NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR
Year:2018 | Volume:16 | Issue:1 |Papers Count:8

Available techniques in handover management in cellular communication networks can’t keep unnecessary events and delay decision at a low level state. The main purpose of this paper is to provide the intelligence method which is able to minimize the number of unnecessary events and allowing the necessary requests to occur and so improves the overall network performance. In order to achieve such a goal, in the proposed method, we have used the geographical knowledge from building maps with spectral clustering in the area covered by femtocell. Therefore, we require to develop the spectral clustering based on geographical information. The experimental results on real dataset and performed simulations indicate that the superiority of the proposed method in allocating the user to appropriate cell and acceptable ability to manage the handover in heterogeneous layer of femtocell-macrocell.

One key challenge in software systems development is changing requirements at development phases or run-time. This might happen as the result of uncertainty in stakeholder requirements. Uncertain requirements drive a flexible and therefore adaptable architecture to manage risks at run-time. Modeling uncertainty to adapt architecture automatically is an effective solution when requirements change. In order to evaluate requirements and handle uncertainty by modeling and self-managing, it is advantageous to quantify requirements, computationally. This study besides understanding the sources of uncertainty, investigates how to quantify requirements and quality attributes. Subsequently, decision making at all software development phases will be based on numerical analysis that leads to autonomic software development.

Classifier is one of the three blocks of a video steganalysis that needs labeled for training. In the blind video steganalysis, due to the lack of access to steganography algorithms, it is difficult to label. In this paper, the semi supervised growing self-organizing map classifier has been used to reach the minimum label. For this purpose, a concept called the geometric redundancy of the lower-layer nodes of the semi supervised self-organizing network has been used. It has been shown that this redundancy will create repetitive patterns of the network, so deleting such nodes is possible. Proven due to the existence of one-to-one correspondence between nodes and labels. Reducing nodes leads to a reduction in the number of labels required. The basic point is the need for a geometric redundancy among a number of nodes, which is a conception of abstraction, is the formation of a group by them. Therefore, the proposed algorithm is based on identifying categories and integrating their members. The classifier obtained on this basis has been named a geometric self-organizing map classifier .It is proven that this classifier can achieve the minimum amount of optimal label. The simulation results show a remarkable superiority over the previous algorithms.

Underwater Wireless Sensor Network (UWSN) is a kind of sensor networks that their operational fields have been developed under water in recent decades, although these networks deal with lots of challenges due to lack of the GPS1. These networks encounter researchers with many challenges by some limitations like high propagation delay, low bandwidth, high bit error rate, movement, limited battery and memory. In comparison with terrestrial sensor networks, sensors in the UWSN consume energy more because they use acoustic technology to communicate. Motivation of this research is proposing a routing protocol for underwater systematic settings with a limited energy. The settled sensor nodes in underwater cannot communicate directly with nodes near surface, so they need prepared multi hop communications with a proper routing plan. In wireless sensor networks, node clustering is a common way to organize data traffic and to decrease intra-network communications along with scalability and load balance improvement plus reducing of overall energy consumption of system. Therefore, in this article a fuzzy clustering routing protocol with data aggregation and balanced energy consumption for UWSNs is proposed. Simulation results show that in the proposed protocol, energy consumption becomes more uniformly distributed in the network and average of the nodes' energy usage and number of routing packets decreases and finally, packet delivery ratio and throughput are improved in the network in comparison with DABC3 and IDACB4 algorithms.

Teenager users apply a limited vocabulary when they proceed to look for their desired materials. Another important issue is that teenagers often click mostly on the first items presented in the list of the search results. This research shows that, in order to amend and compensate these issues, we can extract and suggest a more appropriate query to the teenager user. This can be accomplished by discovering the relevant subject patterns from the behavior of the teenage user according to his or her previous search quarries and based on the already found patterns. In the proposed method, the topic patterns of the user are discovered based on the popularity of the clicks and the most relevant topics from the search logs which are generally massive. Afterwards, by using the binary classification method, the closest query to the query given by the user would be specified. Then, by filtering the subject navigation noise via extraction of the subject patterns of the teen user’s clicks, a user model with a higher accuracy can be obtained. We evaluated performance of the proposed method using the Alteryx and Weka tools, over the AOL search log, which includes about twenty million sample search transactions from six hundred and fifty different users. The results obtained from the experiments indicate that the queries presented by the proposed system are closer to the target user's query, and consequently, leads to achievement of more related results.

Personalized recommender systems and search engines, are two effective key solutions to overcome the information overloading problem. They use the intelligent techniques on users’ interactions to extract their behavioral patterns. These patterns help in making a personalized environment to deliver accurate recommendations. In the technology enhanced learning (TEL) field and in particular resource-based learning, recommendation systems have attracted growing interest. Specially, they are an important module of Adaptive Educational Systems that deliver the appropriate learning objects to their users. Gray-sheep users are a challenge in these systems. They have a little similarity with their peers. So the recommendations to others are not suitable for them. To overcome this problem, we propose the idea of accommodating the user’s learning style to web page features. The user's learning style will be computed according to Felder-Silverman theory. On the other hands, the necessary implicit and explicit meta data will be extracted from OCW web pages. By matching the extracted information, the system delivers the appropriate educational links to user. The user can compare the proposed links, based of our algorithm, to the output of Lucene algorithm. A user’s opinion about every web page as a recommended result would be submitted to the system. The system uses a learning automata algorithm and user’s feedback to deliver best recommendations. The system has been evaluated by a group of engineering students to evaluate its accuracy. Results show that the proposed method outperforms the general search algorithm. This system can be used at formal and informal learning and educational environments for Resource-based learning.

Today, hosting services in geographically distributed data centers is very common among service provider companies, because of more efficiency of energy consumption, high availability of the system, and providing quality of service. Load distribution is the main issue in the geographical data centers. On the one hand, there are several architectures to distribute load between different clusters, e.g., central load balancer, DNS-based systems, and IGP based schemes; one the other hand, the optimum traffic load balancing between clusters is a very challengeable issue. The proposed solutions have different facilities to distribute incoming traffic; nevertheless, they are vulnerable in terms of propagation delay, centralized load balancer failure, and maintaining connections. In this paper, a new architecture based on BGP and Anycast routing protocols in SDN based data centers is proposed to distribute traffic loads between clusters. Simulation result shows improvement in comparison to the existing techniques.

In this paper, we present a new EPC-C1G2 standard based anti-collision algorithm to solve the problem of tags’ starvation in RFID systems. Non deterministic identification of tags in the collisions lots, leads to tags’ starvation phenomenon. In this paper, to address this problem, we use the position of the first different value bits that are the first different bits in the tags’ 16-bit random number (or RN16).In fact, the reader in facing with collision slots, earns the position of the first different-value bit in RN16 and uses it to deterministic identification of tags. Unlike recent anti-collision algorithms, which in them it is assumed that tags send information synchronous or asynchronous, in the proposed anti-collision algorithm in order to obtain the position of the first different-value bit there is not any assumption on synchronous or asynchronous sending information by tags. Based on simulation which is done, the average time of identification for 200 to 2000tags in the proposed anti-collision algorithm relative to average time of identification in EPC-C1G2, BIS and ERN2 anti-collision algorithms is less than 0.92, 0.71, and 0.42 second, respectively.