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The scheduling, control, and management of traffic load in multi-protocol label switching (MPLS) are important aspects supporting the quality of service (QoS) and optimizing the network performance. In addition, ensuring the QoS in the Internet of Things (IoT) is one of the main issues of current research in this area. The IoT provides the ability for humans and computers to learn and interact with billions of things, including sensors, actuators, services, and other Internet-connected objects. MPLS offers the possibility for effective traffic control mechanisms within limited network capabilities. Traffic engineering in the MPLS domain provides alterations of the traffic routes to improve network resources or avoid network traffic congestion. Static management of traffic flows has failed to enhance the QoS of the network performance. Therefore, many researchers have attempted to use a dynamic management of traffic flow to enhance the QoS; however, more enhancements are needed. In this paper, we propose a new scheme to solve the problem of dynamic management of traffic flow through various links and routers in the network, and describe its application to an MPLS network. The proposed scheme works by utilizing queues, re-distribution, and re-balancing of streams before or during periods of congestion. It solves the problem of QoS violation, and tags, upgrades, or downgrades the traffic between queues based on the availability of free space of other queues and QoS parameters. We simulate the proposed scheme using OPNET, and the effect of implementing our method on low- and high-priority queues is analyzed and finally validated in conjunction with the ITU QoS measures.

The presence of communication obstructing entities severely degrades the routing efficiency in Mobile Ad hoc Networks (MANET). While several routing protocols have proposed ways to mitigate this degradation essentially by getting around and avoiding obstacles, the detection of obstacle locations and their contours driven by the very signaling of the underlying routing protocol has not been given that much attention. Yet, the integration of such a capability within the routing protocol presents an adequate leverage of the routing efficiency. In this paper, we first propose a distributed autonomic scheme to locate obstructing entities (coined ALOE) within a MANET using the basic signaling of the Cartography Enhanced Optimized Link State routing (CE-OLSR) protocol. Then, we propose ALOE-CE-OLSR the integration of ALOE within CE-OLSR, and show the resulting improvement in routing validity as compared to that of both CE-OLSR with and without a priori knowledge of the obstructing entities map. While the routing validity measures the pertinence of the routes computed by the underlying routing protocol, two new metrics, namely the coverage and the precision ratios, are defined to properly evaluate the efficiency and performance of our proposed detection scheme, in addition to the throughput as well as the end-to-end delay of received data packets. Simulation results show that our proposed CE-OLSR signaling-based obstacle detection scheme accurately localizes and detects the boundaries of the stationary obstructing obstacles. The integrated ALOE-CE-OLSR achieves the same route validity, throughput and delay as CE-OLSR with a priori precise knowledge of the obstacles map.

The exact string matching is essential in application areas such as Bioinformatics and Intrusion Detection Systems. Speeding-up the string matching algorithm will therefore result in accelerating the searching process in DNA and binary data. Previously, there are two types of fast algorithms exist, bit-parallel based algorithms and hashing algorithms. The bit-parallel based are efficient when dealing with patterns of short lengths, less than 64, but slow on long patterns. On the other hand, hashing algorithms have optimal sublinear average case on large alphabets and long patterns, but the efficiency not so good on small alphabet such as DNA and binary texts. In this paper, the authors present hybrid algorithm to overcome the shortcomings of those previous algorithms. The proposed algorithm is based on q-gram hashing with guaranteeing the maximal shift in advance. Experimental results on random and complete human genome confirm that the proposed algorithm is efficient on various pattern lengths and small alphabet.

Web access and web resources open many horizons, their usage increases in all life aspects including government, education, commerce and entertainment, where the key to such resources lies in Web browsers. Acknowledging the importance of universal accessibility to web resources, the W3C has developed a series of guidelines into a Web Accessibility Initiative (WAI), with the goal of providing access to web resources for people with disabilities. In order to bridge the gap in the digital divide between the disabled and the non-disabled people, the authors believe that the development of novel assistive technologies using new human-computer interfaces will go a long way towards achieving this lofty goal. In this paper, they present a P300 Electroencephalography Brain-controlled Web browser to enhance the accessibility of people with severe motor disabilities to Web resources. It enhances their interaction with the Web taking their needs into account. The proposed Web browser satisfies the Mankoff's requirements of a system that would “allow true web access.”

