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

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

Non-Line of Sight Test Scenario Generation for Connected Autonomous Vehicle

Connected autonomous vehicles (CAV) level 4-5 use sensors to perceive their environment. These sensors are able to detect only up to a certain range and this range can be further constrained by the presence of obstacles in its path or as a result of the geometry of the road, for example, at a junction. This is termed as a non-line of sight (NLOS) scenario where the ego vehicle (system under test) is unable to detect an oncoming dynamic object due to obstacles or the geometry of the road. A large body of work now exist which proposes methods for extending the perception horizon of CAV’s using vehicular communication and incorporating this into CAV algorithms ranging from obstacle detection to path planning and beyond. Such proposed new algorithms and entire systems needs testing and validating, which can be conducted through primarily two ways, on road testing and simulation. On-road testing can be extremely expensive and time-consuming and may not cover all possible test scenarios. Testing through simulation is inexpensive and has a better scenario space coverage. However, there is currently a dearth in simulated testing techniques that provides the environment to test technologies and algorithms developed for NLOS scenarios. This thesis puts forward a novel end-to-end framework for testing the abilities of a CAV through simulated generation of NLOS scenarios. This has been achieved through following the development process of Functional, Logical and Concrete scenarios along the V-model-based development process in ISO 26262. The process begins with the representation of the NLOS environment (including the digital environment) knowledge as a scalable ontology where Functional and Logical scenarios stand for different abstraction levels. The proposed new ontology comprises of six layers: ‘Environment’, ‘Road User’, ‘Object Type’, ‘Communication Network’, ‘Scene’ and ‘Scenario’. The ontology is modelled and validated in protégé software and exported to OWL API where the logical scenarios are generated and validated. An innumerable number of “concrete” scenarios are generated as a result of the possible combinations of the values from the domains of each concept’s attributes. This research puts forward a novel genetic- algorithm (GA) approach to search through the scenario space and filter out safety critical test scenarios. A critical NLOS scenario is one where a collision is highly likely because the ego vehicle was unable to detect an obstacle in time due to obstructions present in the line-of-sight of the sensors or created due to the road geometry. The metric proposed to identify critical scenarios which also acts as the GA’s fitness function uses the time-to-collision (TTC) and total stopping time (TST) metric. These generated critical scenarios and proposed fitness function have been validated through MATLAB simulation. Furthermore, this research incorporates the relevant knowledge of vehicle-to-vehicle (V2V) communication technologies in the proposed ontology and uses the communication layer instances in the MATLAB simulation to support the testing of the increasing number of approaches that uses communications for alerting oncoming vehicles about imminent danger, or in other word, mitigating an otherwise critical scenario