Software defined networking based resource management and quality of service support in wireless sensor network applications

To achieve greater performance in computing networks, a setup of critical computing aspects that ensures efficient network operation, needs to be implemented. One of these computing aspects is, Quality of Service (QoS). Its main functionality is to manage traffic queues by means of prioritizing sensitive network traffic. QoS capable networking allows efficient control of traffic especially for network critical data. However, to achieve this in Wireless Sensor Networks (WSN) is a serious challenge, since these technologies have a lot of computing limitations. It is even difficult to manage networking resources with ease in these types of technologies, due to their communication, processing and memory limitations. Even though this is the case with WSNs, they have been largely used in monitoring/detection systems, and by this proving their application importance. Realizing efficient network control requires intelligent methods of network management, especially for sensitive network data. Different network types implement diverse methods to control and administer network traffic as well as effectively manage network resources. As with WSNs, communication traffic and network resource control are mostly performed depending on independently employed mechanisms to deal with networking events occurring on different levels. It is therefore challenging to realize efficient network performance with guaranteed QoS in WSNs, given their computing limitations. Software defined networking (SDN) is advocated as a potential paradigm to improve and evolve WSNs in terms of capacity and application. A means to apply SDN strategies to these compute-limited WSNs, formulates software defined wireless sensor networks (SDWSN). In this work, a resource-aware OpenFlow-based Active Network Management (OF-ANM) QoS scheme that uses SDN strategies is proposed and implemented to apply QoS requirements for managing traffic congestion in WSNs. This scheme uses SDN programmability strategies to apply network QoS requirements and perform traffic load balancing to ensure congestion control in SDWSN. Our experimental results show that the developed scheme is able to provide congestion avoidance within the network. It also allows opportunities to implement flexible QoS requirements based on the system’s traffic state. Moreover, a QoS Path Selection and Resource-associating (Q-PSR) scheme for adaptive load balancing and intelligent resource control for optimal network performance is proposed and implemented. Our experimental results indicate better performance in terms of computation with load balancing and efficient resource alignment for different networking tasks when compared with other competing schemes.Thesis (PhD)--University of Pretoria, 2018.National Research FoundationUniversity of PretoriaElectrical, Electronic and Computer EngineeringPhDUnrestricte

Flexible network management in software defined wireless sensor networks for monitoring application systems

Wireless Sensor Networks (WSNs) are the commonly applied information technologies of modern networking and computing platforms for application-specific systems. Today’s network computing applications are faced with high demand of reliable and powerful network functionalities. Hence, efficient network performance is central to the entire ecosystem, more especially where human life is a concern. However, effective management of WSNs remains a challenge due to problems supplemental to them. As a result, WSNs application systems such as in monitored environments, surveillance, aeronautics, medicine, processing and control, tend to suffer in terms of capacity to support compute intensive services due to limitations experienced on them. A recent technology shift proposes Software Defined Networking (SDN) for improving computing networks as well as enhancing network resource management, especially for life guarding systems. As an optimization strategy, a software-oriented approach for WSNs, known as Software Defined Wireless Sensor Network (SDWSN) is implemented to evolve, enhance and provide computing capacity to these resource constrained technologies. Software developmental strategies are applied with the focus to ensure efficient network management, introduce network flexibility and advance network innovation towards the maximum operation potential for WSNs application systems. The need to develop WSNs application systems which are powerful and scalable has grown tremendously due to their simplicity in implementation and application. Their nature of design serves as a potential direction for the much anticipated and resource abundant IoT networks. Information systems such as data analytics, shared computing resources, control systems, big data support, visualizations, system audits, artificial intelligence (AI), etc. are a necessity to everyday life of consumers. Such systems can greatly benefit from the SDN programmability strategy, in terms of improving how data is mined, analysed and committed to other parts of the system for greater functionality. This work proposes and implements SDN strategies for enhancing WSNs application systems especially for life critical systems. It also highlights implementation considerations for designing powerful WSNs application systems by focusing on system critical aspects that should not be disregarded when planning to improve core network functionalities. Due to their inherent challenges, WSN application systems lack robustness, reliability and scalability to support high computing demands. Anticipated systems must have greater capabilities to ubiquitously support many applications with flexible resources that can be easily accessed. To achieve this, such systems must incorporate powerful strategies for efficient data aggregation, query computations, communication and information presentation. The notion of applying machine learning methods to WSN systems is fairly new, though carries the potential to enhance WSN application technologies. This technological direction seeks to bring intelligent functionalities to WSN systems given the characteristics of wireless sensor nodes in terms of cooperative data transmission. With these technological aspects, a technical study is therefore conducted with a focus on WSN application systems as to how SDN strategies coupled with machine learning methods, can contribute with viable solutions on monitoring application systems to support and provide various applications and services with greater performance. To realize this, this work further proposes and implements machine learning (ML) methods coupled with SDN strategies to; enhance sensor data aggregation, introduce network flexibility, improve resource management, query processing and sensor information presentation. Hence, this work directly contributes to SDWSN strategies for monitoring application systems.Thesis (PhD)--University of Pretoria, 2018.National Research Foundation (NRF)Telkom Centre of ExcellenceElectrical, Electronic and Computer EngineeringPhDUnrestricte

