Greedy Forwarding Algorithm Research Articles

Linear Monotonic Inter-electrode Associations as Quantitative EEG for Alcoholism Diagnosis

Alcohol use disorders (AUDs) are associated with alterations in EEG patterns that reflect underlying neural dysfunctions, such as impaired cognitive processing, reduced neural connectivity, and disrupted brain rhythms. These EEG alterations can be indicative of several brain disorders, including cognitive deficits, mood disorders, and neurodegenerative diseases, which are often exacerbated by chronic alcohol abuse. Cognitive impairment in alcoholic subjects is caused due to altered functional connectivity between brain regions that can be quantified using statistical measures such as correlation. This paper proposes novel Quantitative EEG (QEEG) features comprising the band-wise absolute value of inter-electrode correlations as a measure of brain functional connectivity to classify alcoholic and non-alcoholic EEG. The EEG signal is first decomposed into five frequency sub-bands. In each sub-band, the absolute value of the linear Pearson product-moment correlation coefficient is computed between time-series EEG recorded by electrode pairs placed over different brain regions. To reduce the dimensionality of the feature vector, an ensemble feature selection approach is adopted, utilizing ANOVA and Chi-square, in conjunction with greedy forward feature selection wrapper algorithm. Four classifiers, namely SVM, KNN, ANN and RF are utilized for the classification. Among them, the SVM classifier achieves the best classification accuracies of 100% on validation with test data and 99.58% on cross-validation with train data, respectively.

SD-GPSR: A Software-Defined Greedy Perimeter Stateless Routing Method Based on Geographic Location Information

To mitigate the control overhead of Software-Defined Mobile Ad Hoc Networks (SD-MANETs), this paper proposes a novel approach, termed Software-Defined Greedy Perimeter Stateless Routing (SD-GPSR), which integrates geographical location information. SD-GPSR optimizes routing functions by decentralizing them within the data plane of SD-MANET, utilizing the geographic location information of nodes to enhance routing efficiency. The controller is primarily responsible for providing location services and facilitating partial centralized decision-making. Within the data plane, nodes employ an enhanced distance and angle-based greedy forwarding algorithm, denoted as GPSR_DA, to efficiently forward data. Additionally, to address the issue of routing voids in the data plane, we employ the A* algorithm to compute an optimal routing path that circumvents such voids. Finally, we conducted a comparative analysis with several state-of-the-art approaches. The evaluation experiments demonstrate that SD-GPSR significantly reduces the control overhead of the network. Simultaneously, there is a notable improvement in both end-to-end latency and packet loss rate across the network.

Sampling design methods for making improved lake management decisions

AbstractThe ecological status of lakes is important for understanding an ecosystem's biodiversity as well as for service water quality and policies related to land use and agricultural run‐off. If the status is weak, then decisions about management alternatives need to be made. We assess the value of information of lake monitoring in Finland, where lakes are abundant. With reasonable ecological values and restoration costs, the value of information analysis can be compared with the survey's costs. Data are worth gathering if the expected value from the data exceeds the costs. From existing data, we specify a hierarchical Bayesian spatial logistic regression model for the ecological status of lakes. We then rely on functional approximations and Laplace approximations to get closed‐form expressions for the value of information of a sampling design. The case study contains thousands of lakes. The combinatorially difficult design problem is to wisely pick the right subset of lakes for data gathering. To solve this optimization problem, we study the performance of various heuristics: greedy forward algorithms, exchange algorithms and Bayesian optimization approaches. The value of information increases quickly when adding lakes to a small design but then flattens out. Good designs are usually composed of lakes that are difficult to manage, while also balancing a variety of covariates and geographic coverage. The designs achieved by forward selection are reasonably good, but we can outperform them with the more nuanced search algorithms. Statistical designs clearly outperform other designs selected according to simpler criteria.

