I Dream of IoT/Chapter 8 : IoT and Case Study

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Internet of Things: Case studies[edit]

The Internet of Things (IoT) represents a changing method of communication between humans and their technology. Typically, IoT is expected to offer advanced connectivity of devices, systems, and services that goes beyond machine-to-machine communications (M2M) and covers a variety of protocols, domains, and applications. The interconnection of these embedded devices (including smart objects), is expected to usher in automation in nearly all fields, while also enabling advanced applications such as the smart grid. IoT can potentially contribute to many aspects of the human lifestyle, including in healthcare, education, transportation, and business. In buildings, IoT devices can be used to monitor and control the mechanical, electrical and electronic systems used in various homes and businesses (e.g., public and private, industrial, institutions, or residential). Home automation systems, like other building automation systems, are typically used to control lighting, heating, ventilation, air conditioning, appliances, communication systems, entertainment and home security devices to improve convenience, comfort, energy efficiency, and security. But to what degree have IoT systems been tested and used? Here are a few case studies.

Case study: Smart Santander parking monitor project[edit]

The smart city is a city that uses IoT and other communication devices to manage its assets.[1] One such asset is the public parking area, which can be monitored for traffic and regulated for usage. The government of Santander, Spain tested such a smart parking system in 22 different zones of the city. This Smart Santander project was developed by several companies and institutions that aimed to design, deploy and valide a collection of sensors, actuators, cameras and screens that supply useful information to Santander’s citizens.[2]

Each zone was provided a Meshlium, an electronic system that gathers the data sensors and moves it to the cloud. Each zone had different network parameters, creating independent networks that work on different frequency channels so as to not interfere with the other networks. In this project, 375 Waspmotes were deployed in different locations within the city to measure the change in magnetic field above it (caused by a vehicle parked over it) to detect whether a parking slot is free. The magnetic field sensor was connected to the Waspmote through a Smart Parking Sensor Board. The sensor itself was buried under the surface of the road inside a waterproof casing. The hole was closed using a specific material and the sensor is barely detectable at a glance.[2]

The information was sent periodically to repeaters and after that to the Meshlium that stored the data, updating a public message board every five minutes, allowing citizens to find a free parking spot in the shortest time. Not only that, parking status was also updated on an interactive online map so that citizens could check for a free parking slot before they got to the city center.[2]

Case study: Smart lighting[edit]

Smart lighting systems allow a smart city to intelligently provide just the right amount of light depending on variables such as time, day, season, and weather. By applying this form of IoT, users could potentially save up to 80 percent energy versus traditional lighting systems, at least according to Janne Aikio at Finland’s VTT Technical Research Centre. "Forecasts suggest that smart lighting will become one of the key trends in the context of the Internet of Things," Aikio told Engineering and Technology Magazine. "Demand for smart lighting is expected to boom over the next 10 years: as much as €7.7bn in 2020. The comparable figure in 2011 was €1.8bn."[3]

Lighting systems utilizing the IoT concept are already available for commercial use, able to integrate with existing building automation systems. For the future, this smart lighting system could improve by integrating with wireless system so that it can be controlled via devices such as mobile phone. "Smart lighting systems are becoming increasingly popular in both new builds and renovation projects. The next major step will be to integrate better sensors and new functions into lighting systems, which will allow the occupants of a room to adjust lighting with increasing accuracy and flexibility according to their movements and activities," explained Aikio.[3]

For even better results, this smart lighting system could be integrated with many more features such as enabling the direction, power, and color of the lighting to be automatically adjusted according to the function of the room or time of day, season, and weather. For example, the lamp could be directed to point towards people in the room, and lighting near a window could change color according to the weather or temperature outside. Additionally, a smart lighting system could perhaps even be self-updating, downloading light filters or "plugins" on demand from the web.

Case study: Smart roads[edit]

The application of IoT varies greatly, thanks to its reliable nature and ability to contribute positively to safety in the home as well as within industry. Yet it even has the potential to positively contribute to our lives while on the road. In fact, IoT stands to positively improve that which is central to much of our existing infrastructure today: the roads that make up our vast transportation network.

