Sensor selection for tracking multiple groups of targets
- Authors: Armaghani, Farzaneh , Gondal, Iqbal , Kamruzzaman, Joarder , Green, David
- Date: 2014
- Type: Text , Journal article
- Relation: Journal of Network and Computer Applications Vol. 46, no. (2014), p. 36-47
- Full Text: false
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- Description: Group target tracking is a challenge for sensor networks. It occurs where large numbers of closely spaced targets move together in different groups. In these applications, the sensor selection scheme plays a vital role in extending network lifetime while providing high tracking accuracy. Existing schemes cause an extreme imbalance between energy usages and tracking accuracy. They are capable of tracking only individual groups and without using prior knowledge about the groups. These problems make them impractical for group target tracking. With the aim of balancing the trade-off between lifetime and accuracy, we present a novel Multi-Sensor Group Tracking (MSGT) scheme. MSGT comprises the following steps to accomplish concurrent tracking of multiple groups: (1) Clustering to capture changes in the behavioural properties of groups, such as formation, merging, and splitting; (2) Sensor selection to activate the contributory sensors for the estimated group regions; and (3) Group tracking using the activated sensors. We develop a probabilistic decision-making strategy that triggers the clustering step adaptively with any detected change in group behavioural patterns. The sensor selection step coordinates periodic selection of leader and tracking sensor nodes in a distributed manner. We introduce cost metrics that include sensor′s energy parameters in the selection of active sensors that fully cover the group regions. The tracking step is a Bayesian modelling of the target groups which uses particle filtering algorithm to estimate the group locations. Simulation results show that our scheme achieves substantial improvements over existing approaches in terms of network lifetime and tracking accuracy.
A HMM-based adaptive fuzzy inference system for stock market forecasting
- Authors: Hassan, Md Rafiul , Ramamohanarao, Kotagiri , Kamruzzaman, Joarder , Rahman, Mustafizur , Hossain, Maruf
- Date: 2013
- Type: Text , Journal article
- Relation: Neurocomputing Vol. 104, no. (2013), p. 10-25
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- Description: In this paper, we propose a new type of adaptive fuzzy inference system with a view to achieve improved performance for forecasting nonlinear time series data by dynamically adapting the fuzzy rules with arrival of new data. The structure of the fuzzy model utilized in the proposed system is developed based on the log-likelihood value of each data vector generated by a trained Hidden Markov Model. As part of its adaptation process, our system checks and computes the parameter values and generates new fuzzy rules as required, in response to new observations for obtaining better performance. In addition, it can also identify the most appropriate fuzzy rule in the system that covers the new data; and thus requires to adapt the parameters of the corresponding rule only, while keeping the rest of the model unchanged. This intelligent adaptive behavior enables our adaptive fuzzy inference system (FIS) to outperform standard FISs. We evaluate the performance of the proposed approach for forecasting stock price indices. The experimental results demonstrate that our approach can predict a number of stock indices, e.g., Dow Jones Industrial (DJI) index, NASDAQ index, Standard and Poor500 (S&P500) index and few other indices from UK (FTSE100), Germany (DAX) , Australia (AORD) and Japan (NIKKEI) stock markets, accurately compared with other existing computational and statistical methods.
Abrasion modeling of multiple-point defect dynamics for machine condition monitoring
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder , Loparo, Kenneth
- Date: 2013
- Type: Text , Journal article
- Relation: IEEE Transactions on Reliability Vol. 62, no. 1 (2013), p. 171-182
- Full Text: false
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- Description: Multiple-point defects and abraded surfaces in rotary machinery induce complex vibration signatures, and have a tendency to mislead defect diagnosis models. A challenging problem in machine defect diagnosis is to model and study defect signature dynamics in the case of multiple-point defects and surface abrasion. In this study, a multiple-point defect model (MPDM) that characterizes the dynamics of n-point bearing defects is proposed. MPDM is further extended to model degradation in a rotating machine as a special case of multiple-point defects. Analytical and experimental results for multiple-point defects and abrasions show that the location of the fundamental defect frequency shifts depending upon the relative location of the defects and width of the abrasive region. This variation in the defect frequency results in a degradation of the defect detection accuracy of the defect diagnostic model. Based on envelope detection analysis, a modification in existing defect diagnostic models is recommended to nullify the impact of multiple-point defects, and general abrasion in machine components.