Design of Efficient routing protocol achieving high QoS and energy Efficiency based for Wireless Sensor Network

Wireless sensor networks (WSN), which until recently were primarily employed to convey modest amounts of data, have started to be used in recent years for multimedia applications. The routing protocol needs to be streamlined and improved in order to handle the demand for higher throughput and real-time characteristics of multimedia data. The smart, greedy forwarding algorithm based on throughput and energy-awareness (HBOA-Honey Badger optimization Algorithm) that we provide in this research offers minimal end-to-end packet delay and low packet loss. Instead of always choosing the shortest path to the sink, this method chooses high-throughput paths to transport multimedia packets over a WSN. A network void-bypass is used when it runs into network gaps to increase network reliability. Energy management is also used to improve the lifespan of sensor nodes by lowering the amount of radio coverage to a sufficient range that the sensor nodes can access. The simulation findings demonstrate that, compared to other algorithms, HBOA reduces end-to-end time by 8%, packet loss ratio by 18% and energy consumption by maintaining. . Load balancing occurs as a result of energy management, which makes the remaining energy of sensor nodes uniformly dispersed throughout the entire network. Through Simulation, the performance of HBOA is also compared to that of AGEM, TPGF, GPSR, and AODV.the esults suggests the HBOA performance better under conditions that are similat to these, such as low packet end-to-end- delay, low packet loss, and load balancing of routing channels.

A self-adapting greedy forwarding algorithm for MANETs

As the population application of GPS, some greedy forwarding algorithms based on geographic information for MANETs (mobile ad hoc networks) are proposed in recent years. These algorithms have been well-designed from different aspects to improve its performance. On the basis of analysis of these algorithms, this paper presents modified methods to address some issues of GPSR (greedy perimeter stateless routing): dynamically adjusting the time interval of sending beacons according to the radio transmission; not only considering the distance between each neighbor and the destination, but also the neighbor node surviving ratio. Some relevant algorithms are described in the paper as well. The simulation results show the improved routing algorithm excels GPSR markedly in terms of delivery ratio and routing overhead while the transmission range is larger or the motion velocity of nodes is greater.

基于物联网的农产品冷链物流安全监控系统设计

In order to improve the safety of cold chain logistics transportation and the accuracy of monitoring results, a design method of agricultural products cold chain logistics safety monitoring system based on the Internet of Things is proposed. The monitoring system includes wireless sensor, embedded and GPS technologies. In order to effectively realize the management of logistics monitoring data, this paper proposes improved Leda criteria to remove outliers in information fusion, and an information transmission method based on multicast greedy forwarding (MGF) algorithm on the basis of traditional management means, thus realizing logistics vehicle monitoring and logistics information tracing. The result shows that the object loss rate of the proposed logistics supervision method is only 1.7%, which is significantly lower than other supervision methods. And the monitoring accuracy of the monitoring method proposed in the study is also significantly higher than other methods, which can achieve effective supervision in the cold chain transport process of agricultural products. The above results show that it is feasible to adopt improved methods to realize the safety monitoring of logistics cold chain transportation, which is of great significance to the external sales of agricultural products and the development of logistics technology.

Neighborhood rough set based multi‐label feature selection with label correlation

SummaryNeighborhood rough set (NRS) is considered as an effective tool for feature selection and has been widely used in processing high‐dimensional data. However, most of the existing methods are difficult to deal with multi‐label data and are lack of considering label correlation (LC), which is an important issue in multi‐label learning. Therefore, in this article, we introduce a new NRS model with considering LC. First, we explore LC by calculating the similarity relation between labels and divide the related labels into several label subsets. Then, a new neighborhood relation is proposed, which can solve the problem of neighborhood granularity selection by using the nearest neighbor information distribution of instances under the related labels. On this basis, the NRS model is reconstructed by embedding LC information, and the related properties of the model are discussed. Moreover, we design a new feature significance function to evaluate the quality of features, which can well capture the specific relationship between features and labels. Finally, a greedy forward feature selection algorithm is designed. Extensive experiments which are conducted on different types of datasets verify the effectiveness of the proposed algorithm.

