Ahmed I. Saleh

Toward SWSs Discovery: Mapping from WSDL to OWL-S Based on Ontology Search and Standardization Engine

Semantic Web Services (SWSs) represent the most recent and revolutionary technology developed for machine-to-machine interaction on the web 3.0. As for the conventional web services, the problem of discovering and selecting the most suitable web service represents a challenge for SWSs to be widely used. In this paper, we propose a mapping algorithm that facilitates the redefinition of the conventional web services annotations (i.e., WSDL) using semantic annotations (i.e., OWL-S). This algorithm will be a part of a new discovery mechanism that relies on the semantic annotations of the web services to perform its task. The “local ontology repository” and “ontology search and standardization engine” are the backbone of this algorithm. Both of them target to define any data type in the system using a standard ontology-based concept. The originality of the proposed mapping algorithm is its applicability and consideration of the standardization problem. The proposed algorithm is implemented and its components are validated using some test collections and real examples. An experimental test of the proposed techniques is reported, showing the impact of the proposed algorithm in decreasing the time and the effort of the mapping process. Moreover, the experimental results promises that the proposed algorithm will have a positive impact on the discovery process as a whole.

Semantic web services matchmaking: Semantic distance-based approach

The semantic web services (SWSs) discovery is the process of finding a service that can possibly satisfy the user requirements, choosing between several services, and composing services to form a single service. The matchmaking between the user request and SWSs is the main task of any SWSs discovery mechanism. In this paper, a semantic distance-based matchmaking algorithm (SDMA) is introduced. The idea behind SDMA is to use the measurement of the semantic distance between the user request and the tested service as an indicator of the degree of relevance between them. In addition to many proposals, a new concepts tree is proposed to perform the process of computing the semantic distance between two concepts. SDMA bases on many concept-to-concept semantic distance measures that are modified and adapted. A deep evaluation process is performed to validate the great impact of using semantic distance measures in the field of SWSs matchmaking.

Face Recognition in Mobile Devices

networked world,mobile phone playsvery important role, it affects all aspects of human daily life.The need to maintain the security of information in mobile device is becoming both increasingly important and increasinglydifficult. Some human features likefingerprints, face, hand geometry, voice and iris are used to provide an authentication for security systems to reach high security level instead of traditional password based systems. This paper presents a deployment of face recognition algorithms on mobile devices. Proposed approach uses PCA algorithm with FPIEand DCVon mobile device.In thispaper all calculations done on a mobile phone,in whicha small number of images were used for testing the system.System accuracy is 92% for appropriately chosen threshold, in which the time taken to recognize a face is approximately 0.35 sec and this can increase when database size increased.

A New Grid Scheduler with Failure Recovery and Rescheduling Mechanisms: Discussion and Analysis

Computational Grids (CGs) have become an appealing research area. They suggest a suitable environment for developing large scale parallel applications. CGs integrate a huge mount of distributed heterogeneous resources for constituting a powerful virtual supercomputer. Scheduling is the most important issue for enhancing the performance of CGs. Various strategies have been introduced, including static and dynamic behaviors. The former maps tasks to resources at submission time, while the latter operates at run time. While static scheduling is unsuitable for the dynamic Grid environment, scheduling in CGs is still more complex than the proposed dynamic solutions. This paper introduces a decentralized Adaptive Grid Scheduler (AGS) based on a novel rescheduling mechanism. AGS has several salient properties as it is; hybrid, adaptive, decentralized, and efficient. Also, AGS is a robust mechanism as it has the ability to; (i) detect resource failures, (ii) continue its functionality in spite of the failure existence, then (iii) recover back. Moreover, it integrates both static and dynamic scheduling behaviors. An initial static scheduling map is proposed for an input Direct Acyclic Graph (DAG). However, DAG tasks may be rescheduled if the performance of the allocated resources changes in away that may affect the tasks’ response time. AGS overcomes drawbacks of traditional schedulers by utilizing the mobile agent unique features to enhance the resource discovery and monitoring processes. Experimental results have shown that AGS outperforms traditional Grid schedulers as it introduces a better scheduling efficiency.

A data mining based load forecasting strategy for smart electrical grids

Smart electrical grids, which involve the application of intelligent information and communication technologies, are becoming the core ingredient in the ongoing modernization of the electricity delivery infrastructure. Thanks to data mining and artificial intelligence techniques that allow the accurate forecasting of power, which alleviates many of the cost and operational challenges because, power predictions become more certain. Load forecasting (LF) is a vital process for the electrical system operation and planning as it provides intelligence to energy management. In this paper, a novel LF strategy is proposed by employing data mining techniques. In addition to a novel load estimation, the proposed LF strategy employs new outlier rejection and feature selection methodologies. Outliers are rejected through a Distance Based Outlier Rejection (DBOR) methodology. On the other hand, selecting the effective features is accomplished through a Hybrid technique that combines evidence from two proposed feature selectors. The first is a Genetic Based Feature Selector (GBFS), while the second is a Rough set Base Feature Selector (RBFS). Then, the filtered data is used to give fast and accurate load prediction through a hybrid KN3B predictor, which combines KNN and NB classifiers. Experimental results have proven the effectiveness of the new outlier rejection, feature selection, and load estimation methodologies. Moreover, the proposed LF strategy has been compared against recent LF strategies. It is shown that the proposed LF strategy has a good impact in maximizing system reliability, resilience and stability as it introduces accurate load predictions.

