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The Internet of Things (IoT) universe will continue to expand with the advent of the sixth generation of mobile networks (6G), which is expected to support applications and services with higher data rates, ultra-reliability, and lower latency compared to the fifth generation of mobile networks (5G). These new demanding 6G applications will introduce heavy load and strict performance requirements on the network. Network Function Virtualization (NFV) is a promising approach to handling these challenging requirements, but it also poses significant Resource Allocation (RA) challenges. Especially since 6G network services will be highly complicated and comparatively short-lived, network operators will be compelled to deploy these services in a flexible, on-demand, and agile manner. To address the aforementioned issues, microservice approaches are being investigated, in which the services are decomposed and loosely coupled, resulting in increased deployment flexibility and modularity. This study investigates a new RA approach for microservices-based NFV for efficient deployment and decomposition of Virtual Network Function (VNF) onto substrate networks. The decomposition of VNFs involves additional overheads, which have a detrimental impact on network resources; hence, finding the right balance of when and how much decomposition to allow is critical. Thus, we develop a criterion for determining the potential/candidate VNFs for decomposition and also the granularity of such decomposition. The joint problem of decomposition and efficient embedding of microservices is challenging to model and solve using exact mathematical models. Therefore, we implemented a Reinforcement Learning (RL) model using Double Deep Q-Learning, which revealed an almost 50% more normalized Service Acceptance Rate (SAR) for the microservice approach over the monolithic deployment of VNFs.

In the new era, the Internet of Everything (IoE) provides distributed services like data, processes, people, and things, etc. The services to the connected IoE significantly increase the time of service, workload, energy consumption, and delay. These objectives conflict with each other. To address the issue, a novel multi-objective based evolutionary algorithm is proposed. In the proposed method, a new rapid mutation operator is incorporated with multi-objective differential evolution (MODE) to overcome the stagnation of the local optimum. The proposed method to maintain the diversity and enhance the convergence speed of the existing MODE algorithm is described. The proposed method provides more diversity and convergence speed for choosing better candidate solutions. The addition of the proposed method is evaluated with the application of IoE services. We have designed the two objective and three objective-based IoE services scenarios. Furthermore, the proposed method optimizes services like service cost, service delay, and the lifetime of sensors. It is interesting to observe that the proposed approach better performs the most recent state-of-the-art multiobjective evolutionary algorithms.

Internet of everything (IoE)-based smart services are expected to gain immense popularity in the future, which raises the need for next-generation wireless networks. Although fifth-generation (5G) networks can support various IoE services, they might not be able to completely fulfill the requirements of novel applications. Sixth-generation (6G) wireless systems are envisioned to overcome 5G network limitations. In this article, we explore recent advances made toward enabling 6G systems. We devise a taxonomy based on key enabling technologies, use cases, emerging machine learning schemes, communication technologies, networking technologies, and computing technologies. Furthermore, we identify and discuss open research challenges, such as artificial-intelligence-based adaptive transceivers, intelligent wireless energy harvesting, decentralized and secure business models, intelligent cell-less architecture, and distributed security models. We propose practical guidelines including deep Q-learning and federated learning-based transceivers, blockchain-based secure business models, homomorphic encryption, and distributed-ledger-based authentication schemes to cope with these challenges. Finally, we outline and recommend several future directions.

Recently, the increase in inexpensive and compact unmanned aerial vehicles (UAVs) and light-weight imaging sensors has led to an interest in using them in various remote sensing applications. The processes of collecting, calibrating, registering, and processing data from miniature UAVs and interpreting the data semantically are time-consuming. In UAV aerial imagery, learning effective image representations is central to the scene classification process. Earlier approaches to the scene classification process depended on feature coding methods with low-level hand-engineered features or unsupervised feature learning. These methods could produce mid-level image features with restricted representational abilities, which generally yielded mediocre results. The development of convolutional neural networks (CNNs) has made image classification more efficient. Due to the limited resources in UAVs, it is hard to fine-tune the hyperparameters and the trade-offs between classifier results and computation complexity. This paper introduces a new multi-objective optimization model for evolving state-of-the-art deep CNNs for scene classification, which generates the non-dominant solutions in an automated way at the Pareto front. We use a set of two benchmark datasets to test the performance of the scene classification model and make a detailed comparative study. The proposed method attains a very low computational time of 80 sec and maximum accuracy of 97.88% compared to all other methods. The proposed method is found to be appropriate for the effective scene classification of images captured by UAVs.

Motivated by complementing the ubiquitous sensor networks and cloud computing technique, a lot of attentions have been drawn to Sensor-Cloud (SC). The idea of SC thrives on the principle of virtualization of physical sensor nodes and has introduced the intermediate processing between physical sensor nodes and end users. This paper proposes an efficient interactive SC control scheme to provide on-demand sensing services for multiple applications. By adopting the game theory, we develop a new two-stage game model, which consists of a judicious mixture of selection and incentive algorithms. In our game model, a user can choose the most adaptable data center to execute its task, and each data center can give appropriate incentives to the participating sensors. The main merit possessed by our two-stage game approach is to shed light on the practical SC control problem while providing excellent adaptability and flexibility to satisfy the different application requirements. To the best of our knowledge, this is the first work to include a novel incentive algorithm in the SC system. Simulation results demonstrate that our approach can outperform existing schemes by about 5%~15% in terms of the normalized user profit, service delay, and SC system throughput. Finally, we discuss future directions for designing SC control frameworks including other issues.