30 research outputs found
An efficient pending interest table control management in named data network
Get PDFNamed Data Networking (NDN) is an emerging Internet architecture that employs a new network communication model based on the identity of Internet content. Its core component, the Pending Interest Table (PIT) serves a significant role of recording Interest packet information which is ready to be sent but in waiting for matching Data packet. In managing PIT, the issue of flow PIT sizing has been very challenging due to massive use of long Interest lifetime particularly when there is no flexible replacement policy, hence affecting PIT performance. The aim of this study is to propose an efficient PIT Control Management (PITCM) approach to be used in handling incoming
Interest packets in order to mitigate PIT overflow thus enhancing PIT utilization and
performance. PITCM consists of Adaptive Virtual PIT (AVPIT) mechanism, Smart Threshold Interest Lifetime (STIL) mechanism and Highest Lifetime Least Request (HLLR) policy. The AVPIT is responsible for obtaining early PIT overflow prediction and reaction. STIL is meant for adjusting lifetime value for incoming Interest packet while HLLR is utilized for managing PIT entries in efficient manner. A specific research
methodology is followed to ensure that the work is rigorous in achieving the aim of the study. The network simulation tool is used to design and evaluate PITCM. The results of study show that PITCM outperforms the performance of standard NDN PIT with 45% higher Interest satisfaction rate, 78% less Interest retransmission rate and 65% less Interest drop rate. In addition, Interest satisfaction delay and PIT length is reduced significantly to 33% and 46%, respectively. The contribution of this study is important for Interest packet management in NDN routing and forwarding systems. The AVPIT and STIL mechanisms as well as the HLLR policy can be used in monitoring,
controlling and managing the PIT contents for Internet architecture of the future
Develop a new handling method for selfish nodes in mobile ad-hoc networks
Get PDFMobile ad-hoc networks (MANETs) have been a crucial element of next-generation wireless networking technologies during the last decade. Because they allow users to access information and communicate with each other without infrastructure. Selfishness is one of the numerous undesirable behaviors that MANET network nodes may exhibit since this selfish node attempts to safeguard its own resources while accessing the services of other nodes and consuming their resources. Hence, a potential that the network's overall performance may degrade. This study developed a new method named detection, reintroduced, and collaborative of selfish node (DRCSN) that proposed detecting selfish nodes based on two factors: energy and the communication ratio (CR) and handling the rate of selfish nodes. Thus, selfish nodes were exploited to the maximum degree and significantly improve network performance. DRCSN was implemented inside ad-hoc on-demand distance vector (AODV) protocol. The test scenarios were implemented using the network simulator-2 (NS-2); many scenarios were created according to two important network parameters: the number of nodes and movement nodes. The proposed method improved the MANET's performance by increasing both the throughput and packet delivery ratio in the network in addition to that it reduced retransmission rate, delay, and power consumption compared to the related methods
Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care
Get PDFRule-based classification in the field of health care using artificial intelligence provides solutions in decision-making problems involving different domains. An important challenge is providing access to good and fast health facilities. Cervical cancer is one of the most frequent causes of death in females. The diagnostic methods for cervical cancer used in health centers are costly and time-consuming. In this paper, bat algorithm for feature selection and ant colony optimization-based classification algorithm were applied on cervical cancer data set obtained from the repository of the University of California, Irvine to analyze the disease based on optimal features. The proposed algorithm outperforms other methods in terms of comprehensibility and obtains better results in terms of classification accuracy
Improving spam email detection using hybrid feature selection and sequential minimal optimisation
Get PDFCommunication by email is counted as a popular manner through which users can exchange in-formation. The email could be abused by spammers to spread suspicious content to the Internet users. Thus, the need to an effective way to detect spam emails are becoming clear to keep this information safe from malicious access. Many methods have been developed to address such a problem. In this paper, a machine learning technique is applied to detect spam emails. In this technique, a detection system based on sequential minimal optimization (SMO) is built to classify emails into two categories: spam and non-spam (ham). Each email is represented by a set of features extracted from its textual content. A hybrid feature selection is developed to choose a subset of these features based on their importance in process of the detection. This subset is then input into the SMO algorithm to make the detection decision. The use of such a technique provides an efficient protective mechanism to control spams. The experimental results show that the performance of the proposed method is promising compared with the existing methods
Performance evaluation of the replacement policies for pending interest table
Get PDFInformation (content) plays an important role in a Named Data Networking (NDN). Hence, an information model is essential in representing information appropriately to supports meaningful information spreading. As a distinction from the current network practice, the NDN shall concentrate on the content itself, rather than the location of the information itself. One important and common feature of NDN is leveraging through its built-in network caches (temporal store) to improve the communication and efficiency of content dissemination. Thus, caching is well thought-out as one of the most crucial features (especially in PIT) of the NDN. Its efficiency is due to it required feature of producing a flexible strategy in deciding what content to store and replace when the PIT overflows. Thus, PIT management in NDN continues being one of the primary concerns of high-speed forwarding. To address this issue, replacement policies, as one of the key factors for determining the effectiveness of a PIT in line with many researcher's haven to propose numerous replacement policies, i.e. LRU, Random and Persistent, which have been projected to attain the improved Interest drop rate, reduce the delay and Interest retransmission as when the PIT is full. However, to the best of our knowledge, there have not been studies that dealt with the performance and evaluation between the mentioned policies under different network topologies. Therefore, in this paper we study the performance of Interest drop rate, delay and Interest retransmission under different network topologies, i.e. Tree, Abilene and Germany when the PIT is full. The significance yearned for this study would be to provide a solid starting point in research directions of new PIT replacement policies for contemporary workload or selectively turning off of fewer used cache ways
