Face detection, which is an effortless task for humans, is complex to perform on machines. The recent veer proliferation of computational resources is paving the way for frantic advancement of face detection technology. Many astutely developed algorithms have been proposed to detect faces. However, there is little attention paid in making a comprehensive survey of the available algorithms. This paper aims at providing fourfold discussions on face detection algorithms. First, we explore a wide variety of the available face detection algorithms in five steps, including history, working procedure, advantages, limitations, and use in other fields alongside face detection. Secondly, we include a comparative evaluation among different algorithms in each single method. Thirdly, we provide detailed comparisons among the algorithms epitomized to have an all-inclusive outlook. Lastly, we conclude this study with several promising research directions to pursue. Earlier survey papers on face detection algorithms are limited to just technical details and popularly used algorithms. In our study, however, we cover detailed technical explanations of face detection algorithms and various recent sub-branches of the neural network. We present detailed comparisons among the algorithms in all-inclusive and under sub-branches. We provide the strengths and limitations of these algorithms and a novel literature survey that includes their use besides face detection.

The proliferation of mobile devices and user applications has continued to contribute to the humongous volume of data traffic in cellular networks. To surmount this challenge, service and resource providers are looking for alternative mechanisms that can successfully facilitate managing network resources in a more dynamic, predictive and distributed manner. New concepts of network architectures such as Software Defined Network (SDN) and Network Function Virtualization (NFV) have paved the way to move from static to flexible networks. They make networks more flexible (i.e. network providers capable of on-demand provisioning), easily customizable and cost effective. In this regard, network slicing is emerging as a new technology built on the concepts of SDN and NFV. It splits a network infrastructure into isolated virtual networks and allows them to manage resources allocation individually based on their requirements and characteristics. Most of the existing solutions for network slicing are computationally expensive because of the length of time they require to estimate the resources required for each isolated slice. In addition, there is no guarantee that the resource allocation is fairly shared among users in a slice. In this paper, we propose a Network Slicing Resource Management (NSRM) mechanism to assign the required resources for each slice in an LTE network, taking into consideration isolation of resources among different slices. In addition, NSRM aims to ensure isolation and fair sharing of distributed bandwidths between users belonging to the same slice. In NSRM, depending on requirements, each slice can be customized (e.g. each can have a different scheduling policy).

One of the key enablers in serving the applications requiring stringent latency in 5G networks is fog computing as it is situated closer to the end users. With the technological advancement of vehicles' on-board units, their computing capabilities are becoming robust, and considering the underutilization of the off-street vehicles, we envision that the off-street vehicles can be an enormously useful computational source for the fog computing. Additionally, clustering the vehicles would be advantageous in order to improve the service availability. As the vehicles become highly connected, trust is needed especially in distributed environments. However, vehicles are made from different manufacturers, and have different platforms, security mechanisms, and varying parking duration. These lead to the unpredictable behavior of the vehicles where quantifying trust value of vehicles would be difficult. A trust-based solution is necessary for task mapping as a task has a set of properties including expected time to complete, and trust requirements that need to be met. However, the existing metrics used for trust evaluation in the vehicular fog computing such as velocity and direction are not applicable in the off-street vehicle fog environments. In this paper, we propose a framework for quantifying the trust value of off-street vehicle fog computing facilities in 5G networks and forming logical clusters of vehicles based on the trust values. This allows tasks to be shared with multiple vehicles in the same cluster that meets the tasks' trust requirements. Further, we propose a novel task mapping algorithm to increase the vehicle resource utilization and meet the desired trust requirements while maintaining imposed latency requirements of 5G applications. Results obtained using iFogSim simulator demonstrate that the proposed solution increases vehicle resource utilization and reduces task drop noticeably. This paper presents open research issues pertaining to the study to lead the way for future research directions.

Fog computing could potentially cause the next paradigm shift by extending cloud services to the edge of the network, bringing resources closer to the end-user. With its close proximity to end-users and its distributed nature, fog computing can significantly reduce latency. With the appearance of more and more latency-stringent applications, in the near future, we will witness an unprecedented amount of demand for fog computing. Undoubtedly, this will lead to an increase in the energy footprint of the network edge and access segments. To reduce energy consumption in fog computing without compromising performance, in this paper we propose the Green-Demand-Aware Fog Computing (GDAFC) solution. Our solution uses a prediction technique to identify the working fog nodes (nodes serve when request arrives), standby fog nodes (nodes take over when the computational capacity of the working fog nodes is no longer sufficient), and idle fog nodes in a fog computing infrastructure. Additionally, it assigns an appropriate sleep interval for the fog nodes, taking into account the delay requirement of the applications. Results obtained based on the mathematical formulation show that our solution can save energy up to 65% without deteriorating the delay requirement performance.