What is Smart Cities?
The phrase “smart cities” refers to geographical regions that have invested heavily in ICT infrastructure to facilitate the management of the region both for business and for the quality of life of its citizens. The word “smart” alludes to the idea that the growth and facilities of the region evolve with coordination, planning, and greater efficiencies, as opposed to unplanned and carefree or laissez-faire growth (which is growth without government or regulatory intervention).
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Definitions of Smart Cities
There are many definitions of smart cities as follows:
The Government of India defines smart cities as – cities that provide core infrastructure and give a decent quality of life to its citizens, a clean and sustainable environment, and application of “smart” solutions. The smart solutions are based on an IT infrastructure and include facilities such as electronic governance, waste management, water management, energy management, urban mobility management, and other services such as education and healthcare.
In Europe, one definition of smart cities that has gained attention is – a city is considered to be smart when “investments in human and social capital and traditional infrastructure (transportation) and modern communication infrastructure (ICT) to fuel sustainable economic growth and a high quality of life, with a wise management of natural resources, through participatory governance.”
Both definitions thus emphasize the use of IT and communications infrastructure, sustainability, quality of life, and the efficient provision of services in cities.
Facilities of Smart Cities
- Sensing and Measuring
- Smart Transportation
- Smart Environment
- Water Conservation
- Pollution Control
- Energy Management
- Smart Buildings
- Smart Living
- Smart Governance
Sensing and Measuring
One of the foundations of smart cities is that of perceiving and recording various parameters of the city environment. This sensing is done by electronic sensors that are distributed across the city and provides data to connected networks that store and process this information. Data sensing is done for a vast number of phenomena, and the most popular ones are as follows:
- Sensing of transport systems, including the movement of individual vehicles, public transport, the density of traffic at physical locations, speeds, and range of travel.
- Sensing of consumption of power and water usage, where smart meters relay data to central networks.
- Sensing of particle density in air and water to gauge pollution levels.
- Sensing of garbage production, accumulation, and removal.
- Sensing of land use, agricultural activity, soil erosion, and construction activity.
- Sensing of building temperature and illumination, arrival and departure of people, consumption of energy and water, and use of parking facilities.
- Sensing of access to healthcare facilities, location of diseases, the spread of diseases, and availability of medical facilities.
With the above-mentioned sensing and measuring facilities, certain management capabilities are enhanced. The sections below delve deeper into the ICT architecture and management aspects of smart cities.
Many cities around the world have implemented ICT infrastructure for the management of traffic. Traffic congestion has become a serious problem for rapidly growing cities where a large number of people need to commute daily and usually rely on their vehicles for doing so.
Traffic congestion often has predictable patterns, such as rush-hour traffic, that sees huge surges in vehicles plying on the roads, over connecting flyovers and bridges, at intersections, and in parking spaces. These patterns become the basis for understanding how traffic can be managed.
Sensing for smart transportation involves collecting data on traffic parameters in different ways. One of the widely used methods is that of using cameras mounted on traffic signals. Images from these cameras are viewed by people, usually traffic police, in large central command centers from where they inform individual policemen of the extent of traffic at particular junctions and whether any action needs to be taken. Policemen on the ground can then re-route traffic to avoid congestion build-up.
An example of this is the Bangalore Traffic Management Centre in Bangalore City. This center receives direct feeds from 180 surveillance cameras mounted on roads across the city. The feed from the cameras is displayed on a massive display screen and individual camera feeds are viewed on smaller monitors. Traffic personnel then instruct traffic policemen on the roads to direct and control traffic in different ways.
Another approach is to analyze the images streaming in from cameras to calculate the amount of traffic automatically. The processing systems are then able to compute the number of cars on different roads at an intersection and also the length of the queue that has built up. This information is then used to directly control traffic signals to slow down or speed up traffic on other roads that are connected. This information may also be used to re-direct traffic by flashing messages on road signs and at traffic intersections.
The information from surveillance cameras may also be used to identify different types of vehicles. For instance, in some cities, public transport buses are identified, and if there are a number of them at an intersection, then the road they are on is given priority for movement. This logic is also applied to vehicles such as ambulances, police patrol cars, and other priority vehicles that are enabled to move faster than others. Sensors can also detect bicycles and pedestrians and enable them to cross intersections.
Some cities have historically used sensors embedded in the road asphalt to detect the presence of a vehicle. These sensors were connected to the traffic signal and the signal would turn green only if a vehicle was present. Modern versions of such sensors sense not only if a vehicle is present, but also how many are waiting in the queue and have been doing so for how long. Based on this information the signal is controlled.
