Earticle

The localization requirements for mobile nodes in wireless (sensor) networks are increasing. However, most research works are based on range measurements between nodes which are often oversensitive to the measurement error. In this paper we propose a location estimation scheme based on moving nodes that opportunistically exchange known positions. The user couples a linear matrix inequality (LMI) method with a barycenter computation to estimate its position. Simulations have shown that the accuracy of the estimation increases when the number of known positions increases, the radio range decreases and the node speeds increase. The proposed method only depends on a maximum RSS threshold to take into account a known position, which makes it robust and easy to implement. To obtain an accuracy of 1 meter, a user may have to wait at the same position for 5 minutes, with 8 pedestrians moving within range on average.

With the growth of the Internet over the last decade, a key challenge for Internet infrastructure has been delivering increasingly large-scale content to a growing user population. CDN and P2P are two dominant technologies to improve distribution effectiveness. CDN may reduce the user-perceived latency, but it has shortage of expensive deployment. P2P can decrease the deployment cost, while it is of weakness to assure QoS when there are insufficient peers. In this paper, we present a hybrid content distribution network (HCDN) integrating complementary advantages of CDN and P2P, which is used to improve efficiency of large-scale content distribution. To achieve in-depth understanding of HCDN’s effectiveness, we carry out a detailed performance evaluation based on deterministic fluid model. We also provide numeric results of HCDN, conventional CDN and pure P2P. Some performance metrics are taken into account in our analysis, such as the evolution of the number of seeds and downloaders, the service capacity of system and the average downloading time.

Recently, the attack trends have been changed from fast and widespread malware propagation attacks to more sophisticated “targeted” attacks such as spy/adware, password stealers, ransom-ware, and botenets etc. and the attacks are tried via the automated malwares. In this situation, the malware is the most powerful weapon for the attackers. So, the attackers do not want their malwares to be reviled by anti-virus analyzer. In order to conceal their malware, malware programmers are getting utilize the anti reverse engineering techniques and code changing techniques such as the packing, encoding and encryption techniques. If the malware is packed or encrypted, then it is very difficult to analyze. Therefore, to prevent the harmful effects of malware and to generate signatures for malware detection, the packed and encrypted executable codes must initially be unpacked. The first step of unpacking is to detect the packed executable files. In this paper, a packed file detection technique based on a PE Header Analysis is proposed. In many cases, to pack and unpack the executable codes, PE files have unusual attributes in their PE headers. In this paper, these characteristics are utilized to detect the packed files. A Characteristic Vector (CV) that consists of eight elements is defined, and the Euclidean distance (ED) of the CV is calculated. The EDs of the packed files are calculated and represent the base threshold for the detection of packed files.

UWB signal reception in a ranging system can be accomplished by either coherent receiver or non-coherent receiver. Correlation receiver is a type of coherent receiver while energy detection (ED) receiver falls into the non-coherent category. In this paper, we evaluate their ranging performances using the channel profiles measured in various indoor environments. The effects of important system parameters such as sampling rate and threshold on the ranging performance are also investigated. Results show that ranging accuracy is heavily dependent on the system parameters settings, especially in LOS environment. The correlation receiver requires much higher sampling rate to achieve the same ranging accuracy as compared to ED receiver. The study also shows that regardless of receiver type used, the resultant ranging error for NLOS environments can be described by a unified model. The proposed model is validated by measurement data.

As data transfer rates become higher, general processor is not competent for works with high computing intensity in wireless communication area. The other hand, existing baseband processors lack adaptive ability to support new applications and newer versions of existing application. Thus the paper puts forward a novel architecture of reconfigurable stream processor along with application-specific instruction set for software radio. The main goal of the processor is to point out an effective way to designing hardware of software radio with high computational performance and adaptive ability. We achieve this by analyzing multi-stream modalities of advanced wireless communication standards, such as 3G and WLAN. Simulation results show that the processor with powerful calculation capability can adapt to applications of wireless communication area easily.

Spectral density approach for distinguishing graphs was studied in this paper. Firstly, Spectral density approach was testified for being effective in distinguishing different graphs by making comparisons among the spectrums of three different kind of graphs, the ER random graph, BA scale-free graph and the Internet topology graph. Secondly, we focused our studies on the properties of Internet graph that its spectrum could represent, and found that in standard spectral density analysis part, we found that the spectral density plot of Internet graph has a feature of having a maximum when λ=0 and the second maximum whenλ=0.5 around. In SLS analysis part, we found the SLS spectrum had a set of highest tuples when SLS=1 and second highest tuples when SLS=2. Besides, a relationship of the power law distribution was observed when SLS>2, but there is no power-law relationship found when SLS<1. What was found here could be used to identify an Internet topology graph properties.

