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Test cases prioritization has been excessively considered for continious regression and integration testing in Internet of Things based systems to apply multilevel testing activities. Various number of devices, sensors and acctuators are connected together through the internet using different technologies, which requires extensively testing the effeciency of these components and the transferred data between them. Due to the number of the connected components has dramatically increased, causing a direct proportional increase in the number of test cases.Studies that handle the augmentation of the number of test cases for traditional systems lack effeciency when applied for Internet of Things based systems. In this paper, we introduce an enhancement for test cases prioritization using Hill Climbing algorithm as a local search based technique, adapted to achieve tangible effeciency. It is integrated with the LSTM deep learning algorithm for test cases classification purposes. The results of the test cases prioritization using Hill Climbing for regression and integration testing are evaluated using precision, where it achieved 80% and 97% for regression testing, and 93% and 88% for integration testing using two Internet of Things-based system datasets.

Tremendous systems are rapidly evolving based on the trendy Internet of Things (IoT) in various domains. Different technologies are used for communication between the massive connected devices through all layers of the IoT system, causing many security and performance issues. Regression and integration testing are considered repeatedly, in which the vast costs and efforts associated with the frequent execution of these inflated test suites hinder the adequate testing of such systems. This necessitates the focus on exploring innovative scalable testing approaches for large test suites in IoT-based systems. In this paper, a scalable framework for continuous integration and regression testing in IoT-based systems (IoT-CIRTF) is proposed, based on IoT-related criteria for test case prioritization and selection. The framework utilizes search-based techniques to provide an optimized prioritized set of test cases to select from. The selection is based on a trained prediction model for IoT standard components using supervised deep learning algorithms to continuously ensure the overall reliability of IoT-based systems. The experiments are held on two GSM datasets. The experimental results achieved prioritization accuracy up to 90% and 92% for regression testing and integration testing respectively. This provides an enhanced and efficient framework for continuous testing of IoT-based systems, as per IoT-related criteria for the prioritization and selection purposes.

Enormous heterogeneous sensory data are generated in the Internet of Things (IoT) for various applications. These big data are characterized by additional features related to IoT, including trustworthiness, timing and spatial features. This reveals more perspectives to consider while processing, posing vast challenges to traditional data fusion methods at different fusion levels for collection and analysis. In this paper, an IoT-based spatiotemporal data fusion (STDF) approach for low-level data in–data out fusion is proposed for real-time spatial IoT source aggregation. It grants optimum performance through leveraging traditional data fusion methods based on big data analytics while exclusively maintaining the data expiry, trustworthiness and spatial and temporal IoT data perspectives, in addition to the volume and velocity. It applies cluster sampling for data reduction upon data acquisition from all IoT sources. For each source, it utilizes a combination of k-means clustering for spatial analysis and Tiny AGgregation (TAG) for temporal aggregation to maintain spatiotemporal data fusion at the processing server. STDF is validated via a public IoT data stream simulator. The experiments examine diverse IoT processing challenges in different datasets, reducing the data size by 95% and decreasing the processing time by 80%, with an accuracy level up to 90% for the largest used dataset.

the dangers generated from synthesized multimedia are increasing every day. The creation of the so-called Deepfakes multimedia is vastly evolving, making the detection task harder every day. Researchers and corporations are interested in exploring the technology limits and are coming up with new tools every year to create more robust fake media. In this paper, a new enhanced fake video detection model is introduced addressing many of the face-swapping threats and the low generalization problem. A preprocessing stage is proposed to minimize the noise in the data to enhance their quality. The proposed architecture uses a modified application of capsule neural networks (CapsNet) with an enhanced routing technique. It does not require a lot of training data and generates a small number of training parameters making it fast to build. The model was trained and tested using the DFDC-P dataset and the results have proven that it outperformed other detectors in terms of detection recall, weighted precision, and F1 score.

Securing electronic medical information has a significant impact on data access which directly affects patient’s privacy and quality care rights. Medical professionals need to have full access to all patients' medical history to make accurate decisions regarding diagnosis and treatment plans. This paper introduces a novel Secure framework that integrates a new hybrid encryption scheme of medical images using chaotic maps and 2D Discrete wavelet transform (DWT) Steganography to increase key size and achieve high security level. In addition, a web-based monitoring platform has been deployed for tracking of electronic medical records during transmission. To validate the efficiency of the proposed framework, an application case-study has been introduced for securely transmitting retinal fundus medical images for diagnostic decisions on diabetic patients. Compared to the state-of-the-art approaches, the proposed framework demonstrated the ability to mask the context of the confidential patient into a transmitted cover image with high imperceptibility and limited degradation in the stego image provided in acceptable cryptographic overhead. Experimental results demonstrated that the proposed framework outperforms other schemes in terms of accuracy, sensitivity and perceptibility.

