Internet of Things for Sunshine Coast-Free-Samples for Students

Question: Discuss about the Network and Communication: Internet of Things for Sunshine Coast Council Smart Parking. Answer: Introduction Smart cities is a paradigm and reality that has been made possible by the advent of the Internet of Things (IoT); a necessity (smart cities) made urgent as people continue flocking towns and cities. Most cities are experiencing growth in population creating pressure on amenities and resources; however there is need for greater efficiency to accommodate increasing population pressure on cities. Smart cities use the IoT devices such as lights, sensors, and meters to collect data and then analyze this data. This data is then used by cities to improve services and amenities, as well as infrastructure and public utilities (Meola, 2016). One of the biggest challenges in modern cities is traffic and getting parking space. Presently, the Sunshine Coast Council uses the manual parking system where drivers have to drive around looking for parking. As part of its smart city initiative, the SCC intends to implement a smart parking system whose network backbone is the IoT infrastructure to be tes ted at the Sippy Downs Campus, and be scaled up and extended across all SCC building locations. This paper proposes two architectures of IoT to design a smart city parking system; the paper first discusses the infrastructure used, then the existing technologies solutions, before discussing the technologies for data processing and distributed storage. The two architectures are then compared and the designs discussed in the context of power use, initial cost, privacy and security, and ease of use Proposed IoT Architectures for a Smart parking System (SPS) This paper proposes the use of a fog architecture and a cloud architecture to design the smart city automatic car parking system, all based on the five layer protocol architecture (Marti?nez, Urraca, Quintia?n Corchado, 2017). In cloud based IoT architecture; the design is such that data processing is undertaken centrally by computers in the cloud. In this architecture, the cloud is at the center of the IoT system with applications lying above the cloud and below it are found the network of smart things. This architecture where the cloud is central is given primacy since it offers greater flexibility as well as scalability. The core infrastructure, software, platform, and storage services are provided by the cloud. Visualization tools, software tools, machine learning tools, and storage tools can be provided through the cloud (Hussain, 2017). Below is a diagram of the cloud based IoT architectur The other IoT system architecture this paper proposes is fog computing IoT architecture in which there are sensors and network gateways undertake part of the data processing and analysis (Buyya Vahid, 2016). The fog architecture is a layered design in which where monitoring, storage, processing, and security layers are inserted between the transport and physical layers of the network protocol. The fog network has a monitoring layer to monitor resources, power, services, and responses while a preprocessing layer undertakes processing, filtering, and analyzes sensor data. There is a temporary storage layer for offering storage functions such as data distribution, replication, and storage. The fog architecture also incorporates a security layer to undertake encryption and decryption for ensuring data integrity (Negash, Rahmani, Liljeberg Jantsch, 2018), (Velasquez, Abreu, Curado Monteiro, 2017). Preprocessing and monitoring in the fog IoT architecture are undertaken on the network ed ge before data is sent to the cloud. The diagram below shows a representation of the Fog IoT architectures Cloud Architecture IoT Automated Parking The smart parking system is proposed for the SCC (Sunshine Coast Council) for its smart city program to make parking more efficiency and improve access to these amenities by residents. The proposed IoT cloud based architecture uses Wireless Sensor networks (WSN) made up of radio frequency identification (RFID) for monitoring the car parks. Using RFID will allow scaling and large scale system implementation at a low cost, while minimizing wasted time and congestion as well as reducing disputes at the parking. The system will have an application for use by mobile devices and computers where users log in and select their preferred parking space in advance and then book it; the user gets a confirmation of the reserved space. The system then updates the chosen parking space as pending and other users will not be able to reserve it (Pham et al., 2015). The system monitors the space over a given time period, upon which it will be changed in real time to available. The system will have a ser ver based in the cloud to handle all processing, analysis, and storage as well as updating of information. The cloud based server is a web entity. The car park will have a local unit that has a control unit with RFID to authenticate and validate user information and open the parking entrance door. The system has a software client running on Android, Windows, and IOS operating systems; users will install this into their mobile devices and access the parking system, via 3 or 4 G. The car park will have a CPN (car park network) in which sensors connect and communicate through wireless radio; the CPN has routers that are self configuring so that the CPN is easily integrated to existing WSN via bridge/ gateway functionality in the routers. Every car park becomes a node in the CPN and every node has a neighbor for maintaining information on the network status and queue with fixed length (Giaffreda, Caga?n?ova?, Riggio Voisard, 2015). The architecture incorporates an application layer that consists of the application software from where the clients log in, search available parkings, and make a reservation through their mobile devices/ smart phones. Next, they must have Internet connectivity turned on to access the Internet using GSM SIM cards on their