Prior to the innovation of information communication technologies (ICT), social interactions evolved within small cultural boundaries such as geo spatial locations. The recent developments of communication technologies have considerably transcended the temporal and spatial limitations of traditional communications. These social technologies have created a revolution in user-generated information, online human networks, and rich human behavior-related data. However, the misuse of social technologies such as social media (SM) platforms, has introduced a new form of aggression and violence that occurs exclusively online. A new means of demonstrating aggressive behavior in SM websites are highlighted in this paper. The motivations for the construction of prediction models to fight aggressive behavior in SM are also outlined. We comprehensively review cyberbullying prediction models and identify the main issues related to the construction of cyberbullying prediction models in SM. This paper provides insights on the overall process for cyberbullying detection and most importantly overviews the methodology. Though data collection and feature engineering process has been elaborated, yet most of the emphasis is on feature selection algorithms and then using various machine learning algorithms for prediction of cyberbullying behaviors. Finally, the issues and challenges have been highlighted as well, which present new research directions for researchers to explore.INDEX TERMS Big data, cyberbullying, cybercrime, human aggressive behavior, machine learning, online social network, social media, text classification.
With the evolution of fog computing, processing takes place locally in a virtual platform rather than in a centralized cloud server. Fog computing combined with cloud computing is more efficient as fog computing alone does not serve the purpose. Inefficient resource management and load balancing leads to degradation in quality of service as well as energy losses. Traffic overhead is increased because all the requests are sent to the main server causing delays which cannot be tolerated in healthcare scenarios. To overcome this problem, the authors are consolidating fog computing resources so that requests are handled by foglets and only critical requests are sent to the cloud for processing. Servers are placed locally in each city to handle the nearby requests in order to utilize the resources efficiently along with load balancing among all the servers, which leads to reduced latency and traffic overhead with the improved quality of service.
The automotive industry is growing day by day and personal vehicles have become a significant transportation resource now. With the rise in private transportation vehicles, getting a free space for parking one's car, especially in populated areas, has not only become difficult but also results in several issues, such as: (i) traffic congestion, (ii) wastage of time, (iii) environmental pollution, and most importantly (iv) unnecessary fuel consumption. On the other hand, car parking spaces in urban areas are not increasing at the same rate as the vehicles on roads. Therefore, smart car parking systems have become an essential need to address the issues mentioned above. Several researchers have attempted to automate the parking space allocation by utilizing state-of-the-art technologies. Significant work has been done in the domains of Wireless Sensor Networks (WSN), Cloud Computing, Fog Computing, and Internet of Things (IoT) to facilitate the advancements in smart parking services. Few researchers have proposed methods for smart car parking using the cloud computing infrastructures. However, latency is a significant concern in cloudbased applications, including intelligent transportation and especially in smart car parking systems. Fog computing, bringing the cloud computing resources in proximate vicinity to the network edge, overcomes not only the latency issue but also provides significant improvements, such as on-demand scaling, resource mobility, and security. The primary motivation to employ fog computing in the proposed approach is to minimize the latency as well as network usage in the overall smart car parking system. For demonstrating the effectiveness of the proposed approach for reducing the lag and network usage, simulations have been performed in iFogSim and the results have been compared with that of the cloud-based deployment of the smart car parking system. Experimental results exhibit that the proposed fog-based implementation of the efficient parking system minimizes latency significantly. It is also observed that the proposed fog-based implementation reduces the overall network usage in contrast to the cloud-based deployment of the smart car parking. INDEX TERMS Fog computing, smart car parking, fog-based smart car parking, image processing.
The future smart cities vision can be developed through leveraging the potentials of Internet of Things (IoT) and wireless sensor network (WSN) technologies. WSN is a resource constrained network where network nodes are tiny devices that are run on battery power. Diverse types of applications such as environmental and habitual monitoring, detection, and tracking, use WSNs. The invention of new network protocols, the establishment of new models for communications, and testing the available solutions in real world environment are some of the current research issues in WSNs. Main challenges in such networks include energy conservation in an efficient way, dealing with variable channel capacity, and the resource constrained nature of such networks. The design of architecture for such networks has a vital role in solving the issues to some extent, i.e., the cross layer design approach is an architectural technique that offers the interaction of different layers together to enhance the performance, minimize the energy consumption, enhance the network life time, and provide Quality of Service (QoS) in real time communications. These are some of the current areas where cross-layer design approaches are being used. This paper presents different types of cross-layer design techniques in wireless multimedia sensor networks. Using such architectural techniques, different state of the art cross-layer optimization approaches are discussed while giving the reader an insight on prominent challenges and issues along with future directions.
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