An adaptive self-configuration scheme for severity invariant machine fault diagnosis
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2013
- Type: Text , Journal article
- Relation: IEEE Transactions on Reliability Vol. 62, no. 1 (2013), p. 116-126
- Full Text: false
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- Description: Vibration signals, used for abnormality detection in machine health monitoring (MHM), exhibit significant variation with varying fault severity. This signal variation causes overlap among the features characterizing different types of faults, which results in severe performance degradation of the fault diagnostic model. In this paper, a wavelet based adaptive training set and feature selection (WATF) self-configuration scheme is presented, which selects the optimum wavelet decomposition level, and employs adaptive selection of the training set and features. Optimal wavelet decomposition level selection is such that the maximum fault signature-signal energy bands are achieved. The severity variant features, which could cause detrimental class overlap for MHM, are avoided using adaptive selection of the training set and features based on the location of a test data in feature space. WATF uses Support Vector Machines (SVM) to build the fault diagnostic model, and its performance and robustness has been tested with data having different severity levels. Comparative studies of WATF with eight existing fault diagnosis schemes show that, for publicly available data sets, WATF achieves higher fault detection accuracy, even when training and testing data sets belong to different severity levels.
Convergence of elitist clonal selection algorithm based on martingale theory
- Authors: Hong, Lu , Kamruzzaman, Joarder
- Date: 2013
- Type: Text , Journal article
- Relation: Engineering Letters Vol. 21, no. 4 (2013), p. 181-184
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- Description: In recent years, progress has been made in the analysis of global convergence of clonal selection algorithms (CSA), but most analyses are based on the theory of Markov chain, which depend on the description of the transition matrix and eigenvalues. However, such analyses are very complicated, especially when the population size is large, and are presented for particular implementations of CSA. In this paper, instead of the traditional Markov chain theory, we introduce martingale theory to prove the convergence of a class of CSA, called elitist clonal selection algorithm (ECSA). Using the submartingale convergence theorem, the best individual affinity evolutionary sequence is described as a submartingale, and the almost everywhere convergence of ECSA is derived. Particularly, the algorithm is proved convergent with probability 1 in finite steps when the state space of population is finite. This new proof of global convergence analysis of ECSA is more simplified and effective, and not implementation specific.
Multi-step support vector regression and optimally parameterized wavelet packet transform for machine residual life prediction
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2013
- Type: Text , Journal article
- Relation: JVC/Journal of Vibration and Control Vol. 19, no. 7 (2013), p. 963-974
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- Description: Condition based maintenance (CBM) in the process industry helps in determining the residual life of equipment, avoiding sudden breakdown and facilitating the maintenance staff to schedule repairs by optimizing demand–supply relationships. One of the prevalent issues in CBM is to predict the residual life of the equipment. This paper proposes a novel framework to predict the remnant life of the equipment, called residual life prediction, based on optimally parameterized wavelet transform and multi-step support vector regression (RWMS). In optimally parameterized wavelet transform, a generalized criterion is proposed to select the wavelet decomposition level which works for all the applications; decomposition nodes are selected by characterizing their dominancy level based upon relative fault signature–signal energy contents. The prediction model is based on multi-step support vector regression to determine the nonlinear crack propagation in the rotary machine according to Paris’s fatigue model. The results both for the simulated as well as the actual vibration datasets validate the enhanced performance of RWMS in comparison with the existing techniques to predict the residual life of the equipment.
