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FEC_CA FOG AND EDGE COMPUTING 1 Fog and Edge Programming MSc Cloud Computing Module Project 40% Introduction The project is designed to deliver on the module learning outcomes, in particular the following outcomes: • Critically evaluate research publications on cloud services and edge computing and deliver oral presentations on selected ones. (This idea to be used for next step) • Implement software using standard open-source cloud and edge computing software for data analytics (or any other application/service that you identify). • Demonstrate in-depth knowledge of different types of (hardware and) software systems used in fog and edge computing As such you must review research papers to identify a FEC project area of sufficient scope and complexity to prove challenging. The problem you are looking to solve and implement, or the service you wish to provide should make use of the open-source simulation software tools. The proposals (see the template provided on page 3) will be submitted; feedback will include additional requirements to bring the projects in line with the appropriate standard if need be. The implemented solution will be demonstrated, with marks awarded for the implementation, demonstration and understanding shown of the research performed. Requirements Review FEC research papers published in the recent past to identify a FEC project. When finalizing an application or project scenario consider the following requirements. Data collection carried out from multiple IoT devices integrated with sensors (and/or actuators). Data collected is then processed at the FEC nodes. FEC nodes forward the results to be stored in a database, which may be located on another server/cloud. Your scenario may use the Gateway device performing data collection of multiple sensors and being responsive to incoming messages from FEC nodes (and Internet e.g., server/applications). Use of standard protocols when exchanging the data across layers is required. The exact same scenario should be implemented using the Cloud-centric architecture (replacing the FEC system). The performance comparison (using relevant metrics) must be carried out to evaluate both the FEC based and CIoT based solutions. Submitted proposal and the final report must be written by you. You ‘re asked to submit your proposal (within one week of the CA release) and the final report via Turnitin for originality check. FOG AND EDGE COMPUTING 2 Deliverables If time permits, there will be a project demo in the last week of the semester (closer to the project deadline), date to be announced later. In addition, the following is required: • A screen captured and recorded video presentation (uploaded as unlisted video on Stream/YouTube and a link provided in the report) which clearly shows every element of the project working. The video length should be between 5-10 minutes. Include the link to this video in your project report. • The results of the project must be presented in the form of report. The report should be 5-6 pages in length (including figures and references) and must follow the double column IEEE conference format in addition to be employing appropriate referencing methods and academic writing style. You can use some content from your project proposal. • The code associated with the project must also be submitted on Moodle and it must be well commented and include references to any code not written by yourself. You should also reference sources in your final report, stating what was taken from each. You should use the NCI’s citation guidelines. Submission should include a zip of all code and datasets used to produce the results. The root directory of the archive should contain a plain text file providing clear instructions that can be carried out for a re-run if required. Marking Scheme - 20% Project proposal o Suitable project idea, system architecture planned, evaluation methodology, project planning, quality of document - 20% Achievement of the requirements to a good standard - 20% Evaluation results computed - 10% Code quality - 10% Video presentation - 10% Written short report on results (must be included within the proposal upon submission) - 10% Suitable functionality beyond the scope of the proposal submitted (must be included within the proposal upon submission) FOG AND EDGE COMPUTING 3 Proposal Template 1 Abstract A single paragraph which explains the project succinctly. This section appears first in the document; however, it is usually written last. 2 Introduction & Methodology Introduce your project and discuss the project scenario i.e., home automation, assisting the elderly, smart agriculture, smart city services etc. This section should provide a high-level system architecture diagram. You must structure the discussion of what you are going to achieve around that diagram. Be clear on the type of IoT devices required, the FEC and cloud services needed, and any other requirements. (Maximum two pages) 3 Application requirements List the requirements (related to data, communication, functionality, etc.) of the application/system you ‘re proposing. Based on these requirements discuss, why does the application need to utilize FEC based solution instead the CIoT? What requirements will be satisfied by the FEC architecture? (Maximum one page) 4 Evaluation Discuss how the approach will be evaluated at the end of the implementation. Formulate clear indicators/metrics