The traditional centralized network management approach presents severe efficiency and scalability limitations in large scale networks. The process of data collection and analysis typically involves huge transfers of management data to the manager which consumes considerable network bandwidth and causes bottlenecks at the manager side. Mobile agent technology provides an effective solution to alleviate this burden by distributing the management functionality over the network elements. A Mobile Agent has the ability to autonomously move among network elements to perform the required tasks locally. Thus, the code is transferred to the data location instead of moving the entire data to the manager's site.The present study aims to investigate the effectiveness of using mobile agents to overcome the limitations of the centralized structure. Focusing on the network performance management functional area, a prototype is developed to assess the effectiveness of a distributed mobile-agent-based network management system. The developed prototype installs itself automatically on remote machines and periodically checks their software and hardware status. Experiments are done to measure the network traffic volume when managing a typical network. Practical measurements are compared for the traffic generated by both the developed prototype and the current centralized network management standard (SNMP). This comparison confirms that mobileagent-based management employs much less traffic than the centralized system. An estimation of the required management delays is provided for both sequential-and paralleldispatching of the mobile agents.
Wireless sensor networks (WSNs) are vulnerable to security attacks due to the unbounded nature of the wireless medium, restricted node resources, and cooperative routing. Standard cryptography and authentication mechanisms help protect against external attacks, but a compromised node can easily bypass them. This work aims to protect WSNs against internal attacks, which are mostly launched from compromised nodes to disrupt the network’s operation and/or reduce its performance. The trust and reputation management framework provides a routing cost function for selecting the best secure next hop. Tuning the trust weights is essential to cope with the constant changes in the network environment, such as the sensor nodes’ behaviours and locations. To allow real-time operation, the proposed framework introduces an artificial neural network (ANN) in each sensor node that automatically adjusts the weights of the considered trust metrics according to the WSN state. A large dataset is generated to train and test the ANN using a multitude of simulated cases. A prototype is developed and tested using the J-Sim simulator to show the performance gain resulting from applying the adaptive trust model. The experimental results showed that the adaptive model has robust performance and has achieved an improved packet delivery ratio with reduced power consumption and reduced average packet loss. The results showed that when sensor nodes were static and malicious nodes were present, the average accuracy was 99.6%, while when they were in motion, it was 88.1%.
The routing problem is one of the most Impor~ant problems facing the development, improvement, and performance of packet switched computer networks (PSN)o In this paper two, quas~statlc tec~miques~ wh!ch proved to be robust with respect to overall performance, are compared. The first one, uses "The Learning Automata" principle, which is a promising technique regarding its simplisity an~ ease of implementation° This technique is thoroughly investigated. The second, uses "The Deterministic Routing" principle, that is selected for comparison sines it uses the deterministic sequence generation like the first one. The goal is to highlight the efficiency of each technique ~Ith respect to the other. This have been realized using a slm~ated ~O-nodes eaaple network to clarify the advantages arid defficieneies of each technique. The relative perfor~nce of the two algor4_thms is judged on the basis of delay ~nd blocking probability. The ~'esultB are in favour of the learning auto~ta specially in cases of networks of big number of no~es~
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.