Social-connectivity-aware vertical handover for heterogeneous wireless networks
- Authors: Haider, Ammar , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2013
- Type: Text , Journal article
- Relation: Journal Of Network And Computer Applications Vol. 36, no. 4 (2013), p. 1131-1139
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- Description: Vertical handover mechanism for a WLAN-cellular heterogeneous network could be made efficient with the use of context aware admission control strategy. Existing admission control methods aim to provide satisfactory quality of service, but rely solely on the availability of wireless resources in the target network. We propose that the admission control in WLAN should make use of social connectivity context of users in its coverage area to classify local and global traffic. In this paper, we introduce a novel Social-Connectivity-aware Vertical Handover (SCVH) scheme, which performs admission control using connectivity graph data from the online social networking services. A higher importance of visiting node for users resident in WLAN, advocates a higher priority for granting admission. We employ graph-theoretic concept of centrality to calculate the social importance of potential handing-over nodes. By giving handover precedence to higher-centrality nodes, we achieve an optimal allocation of wireless resources in addition to improved quality of service. The proposed handover strategy offers an additional advantage of reducing global social network traffic.
A hybrid of multiobjective evolutionary algorithm and HMM-Fuzzy model for time series prediction
- Authors: Hassan, Md Rafiul , Nath, Gupta , Kirley, Michael , Kamruzzaman, Joarder
- Date: 2012
- Type: Text , Journal article
- Relation: Neurocomputing Vol. 81, no. April (2012), p. 1-11
- Full Text: false
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- Description: In this paper, we introduce a new hybrid of Hidden Markov Model (HMM), Fuzzy Logic and multiobjective Evolutionary Algorithm (EA) for building a fuzzy model to predict non-linear time series data. In this hybrid approach, the HMM's log-likelihood score for each data pattern is used to rank the data and fuzzy rules are generated using the ranked data. We use multiobjective EA to find a range of trade-off solutions between the number of fuzzy rules and the prediction accuracy. The model is tested on a number of benchmark and more recent financial time series data. The experimental results clearly demonstrate that our model is able to generate a reduced number of fuzzy rules with similar (and in some cases better) performance compared with typical data driven fuzzy models reported in the literature.
Application of artificial intelligence to improve quality of service in computer networks
- Authors: Ahmad, Iftekhar , Kamruzzaman, Joarder , Habibi, Daryoush
- Date: 2012
- Type: Text , Journal article
- Relation: Neural Computing & Applications Vol. 21, no. 1 (2012), p. 81-90
- Full Text: false
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- Description: Resource sharing between book-ahead (BA) and instantaneous request (IR) reservation often results in high preemption rates for ongoing IR calls in computer networks. High IR call preemption rates cause interruptions to service continuity, which is considered detrimental in a QoS-enabled network. A number of call admission control models have been proposed in the literature to reduce preemption rates for ongoing IR calls. Many of these models use a tuning parameter to achieve certain level of preemption rate. This paper presents an artificial neural network (ANN) model to dynamically control the preemption rate of ongoing calls in a QoS-enabled network. The model maps network traffic parameters and desired operating preemption rate by network operator providing the best for the network under consideration into appropriate tuning parameter. Once trained, this model can be used to automatically estimate the tuning parameter value necessary to achieve the desired operating preemption rates. Simulation results show that the preemption rate attained by the model closely matches with the target rate.
Inchoate fault detection framework: adaptive selection of wavelet nodes and cumulant orders
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2012
- Type: Text , Journal article
- Relation: IEEE Transactions on Instrumentation and Measurement Vol. 61, no. 3 (2012), p. 685-695
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- Description: Inchoate fault detection for machine health monitoring (MHM) demands high level of fault classification accuracy under poor signal-to-noise ratio (SNR) which persists in most industrial environment. Vibration signals are extensively used in signature matching for abnormality detection and diagnosis. In order to guarantee improved performance under poor SNR, feature extraction based on statistical parameters which are immune to Gaussian noise becomes inevitable. This paper proposes a novel framework for adaptive feature extraction based on higher order cumulants (HOCs) and wavelet transform (WT) (AFHCW) for MHM. Features extracted based on HOCs have the tendency to mitigate the impact of Gaussian noise. WT provides better time and frequency domain analysis for the nonstationary signals such as vibration in which spectral contents vary with respect to time. In AFHCW, stationary WT is used to ensure linear processing on the vibration data prior to feature extraction, and it helps in mitigating the impact of poor SNR. K-nearest neighbor classifier is used to categorize the type of the fault. Simulation studies show that the proposed scheme outperforms the existing techniques in terms of classification accuracy under poor SNR.