This paper addresses the routing problem in packet switching free-space optical (FSO) mesh networks. FSO mesh networks are emerging as broadband communication networks because of their high bandwidth (up to Gbps), low cost, and easy installation. Physical layer topology design of degree-constrained FSO mesh networks has been studied in a recent communication [1]. In this paper, we propose four different routing algorithms, and evaluate their performances through simulations for a number of FSO mesh networks with different topologies and nodal degrees. The performance parameter against which we evaluate these algorithms is the mean end-to-end delay. Our proposed least cost path (LCP) routing algorithm, which is based on minimizing the end-to-end delay, is considered as the bench mark. The performance of each of other three proposed algorithms is evaluated against the bench mark. Our proposed minimum hop count with load-balancing (MHLB) routing algorithm is based on the number of hops between the source and the destination node to route the traffic. Simulations show that the MHLB routing algorithm performs best in most cases compared with the other two. It results in minimum average delay and least blocked traffic.

Data warehousing is gaining importance day by day in enterprises, as it helps them to improve their business intelligence. The process of creating a data warehouse needs to be automated so that the transactional sources are generated in least time, with maximum accuracy and with minimum dependability on users. This automation proves its worth particularly when working with small and medium enterprises, where hiring of new people just for creating data warehouses can be unaffordable. The technique presented in this paper automates entity relationship model into data warehouse logical model to generate semi-star schema by using artificial neural networks. More precisely, the step of differentiating dynamic behavior dimensions from static behavior dimensions has been automated by using feedforward back-propagation neural networks. This network ascertains dimensions which are sensitive to changes. The network is trained for all the possible values of inputs and has been tested for actual results. The performance of proposed technique is evaluated by comparing certain metrics like simplicity and minimality with existing data warehouse creation techniques.

Caching is one of the major steps in system designing to reach a higher performance in operating systems, databases and World Wide Web. High performance processors need memory systems with a proper access time, but still there is a big gap between performances of processors and memory systems. Virtual memory management and hierarchical memory models play an important role in system performance. In these architectures caching replacement policy defines the enhancement factor of the memory system and could modify the efficiency of the system. Different caching policies have different effects on the system performance. Because of the highlight role of replacement policies in the systems, there have been lots of work and proposed algorithms to overcome the problem of performance gap between processor and memory. Most of these policies are the enhancement of the Least-Recently-Used (LRU) and Least-Frequently-Used (LFU) schemes. Although most of the proposed schemes could solve the defects of the LRU and LFU, but they have lots of overhead and are difficult to implement. The most profit of LRU and LFU is their simple implementation. This article proposes an adaptive replacement policy which has low overhead on system and is easy to implement. This model is named Weighting Replacement Policy (WRP) which is based on ranking of the pages in the cache according to three factors. Whenever a miss occurs, a page with the lowest rank point is selected to be substituted by the new desired page. The most advantage of this model is it’s similarity to both LRU and LFU, which means it has the benefits of both (i.e. in cases like loops in which LRU fails, it will switch to LFU). Simulations show that this algorithm performs better than LRU and LFU. In addition, it performs similarly to LRU in the worst cases. The new approach can be applied as a replacement policy in virtual memory systems and web caching.

With the proliferation of the Internet and the huge amount of data it transfers, automatic text summarization is becoming more important. In this paper we first analyze some state of the art methods to text summarization., We also try to analyze one of the previous text summarization methods, "Machine learning Approach", and eliminate its shortcomings .Finally we present an approach to the design of an automatic text summarizer that generates a summary using fuzzy logic to obtain better results compared to previous methods.

In this paper, we propose a set of dimensioning rules, which deliver high quality session-based services over a Next Generation Network based IP/MPLS transport infrastructure. In particular, we develop a detailed dimensioning methodology for improving a target QoS requirement. The proposed methodology outlines an optimal equipment allocation strategy for a requested capacity. The benefits of operating a network under the paradigm of generous dimensioning, for converged multiservice traffic flows, include target QoS guarantee, scalability, and network resilience. We present and discuss experimental results which illustrate a practical implementation of the proposed dimensioning strategy and its benefits.

The use of story cards for user stories in many Extreme Programming software development projects has been widespread. Several popular traditional methods for story cards (e.g., Cohen M, Kent B) have been used in successful fashion at some extent, but all lack of the powerful features for story cards guidelines, right sort of information on story cards and quality of user stories on story cards. They also do not involve anybody apart from customer on story writing workshop. This paper has described the INSERT model, new proposed frame work of story cards, and a new improved requirements elicitation process in XP. The experience with INSERT model and new framework of story cards indicates that it is feasible to contemplate improving user stories and story cards in Extreme programming. This paper also introduces best practices guidelines for agile requirements engineering to enhance the quality of requirements (story cards).

Loopy Belief propagation is an increasingly popular method of performing approximate inference on arbitrary graphical models. Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data mining. Influence diagrams provide a compact technique to represent problems of decision making especially multi-criteria decision making (MCDM) under uncertainty. As a number of nodes in the network increases, computing exact solutions and making optimal decision becomes computationally intractable. Approximate solution becomes more efficient in term of the performance of execution and the storage space. In particular, the belief propagation (or sum-product) algorithm has become a well-known means of solving inference problems approximately. Therefore, the loopy belief propagation is the alternative way for approximate solution and is presented in this paper. A solution is approximated where high-probability actions under the policy have a high utility. Actions are then selected which have a high probability under the approximating policy. The loopy belief propagation method is shown to compare favorably to exact methods.