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In recent years, the vehicular ad hoc network (VANET) has received great attention, as it is involved in the design of the intelligent transportation system (ITS). The VANET network includes message flows from vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) where the network is propped by wireless communication technology, such as IEEE 1609 WAVE and IEEE 802.11P. The VANET network implementation faces challenges, and one of these challenges is the design of routing protocols that transfer reliable and efficient packets from vehicle to vehicle. In VANET, steering is a challenging task in the highway and urban environment. Therefore, this paper presents an assessment of ad hoc on-demand distance vector protocol (AODV) performance in the highway and urban environment and to study the effect of vehicle density on protocol performance. The AODV protocol was simulated by MATLAB. In this study, the performance of AODV protocol was evaluated through four measures, namely, packet delivery ratio (PDR), overhead, end-to-end (E2E) delay, and dropped packets. The study in our paper showed that the best performance of the AODV protocol is in an environment where vehicle speed and vehicle density are low.

The development in the field of computer technology, and the increase in the growth rate of database, alongside the extraction of certain data from a huge pool of database involve intricate and complex processes. The processes comprise text mining, pattern recognition, retrieval of information and text processing. Thus, the need for enhancing the performance of string matching algorithms is required, which is considered as one of the challenges to the researchers. Consequently, one of the resolution to address this problem is the parallelization for exact string matching algorithms. In this study, we implemented the parallel exact string matching algorithm termed as E-Atheer with multi-core processing utilizing Pthread (POSIX) for the reduction of time consumption. The Pitch, XML, Protein, and DNA database types are utilized to test the impact of the proposed parallel algorithm. The parallelization algorithm obtained positive results in the parallel execution time, and a more superior expediting capabilities, in comparison to the sequential result. The Pitch database indicated optimal results in parallel execution time, and when utilizing long and short pattern lengths. The DNA database indicated optimal speedup performance when utilizing short and long pattern length, meanwhile the XML and Protein on the other hand indicated the worst results.

String matching is considered as one of the center issues within the field of computer science, where there are numerous computer applications that supply the clients with string matching services. The increment within the number of databases which are created and protected in numerous computer gadgets had impacted researchers with the slant towards getting robust techniques in tending to this issue. In this study, the Maximum-Shift string matching algorithm is chosen to be executed with multi-core innovation through the utilization of OpenMP paradigm, in order to decrease the successive time, and increment the speedup and efficiency of the algorithm. The deoxyribonucleic acid (DNA), protein and the English text datasets are utilized to test the parallel execution that influences the Maximum-Shift algorithm execution when utilized with multi-core environment. The results demonstrated that the execution is affected by the performance between the parallel and consecutive execution of Maximum-Shift algorithm by data type. The English text appeared ideal comes about within the parallel execution time as compared to other datasets, whereas the DNA database set appeared the most elevated comes about when compared to other data types in terms of speedup and efficiency capabilities.

one of the popular things among succeeded companies is an automated system. The reasons behind using the automated system are to reduce human errors, work and to gain efficiency. The design focused on building an automated system for access control and attendance monitoring by the use of RFID technology. all the companies that still doing mural attendance will get a great help when it used the proposed system. this paper used RFID (tags, reader), microcontroller to collect data, ZigBee technology for transmitting and receiving data wirelessly, computer as a control station. the program at control station was written by using Visual C# and connect with database (DB) MSSQL server to store employees’ numbers (RFID tags) and used for attendance calculations. The initial results of the proposed system appear a high profit in time-saving and cost when compared with traditional systems. cost divided into two parts, firstly: a price of ZigBee device, secondly: security men

Every day the networks are expanding in very large scale and running in very high speed. The Intrusion Detection System (IDS) becomes as an essential part in any new network structure. The IDS is relying on the string matching algorithm to detect any signature attack, but the high speed of the modern networks are preventing the IDS string matching algorithms to work properly. There is a need to develop a robust IDS string matching algorithms to overcome these weaknesses. In this paper, we continue to developing Boyer-Moore Horspool algorithm by adding a new multi-pattern hashing feature to its original structure, which is called MPH-BMH. The developed algorithm is able to reduce the matching time because it can compare a group of pattern at one time. The results show that MPH-BMH is around 50% faster than BMH and QS algorithms in which it can scan and match large number of network packets in the given time and consequently it can be very useful to work in high speed networks. 