Machine health monitoring based on stationary wavelet transform and fourth-order cumulants
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2012
- Type: Text , Journal article
- Relation: International Review of Electrical Engineering Vol. 6, no. 1 (2012), p. 238-248
- Full Text: false
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- Description: Early stage faults detection for machine health monitoring demands high level of fault classification accuracy under poor signal-to-noise ratio (SNR). Vibration signal which is used for signature matching in case of abnormality detection and diagnosis, requires robust tools such as wavelet transform (WT) for time-frequency analysis. WT is specifically used to deal with nonstationary signals. In order to guarantee improved performance under poor SNR, this paper proposes a scheme for feature extraction based on fourth-order cumulant and stationary wavelet transform (FoCSWT). Higher order cumulants have the tendency to mitigate the impact of Gaussian noise. Fourth-order cumulant corresponds to the "peakedness" of the random distribution and the fault detection capability quantifies it as the most dominant cumulant among higher order statistics. Stationary wavelet transform is used to avoid down-sampling on the vibration data prior to feature extraction which gives better estimation of statistical parameters of the data distribution and gives performance enhancement in terms of fault classification accuracy. Simulation studies show that FoCSWT outperforms the existing techniques in terms of fault detection accuracies under poor SNR.
Dynamic resource allocation for improved QoS in WiMAX/WiFi integration
- Authors: Rabbani, Md , Kamruzzaman, Joarder , Gondal, Iqbal , Ahmad, Iftekhar , Hassan, Md Rafiul
- Date: 2011
- Type: Text , Journal article
- Relation: Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2011 (Studies in Computational Intelligence series) Vol. 368, no. 2011 (2011), p. 141-156
- Full Text: false
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- Description: Wireless access technology has come a long way in its relatively short but remarkable lifetime, which has so far been led by WiFi technology. WiFi enjoys a high penetration in the market.Most of the electronic gadgets such as laptop, notepad, mobile set, etc., boast the provision ofWiFi. Currently most WiFi hotspots are connected to the Internet via wired connections (e.g., Ethernet), and the deployment cost of wired connection is high. On the other hand, since WiMAX can provide a high coverage area and transmission bandwidth, it is very suitable for the backbone networks of WiFi. WiMAX can also provide the better QoS needed for many 4G applications. WiMAX devices, however, are not as common as WiFi devices and it is also expensive to deploy aWiMAX-only infrastructure. An integrated WiMAX/WiFi architecture (using WiMAX as backhaul connection for WiFi) can support 4G applications with QoS assurance and mobility, and provide high-speed broadband services in rural, regional and urban areas while reducing the backhaul cost. WiMAX and WiFi have different MAC mechanisms to handle QoS. WiMAX MAC architecture is connection-oriented providing the platform for strong QoS control. In contrast,WiFi MAC is not connection-oriented, hence can provide only best effort services. Delivering improved QoS in an integrated WiMAX/WiFi architecture poses a serious technological challenge. The paper depicts a converged architecture of WiMAX and WiFi, and then proposes an adaptive resource distribution model for the access points. The resource distribution model ultimately allocates more time slots to those connections that need more instantaneous resources to meet QoS requirements. A dynamic splitting technique is also presented that divides the total transmission period into downlink and uplink transmission by taking the minimum data rate requirements of the connections into account. This ultimately improves the utilization of the available resources, and the QoS of the connections. Simulation results show that the proposed schemes significantly outperform the other existing resource sharing schemes, in terms of maintaining QoS of different traffic classes in an integratedWiMAX/WiFi architecture
Energy-balanced transmission policies for wireless sensor networks
- Authors: Azad, Arman , Kamruzzaman, Joarder
- Date: 2011
- Type: Text , Journal article
- Relation: IEEE Transactions on Mobile Computing Vol. 10, no. 7 (2011), p. 927-940
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- Description: Transmission policy, in addition to topology control, routing, and MAC protocols, can play a vital role in extending network lifetime. Existing transmission policies, however, cause an extremely unbalanced energy usage that contributes to early demise of some sensors reducing overall network's lifetime drastically. Considering cocentric rings around the sink, we decompose the transmission distance of traditional multihop scheme into two parts: ring thickness and hop size, analyze the traffic and energy usage distribution among sensors and determine how energy usage varies and critical ring shifts with hop size. Based on above observations, we propose a transmission scheme and determine the optimal ring thickness and hop size by formulating network lifetime as an optimization problem. Numerical results show substantial improvements in terms of network lifetime and energy usage distribution over existing policies. Two other variations of this policy are also presented by redefining the optimization problem considering: 1) concomitant hop size variation by sensors over lifetime along with optimal duty cycles, and 2) a distinct set of hop sizes for sensors in each ring. Both variations bring increasingly uniform energy usage with lower critical energy and further improves lifetime. A heuristic for distributed implementation of each policy is also presented.