phones (or Wi-Fi) to access the cloud server. The cloud server receives data and information from the perception layer which has elements including RFID tags, and RFID antennae, RFID reader, Arduino to control gate/ door opening, an Ethernet shield for the Arduino, and a screen at the car park (optional) (Gaglio Lo, 2014). The sensors, through the WSN and CPN collect data, such as on number of parking slots and their location and feeds this via the transport layer of the mobile Internet protocol. Mobile devices can access the Internet through Wi-Fi (for example at the office) while devices will communicate via Zig bee using radio frequency (low power). All the data is stored, processed, analyzed, and updated via the cloud based server. This design is based on the LoRaWAN, (low power wide area network) in which the battery operated sensors have bi-directional operation and localiz ation services. Fog IoT infrastructure for Smart parking This is the second design where sensors and remote devices will undertake as much processing as possible (preprocessing), storage, analysis, and updating, before information is submitted to a central server from where end users can access the smart parking IoT application via an application that is usable on Android, IOS, and Windows devices that users download. The central server maintains information on available parking spaces at the Sippy Downs Campus parking spot. The design will also contain a raspberry Pi micro controller connected with a Pi-camera for image capture this captures images of the parking spots to validate parking slots. The system incorporates a navigation system to signal parking slot availability from the nearest location of the end user. A display monitor for the administrator side to modify parking slots through observation. Users will connect with the smart parking system using their mobile devices such as tablets or smart phones (Hersent, Boswarthick Ellou mi, 2012). The Pi camera is mounted strategically; on top of lamp posts on the street, ceilings at indoor sections of the parking, and at entrances and exits of the parking area. The I camera continuously checks the parking slot and updates parking space information. Each parking slot has control points that the Pi camera uses for reference. The central server is cloud based and is accessed through the HTTP protocol by the smart devices and the mobile devices. The parking system has a website constructed using JSON and the camera mounted with the raspberry micro-computer has an antennae and GSM SIM card incorporated for communication to the Internet. This system will use the Open IoT platform where all sensors are connected as a natural extension of the IoT; however, the sensors have some processing power, which makes them consume slightly more power. This architecture will also use the LoRaWAN technology for bi-directional communication (Sarkar, Chatterjee Misra, 2015). Fog IoT Architecture Compared to Cloud IoT Architecture In comparison, the Fog IoT architecture enabled edge computing where the burden on cloud computing is reduced by gathering huge amounts of data, services, workloads, and applications to the network edge, where sensors also undertake processing. Fog has the advantage of mobility, single hop client and server distance, low latency, very low delay jitters, and has server nodes at the edge of the network. The Fog IoT architecture enjoys awareness location, is more secure and can be defined, low vulnerability probability, can support a very large number of nodes, supports real time interactions, and can use wireless for last mile connectivity. The cloud based IoT architecture on the other hand, has server nodes located in the network, has multiple hops between client and server distance, high latency and high jitter, is less secure, lacks location awareness, and has vulnerability probability. Further, Cloud based IoT architecture has a centralized geographical distribution, supports real time interaction, uses a leased line for last mile connectivity, and has limited support for sensor mobility. However, the cloud IoT uses less power compared to the Fog IoT, though Fog is more expensive to implement than Cloud (Keramidas, Voros Hu?bner, 2017). References Buyya, R., Vahid, D. A. (2016). Internet of things: Principles and paradigms. Amsterdam : Morgan Kaufmann Gaglio, S., Lo, R. G. (2014). Advances onto the Internet of Things: How Ontologies Make the Internet of Things Meaningful. Cham: Springer International Publishing. Giaffreda, R., Caga?n?ova?, D., Li, Y., Riggio, R., Voisard, A. (2015). Internet of Things. IoT Infrastructures: First International Summit, IoT360 2014, Rome, Italy, October 27-28, 2014, Revised Selected Papers, Part II. Hersent, O., Boswarthick, D., Elloumi, O. (2012). The internet of things: Key applications and protocols. Chichester, West Sussex: Wiley. Hussain, F. (2017). Internet of Things: Building blocks and business models. Cham, Switzerland : Springer Keramidas, G., Voros, N., Hu?bner, M. (2017). Components and Services for IoT Platforms: Paving the Way for IoT Standards. Cham: Springer International Publishing. Marti?nez, . P. F. J., Urraca, R., Quintia?n, H., Corchado, E. (2017). Hybrid artificial intelligent system. HAIS (Conference) Meola, A. (2016). How smart cities IoT will change our communities. Retrieved June 22 2017 from, https://www.businessinsider.com/internet-of-things-smart-cities-2016-10?IR=T Negash, B., Rahmani, A. M., Liljeberg, P., Jantsch, A. (January 01, 2018). Fog Computing Fundamentals in the Internet-of-Things. Pham, T. N., Tsai, M.-F., Nguyen, D. B., Dow, C.-R., Deng, D.-J. (2015). A Cloud-Based Smart- Parking System Based on Internet-of-Things Technologies. Ieee Access, 3, 1581-1591. Sarkar, S., Chatterjee, S., Misra, S. (January 01, 2015). Assessment of the suitability of for computing in the context of internet of things. Ieee Transactions on Cloud Computing, 99.) Velasquez, K., Abreu, D. P., Curado, M., Monteiro, E. (February 01, 2017). 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