Content storage and retrieval mechanisms for vehicular delay-tolerant networks

Vehicular delay-tolerant networks (VDTNs) were proposed as a novel disruptive network concept based on the delay tolerant networking (DTN) paradigm. VDTN architecture uses vehicles to relay messages, enabling network connectivity in challenging scenarios. Due to intermittent connectivity, network nodes carry messages in their buffers, relaying them only when a proper contact opportunity occurs. Thus, the storage capacity and message retrieving of intermediate nodes directly affects the network performance. Therefore, efficient and robust caching and forwarding mechanisms are needed. This dissertation proposes a content storage and retrieval (CSR) solution for VDTN networks. This solution consists on storage and retrieval control labels, attached to every data bundle of aggregated network traffic. These labels define cacheable contents, and apply cachecontrol and forwarding restrictions on data bundles. The presented mechanisms gathered several contributions from cache based technologies such as Web cache schemes, ad-hoc and DTN networks. This solution is fully automated, providing a fast, safe, and reliable data transfer and storage management, while improves the applicability and performance of VDTN networks significantly. This work presents the performance evaluation and validation of CSR mechanisms through a VDTN testbed. Furthermore it presents several network performance evaluations and results using the well-known DTN routing protocols, Epidemic and Spray and Wait (including its binary variant). The comparison of the network behavior and performance on both protocols, with and without CSR mechanisms, proves that CSR mechanisms improve significantly the overall network performance

Design Issues of Reserved Delivery Subnetworks, Doctoral Dissertation, May 2006

The lack of per-flow bandwidth reservation in today\u27s Internet limits the quality of service that an information service provider can provide. This dissertation introduces the reserved delivery subnetwork (RDS), a mechanism that provides consistent quality of service by implementing aggregate bandwidth reservation. A number of design and deployment issues of RDSs are studied. First, the configuration problem of a single-server RDS is formulated as a minimum concave cost network flow problem, which properly reflects the economy of bandwidth aggregation, but is also an NP-hard problem. To make the RDS configuration problem tractable, an efficient approximation heuristic, largest demands first (LDF), is presented and studied. In addition, performance improvements with local search heuristic is investigated. A traditional negative cycle reduction and a new negative bicycle reduction algorithms are applied and evaluated. The study of RDS configuration problems is then extended to multi-server RDSs. The configuration problem can be similarly formulated as the single-server RDS configuration problem; however, the major challenge of multi-server RDS configuration is the optimal server locations. A number of server placement algorithms are evaluated using simulations. The simulation results show that a class of greedy algorithms provide the best solutions. In addition to configuration problem, the dynamic load redistribution mechanism is studied to improve the tolerance to server failures. A configuration algorithm to build redistribution subnetworks is proposed and evaluated to deal with single server failures in a group of servers. Besides the exclusive bandwidth access, there are potentials to further improve end-to-end performance in an RDS because end hosts can utilize the knowledge about the underlying networks to achieve better performance than in the ordinary Internet. These improvements are illustrated with a source traffic regulation technique to resolve the unbalanced bandwidth utilization problem in an RDS. A per-connection and an aggregated regulation algorithm for single-server and multi-server RDSs are presented and studied

Modelling and Design of Resilient Networks under Challenges

Communication networks, in particular the Internet, face a variety of challenges that can disrupt our daily lives resulting in the loss of human lives and significant financial costs in the worst cases. We define challenges as external events that trigger faults that eventually result in service failures. Understanding these challenges accordingly is essential for improvement of the current networks and for designing Future Internet architectures. This dissertation presents a taxonomy of challenges that can help evaluate design choices for the current and Future Internet. Graph models to analyse critical infrastructures are examined and a multilevel graph model is developed to study interdependencies between different networks. Furthermore, graph-theoretic heuristic optimisation algorithms are developed. These heuristic algorithms add links to increase the resilience of networks in the least costly manner and they are computationally less expensive than an exhaustive search algorithm. The performance of networks under random failures, targeted attacks, and correlated area-based challenges are evaluated by the challenge simulation module that we developed. The GpENI Future Internet testbed is used to conduct experiments to evaluate the performance of the heuristic algorithms developed

Annual Report, 2013-2014

Beginning in 2004/2005- issued in online format onl

Air Force Institute of Technology Research Report 2010

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physic

Conserving energy through neural prediction of sensed data

The constraint of energy consumption is a serious problem in wireless sensor networks (WSNs). In this regard, many solutions for this problem have been proposed in recent years. In one line of research, scholars suggest data driven approaches to help conserve energy by reducing the amount of required communication in the network. This paper is an attempt in this area and proposes that sensors be powered on intermittently. A neural network will then simulate sensors’ data during their idle periods. The success of this method relies heavily on a high correlation between the points mak- ing a time series of sensed data. To demonstrate the effectiveness of the idea, we conduct a number of experiments. In doing so, we train a NAR network against various datasets of sensed humidity and temperature in different environments. By testing on actual data, it is shown that the predictions by the device greatly obviate the need for sensed data during sensors’ idle periods and save over 65 percent of energ