Data streams have become a widely-adopted data representation format in many real-world applications. This data streaming may be published for different scientific research, mining, or analysis purposes. However, such streams may contain personal-specific data that could be considered as sensitive about individuals. This makes the privacy preserving of data streams against privacy disclosure attacks, while maintaining their utilization, is a real challenge. Some studies have considered privacy-preserving publishing over data streams with only Single Sensitive Attribute, in which they do not protect the published streams from all possible privacy attacks. In this paper, we propose a novel Restricted Sensitive Attributes-based Sequential Anonymization (RSA-SA) approach for privacy-preserving data stream publishing. Besides, two new privacy restrictions are introduced to restrict the published Sensitive Attributes values: Semantic-diversity and Sensitivity-diversity. RSA-SA can protect the sensitive values of the published data streams against the related privacy attacks, including the attribute disclosure, skewness, similarity, and sensitivity attacks. In addition, RSA-SA handles data streams that have either single or multiple sensitive attributes with minimum information loss and delay time. Thus, the data utility of the published data streams is efficiently maintained to provide more accurate mining and analytical results, where robust invulnerability to privacy attacks is sustained.

Malware is the umbrella term that denotes attacking any system by malicious software. During the last few years, the popularity of Android smartphones led to the sneak of several malware applications into different Android markets without any difficulty. As a consequence of this, malware applications have been grown exponentially in the Android markets. Unfortunately, most of these markets suffer from an inability to detect malware, which results in increasing the probability of infecting users’ phones with these malware applications. The present paper focuses on developing an efficient computational framework based on Deep Belief Networks for malware detection. The proposed framework merges high level static analysis, dynamic analysis and system calls in feature extraction in order to achieve the highest accuracy. The evaluation compares the most familiar machine learning approaches that were applied in malware detection with the proposed framework. The obtained results demonstrate that Deep Belief Networks technique can realize 99.1% accuracy with the presented dataset. Over and above that, we develop our complete static analysis jar which adopts different efficient methods in an attempt to facilitate and speed up the static analysis by handling all the Android applications in only one step rather than considering one application at a time. Keywords: Android, Static analysis, Malware detection, Dynamic analysis, System calls, Deep Belief Networks, Deep learning

The amount of data collected by various organizations about individuals is continuously increasing. This includes diverse data sources often for data of high dimensionality. Most of these data are stored in tabular format and can include sensitive content. Preserving data privacy is an essential task in order to allow such data to be published for different research and analysis purposes. In this context, Privacy-Preserving Tabular Data Publishing (PPTDP) has drawn considerable attention, where different approaches have been proposed to preserve the privacy of individuals' tabular data. Such data can include Single Sensitive Attributes (SSA) or Multiple Sensitive Attributes (MSA) or come from data streams. In this paper, we conduct a comprehensive study to analyze and evaluate the main different data anonymization approaches that have been introduced in PPTDP. The study investigates the three broad areas of research: SSA, MSA and data streams. A detailed criticism is presented to highlight the strengths and the weaknesses of each approach including their deployment in the cloud and Internet of Things (IoT) environments. A research gap analysis is discussed with a focus on capturing current state of the art in this field in order to highlight the future directions that can be considered.

Recently, service-oriented computing paradigms have become a trending development direction, in which software systems are built using a set of loosely coupled services distributed over multiple locations through a service-oriented architecture. Such systems encounter different challenges, as integration, performance, reliability, availability, etc., which made all associated testing activities to be another major challenge to avoid their faults and system failures. Services are considered the substantial element in service-oriented computing. Thus, the quality of services and the service dependability in a web service composition have become essential to manage faults within these software systems. Many studies addressed web service faults from diverse perspectives. In this paper, a comprehensive study is conducted to investigate the different perspectives to manipulate web service faults, including fault tolerance, fault injection, fault prediction and fault localization. An extensive comparison is provided, highlighting the main research gaps, challenges and limitations of each perspective for web services. An analytical discussion is then followed to suggest future research directions that can be adopted to face such obstacles by improving fault handling capabilities for an efficient testing in service-oriented computing systems.