Monitoring systems will play an important role on our streets in the future, whether it's to better keep citizens informed or to prepare for the coming of automated vehicles. A series of sensors, circuit boxes, and other IoT technologies will integrate to each other using radio and satellite to enable communication between nodes. That said, there are eight common areas to cover in this monitoring system. These eight areas are based on European roads and weather, though they're still applicable to the network of roads worldwide.

1. Pollution: The first area is setting up a sensor network to monitor traffic-related pollution. The Libelium company offers an example with its Waspmote, which provides a miniature enclosed system that has a solar panel, antenna, and sensors that can be programmed to each node. It's capable of covering large areas with a massive number of networks, thus making it easy for maintenance with the effortless attachment to nodes. As for pollution, the main contributor is from carbon dioxide and nitrogen dioxide from vehicles. To detect this, gas sensors are attached at strategic points throughout the city’s traffic network.[4]

2. Noise: Next up is monitoring noise and generating a noise map. Acoustic sensors can map the noise to those routes in the city, using similar technology as mentioned in the discussion on pollution detection. The microphone used in the system can capture the source of noise, which is turned into usable data that can be placed into a heat map that shows regions of noise with a specific value in decibels.

3. Weather: Next is weather monitoring between points of risk. Aspects to be monitored include temperature, humidity, rainfall, and wind speed and direction. Mini sensor networks with attached pluviometers and anemometers act as cheap weather stations, providing real-time information that can be used to warn drivers in advance so they may opt for other safer routes.

4. and 5. Flooding and icing: These represent the same point of monitoring, only differing in the temperature: the pavement. Flooding can be measured using ground-based liquid sensors. With these sensors, drivers can be alerted to areas with high-water level issues and be alerted to take precautions when choosing their route. As for the icy road, a prediction application can be used driven off of date from temperature and humidity sensors to record likely ice formation along the roads.

6. Structural cracking: As for structural cracking, linear displacement sensors can be used in bridges or tunnels to monitor for any cracks. In addition to displacement monitoring, vibration sensors — similar to those deployed to buildings in earthquake-prone areas — would help in further monitoring and controlling structural cracking as a whole.[1]

7. Parking: As previously discussed, a vehicle detection system can rely on magnetic field sensors to detect traffic jams and the presence of vehicles in parking areas. It is installed in the pavement itself, equipped with material to cope with communication interference and humidity. The information shared between sensors is similar in style to pollution and noise monitoring, with the data being gathered in the Meshlium being sent to the internet network. The deployment of smart parking nodes with monitored cameras can further increase security in parking areas.

8. Traffic flow: Vehicle and pedestrian flow can be monitored using the Meshlium scanner with a Bluetooth and WiFi card to provide the estimation of the traffic and pedestrian flow. The framework is the same in terms of how information is being sent over the internet. In this system, both Bluetooth and WiFi will have its own databases that consist of IP addresses, ports, users, and their passwords. Additionally, it can be synchronized to an external database then shared throughout the network.

Case study: Smart water system[edit]

Smart cities must monitor water supply and distribution to ensure that there is sufficient access for citizen and industry use and also to save money. The goals of a smart water system is to manage water demand and ensure any losses from the water system are minimal. While demand is being better controlled, there are still huge losses to water supply from inefficient distribution and water leakage. Such a system could use wireless sensor networks to more accurately monitor their water systems and identify their greatest water loss risk. Libelium's Smart Metering Sensor Board includes a water flow sensor that can detect pipe flow rates ranging from 0.15 to 60 litres/minute. The system can report pipe flow measurement data regularly, as well as send automatic alerts if water use is outside of an expected normal range. This allows a smart city to identify the location of leaking pipes and prioritize repairs based on the amount of water loss that could be prevented. The sensors on these boards can be used as part of a network that monitors and responds to water pipe leakages across an urban area. Strategic placement of sensors can ensure city-wide coverage. Data from the sensor boards can be collected at regular intervals and sent by wireless network to the city for analysis and for preventative action. Data can also be sent directly to the internet for sharing with the local community and industry, so that everyone can understand and contribute to a city’s responsible water management.[5]

Case study: Using the Meshlium scanner for smartphone detection[edit]