Cross-layer greedy forwarding algorithm for VANET

In recent years, vehicular communication systems have taken an important place in Intelligent Transport Systems (ITS). These systems are designed on self-organizing networks, known as Vehicular Ad hoc NETworks (VANETs). Their objective is to struggle against road safety problems and road traffic congestion by integrating information and communication technologies in road infrastructures and vehicles. VANET differs from other types of ad hoc networks by high mobility and very dynamic topology. Consequently, the design of an efficient VANET routing protocol becomes a challenging task. Many routing schemes with QoS (Quality of Service) have been proposed in the literature for VANET, to satisfy the performances required by the different applications running upon such networks. Cross-layer designs, which make it possible to exchange and share information between the different layers of the network, have shown to be efficient in VANETs. This paper proposes an improved version of the geographic Greedy algorithm as a cross-layer routing protocol for VANETs. The cooperation between the network layer and the lower layers involves three cyber-physical parameters: Speed, position and Signal-to-Noise Ratio (SNR). The simulation results over the Network Simulator-3 (NS3) demonstrated the effectiveness of the proposed solution in terms of end-to-end delay, throughput and packet delivery ratio.

Smart geographical routing protocol achieving high QoS and energy efficiency based for wireless multimedia sensor networks

Over the recent years, multimedia applications have begun to be used over wireless sensor networks (WSN), which hitherto has been primarily used to carry small amount of data. To meet the demand for higher throughput and real-time characteristics of multimedia contents, the routing protocol need to be simplified and optimized. In this paper, we present a routing algorithm that provides a low end-to-end packet delay and low packet loss, referred to as smart, greedy forwarding algorithm based on throughput and energy-awareness (SGFTEM). This algorithm routes multimedia packets across a WSN by choosing high- throughput paths rather than always choosing the shorter path to the sink. When it encounters network holes, a network void-bypass is applied, which enhances the reliability of the network. Energy management is also implemented to extend sensor node lifetime by way of reducing the radio transmission coverage to a suitable distance reachable by the sensor nodes The simulation results shows that, SGFTEM reduces the end-to-end delay by 40% and packet loss ratio by 35% and maintain energy consumption against other algorithms. With energy management, the residual energy of sensor nodes are made to be equally distributed across the entire network which results in load balancing. The performance of SGFTEM are also compared via simulation against AGEM, TPGF, GPSR, and AODV showing superior performance under similar scenarios i.e. low packet end-to-end delay, low packet loss and load balancing of routing paths.

Optimal Intermittent Particle Filter

The problem of the optimal allocation (in the expected mean square error sense) of a measurement budget for particle filtering is addressed. We propose three different optimal intermittent filters, whose optimality criteria depend on the information available at the time of decision making. For the first, the stochastic program filter, the measurement times are given by a policy that determines whether a measurement should be taken based on the measurements already acquired. The second, called the offline filter, determines all measurement times at once by solving a combinatorial optimization program before any measurement acquisition. For the third one, which we call online filter, each time a new measurement is received, the next measurement time is recomputed to take all the information that is then available into account. We prove that in terms of expected mean square error, the stochastic program filter outperforms the online filter, which itself outperforms the offline filter. However, these filters are generally intractable. For this reason, the filter estimate is approximated by a particle filter. Moreover, the mean square error is approximated using a Monte-Carlo approach, and different optimization algorithms are compared to approximately solve the combinatorial programs (a random trial algorithm, greedy forward and backward algorithms, a simulated annealing algorithm, and a genetic algorithm). Finally, the performance of the proposed methods is illustrated on two examples: a tumor motion model and a common benchmark for particle filtering.