A third method of sensing traffic is by monitoring mobile phones carried by drivers in cars. The basic idea is that cell towers for mobile phones near roads will register the mobile phone carried by the driver of the car. If one totals the number of connections to a particular tower, this could be an estimate of the traffic on the road. However, this is a rough estimate, as the tower will connect all people nearby, in homes or offices, walking on the street or in the vicinity, and they do not contribute to vehicular traffic.
Researchers have shown that sensing “handovers”, that is, the movement of phones from one mobile tower to another, as the phone is moving in the car, gives a better estimate of how many cars are on the road. This data on the number of cars is aligned with maps of city streets and this shows the flow of traffic on roads, which includes the volume of traffic, the direction, and the speed at which it is moving. This information is then clubbed with data from cameras to estimate, with more accuracy, the amount of traffic and how it is likely to create congestion at various points in the city.
The latest trends are to use traffic density and flow information to control traffic signals and traffic direction indicators, where available, to manage the flow of traffic in the city and avoid congestion. Experiments in some cities in different parts of the world show promising results, with up to 25% improvements in travel time. Measuring individual movement of cars and then assessing aggregate traffic patterns requires IT infrastructure to collect the data from millions of moving vehicles, aggregate it and then perform analysis on it. This is a significant cost for obtaining the benefit of reduced congestion.
Collecting movement information through GPS-enabled phones also helps individual commuters gauge their position concerning roads, bus stations, and railway stations. Some smartphone-based apps enable the commuter to know how long it will take them to reach a particular place, say a train station, whether parking spots are available at their destination, and when the next train will arrive. This information enables commuters to make individual choices about their travel. The IT infrastructure used for such systems relies on openly available information, such as traffic data, parking availability data, and train arrival and departure date.
The environmental challenge of modern cities is that of conserving resources that are in short supply, such as water, and protecting some resources from pollution – such as water, air, and land, and also protecting the audible environment from sound pollution. The environmental challenge also draws on sustainable practices to limit GHG emissions, control global warming, and protect the biodiversity of flora and fauna.
Many urban regions need to sense and monitor water resources as the growing population of these cities is creating shortages of this commodity. Varying rainfall patterns that replenish water bodies, draining out water from upstream dams or canals, and poor use of available water have forced many cities to manage water through smart methods.
Water bodies such as lakes and reservoirs are monitored by sensors for their levels, the number of inflows they have from streams and rainfall, and the amount that is consumed by urban water needs. These levels are sensed by water meters and the data is provided continually to a central system that computes the water levels. Water-related measurements also include rainfall, humidity, soil composition, sunlight, topography, and temperature. In rural areas, such data is provided to farmers to make decisions about cropping times, cropping patterns, and irrigation.
Monitoring is also done with satellite image data that indicates the spread of lakes and streams, flows in rivers and canals, and the depth of water bodies. When wastewater is released from urban areas into the local lakes and reservoirs, this too is measured and accounted for. Researchers have developed many complex mathematical models that are used to predict the availability of water from these water sources at different times of the year.
In many cities, one of the main challenges of water conservation is of preventing leakages. Most older cities have water pipes that are many decades old and are underground, thus making them difficult to access and check. To address this problem, some cities are installing pressure gauges in the pipes that indicate the level and pressure of the flowing water. Lower pressure is usually desired as that leads to lesser leakage, where leaks are present.
The system of sensors provides an accurate estimate of the extent, or stock, and flows of water in the network of pipes. When more water is demanded in some regions, the flows are increased by automated pump controllers that allow more water to be pumped into the system. Where water is in less demand, the flows are reduced and with that, the water pressure is also reduced. This has the twin advantages of reducing leakages and also of reducing the energy required to pump water (as the water is pumped only when needed).
Cities are also encouraging water conservation at housing complexes and individual homes. In Bangalore, for instance, housing complexes and gated communities have to implement rainwater harvesting, as part of their compliance with municipal laws. It is being used by these communities to monitor water levels in tanks and sumps. This information is used to control pumps to fill water and also to drain water when IT sensors and motors are used to monitor and control stocks and flows of water in urban areas, based on demand and availability.
Pollution control is enabled by monitoring air quality at different points in the city and aggregating this data to arrive at a comprehensive picture. Typical measurement sensors record data about levels of carbon dioxide, carbon monoxide, nitrogen dioxide, humidity, temperature, and pressure. Some sensors will also monitor light quality and vibration levels, whereas others measure particulate matter and ozone. Such sensors are typically mounted on kiosks at points on city streets, on top of traffic signals, or on top of buildings.
