This paper is a continious of and based off of the same Sense & Save business concept. Below is an outline for the paper so all points should be addressed in the 4 pages. Reading through the first 3 papers should definitely help understand the concept and complete the paper. Let me know if you have any questions.<\/p>\n
Final Paper Goal:
\n\u2756 Consider other key digital strategies and technological elements from a practical angle.<\/p>\n
\u2756 Having dealt with platform structures, digital flows, and platform and ecosystem components, incorporate and integrate elements relating to:
\n\u27a2 Data traces and embedded intelligence
\n\u27a2 Wireless networks
\n\u27a2 Artificial Intelligence<\/p>\n
Embedded intelligence issues:
\n\u2756 Take the ecosystem application you have designed for the education sector.
\n\u27a2 Within the ecosystem there are:
\n\u25a0 Connected things
\n\u25a0 Devices
\n\u2756 For each device and activity within the ecosystem
\n\u27a2 What will be the numerous digital traces generated by things as people use the platform?
\n\u27a2 What will be the frequency of such digital traces data?<\/p>\n
\u2756 For each device and activity with things generating digital traces describe in detail:
\n\u27a2 What will be embedded individual intelligence arising when human beings and objects interact?
\n\u25a0 What could you infer about personal contexts and behavior patterns?
\n\u27a2 What will be embedded spatial intelligence that arises when human beings interact with the environment?
\n\u25a0 What could you infer regarding the status of a place or the spatial context?
\n\u27a2 What will be embedded social intelligence that arises when human beings interact with other human beings?
\n\u25a0 What could you infer on interpersonal and group interaction patterns?<\/p>\n
Network Technology Issues:<\/p>\n
\u2756 A technology issue in IOT is wireless network deployment
\n\u2756 Classify the various devices connected to the ecosystem
\n\u27a2 Are they:
\n\u25a0 Concentrated and fixed devices
\n\u25a0 Dispersed and fixed devices
\n\u25a0 Concentrated and mobile devices
\n\u25a0 Dispersed and mobile devices
\n\u2756 For connecting the devices what will be the likely relevant network type
\n\u27a2 Wireless personal area networks (WPAN)
\n\u27a2 3G or 4G wireless technology
\n\u27a2 WiFi or WPAN technology
\n\u27a2 Satellite technology<\/p>\n
Artificial Intelligence Issues:
\n\u2756 The paper will be rounded off by incorporating AI Issues
\n\u27a2 AI will convert data to meaningful knowledge
\n\u25a0 The main AI functionalities are
\n\u25cf Sense
\n\u25cf Comprehend
\n\u25cf Act
\n\u2756 The ecosystem designed will be generating digital traces and data
\n\u27a2 Of these what will be the primary means of sensing such traces?
\n\u25a0 Computer vision
\n\u25a0 Audio Processing
\n\u25a0 Sensor Processing(most likely)
\n\u2756 In converting sensed data to meaningful knowledge for comprehension
\n\u27a2 Identify which digital traces could be comprehended using natural language processing?
\n\u27a2 Which digital traces could be comprehended using knowledge representation?
\n\u2756 In generating insights from the knowledge so that action is possible
\n\u27a2 In what parts of the designed ecosystem would you use
\n\u25a0 Inference engines
\n\u25a0 Expert systems
\n\u25a0 Machine learning<\/p>\n
Final concern:<\/p>\n
\u2756 While the student is the key stakeholder there are many stakeholders who form the designed ecosystem
\n\u27a2 How will insights generated from data turned into intelligence by AI generate unique value for each stakeholder
\n\u27a2 How are stakeholders better off for being on the ecosystem as designed than not being a part of it
\nSense &Save: Embedded intelligence, Wireless Networks, and AI
\nIoT networks and intelligence are closely correlated to giving businesses easier decision–making capabilities to improve customer satisfaction. Sense and Save incorporates similar strategies to ensure students and staff alike enjoy improved experiences within their daily routines or workflows. Through the integration of data traces and embedded intelligence, proper wireless network connections and Artificial Intelligence Sense and Save can work towards its objective of incentivizing users to consume less energy while holding them accountable to their spending in real-time.<\/p>\n
Data Traces and Embedded Intelligence
\nSense and Save represents a complete IoT ecosystem with connected things and devices. Since it is a technology that is based on sensors to create a smart environment, the AWS software collects abundant data traces and manipulates it under its embedded intelligence. The AWS IoT Platform will keep track of the numerous digital traces generated within the educational facility. These data traces will originate from electric meters, thermostats, showerheads, gas faucets, water meters, or even laundry hookups. Through these sensors, data will start flowing in the software platform where the device detection algorithms will make intelligent use of the data. The frequency of such digital data traces will revolve around environmental factors, length of use, and time of day. The availability of these frequencies will aid the Sense and Save algorithms to generate possible correlations that can be used to forecast future behavior patterns for students.