The machine-to-machine (M2M) technology, is expected to be one of the most promising revenue-generating services. However, to ensure the wide spread of this technology, M2M communications should be secure, fault-tolerant and self-managed. In this work, we add to the M2M gateway the self-healing and self-optimising autonomic capabilities. We couple at the M2M gateway level the host identity protocol (HIP) with the reachability protocol (REAP). REAP enables a selfhealed M2M communication as it detects possible failures and rehomes an M2M session to a new overlay path. We modify REAP to ensure self-optimised M2M communications. REAP continuously monitors M2M overlay paths and always selects the best available ones. We implement our solution on the OMNeT++ simulator. Results show that M2M sessions effectively resume after an outage. Results also highlight that M2M sessions autonomically select the best available M2M overlay paths.

Graph coloring was exploited in wireless sensor networks to solve many optimization problems, mainly related to channel assignment. In this paper, we propose to use coloring to jointly manage channel access and routing to the sink. We introduce CHRA, a coloring based hierarchical routing approach. Coloring is exploited to avoid interferences and also to schedule nodes transmissions to the sink. We provide an analytical study assessing the performance of CHRA and present an investigation of various coloring heuristics to study their impact on CHRA performances on maintaining the network connectivity and coverage, while reducing the energy consumption.

Motif discovery is the problem of finding recurring patterns in biological sequences. It is one of the hardest and long-standing problems in bioinformatics. Apriori is a well-known data-mining algorithm for the discovery of frequent patterns in large datasets. In this paper, we apply the Apriori algorithm and use the Trie data structure to discover motifs. We propose several modifications so that we can adapt the classic Apriori to our problem. Experiments are conducted on Tompa's benchmark to investigate the performance of our proposed algorithm, the Trie-based Apriori Motif Discovery (TrieAMD). Results show that our algorithm outperforms all of the tested tools on real datasets for the average sensitivity measure, which means that our approach is able to discover more motifs. In terms of specificity, the performance of our algorithm is comparable to the other tools. The results also confirm both linear time and linear space scalability of the algorithm.

The control of autonomous mobile robot in an unknown environments include many challenge. Fuzzy logic controller is one of the useful tool in this field. Performance of fuzzy logic controlling depends on the membership function, so the membership function adjusting is a time consuming process. In this paper, we optimized a fuzzy logic controller (Fuzzy) by automatic adjusting the membership function using a particle swarm optimization (PSO). The proposed method (PSO-Fuzzy) is implemented and compared with Fuzzy using Khepera simulator. Moreover, the performance of these approaches compared through experiments using a real Khepera III platform.

Web services allow developers to create, generate and compose them at runtime. However, a single web service is not sufficient to achieve most of user demands in its own. This gives rise to the concept of web services composition of is an appropriate solution to maximize the benefits of web services. Web services composition has received a great attention from different communities. A number of different semantic standards/specifications have been proposed to tackle this issue. These standards are Ontology Web Language and Web Service Modeling Ontology. In this paper, we provide an overview of these standards and present a comparison between them. We also overview different applications that have developed based on each of these standards and present comparisons among them.

