How to stimulate cooperation among selfish mobile nodes, however, is not well addressed yet. In this paper, we propose Sprite, a simple, cheat-proof, credit-based system for stimulating cooperation among selfish nodes in mobile ad hoc networks. Our system provides incentive for mobile nodes to cooperate and report actions honestly. Compared with previous approaches, our system does not require any tamper-proof hardware at any node. Furthermore, we present a formal model of our system and prove its properties. Evaluations of a prototype implementation show that the overhead of our system is small. Simulations and analysis show that mobile nodes can cooperate and forward each other's messages, unless the resource of each node is extremely low.
15017783
On the Computational Complexity of Sensor Network Localization
Determining the positions of the sensor nodes in a network is essential to many network functionalities such as routing, coverage and tracking, and event detection. The localization problem for sensor networks is to reconstruct the positions of all of the sensors in a network, given the distances between all pairs of sensors that are within some radius r of each other. In the past few years, many algorithms for solving the localization problem were proposed, without knowing the computational complexity of the problem. In this paper, we show that no polynomial-time algorithm can solve this problem in the worst case, even for sets of distance pairs for which a unique solution exists, unless RP = NP. We also discuss the consequences of our result and present open problems.
4407504
Optimal ISP subscription for Internet multihoming: algorithm design and implication analysis
Multihoming is a popular method used by large enterprises and stub ISPs to connect to the Internet to reduce cost and improve performance. Recently researchers have studied the potential benefits of multihoming and proposed protocols and algorithms to realize these benefits. They focus on how to dynamically select which ISPs to use for forwarding and receiving packets, and assume that the set of subscribed ISPs is given a priori. In practice, a user often has the freedom to choose which subset of ISPs among all available ISPs to subscribe to. We call the problem of how to choose the optimal set of ISPs the ISP subscription problem. In this paper, We design a dynamic programming algorithm to solve the ISP subscription problem optimally. We also design a more efficient algorithm for a large class of common pricing functions. Using real traffic traces and realistic pricing data, we show that our algorithm reduces users' cost. Next we study how ISPs respond to users' optimal ISP subscription by adjusting their pricing strategies. We call this problem the ISP pricing problem. Using a realistic charging model, we formulate the problem as a non-cooperative game. We first prove that if cost is the only criterion used by a user to determine which subset of ISPs to subscribe to, at any equilibrium all ISPs receive zero revenue. We then study a more practical formulation in which different ISPs provide different levels of reliability and users choose ISPs to both improve reliability and reduce cost. We analyze this problem and show that at any equilibrium an ISP's revenue is positive and determined by its reliability.
5645902
On designing incentive-compatible routing and forwarding protocols in wireless ad-hoc networks: an integrated approach using game theoretical and cryptographic techniques
In many applications, wireless ad-hoc networks are formed by devices belonging to independent users. Therefore, a challenging problem is how to provide incentives to stimulate cooperation. In this paper, we study ad-hoc games---the routing and packet forwarding games in wireless ad-hoc networks. Unlike previous work which focuses either on routing or on forwarding, this paper investigates both routing and forwarding. We first uncover an impossibility result---there does not exist a protocol such that following the protocol to always forward others' traffic is a dominant action. Then we define a novel solution concept called cooperation-optimal protocols. We present Corsac, a cooperation-optimal protocol consisting of a routing protocol and a forwarding protocol. The routing protocol of Corsac integrates VCG with a novel cryptographic technique to address the challenge in wireless ad-hoc networks that a link's cost (ie, its type) is determined by two nodes together. Corsac also applies efficient cryptographic techniques to design a forwarding protocol to enforce the routing decision, such that fulfilling the routing decision is the optimal action of each node in the sense that it brings the maximum utility to the node. Additionally, we extend our framework to a practical radio propagation model where a transmission is successful with a probability. We evaluate our protocols using simulations. Our evaluations demonstrate that our protocols provide incentives for nodes to forward packets.
38612898
Transient behaviors of TCP-friendly congestion control protocols
We investigate the fairness, smoothness, responsiveness, and aggressiveness of TCP and three representative TCP-friendly congestion control protocols: GAIMD, TFRC, and TEAR. The properties are evaluated both analytically and via simulation by studying protocol responses to three network environment changes. The first environment change is the inherent fluctuations in a stationary network environment. Under this scenario, we consider three types of sending rate variations: smoothness, short-term fairness, and long-term fairness. For a stationary environment, we observe that smoothness and fairness are positively correlated. We derive an analytical expression for the sending rate coefficient of variation for each of the four protocols. These analytical results match well with experimental results. The other two environment changes we study are a step increase of network congestion and a step increase of available bandwidth. Protocol responses to these changes reflect their responsiveness and aggressiveness, respectively.