\nWhen human beings and objects in the IoT infrastructure interact, the possibilities of individual embedded intelligence increasing inefficiency will occur. IoT in today’s world can be used to tell more about personal contexts or mass behavioral patterns. The sensors installed will generate information based on unique identification codes allocated to each human. Therefore, instead of relying on general reports, the data traces could be narrowed down to individuals. This can give more information about the behavioral patterns of students while in dorms in comparison to other locations within the school (Harrysson, 2012). With advances in machine learning, the sensors can also enhance or respond to environmental conditions as well. Data collected through the AWS software will be able to predict the status of a place-based on previous patterns due to machine-human interactions. Embedded social intelligence can be used for decision making when human beings interact with each other. Sense and Save can then run Assessments of student behavior when they are alone can be placed against their conduct while in a group to detect possible patterns.<\/p>\n
Network Technology Issues:
\nSense and Save employ the use of various devices that are interconnected across their infrastructure. These devices range from sensors, hubs, switches, database servers, and mobile phones. Concentrated and fixed devices include sensors, smart hubs, and smart switches that will be communicating with each other over the network. Dispersed and fixed devices will consist of the database server that is an essential part of the Sense and Saves architecture. IoT devices are ever generating data in real-time, which means that vast amounts of data will be released and stored within the system’s database. There is also the issue of personal devices such as mobile phones and laptops while they use the application. These devices act as the medium that places the human within the Sense and Save network. Through the interaction of these devices, it is possible to achieve the purpose of the technology, which is generating a complete system capable of collecting data and presenting them to users.
\nSuitable network types are assimilated by the Sense and Save initiative to enforce the most efficient method of communication across devices. When selecting a network type, it is always vital to choose an option that serves the objectives of the organization. At Sense and Save, the aim is to keep track of user behavior on campus utilities. It is only reasonable to select options that decrease power consumption over the network while at the same time sustaining the real-time transfer of data over the connection.
\nWireless Personal Area Networks, Low-Power Wide Area Networks (LPWAN), and 3G or 4g wireless technology are the most reasonable selections to serve the network architecture. Databases will perform better on LPWAN since this recently released technology dating 2013, allows for networks to use low power consumption while working under long-range wireless connectivity. The concentrated and fixed sensor devices will use the WPAN technology, which will allow for the communication of devices within a personal range. Its short-range communication is seemingly reasonable for sensors, hub, and switch transmissions as well as human device interactions. Lastly, 3G and 4G will also serve as a necessity to connect the devices to the cloud and allow for data uploads necessary for insight generation.<\/p>\n
Artificial Intelligence Issues:
\nAs mentioned before, sensor processing is the primary means of sending digital traces and data within the ecosystem. This will enable the various AI functionalities ranging from sensing, comprehending, and acting to take place for insight generation. A good AI ecosystem should be able to collect data, read it, and manipulate it to benefit its users.
\nThe human-device interactions within the ecosystem will suffice for digital traces that can be used by Natural Language Processing (NLP). This means that the AWS detect algorithm will be able to record every interaction students make with the devices over the network. Some examples include turning off forgotten lights or the time spent doing laundry using a washing machine. What NLP does is that it converts the personal data fed to the device into a form that the computer can understand (Dr. Garbade, 2018). Furthermore, digital traces comprehended using knowledge representation will comprise of facts, performances, objects, and events
\nTo generate insights, inference engines, machine learning, and expert systems are components that can be used for Sense and Save knowledge base. Inference engines alongside knowledge representations can be used by stakeholders to view insights from the Billing Operations Data. Expert systems are essential as they are efficient in planning, reasoning, and pattern matching based on the Demand Response Operations Data. Machine learning involves the use of algorithms that improve every time the system is fed with information from the Smart Meter Data.<\/p>\n
Conclusion
\nStudents who choose to join the platform are more advantaged since they can control their expenditures in the long run. Using the Sense and Save product, students get insights that focus on their water, gas, and electricity use. Furthermore, the IoT technology allows them to perform further actions from remote locations. These actions involve switching on and off of running taps or raising the alarm for leaking gas. Being a part of the Sense and Save initiative benefits its stakeholders in a number of ways that contribute to conserving the natural resources. With the insights generated by the system, stakeholders are equipped to make better decisions.<\/p>\n
Works Cited
\nDr. Garbade, Michael. \u201cA Simple Introduction to Natural Language Processing\u201d. 2018. Retrieved from https:\/\/becominghuman.ai\/a-simple-introduction-to-natural-language-processing-ea66a1747b32
\nHarrysson, Martin. \u201cHow \u2018Social Intelligence\u2019 Can Guide Decisions.\u201d 2012. Retrieved from https:\/\/www.mckinsey.com\/industries\/technology-media-and-telecommunications\/our-insights\/how-social-intelligence-can-guide-decisions<\/p>\n","protected":false},"excerpt":{"rendered":"
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