A BASELINE GENE EXPRESSION-BASED PROGNOSTIC FOR ANTI-TNFα THERAPY RESPONSE IN PATIENTS WITH INFLAMMATORY BOWEL DISEASE

Abstract Background Although anti-TNFα therapies have revolutionized the management and care of IBD, their administration and usage remain suboptimal because 1) over 50% of patients do not have a lasting therapeutic response, 2) they increase risk of infections, liver problems, arthritis, and lymphoma, and 3) they are expensive. With approximately 1.6 million people suffering from IBD in the US and global prevalence of IBD on the rise, a predictive test for anti-TNFα response would greatly improve the efficacy and cost-to-benefit ratio of these biologics. Methods We hypothesized that a multicohort analysis of the publicly available IBD gene expression datasets would yield a robust set of mRNAs for distinguishing anti-TNFα responders vs non-responders in the IBD patient population prior to treatment. We identified 5 datasets (n = 160) where whole-genome transcriptomic data was derived from colonic mucosal biopsies of IBD patients who were then subjected to anti-TNFα therapy and subsequently adjudicated for response. We used the MetaIntegrator framework which leverages a leave-one-study-out cross-validation technique in conjunction with effect size and FDR adjusted p-value to identify significant differentially expressed (DE) genes associated with a patient’s predisposition to a response outcome. DE genes were subjected to a greedy forward search to derive a parsimonious gene signature for a response score (geometric mean of the expression level for all positive mRNAs minus the geometric mean of the expression level of all negative mRNAs, multiplied by the ratio of counts of positive to negative genes). Area under the receiver operating characteristic curve (AUC) was subsequently calculated in a leave-one-study-out manner to assess discriminatory performance. Results We first identified 170 genes that were present in at least 40% of cohorts and significantly differentially expressed between responders and non-responders with effect size > 0.8 and q value < 0.1. A score based on these genes predicts responder vs non-responder across the 5 discovery cohorts with AUC of 0.82. Optimizing the variables with a greedy forward search algorithm allowed us to downselect to 7 genes from the set, and a score based on this parsimonious set of 7 genes improved the discriminatory performance to an AUC of 0.87. Choosing a high sensitivity (90%) for a rule-in scenario, the score had moderate specificity (60%); alternatively choosing a high specificity (90%) for a rule-out scenario, the score still had a good sensitivity (80%). Conclusions These initial findings suggest that there is a strong signal for predicting anti-TNFα response in colonic biopsies. In particular, we showed using the leave-one-study-out approach that a predictive signature using mRNA can be generalizable (works in independent cohorts). These initial results warrant further investigation.

Cost-Constrained feature selection in binary classification: adaptations for greedy forward selection and genetic algorithms

BackgroundWith modern methods in biotechnology, the search for biomarkers has advanced to a challenging statistical task exploring high dimensional data sets. Feature selection is a widely researched preprocessing step to handle huge numbers of biomarker candidates and has special importance for the analysis of biomedical data. Such data sets often include many input features not related to the diagnostic or therapeutic target variable. A less researched, but also relevant aspect for medical applications are costs of different biomarker candidates. These costs are often financial costs, but can also refer to other aspects, for example the decision between a painful biopsy marker and a simple urine test. In this paper, we propose extensions to two feature selection methods to control the total amount of such costs: greedy forward selection and genetic algorithms. In comprehensive simulation studies of binary classification tasks, we compare the predictive performance, the run-time and the detection rate of relevant features for the new proposed methods and five baseline alternatives to handle budget constraints.ResultsIn simulations with a predefined budget constraint, our proposed methods outperform the baseline alternatives, with just minor differences between them. Only in the scenario without an actual budget constraint, our adapted greedy forward selection approach showed a clear drop in performance compared to the other methods. However, introducing a hyperparameter to adapt the benefit-cost trade-off in this method could overcome this weakness.ConclusionsIn feature cost scenarios, where a total budget has to be met, common feature selection algorithms are often not suitable to identify well performing subsets for a modelling task. Adaptations of these algorithms such as the ones proposed in this paper can help to tackle this problem.