Virtualized dynamic resource allocation algorithm for the internet Diffserv domains

The Differentiated Services (DiffServ) architecture has been proposed for providing different levels of service to the Internet Protocol (IP) traffic. Current discussions in the DiffServ networks are focused on managing resources dynamically according to the traffic conditions of the DiffServ router (Per Hop Behaviour). Software Defined Networks (SDN) and Network Function Virtualisation (NFV) technologies have recently emerged in the research agenda to support researchers in managing network domains and to achieve better use of domain resources. This thesis introduces a new scheduling algorithm called “Dynamic Resource Allocation Management - Network Function Virtualization (DRAM-NFV)” to allocate the service classes resources in the proportional delay DiffServ domains. DRAM-NFV algorithm manages the resources among service classes within the edge routers of the DiffServ domains dynamically according to their traffic conditions and manages these resources between the DiffServ domains in the event of congestion based on their traffic conditions at the egress routers of the upstream domain and ingress routers of the downstream domain. The NFV executes the DRAM-NFV algorithm on a virtualized - Network as a Service (NaaS) - cloud infrastructure to manage the SDN controllers for the edge routers of the DiffServ domains through monitoring the traffic conditions in the service classes at the edge routers and reallocating the out-link resources of the edge routers among service classes. A number of test scenarios were conducted in this research in order to test the performance of the DRAM-NFV algorithm. The performance of DRAM-NFV algorithm is compared with the performance of the DWFQ algorithm by comparing the average End to End Delay for service classes traffic and links utilization. The DWFQ algorithm cannot manage resources between DiffServ domains but can manage the resources locally and dynamically for each DiffServ domain separately. The network simulator NS3 has been used to implement these test scenarios and to test the performance of the DRAM-NFV algorithm. The results show that with the DRAM-NFV algorithm, better balance for DiffServ domains resources can be achieved through monitoring the bandwidth hungry service class at the downstream domain and managing its resources at the upstream domains. As a consequence of this, the utilizations of some service classes traffic are improved and the average End to End Delay for overall traffic are also reduced. An example of the improvement that was achieved by managing resources between (upstream and downstream) DiffServ domains dynamically, in test scenario 3- Case Study 2, the average utilization for the highest priority class (SC1) for whole period of simulation at the destination end is increased by 0.175% and the average End to End Delay for overall traffic is also reduced by 800 msec. As a result of reducing the average End to End Delay for overall traffic and improving the utilizations of service classes traffic, the QoS of applications traffic can be improved during the congestion periods in DiffServ domains