Wireless Sensor Networks (WSNs) are one the most widely adopted information technologies of modern networking and computing platforms. Today's network computing applications are faced with a high demand of powerful network functionalities. Functional network reach is central to customer satisfaction such as in mobile networks and cloud computing environments. However, efficient management of WSNs remains a challenge, due to problems supplemental to them. Recent technology shift proposes Software Defined Networking (SDN) for improving computing networks. This review paper highlights application challenges faced by WSNs for monitored environments and those faced by the proposed approaches, as well as opportunities that can be realized on applications of WSNs using SDN. We also highlight Implementation considerations by focusing on critical aspects that should not be disregarded when attempting to improve network functionalities. We then propose a strategy for Software Defined Wireless Sensor Network (SDWSN) as an effort for application improvement in monitored environments.

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). QoS capable of networking allows efficient control of traffic, especially for network critical data. However, to achieve this in wireless sensor networks (WSNs) is a serious challenge, since these technologies have 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. In this study, a resource-aware OpenFlow-based active network management QoS scheme that uses software defined networking (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 software defined WSNs. The 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.

The need for highly responsive and adaptable computing systems is essential in today's network computing age. This is principally due to the drastic evolution in broad computing platforms operating at highly descriptive and abstracted mediums such as; reconfigurable computing systems, smart automation systems, cognitive and parallel programming systems which communicate using very complex resources or modes. Hence, such systems must incorporate the best forms of technologies to cater for the rapidly growing and heterogeneously connected platforms such as with Internet of Things (IoT). However, to effectively manage these network platforms with such high-end computing resources, requires a well-structured and carefully implemented systems. This work implements a Software Defined Wireless Sensor Network (SDWSN) approach coupled with Discrete Event Simulation (DES) and a highly extensible and scalable Software Defined Networking (SDN) controller-OpenDayLight (ODL), to implement a software-oriented network environment to increase network service adaptability and simplify network management. The implemented approach uses the ODL's Model-Driven Service Abstraction Layer (MD-SAL) to facilitate the forwarding layer by applying state procedures to manage flow rules and introduce software-oriented network services. Experimental results indicate that in this approach, the traffic flow routing is significantly improved, with reduced transmission delays and that the underlying sensor nodes uses less energy since energy demanding tasks are performed on the controller.

No abstract

Recent advances in computing such as the massively parallel GPUs (Graphical Processing Units),coupledwith the need to store and deliver large quantities of digital data especially images, has brought a numberof challenges for Computer Scientists, the research community and other stakeholders. These challenges,such as prohibitively large costs to manipulate the digital data amongst others, have been the focus of theresearch community in recent years and has led to the investigation of image compression techniques thatcan achieve excellent results. One such technique is the Discrete Cosine Transform, which helps separatean image into parts of differing frequencies and has the advantage of excellent energy-compaction.This paper investigates the use of the Compute Unified Device Architecture (CUDA) programming modelto implement the DCT based Cordic based Loeffler algorithm for efficient image compression. Thecomputational efficiency is analyzed and evaluated under both the CPU and GPU. The PSNR (Peak Signalto Noise Ratio) is used to evaluate image reconstruction quality in this paper. The results are presentedand discussed