Deep learning algorithms have achieved high performance accuracy in complex domains such as image classification, face recognition sentiment analysis, text classification, and speech understanding. Due to the nested non-linear structure of deep learning algorithms, these highly successful models are usually applied in a black-box manner, i.e., no information is provided about what exactly causes them to arrive at their predictions. The effectiveness of these systems is thus limited by the machine’s current inability to explain its decisions and actions to human users. Such a lack of transparency can be a major drawback. For instance, in medical applications the development of methods for visualizing, explaining, and interpreting deep learning models has recently attracted increasing attention. Explainable Machine Learning (XAI) or Interpretable Machine Learning (IML) programs aim to create a suite of machine learning techniques that produce more explainable models while maintaining the high level of accuracy. It also enables human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners. Our work summarizes recent developments in this field, and we present three classification tasks wherein we make use of LIME (Local Interpretable Model-Agnostic Explanations) to explain predictions of deep learning models. LIME attempts to make these complex models at least partly understandable by evaluating using three classification tasks. Our first evaluation is performed on natural language processing application for tweet data classification and our second evaluation is on biomedical signal classification cancer detection. Our third evaluation is on Windows PC malware classification in a cybersecurity domain. In all three case studies, the interpreted results are presented in terms of visualization.

One of the most important challenges that face researchers in the medical field is the variety and the extraordinary mass of data types that can be generated even for a single patient. Machine learning models are commonly used to extract knowledge from medical data. However, most of these models have been built based on only a single data type, ignoring learning valuable information existing in other data types. In this paper, we seek to enhance the efficiency of machine learning models in knowledge extraction. We apply data integration via ensemble stacked supervised generalization method. We propose a framework which adopts feature selection techniques to improve the stacked models in order to achieve a better performance than state-of-the-art data integration models. We also consider getting information from multiple data sources using multiple machine learning models to explore data effectively as each data type has a suitable machine learning model. The proposed predictive model is applied to the METABRIC dataset with survival prediction of breast cancer at 2000 days of diagnosis. Our ensemble stacked generalization method achieves 80% prediction accuracy which represents a high-performance index relative to the other known ensemble methods. Also, we demonstrate that some enhanced feature selection methods are able to select relevant features that are close to the features selected by a biological expert from breast cancer pathways.

With recent progress in pervasive healthcare, physical activity recognition with wearable body sensors has become an important and challenging area in both research and industrial communities. Here, we address a novel technique for a sensor platform that performs physical activity recognition by leveraging a class specific regularizer term into the dictionary pair learning objective function. The proposed algorithm jointly learns a synthesis dictionary and an analysis dictionary in order to simultaneously perform signal representation and classification once the time-domain features have been extracted. Specifically, the class specific regularizer term ensures that the sparse codes belonging to the same class will be concentrated thereby proving beneficial for the classification stage. In order to develop a more practical approach, we employ a combination of an alternating direction method of multipliers and a l 1 − l s minimization method to approximately minimize the objective function. We validate the effectiveness of our proposed model by employing it on two activity recognition problem and an intensity estimation problem, both of which include a large number of physical activities. Experimental results demonstrate that classifiers built in this dictionary learning based framework outperforms state of art algorithms by using simple features, thereby achieving competitive results when compared with classical systems built upon features with prior knowledge.

Watermarking is one of the most known techniques for authentication, tampering detection, privacy control, etc. Concerning privacy protection of patients' medical records, efforts have been devoted to guarantee the confidentiality of data and medical images during storage and transmission via an untrustworthy channel. Our developed watermarking system, aims at fulfilling such demands through the utilization of a biometric authentication code sender physician's iris code, encrypted patient data and a fuzzy-based Region-of-Interest ROI segmentation algorithm. In this paper, a new hybrid DCT fuzzy biometric-based watermarking scheme has been introduced for privacy protection and source verification of medical and non-medical images. The proposed scheme integrates forward DCT transform with enhanced fuzzy-based ROI regions segmentation. To comply with the imperceptibility and high image quality requirements, the coefficients selection decision depends on perceptual visibility threshold estimation, based upon characteristics of the human visual system HVS. The inclusion of a just-noticeable distortion JND profile, computed for DCT coefficients, is proved to outperform the traditional DCT model, with the major contributions of a new formula for luminance adaptation adjustment and the incorporation of block classification for contrast masking. A block classification has been utilized to differentiate edge regions and thus effectively avoid over-estimation of JND in the selected regions. Moreover, several enhanced fuzzy-based clustering models have been suggested for extraction of ROI regions in medical patterns, which aim at increasing the robustness to noise and yield more homogeneous regions with less spurious blobs. The proposed hybrid fuzzy-based ROI extraction scheme integrates the effect of the local neighborhood and allow it to influence the membership value of each pixel. A new Hybrid FCM H-FCM algorithm is proposed, which integrates spatial information with a 2D adaptive noise removal SS-FCM model. Experiments have been conducted to verify the proposed model. Several attacks have been applied to the proposed scheme and the experiments revealed promising results in terms of visual quality and extracted watermark distortion.