Optimally parameterized wavelet packet transform for machine residual life prediction
- Authors: Gondal, Iqbal , Yaqub, Muhammad , Kamruzzaman, Joarder
- Date: 2011
- Type: Text , Conference paper , Journal article
- Relation: Australian Acoustical SocietyConference 2011: Breaking New Ground, Acoustics 2011; Gold Coast, Australia; 2nd-4th November 2011; p.1-8
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- Description: One of the prevalent issues in condition based maintenance (CBM) is to predict the residual life of the equipment. This paper propos-es a novel framework to predict the remnant life of the equipment, called Residual life prediction based on optimally parameterized Wavelet transform and Mute-step Support vector regression (RWMS). In optimally parameterized wavelet transform, a generalized criterion is proposed to select the wavelet decomposition level which works for all the applications and decomposition nodes are selected by characterizing their dominancy level based upon relative fault signature-signal energy contents. The prediction model is based on multi-step support vector regression (MSVR) and prediction accuracy is improved in comparison with the techniques based on support vector regression (SVR). Performance of RWMS is evaluated in terms of Root Means Square Error (RMSE), studies show that proposed algorithm predicts the residual life of the equipment accurately.
Severity invariant feature selection for machine health monitoring
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2011
- Type: Text , Journal article
- Relation: International Review of Electrical Engineering Vol. 6, no. 1 (2011), p. 238-248
- Full Text: false
- Reviewed:
- Description: Vibration signals used for abnormality detection in machine health monitoring (MHM) suffer from significant variation in the patterns with fault severity. This variation results in overlap among the features extracted against different fault types and causes severe degradation in fault detection accuracy. This paper identifies a newfangled problem originated by severity variant features and mitigates this impact by using appropriate feature selection based on Fisher linear discriminant (FLD) and Bhattacharyya distance (BCD) to enhance fault classification accuracy. In order to validate the performance of the proposed scheme, training and testing data are obtained from different severity levels. To capture the non-stationary behavior of vibration signal, robust tools such as wavelet transform (WT) for time-frequency analysis is employed. Simulation studies show that the proposed scheme ensures good fault diagnostic accuracy even if training and testing data belong to different severity levels. [ABSTRACT FROM AUTHOR]
An environment-aware mobility model for wireless ad hoc network
- Authors: Ahmed, Sabbir , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2010
- Type: Text , Journal article
- Relation: Computer Networks Vol. 54, no. 9 (2010), p. 1470-1489
- Full Text: false
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- Description: Simulation is a cost effective, fast and flexible alternative to test-beds or practical deployment for evaluating the characteristics and potential of mobile ad hoc networks. Since environmental context and mobility have a great impact on the accuracy and efficacy of performance measurement, it is of paramount importance how closely the mobility of a node resembles its movement pattern in a real-world scenario. The existing mobility models mostly assume either free space for deployment and random node movement or the movement pattern does not emulate real-world situation properly in the presence of obstacles because of their generation of restricted paths. This demands for the development of a node movement pattern with accurately representing any obstacle and existing path in a complex and realistic deployment scenario. In this paper, we propose a general mobility model capable of creating a more realistic node movement pattern by exploiting the concept of flexible positioning of anchors. Since the model places anchors depending upon the context of the environment through which nodes are guided to move towards the destination, it is capable of representing any terrain realistically. Furthermore, obstacles of arbitrary shapes with or without doorways and any existing pathways in full or part of the terrain can be incorporated which makes the simulation environment more realistic. A detailed computational complexity has been analyzed and the characteristics of the proposed mobility model in the presence of obstacles in a university campus map with and without signal attenuation are presented which illustrates its significant impact on performance evaluation of wireless ad hoc networks.
