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The Internet of Things (IoT) and Cloud Computing are considered to be two of the greatest technology revolutions in the last few years. New technology raised in recent years is known as Cloud IoT or the Cloud of Things (CoT), which integrates cloud computing and the Internet of things. Dynamic and heterogeneous environments, energy efficiency, and delay-sensitivity are the major issues with the CoT. Energy efficiency is one of the basic requirements of IoT applications and resources in the cloud, which is a challenging issue in the CoT. This paper proposes the optimization of energy schemes for the CoT by applying a Genetic Algorithm (GA). Extensive numerical simulation is carried out to verify the effectiveness of the proposed method compared to the ETCORA algorithm. The analytical results present better performances of the proposed optimizing energy consumption technique. Results also show that the proposed method indeed outperforms the ETCORA in reducing the energy consumption of task requests.

Optimization problems are common in many fields and different domains of the human activities where we need to find optimal or near-optimal solutions for specific problems with the capability to meet some limitations. More specifically, optimization focuses on the development of efficient and powerful computing infrastructures that among others will be exploited so as to accelerate meta-heuristic techniques by significantly improving their performance. Thus, many heuristic algorithms have been developed for finding faster near-optimal solutions. Heuristic algorithms can quickly generate a solution with acceptable quality. The heuristics and meta-heuristics include the genetic algorithms, ant colony algorithm, the simulated annealing, gray wolf optimization, and so on. This chapter critically reviews various meta-heuristic optimization techniques used for energy optimization in wireless sensor network along with their detailed analysis, and evaluation on the basis of various parameters. The study and evaluation is useful in improving the performance of existing methods as well as helpful in the development of new methods.

Vehicular ad-hoc networks (VANET) have become an important research area due to their ability to allow sharing resources among the users to carry out their application and provide services of transport and traffic management. VANET communication allows exchange of sensitive information among nearby vehicles such as condition of weather and road accidents in order to improve vehicle traffic efficiency through Intelligent Transportation Systems (ITS). Many technologies have been developed to enhance ITS. Recently, vehicular cloud computing (VCC) has been developed in order to overcome the drawbacks VANET. VCC technology provides low-cost services to vehicles and capable of managing road traffic efficiently by using the vehicular sources (such as internet) to make decisions and for storage. VCC is considered as the basis for improving and developing intelligent transportation systems. It plays a major role in people's lives due to its safety, security, trust, and comfort to passengers and drivers. This chapter investigates the vehicular cloud computing. The authors first concentrate on architectures. Then, they highlight applications and features provided by VCC. Additionally, they explain the challenges for VCC. Finally, the authors present opportunities and future for VCC.