An Administrative Cluster-based Cooperative Caching (ACCC) strategy for Mobile Ad Hoc Networks

Recent explosive growth in computing and wireless communication technologies has led to an increasing interest in mobile ad hoc networks (MANET). Among the many challenges for MANET designers and users, data availability is a critical issue. Caching is considered as an effective solution for the availability problem. Although cooperative caching improves the data access by reducing access latency and bandwidth usage in MANETs, it still suffers from several hurdles and technical problems. This paper introduces a new cooperative caching strategy for MANETs, which is called Administrative Cluster-Based Cooperative Caching (ACCC). ACCC keeps at most two copies of the cached data items in each cluster. Moreover, it develops a new administrative module, which perfectly controls the caching process. ACCC is a cluster-based caching strategy, hence, it divides the network into a set of overlapping clusters. Each cluster is managed by a Cluster Manager (CM) as well as a Cluster Backup (CB). ACCC has several salient features that other techniques do not have such as: (i) the integration between CM and CB besides the integration among all the caching modules improves the data availability within the cluster, (ii) ACCC; maximizes the caching hit ratio, reduces access delay, and saves the battery power of network nodes. ACCC is simulated using Java Caching System JCS2 with the efficient platform Java Enterprise Edition EE8 using a client/server model. Experimental results have shown that ACCC outperforms recent cluster-based caching strategies as it introduces higher cache hit ratio as well as better data availability.

High performance adaptive framework for scheduling Grid Workflow applications

The Grid Workflow scheduling is one of the most important issues in Workflow management because of its impact on the performance. Due to its importance, many scheduling approaches are proposed including static, dynamic and adaptive ones. Static approaches performance in Grid environment is questioned due to the continuous environment changes. The main objective of this paper is to introduce an adaptive scheduling approach in which the new Grid changes can be taken into consideration in order to achieve high performance and minimized makespan. The experimental results show that the proposed adaptive approach outperforms the static approaches in Grid environment.

A novel scheduling strategy for an efficient deadlock detection

Deadlock is one of the most important problems in distributed and centralized systems. Many algorithms are introduced for the detection of deadlock; however some of them suffer from increasing the number of processes, while the others take long processing time. This paper studies the impact of deadlock detection on the overall performance of deadlock handling. Network performance largely relies on the accuracy of deadlock detection. This paper proposes a deadlock detection protocol based on threads, processes which are acquired, released or stopped and which thread wait for the other and causes the deadlock. Our algorithm makes the detection goes faster, reliable and schedules the threads in order to detect which thread would initiate the deadlock. Our algorithm simulation results forecasted the deadlock initiator.

A Multi-Aware Query Driven (MAQD) routing protocol for mobile wireless sensor networks based on neuro-fuzzy inference

A Mobile Wireless Sensor Network (MWSN) is composed of mobile sensor nodes, whereby the life time of a sensor is specified by the life time of its battery. MWSN is used to measure a mobile object parameters then transmit the sensed data to center station called Sink Node (SN). Routing of the sensed data is a challenging issue since several parameters and restrictions should be managed carefully in MWSN. Although the sensors power (e.g., sensors battery level) is a critical issue, managing data transmission time is also a considerable subject especially for real time applications. Several routing protocols had been proposed for MWSN, however, each protocol considers a single type of awareness (such as; long life, delay time, total energy). While some protocols consider the sensors energy to maximize the network lifetime, others aim to find the shortest path to reach the destination. In this paper, a multi-aware query driven (MAQD) routing protocol will be proposed for MWSN based on a neuro-fuzzy inference system. MAQD considers four types of awareness; the long life of the sensor, delay time of data transmission, total cost of the network, and the shortest transmission path. Hence, based on the selected type of awareness, a fuzzy inference system selects the proper path for routing data. MAQD is a query driven protocol, accordingly, SN can collect data from some/all sensors by employing a request (REQ) message in which the awareness type is specified. MAQD is simulated and tested using OPNET 14.5 and compared with the latest MWSN routing protocols. Simulation results have shown that MAQD outperforms the selected competitors routing protocols (LEACH, ERTLD, RACE, SPIN, EAR2, DCBM, and Rumor routing) as it introduces the best data delivery with the minimum routing overheads in terms of time penalties and power consumed.

A Reliable Routing Protocol for Vehicular Ad hoc Networks

Abstract This paper introduces a Reliable Routing Protocol (R2P) for Vehicular Ad-hoc Networks (VANETs), which divides the network into overlapping zones. For each zone, a special node is promoted to be the Master Node (MN), which maintains an up-to-date routing boards for inter/intra-zone communication. R2P depends on two types of boards, namely; Internal Routing Board (IRB) and External Routing Board (ERB). Two types of IRB are used, namely; Zone Routing Board (ZRB) that is maintained by MNs, and Private Routing Board (PRB) that is maintained by each network node. Both ZRB and PRB register routes among zone nodes, while ERB, which is maintained by MN, registers available gateways to neighboring zones. R2P employs a special route discovery mechanism to discover available routes to the destination, and then elects the most reliable route. It has been compared against the recent VANETs routing protocols. Experimental results have shown that R2P outperforms the others.