that will be reported assessing the project objectives. (Maximum one page) 5 References Provide references to research papers that you have reviewed when writing your proposal. IEEE Xplore Full-Text PDF: Microsoft Word - Document1 Abstract: In the present scenario surveillance systems are the most important factors in daily life owing to increasing demand of security and safety. It produces huge amount of data while operating 24/7 in public places including enterprises and educational institutions. These massive amounts of data can deluge the overall storage systems and other analytical applications. Hence the data need to be processed and may be stored to give a glimpse of some “interesting” incidents that is in execution in some particular location during a specified time frame. Cloud computing provides such infrastructure to handle these data by providing storing and processing elements. But in real time situation the overall latency and delay due to transmitting data in a cloud datacenter and revert back to end user's application devices are undesirable. Related to aforementioned scenario, a fog based intelligent surveillance system, that enables low latency and faster data processing by localization of data, is presented. Using ifogsim simulator, different scenarios have been created, computed and compared with the deployment of only cloud datacenter. Results depict that fog based system has lower energy consumption and higher processing rate compared to cloud. Published in: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Date of Conference: 6-8 July 2019 Date Added to IEEE Xplore: 30 December 2019 ISBN Information: INSPEC Accession Number: 19277588 DOI: 10.1109/ICCCNT45670.2019.8944815 Publisher: IEEE Conference Location: Kanpur, India SECTION I. Introduction With the development of the Internet of Things (IoT), a numerous of sensors have been deployed, leading to huge amount of data generated by it. To handle such a huge data, the Cloud Computing has been used to perform the storage, processing and data analysis. But the real challenge in cloud computing technology is the necessity for realtime and low- latency amenities along with massive number of devices linked with internet. In summary, it can be concluded as the current era of research is being shifted from cloud computing to edge computing, that is nothing but fog computing. CISCO introduced the term fog computing [1] [2] that permits end user applications to be executed on the edge of the local network devices instead of cloud datacenters. It has resolutions for the problems like location awareness, edge location, reliability and high latency. In simple words, the distributed services i.e. decentralization of services [3] at the edge of the network is achieved through fog computing paradigm. These reliefs the latency minimization in comparison with cloud which is beneficial for live streaming or real-time applications. It can also support mobility and heterogeneousness [4]. There are numerous of use cases of fog computing, such as healthcare systems, smart cities, smart building, smart grid, vehicular networks, software-defined networks etc. Generally, the longterm analytic and storage process occurs in the cloud datacenters while short term data processing is carried out by the fog servers. It is very crucial information to always keep in mind that fog computing cannot replace the concept and principles of cloud computing but can act as a complement of existing cloud structure by trimming the volume of data that needs to be transferred to the cloud for further processing and storing purpose [5]. As a reason, fog computing also helps in conserving the overall network bandwidth and enhance the system response time by retaining the data nearer to the edge of the network and making it available immediately. SECTION II. RelatedWork Fog computing based intelligent security surveillance system is a fresh concept in recent time. Deployment of fog servers shorten the requirement of bandwidth allocation and boosts up the network efficiency by transferring instant info to the end users nearby to the edge of the network. In [6] authors proposed fog computing approach to track visual moving object in various lighting conditions. Ledakis et. al [7] have presented the benefits of serverless implementation of state-less function for generating a distributed data processing solution for large area audiovisual surveillance. Authors in [8] have stated to retain several mobile objects in camera sight-line by regulating mean and variance of a set of image features. In [9] Bevilacqua et. al have proposed background subtraction technique in PTZ control camera using mosaic background subtraction. Openfogconsortium has stated use case scenario of fog computing based visual security and surveillance in [10]. Grambow et. al [11] have used fog computing in public video surveillance to increase privacy. A. Fog Computing In 2015, CISCO proposed the new terminology Fog computing [1] that will able to handle variety of unprecedented volume of data generated by billions of IoT devices. A number of surveys and review articles can be found for IoT and fog computing in [12]. In [13]authors examine the promising scenarios of IoT and shows how fog computing can resolve it by supporting existing cloud architecture. The security and privacy issues are discoursed in [14]. Taxonomy regarding the developments of fog computing and issues of