conversations.from: human
conversations.value: What could be a title for a book about Real-world, M2M, EDN with the genre: Engineering & Transportation, Engineering?
conversations.from: assistant
conversations.value: A title for this book could be 'Internet of Things'
conversations.from: human
conversations.value: What is the first five paragraphs for this book?
conversations.from: assistant
conversations.value: Internet of Things
  The objective of the backend services varies according to the application; generally there will be both compute and storage components. Fig. 15.4 shows the backend requirements for just the SHT15 sensor (temperature and humidity). The data is received from the service bus, which is written to by all IoT devices. The data is cleaned, to ensure basic validation, and to separate data from multiple sensors into a format suitable for storage. The data is then forked into two streams:
  Figure 15.4 Backend Processing for the Weather Station SHT15 Sensor, Showing a Variety of Services Supported
  The postprocessing is split into two categories: data analytics/machine learning and application logic. The data analytics and machine learning is used to explore the datasets and locate anomalies, for example, to detect sensor failure. This is very much work-in-progress, and uses the Microsoft Azure Machine Learning Studio for analysis [20].
  The application logic in this example processes and filters the data to calculate the daily/weekly/monthly minimum and maximum temperature values. The data is also formatted and pushed to external services, such as Twitter and the Weather Observations Website (WOW) [21].
  Managing the compute and storage requirements of an IoT infrastructure will get more complex as the number of devices, users, and supported services increases. This can be made easier by using an existing framework; we have selected Apache Hadoop.
conversations.from: human
conversations.value: What is four more paragraphs?
conversations.from: assistant
conversations.value: Traffic control at intersections has been always a key issue for ITS. The key point is how to schedule traffic signals efficiently, according to traffic volume information, so as to reduce waiting time and improve fairness. There have been many algorithms or systems proposed for intelligent intersection control, which can be categorized as in Fig. 16.7.
  Most existing work on intersection control is traffic-light based, and the key issue is to determine a good signal-scheduling plan. In early work, road detectors have been used to collect traffic volume information, and the traffic-signal plan constantly changes to adapt to the varying traffic conditions. Systems such as SCOOT [30] and SCATS [31] have been deployed for many years. Detailed vehicle information, including ID, speed, and position, are collected via V2V or V2I communication. Then, more accurate and efficient scheduling can be achieved.
  V2I-based traffic-light scheduling is widely studied. In [32,33], a controller node is placed at the intersection to collect queue-length information and computer proper-cycle time of the traffic signal via the Webster formula. In addition to queue-length information, priority of vehicles is considered in [34], and a traffic signal is scheduled with quality-of-service provisioning. In some other work, signal scheduling is modeled as a combinatorial optimization problem to find an optimal scheduling plan of traffic signal. To solve such a problem, various methods such as dynamic programming (DP) [35,36], branch-and-bound [37], and linear programming [38] have been applied. Some researchers introduce intelligent algorithms to traffic-light scheduling, including fuzzy-logic-based scheduling [39], and Q-learning-based scheduling [40,41].
conversations.from: human
conversations.value: Add another sentence about Traffic-light, scheduling, based.
conversations.from: assistant
conversations.value: Traffic control at intersections has been always a key issue for ITS. The key point is how to schedule traffic signals efficiently, according to traffic volume information, so as to reduce waiting time and improve fairness. There have been many algorithms or systems proposed for intelligent intersection control, which can be categorized as in Fig. 16. 7. 
  Most existing work on intersection control is traffic-light based, and the key issue is to determine a good signal-scheduling plan. In early work, road detectors have been used to collect traffic volume information, and the traffic-signal plan constantly changes to adapt to the varying traffic conditions. Systems such as SCOOT [30] and SCATS [31] have been deployed for many years. 
  Traffic-light scheduling based on vehicular networks is the new stage of intelligent intersection control. Detailed vehicle information, including ID, speed, and position, are collected via V2V or V2I communication. Then, more accurate and efficient scheduling can be achieved. 
  V2I-based traffic-light scheduling is widely studied. In [32,33], a controller node is placed at the intersection to collect queue-length information and computer proper-cycle time of the traffic signal via the Webster formula. In addition to queue-length information, priority of vehicles is considered in [34], and a traffic signal is scheduled with quality-of-service provisioning. In some other work, signal scheduling is modeled as a combinatorial optimization problem to find an optimal scheduling plan of traffic signal. To solve such a problem, various methods such as dynamic programming (DP) [35,36], branch-and-bound [37], and linear programming [38] have been applied. Some researchers introduce intelligent algorithms to traffic-light scheduling, including fuzzy-logic-based scheduling [39], and Q-learning-based scheduling [40,41].
conversations.from: human
conversations.value: What is the last paragraph?
conversations.from: assistant
conversations.value: Embedded device programming languages, , See also Virtualization, embedded boards for cloud of things