Meshlium is a Linux router which contains five different radio interfaces: WiFi 2.4GHz, WiFi 5GHz, 3G/GPRS, Bluetooth, and ZigBee. The Meshlium can also integrate a GPS module for mobile and vehicle applications and be solar and battery powered. These features along with an aluminium IP67 enclosure allows Meshlium to be placed anywhere outdoors. Meshlium comes with the Manager System, a web application which allows quick and easy control of WiFi, ZigBee, Bluetooth and 3G/GPRS configurations as well as the storage options of the sensor data received. It can detect iPhone, Android, and other hands-free devices that broadcast on radio channels.[6]

This general idea of the technology is to measure the amount of vehicles and people present at a certain point and time, allowing the study of the evolution of traffic congestion. For this idea to work, users don’t have to do anything to be detected or visible on a network. As long as the WiFi and Bluetooth radio integrated in their mobile device is active, the router can still detect their presence. A user is detected by the Meshlium router depending on the following[6]:

  • the MAC address of the wireless interface, which allows it to be identified uniquely;
  • the strength of the signal (RSSI), which gives the average distance of the device from the scanning point;
  • the vendor of the mobile device (Apple, Nokia, etc.), the access point the user is connected to (WiFi), and the Bluetooth-friendly name (users that are not connected to an access point will identify as a "free user"); and
  • the class of device (CoD), in the case of Bluetooth, which allows the system to differentiate the type of device, enhancing differentiation between vehicles and pedestrians.

Additionally, the coverage areas may be modified by changing the power transmission of the radio interfaces that is allowing the creation of different scanning zones from a few meters, enabling study of a specific point for dozens of meters (to study the whole street or even the entire floor of a shopping mall).

The Meshlium or other such scanner can focus on:

1.Vehicle traffic detection: In this application, the system is able to...

  • monitor in real time the number of vehicles passing for a certain point in highways and roads.
  • detect average time of vehicle stance for traffic congestion prevention.
  • monitor average speed of vehicles on highways and roads.
  • provide travel times on alternate routes when congestion is detected.
  • calculate the average speed of the vehicles which transit over a roadway by tracking time at two different points.

2. Shopping and street activities: Similar to monitoring car traffic, the efficient flow of pedestrians in an airport, stadium, or shopping centre can be monitored to improve user experiences, helping make the difference between a good and a bad visit.

Conclusion[edit]

These IoT case studies suggest ways in which IoT will make our life easier and well-arranged. The infrastructure of smart cities could potentially improve our environment for safer driving experiences. Smart cities may also introduce improvements in terms of public services that include parking spot monitoring, weather alerts, and management of waste typical to the modern city. When integrated with the city, IoT will allow citizen to enjoy their city more and utilize present technologies. The future of IoT may potentially offer even more advancements to basis infrastructure from its current radio technologies to enable each devices to share information with each other over a network for greater coordination and data analysis.

References[edit]

  1. a b Asín, A. (20 June 2011). "Smart Cities platform from Libelium allows system integrators to monitor noise, pollution, structural health and waste management". Libelium. Libelium Comunicaciones Distribuidas S.L. http://www.libelium.com/smart_cities/. Retrieved 8 June 2016. 
  2. a b c Bielsa, A. (22 February 2013). "Smart City project in Santander to monitor Parking Free Slots". Libelium. Libelium Comunicaciones Distribuidas S.L. http://www.libelium.com/smart_santander_parking_smart_city/. Retrieved 8 June 2016. 
  3. a b Pye, A. (10 November 2014). "Internet of things: Connecting the unconnected". E&T 9 (11). http://eandt.theiet.org/magazine/2014/11/internet-of-things.cfm. Retrieved 8 June 2016. 
  4. Asín, A.; Calahorra, M. (30 September 2010). "Sensor networks to monitor air pollution in cities". Libelium. Libelium Comunicaciones Distribuidas S.L. http://www.libelium.com/smart_cities_wsn_air_pollution/. Retrieved 8 June 2016. 
  5. Asín, A.; Boyd, M. (3 August 2011). "Smart Water: Pipe control to reduce water leakages in smart cities". Libelium. Libelium Comunicaciones Distribuidas S.L. http://www.libelium.com/smart_water_wsn_pipe_leakages/. Retrieved 8 June 2016. 
  6. a b "Meshlium scanner for smartphone detection" (PDF). Libelium Comunicaciones Distribuidas S.L. http://www.m2mconnectivity.com.au/sites/default/files/brochures/SMARTPHONE%20DETECTION.pdf. Retrieved 8 June 2016.