During the last few years, the revolution of social networks such as Facebook, Twitter, and Instagram led to a daily increasing of data that are heterogeneous in their sources, data models, and platforms. Heterogeneous data sources have many forms such as the www, deep web, relational databases systems, No-SQL database systems, hierarchal data systems, semi-structured files, in which data are usually allocated on different machines (distributed) and have different data models (heterogeneous). Large-scale data integration efforts demonstrate that their most valuable contribution is implementing a data integration platform that provides a uniform access to the heterogeneous data sources, as well as the different versions of data reported by the same data source over time. Furthermore, the platform must be able to integrate data from a broad range of data authoring devices and database management systems. It also should be accessible by almost types of data querying devices to ensure globally querying the integration platform from any place on earth anytime and receiving the query result in any data format. In this paper, we create a resource oriented heterogeneous data integration platform (ROHDIP) that facilitates the data integration process and implements the objectives discussed above. We use the resource oriented architecture ROA to support the uniform access by most types of data querying devices from anywhere and to improve the query response time.

With the rapidly increase of electricity demand around the world due to industrialization and urbanization, this turns the availability of precise knowledge about the consumption patterns of consumers to a valuable asset for electricity providers, given the current competitive electricity market. This would allow them to provide satisfactory services in time of load peaks and to control fraud and abuse cases. Despite of this crucial necessity, this is currently very hard to achieve in many developing countries since smart meters or advanced metering infrastructures (AMIs) are not yet settled there to monitor and report energy usages. Whereas the communication and information technologies have widely emerged in such nations, allowing the enormous spread of smart devices among population. In this paper, we present mobile-based BLPDA, a novel platform for big data analytics of consumerss’ load profiles (LPs) in the absence of AMIs’ establishment. The proposed platform utilizes mobile computing in order to collect the consumptions of consumers, build their LPs, and analyze the aggregated usages data. Thus, allowing electricity providers to have better vision for an enhanced decision making process. The experimental results emphasize the effectiveness of our platform as an adequate alternative for AMIs in developing countries with minimal cost.

Due to its symbolic role in ubiquitous health monitoring, physical activity recognition with wearable body sensors has been in the limelight in both research and industrial communities. Physical activity recognition is difficult due to the inherent complexity involved with different walking styles and human body movements. Thus we present a correntropy induced dictionary pair learning framework to achieve this recognition. Our algorithm for this framework jointly learns a synthesis dictionary and an analysis dictionary in order to simultaneously perform signal representation and classification once the time-domain features have been extracted. In particular, the dictionary pair learning algorithm is developed based on the maximum correntropy criterion, which is much more insensitive to outliers. In order to develop a more tractable and practical approach, we employ a combination of alternating direction method of multipliers and an iteratively reweighted method to approximately minimize the objective function. We validate the effectiveness of our proposed model by employing it on an activity recognition problem and an intensity estimation problem, both of which include a large number of physical activities from the recently released PAMAP2 dataset. Experimental results indicate that classifiers built using this correntropy induced dictionary learning based framework achieve high accuracy by using simple features, and that this approach gives results competitive with classical systems built upon features with prior knowledge.

Requirements-based testing is a testing approach in which test cases are derived from requirements. Requirements represent the initial phase in software developments life cycle. Requirements are considered the basis of any software project. Therefore, any ambiguity in natural language requirements leads to major errors in the coming phases. Moreover, poorly defined requirements may cause software project failure. There exist many software development models as waterfall model, agile model, etc. In this paper, we propose a novel automated approach to generate test cases from requirements. Requirements can be gathered from different models either waterfall model (functional and non-functional) or agile model. SRS documents, non-functional requirements and user stories are parsed and used by the proposed approach to generate test cases in which requirements with different types are covered. The proposed approach uses text mining and symbolic execution methodology for test data generation and validation, where a knowledge base is developed for multi-disciplinary domains.