A Vehicle Tracking System Using Greedy Forwarding Algorithms for Public Transportation in Urban Arterial

A vehicle tracking system assists public transportation users in their movements by providing real-time information on the locations of vehicles in transit. Public transportation in parts of developing nations, especially Nigeria is ineffective. The system is chaotic and frustrating, especially at peak traffic periods. In a bid to address the problem, a vehicle tracking system was developed as a component of an Advanced Public Transportation System to improve commuting in an urban arterial. The developed system is based on wireless technologies of the Global Positioning System (GPS) and Global System for Mobile Communication module. It records and displays real-time vehicle location using a GPS-based greedy forwarding algorithm, computes route distance information using distance-time based algorithm and radar range sensor (RRS). A pseudo-range mathematical model using the Haversine formula was adopted in determining the accurate position of an object during signal transmission from GPS satellites to the receiver message module. The minimum inversion matrix method was used for the GPS-based geometric dilution of precision (GDoP) selection of satellite approximation and distance. Atmega328P controller chip was used as the logical control unit for processing activities in the tracking system and programming in Arduino IDE using C-language. The system was deployed to a university transportation system in Nigeria: a journey to and from the Bosso and Gidan Kwano Campuses route in the Federal University of Technology, Minna. The vehicle tracking system was tested with 11 tracked satellite and minor dilution error (PDoP error = 1.9, HDoP=0.9, and VDOP=1.7) was recorded. The system is efficient and accurate in distance and time information display with a minor delay. The system would enhance fleet management schemes for urban arterial and can be adopted universally.

A fitting model based intuitionistic fuzzy rough feature selection

Feature subset selection is an essential machine learning approach aimed at the process of dimensionality reduction of the input space. By removing irrelevant and/or redundant variables, not only it enhances model performance, but also facilitates its improved interpretability. The fuzzy set and the rough set are two different but complementary theories that apply the fuzzy rough dependency as a criterion for performing feature subset selection. However, this concept can only maintain a maximal dependency function. It cannot preferably illustrate the differences in object classification and does not fit a particular dataset well. This problem was handled by using a fitting model for feature selection with fuzzy rough sets. However, intuitionistic fuzzy set theory can deal with uncertainty in a much better way when compared to fuzzy set theory as it considers positive, negative and hesitancy degree of an object simultaneously to belong to a particular set. Therefore, in the current study, a novel intuitionistic fuzzy rough set model is proposed for handling above mentioned problems. This model fits the data well and prevents misclassification. Firstly, intuitionistic fuzzy decision of a sample is introduced using neighborhood concept. Then, intuitionistic fuzzy lower and upper approximations are constructed using intuitionistic fuzzy decision and parameterized intuitionistic fuzzy granule. Furthermore, a new dependency function is established. Moreover, a greedy forward algorithm is given using the proposed concept to calculate reduct set. Finally, this algorithm is applied to the benchmark datasets and a comparative study with the existing algorithm is presented. From the experimental results, it can be observed that the proposed model provides more accurate reduct set than existing model.

A fuzzy rough set based fitting approach for fuzzy set-valued information system

Information systems comprising of multiple attribute values are known as set-valued information systems (SIS). This paper proposes a novel fuzzy set-valued information system (FSIS) as a generalized model of SIS. SIS is known as FSIS if some of its set-values are fuzzy set-values. Existing fuzzy rough set (FRS) model for SIS does not fit a dataset well and can lead to samples misclassification since sometimes there exist a large overlap between samples, so a fitting FRS model is introduced for handling FSIS. Fuzzy similarity relations obtained from each fuzzy set-valued attribute are parameterized using two parameter values to characterize the fuzzy information granules and then based on it, fuzzy lower and upper approximations are reconstructed. This approach can fit FSIS well and prevent samples misclassification. Later, the relative reduct of FSIS is computed using greedy forward algorithm. The effectiveness of proposed FSIS using fitting FRS model approach is proved by conducting a comparative study of proposed FSIS with existing information systems.

Virtual Position based Olsr Protocol for Wireless Sensor Networks

In wireless sensor networks usually taken in routing void problem in geographical routing in high control overhead and transmission delay .The routing void protocol is proposed in this paper is efficient bypassing void routing protocol. This protocol based on virtual co-ordinates is to transform a structure of random process in virtual circle .The circle are composed by void edge in to by mapping of edge nodes. In this paper to used the greedy forwarding algorithm. This algorithm can be process on virtual circle. The virtual circle greedy forwarding failing on routing void process. There are forwarding process are source to destination. The proposed protocol as find the shortest path, long transmission and High quality link maintenance.

