authors have proposed another application where traffic management is done at intersections using traffic lights, thus scheduling the traffic. Sensor nodes are placed on traffic lights, usually one per lane so that it is possible to determine the number of traffic arrivals at the intersection of every segment. The placement of sensor nodes can also be done after the traffic lights so that the queue length at each traffic light is obtained. All these techniques need a small number of sensor nodes, thereby reducing the cost. Due to certain constraints of sensor nodes, in some works acoustic detectors based on neural networks [43] and vibration sensors in vehicles [44] are used for providing high accuracy. As these techniques employ costly mechanisms, a purely collaborative WSN solution, for example shockwave detection algorithm is more suitable [45]. Shockwave detection algorithm is based on the fact that when an accident occurs, two shockwaves are generated in the traffic flow. One of the shockwave’s propagates in the opposite direction of traffic while the other travels in the direction of traffic. In [46], the authors have imple-mented this method by placing sensor nodes along the road for estimating traffic volume and detecting potential shockwaves that are validated by adjacent nodes.
The lack of parking spaces in cities is a major concern which results in illegal parking, congestion due to low speed driving and long searching times forfinding empty parking space by drivers. To reduce this problem for drivers, several smart parking systems have been developed which guide drivers to vacant parking spots (PGIS—Parking Guidance Information System) and enable smart payment and reservation options. For the deployment of smart parking systems, WSNs are a much better substitute against more expensive wired sensors. In [47], the authors proposed applications for detecting parking spaces using WSNs that detect the distribution of vacant parking lots where sensor nodes are placed at the entrance of eachfloor. In [48–50], the authors have used WSN that is deployed in a grid layout manner over the parking area. Here, the sensor nodes perform the task of vehicle detection that leaves or enter the parking area. WSNs are also used for on street parking applications where it is not cost effective to use VMS (Variable Message Sign) or other informative panels in the streets just for parking purposes. Thus, on-street parking systems depend on smart vehicles that are incorporated with On Board Units for receiving parking information.
5.4 Issues and Challenges of Building Intelligent Systems
5.4.1 Issues
There are a number of issues related to building intelligent system for smart cities, both of technical and social nature. Some of the specific technical issues are as follows:
• Public safety: Information analysis to be done in real time for reducing crime and act quickly to threats faced by the public.
• Health care: More improvised connections and advanced analytics for inter-preting a large amount of data collected is required for providing better healthcare.
• Transport: Integrating all transport modes with each other, removing congestion and enabling issuance of new avenues are areas to be taken care of while designing the factor of transport in smart cities.
• Connectivity: In smart cities all businesses, citizens and systems should be provided with high speed connectivity for the smooth running of all applications in such cities.
• Energy issue: Areas should be explored where energy from conventional sys-tems can be procured that can be utilized for operating the various applications in smart cities. Also, people should be made aware of their individual energy consumptions and devise mechanisms for reducing such consumptions.
• Water: Raising awareness to reduce wastage of water as well as analyze the entire ecosystems that will help to estimate the amount of water supply to homes, industries and any other organizations.
5.4.2 Challenges
Like any emergingfield, smart cities also face several genuine challenges that need to be addressed while developing such cities. Some of the challenges are briefly discussed below:
• Implementation cost: The development of smart cities involves technologies that are incurring a huge cost. Many cities that have tried to implement technologies with the ultimate objective of making the city ‘smart’ have invested lots of money. For example, the bus system in Copenhagen, Denmark, costs €125 million annually [12].
• High energy consumption: The sustaining of the technologies needed for the running of smart cities requires high energy that can be a major challenge for the upcoming of smart cities.
• Privacy and security: Privacy and security will play a very vital role for smart cities. The people in smart cities will use smart city services with their smart-phones and computers that are connected through networks. Also, in the context of smart cities, enabling technologies for sensing applications makes the
communication networks highly vulnerable to threats related to cybersecurity and cyber vandalism. So handling these security issues related with the func-tioning of smart cities is a real challenge that needs utmost attention.
