FILE TRANSFER SCHEDULING ON NETWORK ELEMENTS USING SWIM FRAMEWORK

File Transfer Scheduling (FTS) is an application in a network management system which is used to launch multiple File transfer session between a network management system and a managed Network Element (NE). The FTS application supervises parallel NE software download or NE Management Information Base (MIB) backup sessions for multiple supported NEs. FTS operations are based on “JOB”, which can be defined as a descriptor file which contains list of NEs and all the parameters required for action to be scheduled. This paper proposes a system which could be used for file transfer operations using SWIM framework

Semantic reasoning in cognitive networks for heterogeneous wireless mesh systems

The next generation of wireless networks is expected to provide not only higher bandwidths anywhere and at any time but also ubiquitous communication using different network types. However, several important issues including routing, self-configuration, device management, and context awareness have to be considered before this vision becomes reality. This paper proposes a novel cognitive network framework for heterogeneous wireless mesh systems to abstract the network control system from the infrastructure by introducing a layer that separates the management of different radio access networks from the data transmission. This approach simplifies the process of managing and optimizing the networks by using extendable smart middleware that automatically manages, configures, and optimizes the network performance. The proposed cognitive network framework, called FuzzOnto, is based on a novel approach that employs ontologies and fuzzy reasoning to facilitate the dynamic addition of new network types to the heterogeneous network. The novelty is in using semantic reasoning with cross-layer parameters from heterogeneous network architectures to manage and optimize the performance of the networks. The concept is demonstrated through the use of three network architectures: 1) wireless mesh network; 2) long-term evolution (LTE) cellular network; and 3) vehicular ad hoc network (VANET). These networks utilize nonoverlapped frequency bands and can operate simultaneously with no interference. The proposed heterogeneous network was evaluated using ns-3 network simulation software. The simulation results were compared with those produced by other networks that utilize multiple transmission devices. The results showed that the heterogeneous network outperformed the benchmark networks in both urban and VANET scenarios by up to 70% of the network throughput, even when the LTE network utilized a high bandwidth

Fighting the network: MANET management in support of littoral operations

Advances in computer processing and communications capabilities have contributed to the recent explosion of mesh network technologies. These technologies’ operational benefits are of particular interest for those operating in the littorals. The dynamic complexities of the littorals force tactical decision-makers to adapt to a constantly changing battlespace in a constrained temporal and spatial environment. Ongoing research into the integration of unmanned systems and sensors as mobile ad-hoc network (MANET) nodes highlights the significant potential to improve situational awareness and force efficiency in the littoral environment. However, difficulties associated with tactical network operations and management make the littorals particularly challenging. There remains a need for a unified approach to managing these networks in a coherent and effective manner. The complexity of the littorals emphasizes the inherent interconnectedness of MANET management and command and control (C2). As a result, new and innovative approaches to C2 are also required. This thesis explores the value of modern network management systems as they contribute to the richness of the human-network interface, as well as the integration of network management and maneuver at the tactical level. The result is a proposal for a novel framework for littoral MANET management and C2 as a corollary of cyber-physical maneuver.http://archive.org/details/fightingnetworkm1094548561Outstanding ThesisLieutenant, United States NavyApproved for public release; distribution is unlimited

Cognitive network framework for heterogeneous wireless mesh systems

Heterogeneous wireless mesh networks (WMN) provide an opportunity to secure higher network capacity, wider coverage and higher quality of service (QoS). However, heterogeneous systems are complex to configure because of the high diversity of associated devices and resources. This thesis introduces a novel cognitive network framework that allows the integration of WMNs with long-term evolution (LTE) networks so that none of the overlapped frequency bands are used. The framework consists of three novel systems: the QoS metrics management system, the heterogeneous network management system and the routing decision-making system. The novelty of the QoS metrics management system is that it introduces a new routing metric for multi-hop wireless networks by developing a new rate adaptation algorithm. This system directly addresses the interference between neighbouring nodes, which has not been addressed in previous research on rate adaptation for WMN. The results indicated that there was a significant improvement in the system throughput by as much as to 90%. The routing decision-making system introduces two novel methods to select the transmission technology in heterogeneous nodes: the cognitive heterogeneous routing (CHR) system and the semantic reasoning system. The CHR method is used to develop a novel reinforcement learning algorithm to optimise the selection of transmission technology on wireless heterogeneous nodes by learning from previous actions. The semantic reasoning method uses ontologies and fuzzy-based semantic reasoning to facilitate the dynamic addition of new network types to the heterogeneous network. The simulation results showed that the heterogeneous network outperformed the benchmark networks by up to 200% of the network throughput