Entropy Based Feature Selection for Fuzzy Set-Valued Information Systems

In Set-valued Information Systems (SIS), several objects contain more than one value for some attributes. Tolerance relation used for handling SIS sometimes leads to loss of certain information. To surmount this problem, fuzzy rough model was introduced. However, in some cases, SIS may contain some real or continuous set-values. Therefore, the existing fuzzy rough model for handling Information system with fuzzy set-values needs some changes. In this paper, Fuzzy Set-valued Information System (FSIS) is proposed and fuzzy similarity relation for FSIS is defined. Yager's relative conditional entropy was studied to find the significance measure of a candidate attribute of FSIS. Later, using these significance values, three greedy forward algorithms are discussed for finding the reduct and relative reduct for the proposed FSIS. An experiment was conducted on a sample population of the real dataset and a comparison of classification accuracies of the proposed FSIS with the existing SIS and single-valued Fuzzy Information Systems was made, which demonstrated the effectiveness of proposed FSIS.

On the performance of greedy forwarding on Yao and Theta graphs

Greedy Forwarding algorithm is an important geometric routing algorithm for wireless networks. However, it can fail if the network topologies contain voids, in which a packet cannot be moved closer to destination. Since Yao and Theta graphs are two types of geometric graphs exploited to construct wireless network topologies, this paper first gives theoretical results on whether these two types of graphs can contain voids with respect to their cone numbers. Then, this paper examines the performance of Greedy Forwarding on Yao and Theta graphs in terms of stretch (the ratio between the path length found by Greedy Forwarding and the shortest path length from a source to a destination). Both worst-case and average-case stretches are studied. For the worst case, this paper shows that the stretches of Greedy Forwarding on both Yao and Theta graphs do not have a constant upper bound (i.e., not competitive) in hop metric. For the average case, this paper studies the stretch experimentally by running Greedy Forwarding on a large number of Yao and Theta graphs with randomly generated node sets. The average-case stretches in both hop and Euclidean metrics are measured, and several interesting findings are revealed.

Dual reduced kernel extreme learning machine for aero-engine fault diagnosis

In order to improve the sparsity of kernel-based extreme learning machine (KELM), this paper proposed a novel method named dual reduced kernel extreme learning machine (DR-KELM). The proposed algorithm incorporates traditional greedy forward learning algorithm into backward learning algorithm to gain more sparsity and enhance testing time further. Compared to original KELM, the proposed method produces satisfactory performance of pattern recognition with fewer nodes, and reduces diagnostic consuming time from the tests on benchmark dataset. The DR-KELM application to aero-engine fault diagnosis also demonstrates its superior performance with more sparse structure.

A Fitting Model for Feature Selection With Fuzzy Rough Sets

A fuzzy rough set is an important rough set model used for feature selection. It uses the fuzzy rough dependency as a criterion for feature selection. However, this model can merely maintain a maximal dependency function. It does not fit a given dataset well and cannot ideally describe the differences in sample classification. Therefore, in this study, we introduce a new model for handling this problem. First, we define the fuzzy decision of a sample using the concept of fuzzy neighborhood. Then, a parameterized fuzzy relation is introduced to characterize the fuzzy information granules, using which the fuzzy lower and upper approximations of a decision are reconstructed and a new fuzzy rough set model is introduced. This can guarantee that the membership degree of a sample to its own category reaches the maximal value. Furthermore, this approach can fit a given dataset and effectively prevents samples from being misclassified. Finally, we define the significance measure of a candidate attribute and design a greedy forward algorithm for feature selection. Twelve datasets selected from public data sources are used to compare the proposed algorithm with certain existing algorithms, and the experimental results show that the proposed reduction algorithm is more effective than classical fuzzy rough sets, especially for those datasets for which different categories exhibit a large degree of overlap.