International Journal of Information and Communication Technology Research
Year:2018 | Volume:10 | Issue:2|Papers Count:6

Software defined networking (SDN) is an emerging networking paradigm which gained a lot of interests from both academia and industry. SDN decomposes the control plane from the data plane and allows for the network logic to run on a software-based controller. This decomposition of network operations offers the simplification of network management and greater ease of configuration through network programmability. In this paper, we first comprehensively discuss the state-of-the-art experiences in this field and their existing challenges. We also categorize the challenges facing SDN and its integration with other technologies such as network function virtualization (NFV), internet of thing (IoT), 5G networks, big data, and optical networks. Then, considering the key issues, we revisit the SDN architecture and propose a comprehensive conceptual framework and some detailed architectural considerations for enhancing the capabilities of SDN networks. The proposed conceptual framework also applies IoT controllers and hypervisors to support IoT applications and virtualized networks respectively. Moreover, we present future research works according to the proposed framework.

Internet of things (IoT) is the intersection of many different technologies and networks. The management of these is very complex and important. One of the dimensions that must be considered in IoT management is the fault tolerance level of the systems. Of particular importance is the fact that lack of, or a weakness of this feature would threaten the survival and existence of the system. In this paper, a framework called FISWSN is proposed to evaluate the fault tolerance feature of protocols and frameworks in the first layer of the IoT architecture (Sensing layer). Given that the Fault Tolerance is a qualitative parameter, the linguistic variables and fuzzy logic are used to implement the proposed framework. FISWSN is used to elect optimal protocols and architectures with the aim of increasing fault tolerance and higher efficiency in the final output but at a lower cost.

Many process-aware organizations need to monitor the execution of their Business Processes (BP). Changes in BP execution can be reported as events while real-time detection of event patterns from such events can help the monitoring of model-execution conformance or business activities. Complex Event Processing (CEP) techniques can detect event patterns that are specified as CEP rules. Given the high rate of events and numerous number of complex rules, existing CEP-based solutions are not scalable. We present a novel scalable Event Driven Process Monitoring Mechanism (EDBPM) using distributed CEP. Events are partitioned by process instance identifier and the events of each partition is dispatched to a compute node. As such, the processing load of BP monitoring is distributed adaptively to compute nodes in a load balanced manner. Using a prototyped implementation of EDBPM we show that EDBPM scales well horizontally, i. e. increases in throughput are nearly linear when the number of compute nodes increases. Compared to CPU and memory balancing in a general purpose distributed CEP-based solution, EDBPM keeps the CPU load doubly balanced and does balance the memory too, which is lacking in similar solutions

The technological advancement has tremendously evolved over the last two decades with continuing changes throughout their system and communication practices. The association between the elements of communication technology and service performance, however, has yet to be explored with special focus on employee empowerment from an organizational behavior point of view. This research’ s main aim is examine the impact of communication technology practices on service performance in the Information Technology (IT) sector. Therefore the mixed method research approach has been selected for the purposes of this study. 20 interviews have been conducted with experts in the field from the Iran Telecommunication Research Centre. 254 data samples were collected from IT sector employees in Iran. Data was used to test the conceptually developed model via the structural equation modeling-partial least square (SEM-PLS) approach. Based on the qualitative data, communication technology practices can be grouped into six areas including technology application, technology of equal opportunity, individual ability in technology, individual knowledge and attitudes towards technology, technological infrastructure, and security. The results show that among other factors, those variables related to technology application, individual knowledge and attitudes towards technology, organizational infrastructure for technology, and technology security have a positive impact on employees’ empowerment; whereas technology application has a reverse effect. Moreover, the present study’ s results show that employee empowerment has a significant relationship to service performance in the ITsector.

Nowadays, literature has been explored adult image detection automatic which is a replacement for human action in the boring task of moderating online content. One of the mistake scenes with high skin exposure, such as people swimming and get a tan, can be have many wrong alarms. Some condition factors like illumination, occlusion, and poses are more important to image-recognize which any system has to able to recognize. Reasonable amounts of illumination variation between the gallery and probe images need to be taken into account in image recognition algorithms. In the context of image verification, two items are important; illumination variation and skin classification, and these two factors will most likely result in misclassification. There is a lack of research in combining two factors of imaging condition for illuminating and determining skin in image recognition system. The purpose of this paper is to determine and design the proposed scheme to solve illumination variation and integrate with skin classification in image recognition. The proposed method will be analyzed and evaluated based on its performance in terms of accuracy and effectiveness. In this paper, image processing is divided into two phases; preprocessing and image processing. We have used 8, 650 images, which are imported from Compaq and Poesia datasets.

Most of today’ s Internet services utilize user feedback (e. g. clicks) to improve the quality of their services. For example, search engines use click information as a key factor in document ranking. As a result, some websites cheat to get a higher rank by fraudulently absorbing clicks to their pages. This phenomenon, known as “ Click Spam” , is initiated by programs called “ Click Bot” . The problem of distinguishing bot-generated traffic from the user traffic is critical for the viability of Internet services, like search engines. In this paper, we propose a novel classification-based system to effectively identify fraudulent clicks in a practical manner. We first model user sessions with three different levels of features, i. e. session-based, user-based and IP-based features. Then, we classify sessions with two different methods: a one-class and a two-class classification that both work based on the well-known K-Nearest Neighbor algorithm. Finally, we analyze our methods with the real log of a Persian search engine. Experimental results show that the proposed algorithms can detect fraudulent clicks with a precision of up to 96% which outperform the previous works by more than 5%.