The accelerated growth of data traffic in data centers (DCs) globally is driven by the dominance of multiple emerging data-intensive applications hosted by edge/cloud DCs, such as Internet of Things (IoT), augmented and virtual reality etc. The advent of machine learning/artificial intelligence (ML/AI) and its exploitation in several applications for prediction and optimization purposes has further burdened the traffic conditions within existing DC networks (DCNs) where traffic burstiness and high variability dominates. Most of the existing DCNs worldwide use conventional electrical switches which cannot effectively serve the highly differentiated traffic, while optical switching is currently used by few big-data companies’ DCs and only in the core and aggregation DCN tiers. As a result, limited DCN performance in terms of end-to-end delay and bandwidth utilization is provided. Therefore, the investigation on all aspects of optical networking (switching, transmission techniques etc.) in DCs attracts high attention in both academia and industry, aiming to provide high DCN bandwidth utilization, low end-to-end latency (in the order of few μs), and low data loss. In this paper, a comprehensive review on traffic characteristics, switching techniques, and bandwidth allocation schemes for next-generation optical DCNs is extensively given. Moreover, trends of optical technology enabled in optical DCNs are comprehensively discussed, putting in the agenda the technical challenges towards efficient optical DCNs capable of serving the stringent traffic classes demands for guaranteed low completion time of diverse edge/cloud data-intensive applications.

The Internet of Vehicles (IoV) is an emerging paradigm, which is expected to be an integral component of beyond-fifth-generation and sixth-generation mobile networks. However, the processing requirements and strict delay constraints of IoV applications pose a challenge to vehicle processing units. To this end, multi-access edge computing (MEC) can leverage the availability of computing resources at the edge of the network to meet the intensive computation demands. Nevertheless, the optimal allocation of computing resources is challenging due to the various parameters, such as the number of vehicles, the available resources, and the particular requirements of each task. In this work, we consider a network consisting of multiple vehicles connected to MEC-enabled roadside units (RSUs) and propose an approach that minimizes the total energy consumption of the system by jointly optimizing the task offloading decision, the allocation of power and bandwidth, and the assignment of tasks to MEC-enabled RSUs. Due to the original problem complexity, we decouple it into subproblems and we leverage the block coordinate descent method to iteratively optimize them. Finally, the numerical results demonstrate that the proposed solution can effectively minimize total energy consumption for various numbers of vehicles and MEC nodes while maintaining a low outage probability.

Human-Centric Sensing (HCS), a novel approach in the evolution of the Next Generation Internet of Things (NG-IoT), exploits the ubiquity of diverse smart devices, including smartphones or wearable devices, in conjunction with their enhanced sensing capabilities to collect information, leveraging human intelligence for the common benefit of the crowd. The main feature of HCS is the involvement of mobile users in data collection, processing, analysis and sharing. Thus, the main challenge in HCS systems is to ensure users’ participation and trustworthiness as well as data quality. The aim of this work is, as a first step, to identify and discuss the factors that affect data quality in HCS-based NG-IoT systems, as well as elaborate on their interrelation. Furthermore, potential solutions that could be adopted to ensure the highest possible degree of data quality are highlighted, in conjunction with critical aspects that should be considered, proposing a novel classification with three major categories: task assignment, reputation mechanisms and blockchain technology. Finally, a trust-aware task assignment model is proposed to effectively address the data quality challenge in HCS-based IoT systems, reflecting users’ trustworthiness, willingness, experience, and ability to collect and share high-quality data contributions. The proposed trust-aware task assignment model exploits a reputation mechanism and is designed using blockchain and smart contract technologies to enable the decentralized provision of trustworthy services among entities and preserve users’ privacy, harnessing the decentralization, transparency and immutability offered by blockchain. Trust-based task assignment offers an effective solution for trustworthy users’ selection while ensuring high-quality contributions and users’ privacy.

In this paper, we present a wavelength division multiplexing multiring metropolitan area network architecture using a separate ring as control channel and a finite number of access nodes. Each access node is equipped with a fixed tuned transmitter and a fixed tuned receiver to exchange control information over the control wavelength. Also, each access node has a tunable transmitter to efficiently exploit all data wavelengths for data transmission. The set of data wavelengths is divided into wavelength bands. This allows a flexible node design with a number of parallel tunable receivers per node, each operating in a specific wavelength band and providing limited tuning time. On the one hand, our network strategy confronts more efficiently the scalability and maintenance problems comparatively with most of the access ring protocols. On the other hand, the proposed access algorithm avoids both the data wavelengths and the receiver collisions, improving even more the network utilization. An analytic model is developed for the performance measures evaluation. Also, we develop another analysis approach using discrete event simulation model based on self-similar statistics. Analysis is accomplished studying various numbers of access nodes, data wavelengths, and buffer size.