Indiana University-Purdue University Indianapolis (IUPUI) / Asynchronous JavaScript and XML (AJAX) is the primary method for enabling asynchronous communication over the Web. Although AJAX is providing warranted real-time capabilities to the Web, it requires unconventional programming methods at the expense of extensive resource usage. WebSockets, which is an emerging protocol, has the potential to address many challenges with implementing asynchronous communication over the Web. There, however, has been no in-depth study that quantitatively compares AJAX and WebSockets. This thesis therefore provides two contributions to Web development. First, it provides an experience report for adding real-time monitoring support over the Web to the Open-source Architecture of Software Instrumentation of Systems(OASIS), which is open-source real-time instrumentation middleware for distributed real-time and embedded (DRE) systems. Secondly, it quantitatively compares using AJAX and WebSockets to stream collected instrumentation data over the Web in real-time. Results from quantitative comparison between WebSockets and AJAX show that a WebSockets server consumes 50% less network bandwidth than an AJAX server; a WebSockets client consumes memory at constant rate, not at an increasing rate; and WebSockets can send up to 215.44% more data samples when consuming the same amount network bandwidth as AJAX.

<p>Technological advances have a profound impact on people and the world in which they live. People use a wide range of smart devices, such as the Internet of Things (IoT), smartphones, and wearable devices, on a regular basis, all of which store and use location data. With this explosion of technology, these devices have been playing an essential role in digital forensics and crime investigations. Digital forensic professionals have become more able to acquire and assess various types of data and locations; therefore, location data has become essential for responders, practitioners, and digital investigators dealing with digital forensic cases that rely heavily on digital devices that collect data about their users. It is very beneficial and critical when performing any digital/cyber forensic investigation to consider answering the six Ws questions (i.e., who, what, when, where, why, and how) by using location data recovered from digital devices, such as where the suspect was at the time of the crime or the deviant act. Therefore, they could convict a suspect or help prove their innocence. However, many digital forensic standards, guidelines, tools, and even the National Institute of Standards and Technology (NIST) Cyber Security Personnel Framework (NICE) lack full coverage of what location data can be, how to use such data effectively, and how to perform spatial analysis. Although current digital forensic frameworks recognize the importance of location data, only a limited number of data sources (e.g., GPS) are considered sources of location in these digital forensic frameworks. Moreover, most digital forensic frameworks and tools have yet to introduce geo-contextualization techniques and spatial analysis into the digital forensic process, which may aid digital forensic investigations and provide more information for decision-making. As a result, significant gaps in the digital forensics community are still influenced by a lack of understanding of how to properly curate geodata. Therefore, this research was conducted to develop a transdisciplinary framework to deal with the limitations of previous work and explore opportunities to deal with geodata recovered from digital evidence by improving the way of maintaining geodata and getting the best value from them using an iPhone case study. The findings of this study demonstrated the potential value of geodata in digital disciplinary investigations when using the created transdisciplinary framework. Moreover, the findings discuss the implications for digital spatial analytical techniques and multi-intelligence domains, including location intelligence and open-source intelligence, that aid investigators and generate an exceptional understanding of device users' spatial, temporal, and spatial-temporal patterns.</p>