Vehicular Ad-hoc Network (VANET) is a technology of importance for the Intelligent Transportation System, which provides safe and comfortable driving, infotainment, traffic management, route optimization, accident prevention, etc. Imparting suitable Quality of Service (QoS), high reliability, and low latency in VANET is a task that is challenging due to the presence of various constraints such as dynamic topology, an imperfection in hardware, congestion in the channel, fluctuation in vehicle density, etc. It is essential to allocate and utilize the resources effectively to maximize the benefit and enhance network performance. It is vital to use the available bandwidth, power, computing, and storage resources efficiently. Along with resource allocation, security and privacy challenges are of equal importance for VANET. One of the major concerns is to make the communication channel secure and prevent the network from attacks by malicious agents. Also, preserving the privacy of vehicles by hiding real identities to circumvent tracking is a challenging task. The authorized agents are allowed to know the actual identity of vehicles. This paper exhibits an in-depth survey of resource allocation schemes in different aspects of VANET. It analyzes the security and privacy of messages, vehicles, and the overall network. Also, the paper presents and discusses the open issues and future research areas in resource allocation, security, and privacy of VANET.

Virtual machine (VM) placement strategies reported in the literature focuses mainly on minimization of power consumption and maximization of placed VMs. The revenue earned by a cloud service provider (CSP) depends on the number of VMs placed. Increasing the number of VMs placed by a CSP not only increases the power consumption but also decreases the profit margin of the CSP. In this paper, we propose a technique called maximum VM placement with minimum power consumption (MVMP) to maximize the profit earned by a CSP. The proposed technique attempts to maximize the revenue and minimize the power budget. It is formulated as a bi-objective optimization problem, and is solved using simulated annealing (SA) technique. To reach a sub-optimal solution more randomness is applied to SA. Our MVMP algorithm is compared to five state of the art algorithms in the realm of strategic VM placement, namely Marotta and Avallone (MA) approach, Hybrid genetic algorithm (HGA), Modified Best-Fit decreasing (MBFD), First-Fit decreasing (FFD) and Random deployment. We observe that MVMP performs better than Marotta and Avallone (MA) approach, HGA, MBFD, FFD and Random placement in terms of number of servers used, energy consumption, profit and execution time. Scalability of MVMP is verified using two different scenarios: (i) fixed number of VMs and, (ii) fixed number of servers. It is observed that MVMP is scalable too

The evolution of cloud computing facilitates applications with varying demands to operate in a virtualized environment. For instance, applications like the healthcare system, video streaming, Internet of Things (IoT) that are moving to the cloud, demand responses within a particular time limit, i.e., deadline. However, the cloud computing system consumes a considerable amount of electric energy while providing services to these type of applications, which in turn contribute to the high operational cost. Specifically, it becomes cumbersome to offer services to deadline sensitive task while minimizing energy consumption. In this regard, efficient task scheduling is an attractive way to cut down energy usage while ensuring satisfactory services for cloud users. In this paper, the task scheduling problem is considered as a bi-objective minimization problem which includes minimization of energy consumption and makespan. First, we proposed a novel learning automata-based scheduling framework for deadline sensitive tasks in the cloud. Later, the LA-based Scheduling (LAS) algorithm is introduced which exploits the heterogeneity of tasks and virtual machines (VMs) while ensuring the timing requirements of the tasks. Extensive simulation is carried out to designate the effectiveness and applicability of LAS for deadline sensitive task scheduling in the heterogeneous cloud environmen

Free space optics (FSO) is considered primarily for establishing point-to-point (PtP). However, due to the increasing demand for mobile platforms and mechanisms to establish point-to-multipoint (PtM) connections, this research proposes and implements a novel PtM tracking mechanism for FSO communication. The alignment mechanism consists of two tracking systems, i.e., coarse tracking and fine-tuning. The coarse tracking is responsible to locate FSO transceivers placed at different positions by using magnetometer sensors connected to the cloud. On the other hand, the fine-tuning mechanism overcomes high-frequency amplitude oscillation or jitter with a displacement range of 8 cm. The link is continuously monitored and if the link is lost or not established after a pre-determined time, then the reference line-of-sight (LoS) angle is updated in the cloud to provide an alternate connection. The stability of the closed-loop system is analyzed in terms of root locus and step response. The proposed PtM system is experimentally validated with FSO terminals located at different locations and distances over indoor and outdoor environments. The proposed method achieved an alignment time of less than 5 s for ten degrees of misalignment.