代做Mobile Technologies Coursework 2代做Python编程

Mobile Technologies

Coursework 2

Wireless Sensor Network Technology in Smart Farming

Background

This coursework is building on the skills and knowledge acquired from the instructional plans of Weeks 1 to 10, students will apply the principles of wireless communications, network infrastructure,IoT and mobile communication architectures to a practical scenario. The coursework is designed to align with topics covered such as  mobile technologies, networking, connectivity, radio propagation, multiple access schemes, and the evolution of wireless standards from 2G to 5G. Furthermore, considerations for sensor networks as explored in wireless sensor networks and IoT applications will be a significant component of this coursework.

Scenario

In the role of anIoT engineer, you are required to design a robust and efficient wireless sensor network for avast smart farm measuring approximately 1000 mx 3000 m. The network's design must facilitate the collection of environmental data from numerous sensors strategically placed throughout the farm to ensure the health of crops and efficiency of farm operations. A minimum of 200 environmental sensors, each with a 50 kbps data rate, are needed to achieve comprehensive coverage. Despite an existing Wi-Fi network, its reach is inconsistent over the large area. Furthermore, 5G coverage is only available at the farm's periphery. The network should operate autonomously for at least one year, and data must be accessible in the cloud.

Learning Outcome

To decide and discuss the selection of an appropriate wireless technology for the smart farm's sensor network, considering the size of the farm, signal strength variability, and the partial availability of 5G  coverage.

Tasks:

(a) Technology Evaluation and Selection

•    Evaluate various wireless technologies covered in the course, considering the specific challenges of the smart farm's size and remote location.

•    Discuss each technology's suitability for sensor data rates, energy consumption, and long- term autonomous operation.

•    Decide on the most appropriate technology for this scenario, with a comprehensive justification referencing course material.

(b) Network Design and Justification

•    Propose a detailed network design using the selected technology, accounting for the extensive area and uneven terrain of the farm.

•    Justify the design based on connectivity principles, data transmission requirements, and environmental sensor distribution.

•    Address the potential connectivity issues due to the varying Wi-Fi signal strength and limited 5G access.

(c) Critical Analysis and Implementation

•    Conduct a critical analysis of the proposed technology, considering the scalability, reliability, and maintenance of the network.

•    Outline a clear implementation strategy that reflects the course's teachings, including deployment phases, necessary infrastructure, and testing protocols.

•    Develop a plan for integrating the sensor data with cloud services to ensure data availability and the possibility of real-time analytics.

Submission Guidelines

•    The coursework report should not exceed 5 pages and must include diagrams, schematics, and tables where appropriate.

•    References to the instructional material, concepts, and specific technologies discussed during the course are expected.

•    The  report  must  be  formatted  according  to the  provided template  and submitted via the designated online portal by the deadline.

• Deadline: Monday, 27th May 2024 Marking Rubric

Technical Accuracy (30%): Essential, as the students must demonstrate their grasp of the wireless technologies, network infrastructure, and IoT architectures covered in Weeks 1 to 10. The application of theoretical knowledge to a real-world scenario is central to this module, and the grading bands offer clear differentiation according to the students'level of understanding.

Innovation and Creativity (20%): The scenario demands that students design a network for a large smart farm, presenting unique challenges. This criterion encourages them to develop creative and innovative solutions that contribute functional enhancements.

Clarity and Organisation (20%): The clarity with which students communicate their ideas and the organisation of their report are crucial. The structure must be logical, aiding in the clear presentation of their network design and rationale.

Implementation Feasibility (15%): The students' proposals must be pragmatic, reflecting the real- world constraints of the smart farm environment. This element of the rubric underscores the necessity for plans that are not only theoretical but also actionable within the context of a large and remote farm.

Evaluation Depth (15%): A critical skill for engineers is the ability to evaluate their work thoroughly and devise a comprehensive performance assessment plan. This section of the rubric stresses the importance of self-evaluation and the measurement of the network's effectiveness in practical terms.