The data from sensors is aggregated and overlaid on a map of a city to obtain a visual image of the different components of air quality and their presence in the city. “Hotspots” of pollution are thus identified, where air quality is particularly poor and this is used to direct executive action. For instance, cities may divert traffic away from hotspots of pollution to control air quality.
Sensors are also used to visually observe sensitive areas in the city – such as factories or warehouses, where there is a possibility of an increase in polluting activity. These are surveillance cameras that may be observed by citizens on a volunteer basis and help to identify potential problem areas. The city of Pittsburgh in the USA has experimented with such an approach.
Another approach for measuring air quality is to provide citizens with sensors that are connected to GPS-enabled smartphones that feed data from different points in the city to a central server. The advantage of this approach is that the sensors are mobile and check air quality where people congregate.
Water pollution is also a matter of concern for urban areas. Some cities have implemented sensors to monitor the extent of sewage flowing through the underground sewage system, both to treat the sewage before it is permitted to mix with other water bodies and also to control the extent of sewage that is produced and unleashed into rivers and oceans.
Weather and rainfall measurement sensors are used to predict if excessive rainfall is likely, which often leads to overfilling of storm-water drains that let untreated sewage into water bodies. With these measurements, infrastructure managers in cities can divert excessive water to alternate drains, without letting them enter the sewage system.
Conservation of energy remains a strong rationale for urban centers to use smart technologies. It is estimated that cities consume about 75% of the total energy requirements in the world. This consumption is for industrial production, in factories and offices, and also for running services such as transportation, and domestic consumption. A key goal of smart cities is to monitor and control energy consumption.
Energy management requires locating and measuring the generation, storage, distribution, and consumption of energy. Each of these stages of the energy life cycle requires sensing and monitoring for efficient management.
Energy generation is usually carried out by the traditional means of central power generation plants that use hydro power or are based on fossil fuels. In many cities, owing to uncertainty in power supply from these centralized sources, many industrial and other organizations install their captive power generation plants that essentially rely on fossil fuels. Solar power has gained prominence in recent years, and many cities have begun to invest in massive solar panel installations, sometimes over rooftops and on buildings.
Photovoltaic panels convert solar power directly into electricity, whereas thermal collectors convert solar energy into heat energy (such as solar water heaters). These sources of electricity generation may be located on individual buildings and homes in cities, thus creating a large mass of micro-generators that may be connected to a grid.
Cities in some regions that have windy environments rely on wind energy obtained from massive wind turbines located on hilltops. In all these cases, the challenge is to sense and monitor the extent of energy production at all these sources and to decide how these can be stored or distributed.
Electrical energy is typically stored in batteries of various forms. These batteries may be monitored to see when and how they lose charge and then have to be recharged. Energy is also stored in superconducting magnetic energy coils and as potential energy in water pumped to higher positions, from which it can be released and converted to energy.
These different storage mechanisms can be monitored by different sensors and their potential to release energy can be tapped for different requirements. Typical variations in energy needs include sudden peaks in demand, owing to weather changes, or gradual demand growth owing to industrialization. Cities try to balance the different needs with different storage and monitoring facilities. The key objective of these efforts is to retain sustainability.
Energy distribution in smart cities follows the “smart-grid” concept where each energy generation and storage location is connected to a grid, along with the consumption locations. The function of the smart grid is to distribute energy from supply sources to demand points based on price expectations, contractual obligations, and sustainability criteria.
The key idea of smart grids is to meet customer demand with existing energy sources without resorting to building new facilities, with an architecture that is resilient and can withstand natural forces and calamities. The smart grid is usually an interconnection of many microgrids that operate independently of each other and are called to pull together only in times of need. Such an arrangement requires a strong communication backbone that is robust and constantly online.
One of the key aspects of energy management in cities is “smart buildings”. Since buildings are the main consumers of energy, for air-conditioning, heating, lighting, ventilation, running machines, and for facilities such as parking, security, and running elevators and doors, each aspect of energy consumption within buildings must be sensed, monitored, and controlled.
All the office floors of the building have sensors for measuring the quantity of light, the ambient temperature, and the extent of ventilation. These sensors would connect with nearby mesh routers that signal the conditions to a central control unit. In case the temperature or light condition has to be changed, the central control unit will send a signal using the mesh network to the actuators (or motors) to change the temperature by giving commands to an air-conditioner or to light switches to turn on or turn off lights.
