Delivery of data-enriched applications has become a top priority on the Internet, and Internet users are demanding faster and higher-quality services. Cater such requirements, Content Delivery Networks (CDNs) were introduced. However, the growth rate of information on the Internet requires infrastructural modifications to keep the consistency while maintaining quality of the Internet services. To this end, the Service-oriented Router is introduced to provide content based services by shifting the current Internet infrastructure to information-based open innovation platform. In this study, initially we provide implementation notes of a software-designed SoR. Then we propose a new method of CDN Request Redirection (RR) (SoR-based RR), which is designed to redirect packets based on the content of packets and the status of content servers using an SoR as an edge router of a CDN. Furthermore, we present the design and implementation of a prototype to realize the SoR-based RR in a testing network. By analyzing the result of the prototype implementation, we show that the SoR-based RR can enhance the both client experience and faster adaptations to the server changes in CDN environments.

Not very long ago, organizations used to identify their customers by means of one-factor authentication mechanisms. In today's world, however, these mechanisms cannot overcome the new security threats at least when it comes to high risk situations. Hence, identity providers have introduced varieties of two-factor authentication mechanisms. It may be argued that users may experience difficulties at time of authentication in systems that use two-factor authentication mechanisms for example because they may be forced to carry extra devices to be authenticated more accurately. This is however the tradeoff between ease-of-use and having a secure system that may be decided by the users and not the security providers. In this paper we present a new two-factor authentication mechanism that secures systems and at the same time is easier to use. We have used mnemonic features and the cache concept to achieve ease-of-use and security, respectively. Also, we have tested our method with almost 6500 users in real world using The Mechanical Turk Developer Sandbox.

For more than half a century, Forward Error Correction Convolutional Codes (FEC-CC) have been in use to provide reliable data communication over various communication networks. The recent high increase of mobile communication services that require both bandwidth intensive and interactive Real Time Applications (RTAs) impose an increased demand for fast and reliable wireless communication networks. Transmission burst errors; data decoding complexity and jitter are identified as key factors influencing the quality of service of RTAs implementation over wireless transmission media. This paper reviews FEC-CC as one of the most commonly used algorithm in Forward Error Correction for the purpose of improving its operational performance. Under this category, we have analyzed various previous works for their strengths and weaknesses in decoding FEC-CC. A comparison of various decoding algorithms is made based on their decoding computational complexity.

The present work compares single and multiple watermarking by using discrete wavelet transform and different embedding methods. The different embedding methods are additive, multiplicative and hybrid watermarking with importance on its robustness versus the imperceptibility of the watermark. The objective quality metrics are demonstrated that, the additive embedding method achieves superior performance against watermark attacks on multiple watermarking technique.

Since distribution substations connect transmission lines and distribution system, planning these substations is a very important step in scheduling process of power system and optimal placement of distribution transformers for reducing the involved loss is of concern. In this paper a new model is [proposed for solving optimal substation planning of distribution networks. In the objective function, we have included both capital and operation (loss) costs. The optimization problem is solved by genetic algorithm (GA). In order to evaluate the effects of load imbalance in low-voltage system, a group of balanced one-phase and three-phase and imbalanced three-phase in 4-wire system are used. As a case study, the optimization model is tested on the 33-bus system. Simulation results show that the total cost is reduced by balancing the load of different phases. Also, the results confirm a 48 % reduction in loss and an 11 % reduction in costs of the network planning. Moreover influence of energy cost change and load change on distribution network planning is evaluated by investigating on 33-bus system.

Clustering in Mobile Ad-hoc Networks (MANETs) has become a crucial research issue in modern days, because clustering can improve system performance of MANETs. As MANETs have limited battery power supply, cluster formation is costly in terms of power depletion of nodes in the cluster. This is due to the huge amount of messages transmission required during cluster formation and further data transfer. In this paper, an extensive literature survey of MANET clustering algorithm was done and lastly we proposed an algorithm for cluster head selection and cluster maintenance. The proposed algorithm uses self-organizing principle for binding a node with a cluster, which can reduce the explicit message passing in cluster maintenance. Thus, there is no need of extra message passing during further cluster maintenance. Unlike most usual methods, a cluster head in our proposed algorithm acts only as an identifying tag for cluster entity, further it performs as a typical node when it is acting as head. It does not perform any cluster maintenance function or hierarchical routing and hence is not a bottleneck to the functioning of the cluster.

The problem of task assignment is one of the most fundamental among combinatorial optimization problems. Solving the Task Assignment Problem is very important for many real time and computational scenarios where a lot of small tasks need to be solved by multiple processors simultaneously. A classic problem that confronts computer scientists across the globe pertaining to the effective assignment of tasks to the various processors of the system due to the intractability of the task assignment problem for more than 3 processors. Several Algorithms and methodologies have been proposed to solve the Task Assignment Problem, most of which use Graph Partitioning and Graph Matching Techniques. Significant research has also been carried out in solving the Task Assignment Problem in a parallel environment. Here we propose a modified version of iterated greedy algorithm that capitalizes on the efficacy of the Parallel Processing paradigm, minimizing the various costs along with the duration of convergence. The central notion of the algorithm is to enhance the quality of assignment in every iteration, utilizing the values from the preceding iterations and at the same time assigning these smaller computations to internal processors (i.e. parallel processing) to hasten the computation. On implementation, the algorithm was tested using Message Passing Interface (MPI) and the results show the effectiveness of the said algorithm.

Feature selection is always beneficial to the field like Intrusion Detection, where vast amount of features extracted from network traffic needs to be analysed. All features extracted are not informative and some of them are redundant also. We investigated the performance of three feature selection algorithms Chi-square, Information Gain based and Correlation based with Naive Bayes (NB) and Decision Table Majority Classifier. Empirical results show that significant feature selection can help to design an IDS that is lightweight, efficient and effective for real world detection systems.