Interest in smart cities is rapidly rising due to the global rise in urbanization and the wide-scale instrumentation of modern cities. Due to the considerable infrastructural cost of setting up smart cities and smart communities, researchers are exploring the use of existing vehicles on the roads as "message ferries" for the transport data for smart community applications to avoid the cost of installing new communication infrastructure. In this paper, we propose an opportunistic data ferry selection algorithm that strives to select vehicles that can minimize the overall delay for data delivery from a source to a given destination. Our proposed opportunistic algorithm utilizes an ensemble of online hiring algorithms, which are run together in passive mode, to select the online hiring algorithm that has performed the best in recent history. The proposed ensemble based algorithm is evaluated empirically using real-world traces from taxies plying routes in Shanghai, China, and its performance is compared against a baseline of four state-of-the-art online hiring algorithms. A number of experiments are conducted and our results indicate that the proposed algorithm can reduce the overall delay compared to the baseline by an impressive 13 to 258 .
@cite_7 present a system where public and semi-public vehicles are used for transporting data between stations distributed around the city and the main server. @cite_4 introduce the concept of Smart Vehicle as a Service (SVaaS). They predict the future location of the vehicle in order to guarantee a continuous vehicle service in smart cities. In another work @cite_12 , the authors indicate that cars will be the building blocks for future smart cities due to their mobility, communications, and processing capabilities. They propose Car4ICT, an architecture that uses cars as the main ICT resource in a smart city. The authors in @cite_10 propose an algorithm for collecting and forwarding data through vehicles in a multi-hop fashion in smart cities. They proposed a ranking system in which vehicles are ranked based on the connection time between the OBU and the RSU. The authors claim that their ranking system results in a better delivery ratio and decrease the number of replicated messages.
@cite_10 @cite_4 @cite_12 @cite_7
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Efficient and cost effective data collection from smart city sensors through vehicular networks is crucial for many applications, such as travel comfort, safety and urban sensing. Static and mobile sensors data can be gathered through the vehicles that will be used as data mules and, while moving, they will be able to access road side units (RSUs), and then, send the data to a server in the cloud. Therefore, it is important to research how to use opportunistic vehicular networks to forward data packets through each other in a multi-hop fashion until they reach the destination. This paper proposes a novel data forwarding algorithm for urban vehicular networks taking into consideration the rank of each vehicle, which is based on the probability to reach a road side unit. The proposed forwarding algorithm is evaluated in the mOVERS emulator considering different forwarding decisions, such as, no restriction on broadcasting packets to neighboring On-Board Units (OBUs), restriction on broadcasting by the average rank of neighboring OBUs, and the number of hops between source and destination. Results show that, by restricting the broadcast messages in the proposed algorithm, we are able to reduce the network's overhead, therefore increasing the packet delivery ratio between the sensors and the server. The Smart City vision is to improve quality of life and efficiency of urban operations and services while meeting economic, social, and environmental needs of its dwellers. Realizing this vision requires cities to make significant investments in all kinds of smart objects. Recently, the concept of smart vehicle has also emerged as a viable solution for various pressing problems such as traffic management, drivers' comfort, road safety and on-demand provisioning services. With the availability of onboard vehicular services, these vehicles will be a constructive key enabler of smart cities. Smart vehicles are capable of sharing and storing digital content, sensing and monitoring its surroundings, and mobilizing on-demand services. However, the provisioning of these services is challenging due to different ownerships, costs, demand levels, and rewards. In this paper, we present the concept of Smart Vehicle as a Service (SVaaS) to provide continuous vehicular services in smart cities. The solution relies on a location prediction mechanism to determine a vehicle's future location. Once a vehicle's predicted location is determined, a Quality of Experience (QoE) based service selection mechanism is used to select services that are needed before the vehicle's arrival. We provide simulation results to show that our approach can adequately establish vehicular services in a timely and efficient manner. It also shows that the number of utilized services have been doubled when prediction and service discovery is applied. Vehicular Networks implementation has become a necessity in Smart Cities, due to the opportunistic network connectivity it offers within the city, where researches has began to focus more on Vehicular Networks, to improve the control over traffics and road safety inside the city; other researches on Smart Cities are more concerned about the transmission of the data collected by sensors, nodes, devices installed around the city to the back-end servers where they can be processed and analyzed. In this paper, we propose possible alternative network for Smart Cities, to forward informative data stored in stations implemented around the city without the needs of pre-installed infrastructures, by relying on public and semi-public vehicle transports for network connectivity between the stations and the back-end server.