With the recent increase in urban drift, which has led to an unprecedented surge in urban population, the smart city (SC) transportation industry faces a myriad of challenges, including the development of efficient strategies to utilize available infrastructures and minimize traffic. There is, therefore, the need to devise efficient transportation strategies to tackle the issues affecting the SC transportation industry. This paper reviews the state-of-the-art for SC transportation techniques and approaches. The paper gives a comprehensive review and discussion with a focus on emerging technologies from several information and data-driven perspectives including (1) geoinformation approaches; (2) data analytics approaches; (3) machine learning approaches; (4) integrated deep learning approaches; (5) artificial intelligence (AI) approaches. The paper contains core discussions on the impacts of geo-information on SC transportation, data-driven transportation and big data technology, machine learning approaches for SC transportation, innovative artificial intelligence (AI) approaches for SC transportation, and recent trends revealed by using integrated deep learning towards SC transportation. This survey paper aimed to give useful insights to researchers regarding the roles that data-driven approaches can be utilized for in smart cities (SCs) and transportation. An objective of this paper was to acquaint researchers with the recent trends and emerging technologies for SC transportation applications, and to give useful insights to researchers on how these technologies can be exploited for SC transportation strategies. To the best of our knowledge, this is the first comprehensive review that examines the impacts of the various five driving technological forces—geoinformation, data-driven and big data technology, machine learning, integrated deep learning, and AI—in the context of SC transportation applications.

The advances and convergence in sensor, information processing, and communication technologies have shaped the Internet of Things of today. The rapid increase of data and service requirements brings new challenges for Internet of Thing. Emerging technologies and intelligent techniques can play a compelling role in prompting the development of intelligent architectures and services in Internet of Things to form the artificial intelligence Internet of Things. In this article, we give an introduction and review recent developments of artificial intelligence Internet of Things, the various artificial intelligence Internet of Things computational frameworks and highlight the challenges and opportunities for effective deployment of artificial intelligence Internet of Things technology to address complex problems for various applications. This article surveys the recent developments and discusses the convergence of artificial intelligence and Internet of Things from four aspects: (1) architectures, techniques, and hardware platforms for artificial intelligence Internet of Things; (2) sensors, devices, and energy approaches for artificial intelligence Internet of Things; (3) communication and networking for artificial intelligence Internet of Things; and (4) applications for artificial intelligence Internet of Things. The article also discusses the combination of smart sensors, edge computing, and software-defined networks as enabling technologies for the artificial intelligence Internet of Things.

Crowdsourcing can play an important role in the Internet of Things (IoT) applications for information sensing and gathering where the participants are equipped with geolocated devices. Mobile crowdsourcing can be seen as a new paradigm contributing to the development of the IoT. They can be merged to form a new and essential platform in crowdsourcing IoT paradigm for data collection from different sources and communication mediums. This paper presents a comprehensive survey for this new Crowdsourcing IoT paradigm from four different perspectives: (1) Architectures for Crowd-IoT; (2) Trustworthy, Privacy and Security for Crowd-IoT; (3) Resources, Sharing, Storage and Energy Considerations for Crowd-IoT; and (4) Applications for Crowd-IoT. This survey paper aims to increase awareness and encourage continuing developments and innovations from the research community and industry towards the Crowdsourcing IoT paradigm.

Recent technology developments and the numerous availabilities of mobile users, devices and Internet technologies together with the growing focus on reducing traffic congestion and emissions in urban areas have led to the emergence of new paradigms for ridesharing and crowdsourcing for smart cities. Compared to carpooling approaches where the driver and participant passengers or riders are usually prearranged and the journey details known beforehand, the paradigm for ridesharing requires the participants to be selected at short notice and the rider trips are often dynamically formed. Crowdsourcing techniques and approaches are well suited to match drivers and riders for these dynamic scenarios, although there are many challenges to be addressed. This paper aims to survey this new paradigm of ridesharing and crowdsourcing for smart city transportation environments from several technological and social perspectives including: (1) Ridesharing and architecture in transportation; (2) Techniques for ridesharing; (3) Artificial intelligence for ridesharing; (4) Autonomous vehicles and systems ridesharing; and (5) Security, policy and pricing strategies. The paper concludes with some use cases and lessons learned for the practical deployment of ridesharing and crowdsourcing platforms for smart cities.

The electric network frequency (ENF) represents the transmission frequency of the electrical grid and fluctuates constantly around 50 Hz or 60 Hz, subject to the region. This constant fluctuation, caused by the continuous mismatch in power demand and supply, makes the ENF a unique signature, which can be utilized for several applications. According to studies, the ENF may be intrinsically implanted in audio recordings captured by digital audio recorders (e.g., microphone-based voice recorder) plugged into the mains supply or are situated close to sources of power and power transmission cable due to electromagnetic field interference originated from power source or acoustic hum and mechanical vibrations emitted by electrically operated devices such as regular household appliances. Recent studies further observed that video recordings made under an illumination source powered by main power can pick up the ENF signal. Following this discovery, several research efforts have been invested towards successful and accurate extraction of the ENF signal, and utilizing the ENF signal retrieved for several applications, including time stamp verification, audio/video authentication, location of recording estimation, power grid identification, estimation of camera read-out time, and video record synchronization. To the best of our knowledge, there has been no comprehensive survey on ENF-based multimedia forensics. Thus, in this paper, we present a comprehensive survey of studies conducted in this field, identifying several application specifics, current challenges, and future research directions.