Outlier detection is an important issue in the realm of data mining. Several applications relay on outlier detection such as intrusion detection, fraud detection, medical and public health data, image processing, etc. Clustering-based outlier detection algorithms are considered as the most important outlier detection approaches. They provide high detection rate, however, they suffer from high false positives. In this paper, we propose a clustering-based outlier detection algorithm that supports searching for outliers not only in small clusters but also in large clusters with an optimized calculation methodology. The experimental results demonstrate the good performance of the algorithm in terms of detection accuracy by increasing the detection rate, decreasing the false positives, and minimizing outlierness factor calculations.

Trajectory data analysis has recently become an active research area. This is due to the large availability of mobile tracking sensors, such as GPS-enabled smart phones. However, those GPS trackers only provide raw trajectories (x, y, t), ignoring information about the activity, transportation mode, etc. This information can contribute in producing significant knowledge about movements, which transforms raw trajectories into semantic trajectories. Therefore, research lately has focused on semantic trajectories; their representation, construction, and applications. This paper investigates the current studies on semantic trajectories so far. We propose a new classification schema for the research efforts in semantic trajectory construction and applications. The proposed classification schema includes three main classes: semantic trajectory modeling, computation, and applications. Besides, we discuss the current research gaps found in this research area.

Regression testing is an important and expensive activity that is undertaken every time a program is modified to ensure that the changes do not introduce new bugs into previously validated code. Instead of re-running all test cases, different approaches were studied to solve regression testing problems. Data mining techniques are introduced to solve regression testing problems with large-scale systems containing huge sets of test cases, as different data mining techniques were studied to group test cases with similar features. Dealing with groups of test cases instead of each test case separately helped to solve regression testing scalability issues. In this paper, we propose a new methodology for regression testing of large-scale systems using data mining techniques to prioritize and select test cases based on their coverage criteria and fault history.

In recent years data mining (DM) has been heavily applied in customer relationship management (CRM). The objective of this paper is the categorization of customer records into several groups in the Business-to-Business (B2B) context, using clustering analysis. The obtained categories are then used to make suitable marketing strategy recommendations for each group of customers. This is accomplished by first using the Length, Recency, Frequency, Monetary (LRFM) customer lifetime value model, which scores customers according to four attributes, the relationship length with the company (L), recency of latest transaction (R), purchasing frequency (F), and monetary value of customer (M). Secondly, the paper introduces a proposed enhanced clustering model using the k-means++ algorithm, where customer records are segmented based on their respective LRFM values. Also, the proposed model is integrated with a bootstrapping phase, where the selection of the number of clusters is performed by employing both the Calinski-Harabasz and Rand cluster validity indices. In addition, the firmographics data of each customer are taken into account by analyzing groups based on both the sale sector, and the location of customers, as means of enhancing the clustering analysis. Finally, the clustering results are evaluated and discussed. This study is performed on a dataset obtained from a well-known, multi-national, Fast-Moving Consumer Goods (FMCG) company in Egypt, resulting in useful insights into the nature of their customers.

In this paper, a novel Hybrid Fuzzy Self-Adaptive digital Audio Watermarking scheme (HFSA-AW) is proposed based on local audio features. Firstly, the original audio signal is partitioned into audio frames. These audio frames are transformed into discrete wavelet transform (DWT) domain. A method for performing automatic segmentation based on features related to rhythm, timbre and harmony is presented. A proposed segmentation approach, combining audio signal characterization by statistical features and fuzzy clustering approach, is proposed. The local features of each audio frame are extracted respectively, and these features are used to train a fuzzy c-means clustering algorithm. Since fuzzy set theory is capable of performing complex nonlinear mappings between input and output spaces, it can effectively estimate the strength of a frame for each sub-band and ensure that the embedded watermark in the original audio is self-adaptive. A new watermark embedding scheme, based on fuzzy adaptive embedding strength, is used to embed watermark into the statistics average value of low frequency components. In order to evaluate the performance of the proposed audio watermarking method, subjective and objective quality tests including bit error rate (BER) and signal to noise ratio (SNR) are conducted. Experimental results show that the proposed scheme is inaudible and robust against common signal processing, including low-pass filtering, noise addition, and cropping.

Regression testing repeatedly executes test cases of previous builds to validate that the original features are not affected with any new changes. In recent years, regression testing has seen a remarkable progress with the increasing popularity of agile methods, which stress the central role of regression testing in maintaining software quality. The optimum case for regression testing in agile context is to run regression set at the end of each sprint and release, which requires a lot of cost and time. In this paper, we present an automated agile regression testing approach on both the sprints and release levels. The proposed approach addresses both weighted sprint test cases prioritization technique, which prioritizes test cases based on several parameters having real practical weight for testers, and Cluster-based Release Test cases Selection technique that clusters user stories based on the similarity of covered modules to solve the scalability issue. Test cases are then selected based on issues logged for failed test cases using text mining techniques. The proposed approach achieves enhancement for both the prioritization and selection of test cases for agile regression testing. Copyright © 2016 John Wiley & Sons, Ltd.

