Identification of metrics used by decision makers to determine the efficacy of wireless communication systems in higher education

This research described the wireless network technologies that are available for use in higher education, determined the categories of metrics used to evaluate wireless network efficacy, and yielded a self-assessment instrument for guiding small college administrators considering wireless local area network systems.;The features and benefits of contemporary wireless systems in higher education were identified through a review of the professional journals, government publications, and standards industry documentation. The literature identified three categories of metrics beneficial for the evaluation of efficacy of wireless campus local area networks: cost, speed, and reliability. After identification of these categories of metrics, a modified Delphi technique was administered to ten wireless network experts in higher education. The expert group was made up of seven higher education wireless decision makers and three wireless industry professionals.;The wireless experts responded to Instrument One which identified 27 metrics in the three categories of metrics. The experts generated 19 essential metrics: four in the category of cost, seven in the category of speed, and eight in the category of reliability. Eight supplemental metrics were also identified in Instrument One: four in the category of cost, two in the category of speed, and two in the category of reliability.;Instrument Two generated 27 questions that could guide wireless decision makers in higher education. These metrics offer a timeless guide to wireless system planning on small college campuses. The self-assessment instrument will assist in gathering information specific to the small college environment, and in gathering current specifications for wireless network systems. The analysis of information gained from the use of this tool will help wireless campus networks to operate as an integrated part of teaching and learning

Leveraging Resources on Anonymous Mobile Edge Nodes

Smart devices have become an essential component in the life of mankind. The quick rise of smartphones, IoTs, and wearable devices enabled applications that were not possible few years ago, e.g., health monitoring and online banking. Meanwhile, smart sensing laid the infrastructure for smart homes and smart cities. The intrusive nature of smart devices granted access to huge amounts of raw data. Researchers seized the moment with complex algorithms and data models to process the data over the cloud and extract as much information as possible. However, the pace and amount of data generation, in addition to, networking protocols transmitting data to cloud servers failed short in touching more than 20% of what was generated on the edge of the network. On the other hand, smart devices carry a large set of resources, e.g., CPU, memory, and camera, that sit idle most of the time. Studies showed that for plenty of the time resources are either idle, e.g., sleeping and eating, or underutilized, e.g. inertial sensors during phone calls. These findings articulate a problem in processing large data sets, while having idle resources in the close proximity. In this dissertation, we propose harvesting underutilized edge resources then use them in processing the huge data generated, and currently wasted, through applications running at the edge of the network. We propose flipping the concept of cloud computing, instead of sending massive amounts of data for processing over the cloud, we distribute lightweight applications to process data on users\u27 smart devices. We envision this approach to enhance the network\u27s bandwidth, grant access to larger datasets, provide low latency responses, and more importantly involve up-to-date user\u27s contextual information in processing. However, such benefits come with a set of challenges: How to locate suitable resources? How to match resources with data providers? How to inform resources what to do? and When? How to orchestrate applications\u27 execution on multiple devices? and How to communicate between devices on the edge? Communication between devices at the edge has different parameters in terms of device mobility, topology, and data rate. Standard protocols, e.g., Wi-Fi or Bluetooth, were not designed for edge computing, hence, does not offer a perfect match. Edge computing requires a lightweight protocol that provides quick device discovery, decent data rate, and multicasting to devices in the proximity. Bluetooth features wide acceptance within the IoT community, however, the low data rate and unicast communication limits its use on the edge. Despite being the most suitable communication protocol for edge computing and unlike other protocols, Bluetooth has a closed source code that blocks lower layer in front of all forms of research study, enhancement, and customization. Hence, we offer an open source version of Bluetooth and then customize it for edge computing applications. In this dissertation, we propose Leveraging Resources on Anonymous Mobile Edge Nodes (LAMEN), a three-tier framework where edge devices are clustered by proximities. On having an application to execute, LAMEN clusters discover and allocate resources, share application\u27s executable with resources, and estimate incentives for each participating resource. In a cluster, a single head node, i.e., mediator, is responsible for resource discovery and allocation. Mediators orchestrate cluster resources and present them as a virtually large homogeneous resource. For example, two devices each offering either a camera or a speaker are presented outside the cluster as a single device with both camera and speaker, this can be extended to any combination of resources. Then, mediator handles applications\u27 distribution within a cluster as needed. Also, we provide a communication protocol that is customizable to the edge environment and application\u27s need. Pushing lightweight applications that end devices can execute over their locally generated data have the following benefits: First, avoid sharing user data with cloud server, which is a privacy concern for many of them; Second, introduce mediators as a local cloud controller closer to the edge; Third, hide the user\u27s identity behind mediators; and Finally, enhance bandwidth utilization by keeping raw data at the edge and transmitting processed information. Our evaluation shows an optimized resource lookup and application assignment schemes. In addition to, scalability in handling networks with large number of devices. In order to overcome the communication challenges, we provide an open source communication protocol that we customize for edge computing applications, however, it can be used beyond the scope of LAMEN. Finally, we present three applications to show how LAMEN enables various application domains on the edge of the network. In summary, we propose a framework to orchestrate underutilized resources at the edge of the network towards processing data that are generated in their proximity. Using the approaches explained later in the dissertation, we show how LAMEN enhances the performance of applications and enables a new set of applications that were not feasible

Interoperability enhancement of IoT devices using open web standards in a smart farming use case

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesSince its rst appearance the Internet of Things has been subject to constant evolution, development and change. Now it has stepped out of its infancy with billions of devices embedded in the world wide web. However, IoT providers mostly de ne their own data formats and protocols and there is still a lack of a common standard that connects these devices in an interoperable manner. There are several organisations dedicated to developing common standards for IoT devices and research is focusing on de ning an e ective standard to be used by embedded devices. Unsurprisingly, IoT has also found its way into the spatial web and into environmental monitoring and sensing platforms connected over the web by wireless sensor networks are now a common way to monitor natural phenomena. This study compares three open Web Standards in the use case of SEnviro for Agriculture, a full stack IoT for monitoring vineyards. The interoperability potential of the OGC's Sensor Observation Service and SensorThings API are evaluated by integrating Web Standard implementations for each standard and contrasting their qualitative and quantitative traits. In a further step the Mozilla Corporation's Web Thing API was implemented and evaluated in an environmental monitoring and Smart Farming context. The results of the study show that the SensorThings API proves to be the most adequate Web Standard for SEnviro and IoT applications for environmental monitoring and Smart Farming in terms of interoperability. It outperforms the contesting Web Standards in terms of exibility and scalability, which strongly impacts on developer and user experience

Experimental Evaluation of Desktop Operating Systems Networking Performance

The rapid advancement of network, communication and Internet technology resulted with always-on, always-connected, device-independent and remote online working, business, education and entertainment environment. Consequently, users are searching for solutions and technologies that enable fast and reliable wide area network connection and the typical solution is through using personal computers connected with ethernet cable to network equipment and infrastructure that supports gigabit ethernet connection. Besides the complex network infrastructure that can influence performance, the bottleneck can also be caused by insufficient hardware, operating system and software resources on clients’ machines. Therefore, in this paper a networking performance evaluation of three globally most common and most used versions of Windows operating systems; namely Windows 7TM, Windows 8.1TM and Windows 10TM, on two identical computer systems, is conducted. Networking performance measurements are performed with three different benchmarks: namely iPerf, D-ITG and NetStress. Performance evaluation results showed that a newer versions of an operating system bring certain networking performance improvements but by sacrificing other performances