2096552
Keyword search on relational data streams
Increasing monitoring of transactions, environmental parameters, homeland security, RFID chips and interactions of online users rapidly establishes new data sources and application scenarios. In this paper, we propose keyword search on relational data streams (S-KWS) as an effective way for querying in such intricate and dynamic environments. Compared to conventional query methods, S-KWS has several benefits. First, it allows search for combinations of interesting terms without a-priori knowledge of the data streams in which they appear. Second, it hides the schema from the user and allows it to change, without the need for query re-writing. Finally, keyword queries are easy to express.
 Our contributions are summarized as follows. (i) We provide formal semantics for S-KWS, addressing the temporal validity and order of results. (ii) We propose an efficient algorithm for generating operator trees, applicable to arbitrary schemas. (iii) We integrate these trees into an operator mesh that shares common expressions. (iv) We develop techniques that utilize the operator mesh for efficient query processing. The techniques adapt dynamically to changes in the schema and input characteristics. Finally, (v) we present methods for purging expired tuples, minimizing either CPU, or memory requirements.
891317
Translingual Information Retrieval: A Comparative Evaluation
Translingual information retrieval TIR con sists of providing a query in one language and searching document collections in one or more di erent languages This paper introduces new TIR methods and reports on comparative TIR experiments with these new methods and with previously reported ones in a realistic setting Methods fall into two categories query trans lation based and statistical IR approaches es tablishing translingual associations The re sults show that using bilingual corpora for au tomated extraction of term equivalences in con text outperforms other methods Translin gual versions of the Generalized Vector Space Model GVSM and Latent Semantic Indexing LSI perform relatively well as does translin gual pseudo relevance feedback PRF All showed relatively small performance loss be tween monolingual and translingual versions Query translation based on a general machine readable bilingual dictionary heretofore the most popular method did not match the per formance of other more sophisticated methods Also the previous very high LSI results in the literature were discon rmed by more realistic relevance based evaluations
13584016
kNN, Rocchio and Metrics for Information Filtering at TREC-10
We compared a multi-class k-nearest neighbor (kNN) approach and a standard Rocchio method in the filtering tasks of TREC-10. Empirically, we found kNN more effective in batch filtering, and Rocchio better in adaptive filtering. For threshold adjustment based on relevance feedback, we developed a new strategy that updates a local regression over time based on a sliding window over positive examples and a sliding window over negative examples in the history. Applying this strategy to Rocchio and comparing its results to those by the same method with a fixed threshold, the recall was improved by 37-39% while the precision was improved by as much as 9%. Motivated by the extremely low performance of all systems on the T10S metric, we also analyzed this metric, and found that it favors more frequently occurring categories over rare ones and is somewhat inconsistent with its most straightforward interpretation. We propose a change to this metric which fixes these problems and brings it closer to the C trk metric used to evaluate the TDT tracking task.
1977839
Effective classification of noisy data streams with attribute-oriented dynamic classifier selection
Recently, mining from data streams has become an important and challenging task for many real-world applications such as credit card fraud protection and sensor networking. One popular solution is to separate stream data into chunks, learn a base classifier from each chunk, and then integrate all base classifiers for effective classification. In this paper, we propose a new dynamic classifier selection (DCS) mechanism to integrate base classifiers for effective mining from data streams. The proposed algorithm dynamically selects a single “best” classifier to classify each test instance at run time. Our scheme uses statistical information from attribute values, and uses each attribute to partition the evaluation set into disjoint subsets, followed by a procedure that evaluates the classification accuracy of each base classifier on these subsets. Given a test instance, its attribute values determine the subsets that the similar instances in the evaluation set have constructed, and the classifier with the highest classification accuracy on those subsets is selected to classify the test instance. Experimental results and comparative studies demonstrate the efficiency and efficacy of our method. Such a DCS scheme appears to be promising in mining data streams with dramatic concept drifting or with a significant amount of noise, where the base classifiers are likely conflictive or have low confidence.
17445118
User Group Profile Modeling Based on User Transactional Data for Personalized Systems
In this paper, we propose a framework named UMT (User-profile Modeling based on Transactional data) for modeling user group profiles based on the transactional data. UMT is a generic framework for application systems that keep the historical transactions of their users. In UMT, user group profiles consist of three types: basic information attributes, synthetic attributes and probability distribution attributes. User profiles are constructed by clustering user transaction data and integrating cluster attributes with domain information extracted from application systems and other external data sources. The characteristic of UMT makes it suitable for personalization of transaction-based commercial application systems. A case study is presented to illustrate how to use UMT to create a personalized tourism system capable of using domain information in intelligent ways and of reacting to external events.