Networks expand every day at a large scale and operate at high speeds. People have become increasingly dependent on the Internet to perform various tasks. Networks require a system that protects against intrusion activities. Therefore, Intrusion Detection Systems (IDSs) have been created. Subsequently, IDSs have become an important part of modern networks to detect suspicious attacks. Modern networks expand and operate at high speeds, which is why handling all network traffic without overlooking security issues is difficult for IDS that run on a single processor. In this paper, we proposed a shared memory design called Hybrid Multi-Pattern Algorithm (HMA) as extension of our previous work, multi-core based HMA. The experiments show that GPGPU based HMA performs better than multi-core based HMA. This finding proves that GPGPU-based HMA is robust and can function properly in a high-speed network.

Computer networks are developing exponentially and running at high speeds. With the increasing number of Internet users, computers have become the preferred target for complex attacks that require complex analyses to be detected. The Intrusion detection system (IDS) is created and turned into an important part of any modern network to protect the network from attacks. The IDS relies on string matching algorithms to identify network attacks, but these string matching algorithms consume a considerable amount of IDS processing time, thereby slows down the IDS performance. A new algorithm that can overcome the weakness of the IDS needs to be developed. Improving the multi-pattern matching algorithm ensure that an IDS can work properly and the limitations can be overcome. In this paper, we perform a comparison between our three multi-pattern matching algorithms; MP-KR, MPHQS and MPH-BMH with their corresponding original algorithms Kr, QS and BMH respectively. The experiments show that MPH–QS performs best among the proposed algorithms, followed by MPH–BMH, and MP–KR is the slowest. MPH–QS detects a large number of signature patterns in short time compared to other two algorithms. This finding can prove that the multi-pattern matching algorithms are more efficient in high-speed networks.

The advancement of computer hardware has opened up new challenges to algorithms experts. One of the challenges is designing new algorithm that exploit the parallel nature of the new architecture of current computer hardware. In this study, we redesign the hybrid exact string matching algorithm named AKRAM on parallel platform with the goal of speeding up the algorithm. The parallel of this algorithm showed optimal results, in parallel time, speed up, efficiency and percentage of performance gain when compared to the sequential version. The result demonstrated that the performance of both parallelized and sequential of AKRAM algorithm was influenced by type of data. The DNA sequence showed the highest performance than other data types after parallelization.

String matching algorithms play a significant role in solving the fundamental problems of computer sciences. This study proposed a new hybrid string matching algorithm called AbdulRazzaq. This algorithm consisted of a combination between the new searching technique and selecting the good features from two original algorithms, namely, Smith and Karp–Rabin. The proposed algorithm showed lower number of attempts and character comparisons than those of the original algorithms and common algorithms in the string matching field. AbdulRazzaq algorithm used six types of databases, namely, DNA, Protein, XML, Pitch, English, and Source. The best database regarding the number of attempts of AbdulRazzaq algorithm was Pitch, whereas DNA was the worst. This algorithm can work with good performance in the number of character comparisons with all databases.

This paper is to study and identify suitable algorithm that use in searching regulatory element where PhyloCon algorithm is one of the algorithms. Since PhyloCon has complexity limitation, the time performance is reduced. Therefore, a parallel technique is identified to improve performance of PhyloCon algorithm. The identified parallel technique is implemented on multicore architecture using OpenMP. This technique use multilevel parallelism which consists of outer parallelism and inner parallelism implemented in Phylocon algorithm. In each level, the data decomposition technique is applied to achieve computational load balancing. The implementation result produced maximum speed up until 2.62 on four processors compared to the sequential PhyloCon results.