Combining segmental semi-Markov models with neural networks for protein secondary structure prediction
- Authors: Bidargaddi, Niranjan , Chetty, Madhu , Kamruzzaman, Joarder
- Date: 2009
- Type: Text , Journal article
- Relation: Neurocomputing Vol. 72, no. 3943-3950 (2009), p.3943-3950
- Full Text: false
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- Description: Predicting the secondary structure of proteins from a primary sequence alone has been variously approached from either a classification or a generative model perspective. The most prominent classification methods have used neural networks, which involves mappings from a local window of residues in the sequence to the structural state of the central residue in the window, thus capturing the local interactions effectively. However, they fail to capture distant interactions among residues. The generative models based on Bayesian segmentation capture sequence structure relationships using generalized hidden Markov models with explicit state duration. They capture non-local interactions through a joint sequence-structure probability distribution based on structural segments. In this paper, we investigate a combined architecture of Bayesian segmentation at the first stage and neural network at the second stage which captures both local and non-local correlation, to increase the single sequence prediction accuracy. The combined architecture is further enhanced by using neural network optimization and ensemble techniques.
Predicting protein protein interfaces as clusters of optimal docking area points
- Authors: Arafat, Yasir , Kamruzzaman, Joarder , Karmakar, Gour , Fernandez-Recio, Juan
- Date: 2009
- Type: Text , Journal article
- Relation: International Journal of data mining and bioinformatics Vol. 3, no. 1 (2009), p. 55-67
- Full Text: false
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- Description: Abstract: Desolvation property is used here to predict protein-protein binding sites exploiting the fact that lower-valued 'optimal docking area' ODA (Fernandez-Recio et al., 2005) points form cluster at the interface. The proposed method involves two steps; clustering the ODA points and representing ODA points by average ODA values. On 51 nonredundant proteins, results show the success rate improved considerably. Considering only significant ODA, the previous ODA method has obtained a success rate of 65% with overall success rate of 39%. The proposed method improved the overall success rate to 61%. Further, comparable results were found for X-ray and NMR structures.
Hidden Markov models Incorporating fuzzy measures and integrals for protein sequence identification and alignment
- Authors: Bidargaddi, Niranjan , Chetty, Madhu , Kamruzzaman, Joarder
- Date: 2008
- Type: Text , Journal article
- Relation: Genomics Proteomics & Bioinformatics Vol. 6, no. 2 (2008), p.98–110
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- Description: 2014060227
Rerouting in advance for preempted IR calls in QoS-enabled networks
- Authors: Ahmad, Iftekhar , Kamruzzaman, Joarder , Habibi, Daryoush
- Date: 2008
- Type: Text , Journal article
- Relation: Computer Communications Vol. 31, no. 17 (2008), p. 3922-3928
- Full Text: false
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- Description: When network resources are shared between Instantaneous Request (IR) and Book-Ahead (BA) connections, activation of future BA connections may cause preemption of on-going IR connections due to resource scarcity. Rerouting of preempted calls via alternative feasible paths is often considered as the final option to restore and maintain service continuity. Existing rerouting techniques, however, do not ensure acceptably low service disruption time and suffer from high failure rate and low network utilization. In this work, a new rerouting strategy is proposed that estimates the future resource scarcity, identifies the probable candidate connections for preemption and initiates the rerouting process in advance for those connections. Simulations on a widely used network topology suggest that the proposed rerouting scheme achieves a higher successful rerouting rate with lower service disruption time, while not compromising other network performance metrics like utilization and call blocking rate.