Journal of Advances in Computer Engineering and Technology
Volume:4 Issue: 2, Spring 2018

Nowadays the active traffic management is enabled for better performance due to the nature of the real-time large data in transportation system. With the advancement of large data, monitoring and improving the traffic safety transformed into necessity in the form of actively and appropriately. Per-formance efficiency and traffic safety are considered as an im-portant element in measuring the performance of the system. Although the productivity can be evaluated in terms of traffic congestion, safety can be obtained through analysis of incidents. Exposure effects have been done to identify the Factors and solutions of traffic congestion and accidents.In this study, the goal is reducing traffic congestion and im-proving the safety with reduced risk of accident in freeways to improve the utilization of the system. Suggested method Man-ages and controls traffic with use of prediction the accidents and congestion traffic in freeways. In fact, the design of the real-time monitoring system accomplished using Big Data on the traffic flow and classified using the algorithm of random-ized forest and analysis of Big Data Defined needs. Output category is extracted with attention to the specified characteristics that is considered necessary and then by Alarms and signboards are announced which are located in different parts of the freeways and roads. All of these processes are evaluated by the Colored Petri Nets using the Cpn Tools tool.

Nowadays, the growing demand for supporting multiple applications causes to use multiple IPs onto the chip. In fact, finding truly scalable communication architecture will be a critical concern. To this end, the Networks-on-Chip (NoC) paradigm has emerged as a promising solution to on-chip communication challenges within the silicon-based electronics. Many of today’s NoC architectures are based on grid-like topologies which are also used in application-specific design.The small world network idea recently has been introduced in order to optimize the performance of the Networks-on-chip. Based on this method the architecture will be neither fully customized nor completely regular. Results have shown that by using the long-range links which optimized the network power and performance, the area consumption will exceed. We can derive from this that an acceptable bound on the area consumption should be considered. Based on the restriction of a designer, in this paper we want to present a methodology that will automatically optimize an architecture while at the same time considering the area consumption.

Over the past few decades great efforts were made to solve uncertain hybrid optimization problems. The n-Queen problem is one of such problems that many solutions have been proposed for. The traditional methods to solve this problem are exponential in terms of runtime and are not acceptable in terms of space and memory complexity. In this study, parallel genetic algorithms are proposed to solve the n-Queen problem. Parallelizing island genetic algorithm and Cellular genetic algorithm was implemented and run. The results show that these algorithms have the ability to find related solutions to this problem. The algorithms are not only faster but also they lead to better performance even without the use of parallel hardware and just running on one core processor. Good comparisons were made between the proposed method and serial genetic algorithms in order to measure the performance of the proposed method. The experimental results show that the algorithm has high efficiency for large-size problems in comparison with genetic algorithms, and in some cases it can achieve super linear speedup. The proposed method in the present study can be easily developed to solve other optimization problems.

Effective and congestion-aware routing is vital to the performance of network-on-chip. The efficient routing algorithm undoubtedly relies on the considered selection strategy. If the routing function returns a number of more than one permissible output ports, a selection function is exploited to choose the best output port to reduce packets latency. In this paper, we introduce a new selection strategy that can be used in any adaptive routing algorithm. The intended selection function is named Modified-Neighbor-on-Path, the purpose of that is handling the condition of hesitation happening when the routing function provides a set of acceptable output ports. In fact, number of inquiries that each router has sent to its neighbors in determined past cycles is a new parameter that can be combined with number of free slots of adjacent nodes in the latest selection function named Neighbor-on-Path. Performance analysis is performed by using exact simulation tools under different traffic scenarios. Outcomes show how the proposed selection function applied to West-first and North-last routing algorithms outperforms in average delay up to 20 percent on maximum and an acceptable improvement in total energy consumption.

Security is the major area of concern in communication channel. Security is very crucial in wireless sensor networks which are deployed in remote environments. Adversary can disrupt the communication within multi hop sensor networks by launching the attack. The common attacks which disrupt the communication of nodes are packet dropping, packet modification, packet fake routing, badmouthing attack and Sybil attack. In this paper we considered these attacks and presented a solution to identify the attacks. Many approaches have been proposed to diminish these attacks, but very few methods can detect these attacks effectively. In this simple scheme, every node selects a parent node to forward the packet towards base station or sink. Each node append its unique identity and trust to the parent as a path marker. It encrypts the bytes using a secret key generated and shared among the sink. The encrypted packet is then forwarded to the parent node. Base station can identify the malicious nodes by using these unique identity and trust value.

Internet of Things (IoT) is a novel and emerging paradigm to connect real/physical and virtual/logical world together. So, it will be necessary to apply other related scientific concepts in order to achieve this goal. The main focus of this paper is to identify the research topics in IoT. For this purpose, a comprehensive study has been conducted on the vast range of research articles. IoT concepts and issues are classified into some research domains and sub-domains based on the analysis of reviewed papers that have been published in 2015 & 2016. Then, these domains and sub-domains have been discussed as well as it is reported their statistical results. The obtained results of analysis show the most of the IoT research works are concentrated on technology and software services domains similarly at first rank, communication at second rank and trust management at third rank with 19%, 14% and 13% respectively. Also, a more accurate analysis indicates the most important and challenging sub-domains of mentioned domains which are: WSN, cloud computing, smart applications, M2M communication and security. Accordingly, this study will offer a useful and applicable broad viewpoint for researchers. In fact, our study indicates the current trends of IoT area.