above. Examples of such architectures include combinations of IoT-AL [26], IoT-SOA [27,28], IoT-SOA-AL [29], IoT-EDA [30], etc.
that will make it possible for a better integrated management of energy flows between different subsystems that will form an integral part of smart city. The SEM is conceptualized as a flexible energy hub used for supplying and storing many energy carriers. Therefore, this work actually proposes new tools that are aimed at optimizing the design and the e-governance of SEM. Here, the new tools are integrated based on complexity science and risk analysis. Also, reliability and quality are assured by proposing new technologies for service optimization and reconfiguration. The authors have also tried to devise mechanisms using the new technologies for minimizing the infrastructure vulnerabilities. Further, they have analyzed the control of interdependencies among the infrastructures and also between the environment and the infrastructure. Finally, organizational problems and human factor impacts on SEM control and management are studied in the work.
In another work [33], the authors have proposed solutions through the project DC4Cities that aim to optimize the share of local renewable power sources while operating data centres in smart cities. Basically, data centres are important func-tionaries in smart cities as they perform the dual function as IT service providers and energy consumers. A major challenge for future smart cities is that of inte-grating intermittent renewable energy sources into the local power grid with an objective of IT-based low carbon economy. The authors have proposed methods for power management options between the data centre and the smart city together with internal adaptation strategies. Finally, the authors have implemented the proposed mechanism and evaluated the same through simulation.
5.3.3 Transport Management
Intelligent transport systems are a fundamental part of smart city [34] without which the operation of such a city would be incomplete. Intelligent transport systems include applications related to traffic safety, traffic law enforcement, traffic control and smart parking. In Fig.5.2, reference architecture of a typical WSN-based ITS is shown. It is worth noting that the architecture has four main subsystems, namely, sensing, distribution, decision-making and execution. Each of these subsystems may carry out their work independently. The discussion given below highlights some of the works done in intelligent transport system area.
Traffic safety applications have been dealt with in [35, 36] that monitor the presence of traffic or animals within a safety zone as defined by the application. The objective of traffic applications is to deal with prevention of accidents. The sensor devices are made to work proactively for alerting drivers against some possible dangers such as the presence of obstacles, animals, bad road conditions or vehicles driving in the wrong direction. These devices communicate among themselves for warning the drivers of events that are not within their sight, thereby averting any major accident. These sensor devices function using a particular approach or a
combination of both approaches. In the first approach, when a static sensor node detects the arrival of a vehicle, it activates the remaining static nodes for obtaining the conditions of the road that follows. The second approach makes the road information available to the nodes prior to the vehicles reaching them. This means that whenever any required data is obtained, it is transferred to a particular area so that they are collected by the passing vehicles later on. The second approach is very much applicable for detecting non-ephemeral events. In [37], the authors have suggested placing static sensor nodes at the beginning of every road that makes it possible for all vehicles to know beforehand the conditions of the road that were gathered by previous vehicles. The authors in [38] have combined WSNs and Vehicular Ad hoc Network (VANET) for implementing this technique. Here, WSN monitors the road and VANET circulates the information to other vehicles travel-ling on roads without WSNs or to distant static sensor nodes for warning drivers of absence of other vehicles.
In other works (e.g. [39, 40]), the authors use techniques for detecting speed limit breaches that form a part of traffic law enforcement application by collabo-rating between two sensor nodes. Here, cameras are used which trigger when such a speed violation is detected and the photographs are sent to Traffic Management Centre (TMC) where they are processed and stored. Also, it is possible to warn the drivers by using Variable Message Signs (VMSs) before issuing anyfine. In [40], detection of illegal parking is handled by placing sensor nodes that take a picture of the number plate of the vehicle that has created this issue. In another work [41], the authors have used techniques for findings of post accident investigation. The post-accident investigation is necessary for determining the responsibilities after an accident.
In [42], the authors use traffic guidance applications such as path planning that are needed for determining the best urban scenarios. Here, sensor nodes are used for monitoring small-to-medium size road networks and estimate the time cost of each road segment for obtaining the optimum path in a particular direction. Also, the
Traffic Management Center
Distribution
Sensing
Cluster head
Execution Decision Making
Fig. 5.2 Architecture of a WSN-based ITS
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.