14550653
QoS Topology Control with Minimal Total Energy Cost in Ad Hoc Wireless Networks
This paper discusses the energy efficient QoS topology control problem in ad hoc wireless networks. Given a set of nodes in a plane, end-to-end traffic demands and delay bounds between node pairs, the problem is to find a network topology that can meet the QoS requirements and the total transmission power of nodes is minimized. We consider two cases of the problem: 1) the traffic demands are not splittable, and 2) the traffic demands are splittable. The first case is formulated as an integer linear programming problem. The latter case is formulated as a mixed integer linear programming problem. A greedy algorithm and an approximation algorithm with ratio n are proposed to solve the problem, where n is the number of nodes. Extensive simulations are conducted to evaluate the performance of proposed algorithms.
893749
A Distributed and Efficient Flooding Scheme Using 1-Hop Information in Mobile Ad Hoc Networks
Flooding is one of the most fundamental operations in mobile ad hoc networks. Traditional implementation of flooding suffers from the problems of excessive redundancy of messages, resource contention, and signal collision. This causes high protocol overhead and interference with the existing traffic in the networks. Some efficient flooding algorithms were proposed to avoid these problems. However, these algorithms either perform poorly in reducing redundant transmissions or require each node to maintain 2-hop (or more) neighbors information. In the paper, we study the sufficient and necessary condition of 100 percent deliverability for flooding schemes that are based on only 1-hop neighbors information. We further propose an efficient flooding algorithm that achieves the local optimality in two senses: 1) the number of forwarding nodes in each step is minimal and 2) the time complexity for computing forwarding nodes is the lowest, which is O(nlogn), where n is the number of neighbors of a node. Extensive simulations have been conducted and simulation results have shown the excellent performance of our algorithm
10761342
Placement of mirrored web servers in ring networks with budget constraint
This paper investigates the placement problem of mirrored web servers in ring networks, where each client accesses the closest mirrored server. Without budget constraint, we propose an efficient algorithm that computes the optimal placement with time complexity O(|C|/sup 2/ /spl times/ |V|). With budget constraint, if all candidate servers charge the same fee, we propose an algorithm to compute the optimal placement in time O(k/sup 2/|C|/sup 3/|V|) by using dynamic programming; if candidates charge different fees, we prove the problem is NP-hard, and further propose two heuristic algorithms. The performance of the proposed placement schemes is evaluated with the traffic-reduction ratio and the budget-usage ratio over a wide range of system parameters.
13107702
Maximal lifetime scheduling in sensor surveillance networks
This paper addresses the maximal lifetime scheduling problem in sensor surveillance networks. Given a set of sensors and targets in a Euclidean plane, a sensor can watch only one target at a time, our task is to schedule sensors to watch targets, such that the lifetime of the surveillance system is maximized, where the lifetime is the duration that all targets are watched. We propose an optimal solution to find the target watching schedule for sensors that achieves the maximal lifetime. Our solution consists of three steps: 1) computing the maximal lifetime of the surveillance system and a workload matrix by using linear programming techniques; 2) decomposing the workload matrix into a sequence of schedule matrices that can achieve the maximal lifetime; 3) obtaining a target watching timetable for each sensor based on the schedule matrices. Simulations have been conducted to study the complexity of our proposed method and to compare with the performance of a greedy method.
11860267
On optimal placement of relay nodes for reliable connectivity in wireless sensor networks
The paper addresses the relay node placement problem in two-tiered wireless sensor networks. Given a set of sensor nodes in Euclidean plane, our objective is to place minimum number of relay nodes to forward data packets from sensor nodes to the sink, such that: 1) the network is connected, 2) the network is 2-connected. For case one, we propose a (6+ε)-approximation algorithm for any ε > 0 with polynomial running time when ε is fixed. For case two, we propose two approximation algorithms with (24+ε) and (6/T+12+ε), respectively, where T is the ratio of the number of relay nodes placed in case one to the number of sensors. We further extend the results to the cases where communication radiuses of sensor nodes and relay nodes are different from each other.
