Mohamed Zaki

ICSSSS: An intelligent channel selection scheme for cognitive radio ad hoc networks using a self organized map followed by simple segregation

Abstract In Cognitive Radio Ad Hoc Networks (CRAHNS), several spectrum bands with different channel characteristics may be available over a large frequency range. It is essential to identify the most appropriate spectrum band correctly which allow the Secondary Users (SUs) to exploit the band without disturbing the Primary Users (PUs). Many channel selection solutions, based on cooperative spectrum sensing, have been employed for this purpose depending on their prediction models for primary users’ activities. In practice, cooperative spectrum sensing cannot completely solve the sensing problems which are false alarm and miss detection, especially in heavily shadowed or fading environment. This paper presents, ICSSSS, as an Intelligent Channel Selection Scheme for cognitive radio ad hoc network using Self organized map followed by simple Segregation. The contribution of the proposed scheme is twofold: using an unsupervised learnable Self Organizing Map (SOM) method to efficiently minimize the probability of the sensing errors (false alarm and miss detection), in addition to segregated channel selection strategy to speed up the search for the available best channel. Simulation results based on NS2 simulations show that the proposed scheme can be used with the advantage of better performance than other existing channel selection strategies.

A generalized object detection system using automatic feature selection

The problem of object detection in image and video has been treated by a large number of researchers. Many design factors degrade the reliability of the problem solutions, such as manual modeling of the object, manual features selection, handcrafting architecture, and learning algorithm selection. Here, a generalized object detection and localization system is presented. It has the ability to learn the object model with the processes of feature selection and architecture building automated by adopting the AdaBoost algorithm as a feature selection and meta-learning algorithm. The output of the training phase is a cascade of classifiers which can be used to classify parts of an image within a search window as either object or non object.

Location Management in PCS Networks by Caching Two-Level Forwarding-Pointers

One of the challenging tasks in Personal Communication Services (PCS) is to efficiently maintain the location of PCS subscribers who move from one region to another (hereafter called mobile users). When a mobile user receives a call, the network has to quickly determine its current location. The existing location management scheme suffers from high signaling traffic in locating the mobile users. Two-level forwarding pointer scheme has been proposed from per-user forwarding pointer scheme to reduce the cost of signaling traffic. In this paper, we enhance the two-level forwarding pointer scheme. When a mobile user moves from its current Registered Area (RA), which is served by Mobile Switching Center (MSC), to another RA the local switch that acts as a parent of those two MSCs maintains this movement in its memory (hereafter called cache entry). A cache entry is used to locate rapidly the mobile user instead of querying the Home Location Register (HLR) and waiting for its reply. HLR is centralized in the network and far away from the mobile users so that the signaling traffic crossing it is expensive. Sometimes the cache entry may be failed to reach the mobile user then a two-level forwarding pointers will be created from the corresponding Visitor Location Register (VLR), attached to its MSC, through a correct path to locate the mobile user. Thus, there is a saving in cost of querying the underlying HLR. The analytical results indicate that such proposal efficiently reduces the signaling traffic cost for all values of Call to Mobility Ratio (CMR), this is especially considerable when CMR ≥1, without any increase in the call setup delay.

Two-level forwarding pointers-based cache scheme for location management in PCS networks

One of the challenging tasks in a personal communication services (PCS) environment is to efficiently maintain the location of PCS subscribers who move from one region to another (hereafter called mobile users). When a mobile user receives a call, the network has to quickly determine its current location. The existing location management scheme suffers from high signaling traffic in locating the mobile users. Two-level forwarding pointer has been proposed from a per-user forwarding pointer scheme to reduce the cost of signaling traffic. In this paper, we elaborate the two-level forwarding pointer scheme; we use the cache information that stored in a local switch to locate rapidly the mobile user. The analytical results indicate that our proposal efficiently reduces the signaling traffic cost for users without increasing much of the call setup delay

Anti-spyware Security Design Patterns

Spyware is deemed a big danger to the privacy and secrecy of both individuals and firms, it frequently results in the damage and loss of electronic data stored on all types of computers. The main challenges in designing anti-spyware systems are the need for updating data describing spywares and changing the structure of old programs to detect new or unknown spywares. In this paper, we aimed to propose a security pattern for spyware detection. We achieved our objective by introducing a framework for anti-spyware system that combine data mining and design patterns in creating security patterns able to detect and classify spyware efficiently. The proposed anti-spyware system is characterized by its reusability, in sense that it can modify itself for any new or unknown spyware. The experimental results of the proposed approach outperform all the previous competing approaches.