Most queries that applied on database management systems (DBMS) depend heavily on the performance of the used sorting algorithm. In addition to have an efficient sorting algorithm, as a primary feature, stability of such algorithms is a major feature that is needed in performing DBMS queries. In this paper, we study a new Sorting Algorithm for Join Queries (SAJQ) that has both advantages of being efficient and stable. The proposed algorithm takes the advantage of using the m-way-merge algorithm in enhancing its time complexity. SAJQ performs the sorting operation in a time complexity of O(nlogm), where nis the length of the input array and mis number of sub-arrays used in sorting. An unsorted input array of length nis arranged into msorted sub-arrays. The m-way-merge algorithm merges the sorted msub-arrays into the final output sorted array. The proposed algorithm keeps the stability of the keys intact. An analytical proof has been conducted to prove that, in the worst case, the proposed algorithm has a complexity of O(nlogm). Also, a set of experiments has been performed to investigate the performance of the proposed algorithm. The experimental results have shown that the proposed algorithm outperforms other Stable–Sorting algorithms that are designed for join-based queries.

Fuzzy logic has been incorporated in many expert systems to solve real world problems that are inherently ambiguous. With fuzzy logic it is possible to program human intuition through the development of fuzzy expert system shells. A fuzzy expert system shell is a tool that helps build expert systems to manage fuzzy problems. Commercial as well as non-commercial fuzzy expert system shells are available. These shells provide variety of functions to facilitate the development of fuzzy expert systems for real world problems in different application areas such as medicine, engineering, and finance. To the best of our knowledge, none of the available fuzzy shells is natively developed for the Arabic language. This paper describes the development and the experimentation of a bilingual fuzzy expert system shell. This shell is intended to be a research tool for fuzzy expert systems developers in bilingual environments similar to those in the Arab world where users and developers use multi-languages due to their educational backgrounds and working environments. The shell processes fuzzy terms of the Arabic language as well as the English language. The shell is a general purpose shell that provides users with the ability to develop Arabic/English fuzzy expert systems using a simple Graphical User Interface. It applies implication methods that bear resemblance to human intuition. In the process of the development, a comparison of various fuzzy expert system shells has been performed to identify strengths and weaknesses of available shells. Experiments with our shell are reported and its performance is compared to existing shells that use different implication methods.

The General Abstract Model for Multi-set mAnipulation (GAMMA) is a parallel computational and programming paradigm. It utilizes the multi-set data structure and a program structure that is defined as a pair of

Comparative genomic analysis between variant datasets of same specie is considered to be vital to discover the degree of relevancy in them. This analysis helps in the categorisation of diversity of features in species. An immense need was felt to build sophisticated tools for efficient and robust comparative analysis. The accuracy of methodologies is directly proportional to sensitivity involved in comparing datasets for optimality. This paper is a depiction of an effort for the discovery of variant features between genetic datasets of Mus musculus. The approach described is demonstrated phase-wise with the inclusion of specific filters at each stage. At first instance, cleansing filter refines the datasets. Further series of filters depict the layered process for comprehensive comparative analysis. Numerical results have been evaluated. The protein translation phase has been introduced with conceptual demonstration of codon composition phenomenon. Characteristics of density, nucleotide strengths and codon composition better reflect the relevancy in genetic datasets of Mus musculus.

Most of the traditional and legacy systems were designed using traditional methodologies such as Structured Analysis/Structured Design (SA/SD) methodology. Design of such a system is called an imperative design. After the introduction of the object-oriented technology, there are compelling reasons to redevelop those systems using this new technology to benefit from its merits. To redevelop them, there are two possible choices: either develop them from scratch using some object-oriented methodology, or use the available design documents (i.e., imperative design) of those systems and transform their designs into object-oriented designs. The second choice clearly results in saving both the development cost and time.

This paper presents a motion control for an autonomous robot navigation using fuzzy logic motion control and stereo vision based path-planning module. This requires the capability to maneuver in a complex unknown environment. The mobile robot uses intuitive fuzzy rules and is expected to reach a specific target or follow a prespecified trajectory while moving among unforeseen obstacles. The robot's mission depends on the choice of the task. In this paper, behavioral-based control architecture is adopted, and each local navigational task is analyzed in terms of primitive behaviors. Our approach is systematic and original in the sense that some of the fuzzy rules are not triggered in face of critical situations for which the stereo vision camera can intervene to unblock the mobile robot.