• Integration of technologies: Smart cities are fast emerging as a possible solution for brighter future prospects. While information and communication technolo-gies are being used for development of such cities in terms of materials and infrastructure, the same technologies are also used for the planning of smart cities that involve the usage of computations and data. Therefore, a vital chal-lenge is to demonstrate how the technologies utilized for the above mentioned developments are interoperable so that cities can really be termed as smart ones.
• Traffic management system: Challenges in WSNs used in smart cities for traffic management systems range from the need for a highly reliable and fast MAC access protocol to data forwarding mechanisms for ensuring critical message transmissions that carry information regarding emergency situations on roads.
• Mobility: In smart cities, it is very much essential to guarantee uninterrupted service to mobile users while shifting between different access networks. This is important so as to enableflawless running of applications in smart cities.
• Scalability: The limitations with respect to restrictions in storage, bandwidth and computational abilities that act as an hindrance to service providers while handling a large number of users should not come in the way of proper func-tioning of the different services applicable to smart cities.
• Fault tolerance: This is a real challenge while designing smart cities as infor-mation and communication technologies should be implemented in such a way such that they are highly resilient to system failures.
• Upgradation: This feature is true for the development of any technology and likewise for smart cities too. Upgrading a smart city will incur a huge cost as such a city is very much dependant on communication and technologies, thereby posing a tough challenge.
5.4.3 State-of-the-Art Solutions
In this section, a brief description of the various key issues that were addressed in recent past for the development of intelligent systems for smart cities is provided.
In [51], Suryadevara et al. developed a low cost,flexible, robust and data driven intelligent system for determining the wellness of elderly persons living alone in a smart home. The system model comprises of two modules, viz. WSN and intelli-gent home monitoring software system. These modules are vested with the task of collecting sensor data and performing data analysis for detection of changes in the behavioural pattern of the elderly. Based on the behavioural pattern of the elderly person, the healthcare service providers provide accurate assistance to the elderly persons. The authors have tested the models at several elderly homes and the results are found to be convincing. Similar to [51], a WSN based intelligent elderly care
monitoring system was designed by Dasios et al. [52]. Initially, the system monitors and records various environmental parameters such as temperature, humidity and light intensity to gather daily activity like moving, sitting and sleeping of the elderly person. If any significant deviation from the normal activity pattern of an individual is detected, the system issues automated alarms to authorized persons.
In [53], Semertzidis et al. proposed a real-time traffic monitoring system using visual sensor networks. The proposed system consists of a number of visual sensor nodes and personal computers. The visual sensor node captures imagery data and sends them to the personal computer for processing and subsequent recognition and tracking of any car. An intelligent vehicle speed controlling system is proposed in [54]. In the proposed system, RFID is used for communication between vehicle and traffic signs in order to control the vehicle speed. In another work [55], Magpantay et al. implemented and deployed a WSN for the monitoring of usage of electric energy in smart buildings. Here, WSN is developed using the Granular Radio Energy (GRE) sensing node. The GRE sensing node consists of a micro-controller, a radio, a battery and a giant magnetoresistive magneticfield sensor. The authors have implemented these GRE sensing nodes based WSN in smart building for reducing energy consumptions and have obtained effective results. Similar to [55], an intelligent energy monitoring system called EnerISS (Energy Integrated urban planning & managing Support System) is developed by Kim et al. [56]. In EnerISS, a WSN is used to collect energy usage data from the buildings. The collected energy usage data coupled with location information (i.e. GIS data) are stored in EnerISS database. Finally, based on the rules generated from the interaction between the consumer and producer, EnerISS takes in-time decision for efficient energy supply management.
In [57], Metje et al. proposed an intelligent water pipeline monitoring system based on WSNs. In the proposed system, sensor nodes are used to monitor specifically three parameters, namely, vibration, pressure and sound. By monitoring these parameters, the system successfully detects a leakage in the pipeline.