Standard Fuzzy C-Means (FCM) clustering has been widely used as an effective method for image segmentation. It gained a huge popularity because it efficient, straightforward, and easy to implement. However, FCM is sensitive to initialization and is easily trapped in local optima. In this paper, several enhanced models for FCM clustering were proposed, namely W_SS_FCM, LAWS_SS_FCM and H_FCM, to promote the performance of standard FCM. The proposed algorithms merge partial supervision with spatial locality to increase conventional FCM's robustness. The proposed models includes integrating adaptive filtering as a preprocessing step to FCM, using Laws level masks to obtain weighted-sum image for clustering, and Integrating both spatial information with partial expert knowledge in the FCM model to formulate a new Hybrid FCM (H_FCM) model. A comparison study was conducted to validate the proposed methods' performance applying well established measures. Evaluation was done on three datasets: Brain MR phantoms, real Brain MR images and chest CT scans. Experimental results show considerable improvement over standard FCM and other variants of the algorithm. It also manifests high robustness against noise attacks.

Smart homes have come into reality with the integration of many technologies to provide maximum satisfaction to their inhabitants. In this paper, we present a new approach for resources management in multi-inhabitant smart homes using encrypted mobile agents. Mobility is combined with encryption in order to provide maximum privacy to the inhabitants. Conflict resolution on smart devices control is also considered between agents. The efficiency of our approach would be evaluated using our smart space simulation tool, Bosthan.

This paper introduces a novel approach for dynamic structuring of contextual lattices. It is anticipated that the approach can be applied to improve the accuracy of word-segmentation patterns in autonomous text recognition systems. A multi-level hierarchical structure of lattices is used to implement the algorithm, and the approach can be applied in a generic manner to other pattern recognition problems. We apply a top-down structural model in parallel with a constrained probabilistic model and intelligent distributed searching paradigm. This paradigm is based on the integration between probabilistic bi-grams and adaptive intelligent swarm-based agent search to identify the most likely sentence structures. The searching paradigm allows the exploitation of positive feedback as a search mechanism and, consequently, makes the model amenable to parallel implementation. The distributed intelligence of the proposed approach enables the dynamic structuring of contextual lattices and has proved to scale well with large lattice sizes. Moreover, we believe that the proposed architecture solves the ill-conditioned nature of most pattern recognition problems that lies in the effect of noise in the segmentation phase. To verify the developed Swarm-based Intelligent Search Algorithm (SISA), a simulation study was conducted on a set of variable size scripts. The proposed paradigm proved to be efficient in identifying the most highly segmented patterns and also returned good decisions concerning lower probability segments enabling further re- segmentations and re-combinations to take place. The paper is the first to apply the intelligent swarm-based paradigm for the identification of optimal segmented patterns in contextual recognition models. The algorithm is compared with other algorithms for the same problem, and the computational results demonstrate that the proposed approach is very efficient and robust for large-scale statistical contextual-lattice structures.

Mobile agents (MA) technology has been proposed for network and system management to solve the problem of scalability limitation of centralized models and the flexibility problems of static hierarchical networks. Mobile software agents represent a very attractive approach to the distributed control of computer networks and a valid alternative to the implementation of strategies for the management of QoS. Distributing intelligence across the network allows the fast exploration of more advanced services that can dynamically adapt to the user's requirements. This paper describes the efficient integration and adoption of mobile agents and swarm optimization in the implementation of an effective strategy for the development of efficient dynamic resource reservation by optimal rerouting (optimal alternative path allocation) under unpredicted dynamics which leads to an optimal migration strategy of tunnel agents in a constrained resource reservation model. We assume the RSVP protocol to be available at least in a subset of the network equipment, and investigate the use of swarm-based optimization with the problem of operating resource reservation in an IntServ model when some of the routers along the path do not support the RSVP protocol.. The proposed swarm intelligence optimization provides an adaptive, distributed searching process in order to identify the most effective resource allocation for QoS guarantee in large dynamic network between edge routers. We ran the experiments under various dynamics and the performance is compared with other routing algorithms. Our presented approach showed both very good performance and robustness under all the experimental conditions with respect to its competitors.