A quality of service distributed optimizer for Cognitive Radio Sensor Networks

In Cognitive Radio Sensor Networks (CRSNs), a sensor node is provided with a cognitive radio unit to overcome the problem of frequency spectrum being crowded. Sensor nodes sense frequency gaps for Primary Users (PUs) to work as Secondary Users (SUs). However, Quality of Service (QoS) requirements for sensor nodes such as maximizing throughput and minimizing transmission power conflicts with minimizing interference between sensor nodes and PUs. Existing works have optimized QoS parameters considering frequency interference problem using Genetic Algorithms (GA) and Simulating Annealing (SA). In this paper, a distributed optimizer for CRSNs based on advanced multi-objective evolutionary algorithms named Non-dominated Sorting Genetic Algorithm (NSGA-II) has been proposed. A set of accurate fitness functions for NSGA-II implementation that fully control evolution of the algorithm have been employed. To the best of our knowledge, there is no published research in CRSN that contains all these intrinsic fitness functions in one system model. Simulation results show that the proposed optimizer can work as a distributed solution for CRSNs because it achieves a minimum number of iterations and minimum coverage time to reach an optimal solution compared to GA and SA. Such minimization matches the energy requirement for the underlying sensor nodes.

A Best Fit Relocation Approach for Heterogeneous Sensor Networks

The heterogeneity of sensing devices has to be taken into account for increasing the network performance and lifetime. This paper presents a study for the sensor relocation problem based on the heterogeneity point of view. A novel approach named Best Fit Relocation Approach, BFRA, is proposed for heterogeneous sensors in order to maximize the coverage of the monitored field and guarantee the connectivity of the deployed sensors. This approach proposes new computational geometry algorithms with perfect complexity to be exploited in small and large-scale sensor networks. A simulation tool is proposed to perform a set of experiments to evaluate the proposed algorithms for different sensor characteristics taking into consideration the curly of field boundaries and the presence of obstacles. Simulation results show that near-optimal coverage performance could be achieved in much less both running time and average moving distance.

Semantic mediation between ontologies

Mediators describe elements that handle interoperability problems between different web services. A particular type of mediators is the data mediator, which is used to solve the terminological mismatches between ontologies. Ontology is a specification of conceptualisation which represents a key element of Semantic Web Services (SWS). Formally, it specifies the terminology used to describe a SWS functionality and behaviour. When the web services interact with each other and there are some requests needed from these services, the instance data must be translated from terms of a service into terms of the other service which is called instance transformation. This paper presents a design and an implementation of a mediator, to handle interoperability between two different ontologies. Different types of transformation services are presented and several transformation operations are explained in order to enable the mapping process. A new tool is proposed which has a facility to build ontologies with the ability for creating mediators. The proposed mediator can exploit many types of transformation services. The advantages of that mediator over the previous ones are explained and its implementation is investigated.

An Integrated Model to Design PCS Networks

An integrated model is proposed that comprises essentially, an Enhanced Profile-Based Strategy (EPBS) for small-scale roaming and a Caching Two-Level Forwarding Pointer (C2LFP) strategy for large-scale roaming. The idea behind the integrated model is how those two location management solutions are applied, and what is the suitable approach to specify the physical parameters of PCS networks from mobility management’s point of view so that our solutions can be more cost effective for location management. An evolutionary method, using a constrained Genetic Algorithm (GA) has been used to achieve network parameters optimization. For convenience, we selected the underlying planning problem with an appropriate set of parameters so that it can be treated, in what follows, both genetically and analytically. Thus one can easily verify the accuracy and efficiency of the evolutionary solution that would be obtained from the genetic algorithm. For more realistic environments, GA could be used reliably to build up sophisticated models that integrate the small-scale and large-scale roaming parameters of PCS networks. The results that have been obtained from a case study are presented in order to provide a deep explanation for the proposed integration approach.