16166828
Maximal lifetime scheduling for K to 1 sensor-target surveillance networks
This paper addresses the maximal lifetime scheduling for k to 1 sensor-target surveillance networks. Given a set of sensors and targets in Euclidean plane, a sensor can watch only one target at a time and a target should be watched by k sensors (k ≥ 2) at anytime. Our task is to schedule sensors to watch targets, such that the lifetime of the surveillance system is maximized, where the lifetime is the duration that all targets are watched. We propose an optimal solution to find the target watching schedule for sensors that achieves the maximal lifetime. This is the first time in the literature that this scheduling problem of sensor surveillance systems has been formulated and the optimal solution has been found. Our solution consists of three steps: (1) computing the maximal lifetime of the surveillance system and a workload matrix by using linear programming techniques; (2) decomposing the workload matrix into a sequence of schedule matrices by extending the Hall's theory, to achieve the maximal lifetime; (3) obtaining a target watching timetable for each sensor based on the schedule matrices. The time complexity of our optimal method is O(m2n3), where m, n are the number of targets and the number of sensors, respectively. We illustrate our optimal method by a numeric example and experiments in the end.
12824352
On Issues of Instance Selection
The digital technologies and computer advances with the booming internet uses have ledto massive data collection (corporate data, data warehouses, webs, just to name a few) andinformation (or misinformation) explosion. Szalay and Gray described this phenomenon as“drowning in data” (Szalay and Gray, 1999). They reported that each year the detectors attheCERNparticlecolliderinSwitzerlandrecord1petabyteofdata;andresearchersinareasof science from astronomy to the human genome are facing the same problems and chokingon information. A very natural question is “now that we have gathered so much data, whatdo we do with it?” Raw data is rarely of direct use and manual analysis simply cannotkeep pace with the fast growth of data. Data mining and knowledge discovery (KDD), as anew emerging ﬁeld comprising disciplines such as databases, statistics, machine learning,comes to the rescue. KDD attempts to turn raw data into nuggets and create special edgesin this ever competitive world for science discovery and business intelligence.TheKDDprocessisdeﬁnedinFayyadetal.(1996)as
23957657
Terminology model discovery using natural language processing and visualization techniques
Medical terminologies are important for unambiguous encoding and exchange of clinical information. The traditional manual method of developing terminology models is time-consuming and limited in the number of phrases that a human developer can examine. In this paper, we present an automated method for developing medical terminology models based on natural language processing (NLP) and information visualization techniques. Surgical pathology reports were selected as the testing corpus for developing a pathology procedure terminology model. The use of a general NLP processor for the medical domain, MedLEE, provides an automated method for acquiring semantic structures from a free text corpus and sheds light on a new high-throughput method of medical terminology model development. The use of an information visualization technique supports the summarization and visualization of the large quantity of semantic structures generated from medical documents. We believe that a general method based on NLP and information visualization will facilitate the modeling of medical terminologies.
17123515
A Probabilistic Approach to Feature Selection - A Filter Solution
Feature selection can be de ned as a problem of nding a minimum set of M relevant at tributes that describes the dataset as well as the original N attributes do where M N After examining the problems with both the exhaustive and the heuristic approach to fea ture selection this paper proposes a proba bilistic approach The theoretic analysis and the experimental study show that the pro posed approach is simple to implement and guaranteed to nd the optimal if resources permit It is also fast in obtaining results and e ective in selecting features that im prove the performance of a learning algo rithm An on site application involving huge datasets has been conducted independently It proves the e ectiveness and scalability of the proposed algorithm Discussed also are various aspects and applications of this fea ture selection algorithm
7205040
Effective Data Mining Using Neural Networks
Classification is one of the data mining problems receiving great attention recently in the database community. The paper presents an approach to discover symbolic classification rules using neural networks. Neural networks have not been thought suited for data mining because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by humans. With the proposed approach, concise symbolic rules with high accuracy can be extracted from a neural network. The network is first trained to achieve the required accuracy rate. Redundant connections of the network are then removed by a network pruning algorithm. The activation values of the hidden units in the network are analyzed, and classification rules are generated using the result of this analysis. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of standard data mining test problems.
3660238
Active Feature Selection Using Classes
Feature selection is frequently used in data pre-processing for data mining. When the training data set is too large, sampling is commonly used to overcome the difficulty. This work investigates the applicability of active sampling in feature selection in a filter model setting. Our objective is to partition data by taking advantage of class information so as to achieve the same or better performance for feature selection with fewer but more relevant instances than random sampling. Two versions of active feature selection that employ class information are proposed and empirically evaluated. In comparison with random sampling, we conduct extensive experiments with benchmark data sets, and analyze reasons why class-based active feature selection works in the way it does. The results will help us deal with large data sets and provide ideas to scale up other feature selection algorithms.