Many buildings also have motion and occupancy sensors that can detect the presence of humans, and maintain certain light and temperature conditions. If humans are not present then the lights and temperature maintenance are automatically turned off. The building also has parking sensors that sense the number of vehicles in the building and also the number of parking spots available. This information would be available online and commuters intending to arrive at the building would know if they will find parking or not. If parking is already full, they may choose to rely on public transportation to commute.
The key challenge of smart buildings is to use IT to manage their energy requirements in a sustainable manner, where they draw on the grid only when required and try to remain dependent only on their generation capacities. Meeting this challenge requires collecting data through many sensors and devising algorithms or programs that can achieve these sustainability goals.
There are many ways in which information technology is being used by people to enrich their lives. The idea of smart living in urban areas is that of people collaborating on online or peer-to-peer communities to sustainably impact and improve some aspect of their lives.
Some examples that highlight aspects of smart living are as below:
Open street maps are maps created by masses of people living in cities or rural areas. The idea behind OpenStreetMap (www.openstreetmap.org) was to have publicly available maps that are not owned by any corporation and are created and updated by people living in the areas that are mapped.
The technology basis of OpenStreetMap allows registered users to walk, bicycle, or drive around territories and mark them on digital maps that are uploaded to a central database. The mapping work is done by volunteers who are registered on the OpenStreetMap.org site. In February 2017, there were over 3.7 million registered users of OpenStreetMap.
Citizens of cities around the world now collaborate through smartphones to rate facilities and services in their region. For example, many apps rate restaurants, laundries, healthcare providers, such as dentists, schools and colleges, and also government offices that provide citizen services.
These ratings are provided by citizens who have availed of these services or consumed the products and provide feedback to other citizens and providers, largely to help improve the services. The breadth and quality of such feedback from citizens have improved vastly across countries, where citizens comment on all aspects of living in urban areas.
The role of information technology in governance, referred to as e-governance in Chapter 5, is further enhanced through the concept of smart governance. The main idea is that the data and information required for governance and decision-making are obtained from distributed and decentralized sources. Functions of governance such as monitoring, planning, and execution of various policies are supported and enhanced by smart governance technologies and infrastructure. A few examples highlight these aspects.
Citizen participation in budget monitoring and the setting was the goal of a smart governance project in some neighborhoods in the city of Amsterdam. The idea was to have an IT infrastructure that could inform citizens about various allocations of budgets for neighborhood development, how the budgets were spent, and the outcome of the spending. The system allowed citizens to see and understand the budget allocation process, the details of where the money was being allocated, and see how the money was being spent.
Further, citizens could also participate in the allocation process, helping set priorities for the spending – such as on roads, schools, playgrounds, etc. The system was a web-based application that enabled various neighborhood councils to upload their data on a common platform, after which the data was presented in different views for citizens to understand and comment upon.
The coastal town of Rikuzentaka, in the Iwate Prefecture of Japan, was struck by a massive earthquake in March 2011. This was followed by a colossal tsunami, with waves more than 10 meters high, that effectively wiped out the entire downtown area of the city. Hundreds of buildings were destroyed, with a human toll of close to 2000 dead.
The town was devastated, but its citizens resolved to rebuild it and do it in such a manner that it would be environmentally friendly and also be able to withstand future shocks like earthquakes and tsunamis. The challenge was to come up with a design that would be acceptable to those wanting to live there and also meet the cost and geographical constraints of the region.
The central government of Japan provided funds for the reconstruction, and by 2013, the authorities had drawn up a detailed plan for the new downtown and residential areas. The idea was to create a raised hill near the seaside that would host the new downtown area, including the business and shopping areas, and have the residences move uphill nearby.
Residents were shown the plan; however, not all were convinced of its feasibility and how it would affect their lives. The authorities then drew up a virtual reality reconstruction of the entire plan, including details such as landscaping, roads, parks, forested areas, buildings, residences, schools, and other facilities. Citizens could now see the plan in three dimensions, with aerial views, cross-section views, and also ground-level views.
This helped them understand how their residences, neighborhoods, offices, and shopping areas would be located and would appear when rebuilt. Many then made suggestions for improvement and changes, which were visualized on the software. Citizens then gave their approval for the new plans and the construction of the new town could proceed. It was used to build consensus for a new plan for a city, with the active participation of citizens in both the design and approval processes.