6.3 IoT Approaches to Smart City Governance
6.3.5 Data and Citizen-centric Approach (DCA)
The above-mentioned approaches address IoT challenges in smart city governance such as identity, scalability, resource management, privacy, and security. However, in all approaches, the focus is on safeguarding the integrity of network architecture.
So the focus of smart city governance was also managing smart city governance infrastructure. But what makes IoT technology relevant and popular in smart city governance is not so much the architecture but creation of knowledge hidden behind these voluminous data. What makes IoT environment and city governance smart is the possibility of knowledge creation (kc) for different needs of the smart city. In this regard, data and citizen-centric IoT environment for smart city governance is the need of the hour. Data and citizen-centric smart city governance has immense
potential to meet challenging smart city applications such as smart transport, smart home, smart business, smart health, smart electricity and water, smart parking, and many more. The idea of smartness lies in the inherent knowledge that is hidden beneath acquired city data. The data volume is directly proportional to the size of the smart city governance network. In this way, kc for various smart city services will be thefirst step in meeting data and citizen-centric approach.
Knowledge creation (kc) process eventually is developed in to Knowledge Centres (KCs). The creation of Knowledge Centre (KC) for smart city governance involves not only data but also citizens. As IoT technology is becoming popular, citizens are awakening to the resourcefulness of citizens with their handheld smart devices. The real live data of the smart city can be obtained from citizens them-selves. In DCA, citizens are also sensors (data gatherers). Weather forecast, emergency relief operations, traffic updates, market analysis, delivery of govern-ment services, citizen database, etc. can be obtained from citizens themselves. This mode of participatory data collection from citizens will be more authentic, fault tolerant, and above all citizens will own the IoT vision as their own.
6.3.5.1 Data and Citizen-centric Smart City Governance
Data and citizen-centric smart city governance model is presented in Fig.6.6. This model has four layers: Sensing Layer, Network layer, Data and Service Management Layer, and Application Layer. Sensing Layer consists of devices such as Sensors, Actuators, RFID tags, Meters, Video devices, and citizens. The function of sensing layer is to sense the physical reality, aggregate and transmit the data to the network layer. Network Layer deals with both routing and transporting acquired data to the next layer. This is done via communication medium such as wired, wireless, mobile communications (2G/3G/4G/LTE), GPRS, bluetooth, Wi-Fi, WiMAX, NFC, ZigBee, etc. Framing, header compression mechanisms, secure connection, etc. are also handled here. The data and service management layer is special to data and citizen-centric model. This layer has three parts: Data Analytics, Storage, and Knowledge Centre (KC). The received data are aggregated at the border router or gateway at the sensing layer. Eventually, aggregated data are stored in Cloud for data analytics using advanced techniques in data mining and big data.
The result is the creation of KC which is useful to make appropriate decision followed by desired action. This is explained in detail in Fig.6.7. The application layer provides interface for users to interact with the system. The security system provides security to all the layers at various degrees.
6.3.5.2 Data Aggregation
In the coming years, handling IoT data will be a big challenge. Every knowledge creation (kc) exercise involves managing big data. At the sensing level, all kinds of data such as integer, Boolean, audio, and video images are generated. Each type of
Fig. 6.6 Data and citizen-centric model for smart city governance
Fig. 6.7 Process in creation of knowledge centre (KC)
data has varying length, size, memory occupation, and need-specific mode to transmit them. When many sensors or data gathering devices are within the vicinity of one another, one data may be reported by multiple sensors giving chance for redundancy in data. The mode of data collection may vary from sensor to sensor and from application to application. Some sensors may transmit data periodically and other may report when some change occurs in the smart city environment.
Apart from these, IoT networks are resource constrained and they are also sus-ceptible to short communication ranges and power failure; and thus, face frequent retransmission of packets. All these difficulties at network level duplicate data generation from these devices. Therefore, many of these difficulties with sensed data need to be handled effectively. Data filtering mechanisms, data aggregation techniques, removal of data noise, and checking the authenticity of received data are important tasks. The data aggregation can be done at the sensing level or border router gateway level. Clustering techniques are also being developed to manage the aggregation of data. Thus, aggregated data is received by the network layer to data and service management layer for creation of Knowledge Centres (KCs).
6.3.5.3 Creation of Knowledge Center (KC)
Aggregated data at the sensing level are fed to data analytics framework. Here, the data is stored and retrieved in cloud. Advanced data analytic processes or data mining tools are applied to construct patterns to obtain meaningful information.
This information is correlated with physical environments to create Knowledge Centre (KC). Creation of knowledge centres for various needs of smart city gov-ernance can be achieved. Repeated knowledge creation with citizen experience brings wisdom to smart city governance [14]. Therefore, data is the raw material for data and citizen-centric smart city governance. While knowledge creation changes over time, wisdom is timeless, and it all begins with the acquisition of data.
Collection of voluminous data leads to more Knowledge Centres (KCs) for right judgment and action [15].
Hence, creation of knowledge centres (KCs) is collaborative, citizen-centric, and efficient. Due to Knowledge Centres (KCs) many smart city governance services can be automated. Creation of Knowledge Centres (KCs) in smart city governance for a long period of time helps governments to do predictive analysis, better pre-pared for untoward situations in advance, and many such innovations. KCs can be shared with all stakeholders of smart city governance to bring the desired trans-parency, efficiency, and participatory democracy.