Peristera A. Baziana

In this paper, we introduce an all-optical metro network architecture, called MOON, to serve converged multigranular traffic from fixed, mobile, and edge computing services. Since traffic is characterized by high dynamicity and diverse access requirements, MOON uses network slicing to provide quality of service (QoS) aware wavelength allocation to fulfill the various applications traffic demands. MOON incorporates hybrid optical switching (HOS) combining optical circuit switching (OCS) and optical time slotted switching (OTS) capabilities that appropriately maps different traffic types to them. Specifically, the OCS network slice explicitly serves aggregated traffic of long duration and high volume, while OTS network slice serves short bursty traffic. In order to provide flexibility, separate sets of wavelengths are used for OCS and OTS traffic service, both within a metro-access network (MAN) (intra-MAN) and between different MANs (inter-MAN). We extensively study the required number of wavelengths to efficiently serve OCS and OTS traffic for intra- and inter-MAN communication scenarios, taking into account their specific traffic access requirements in an effort to optimize wavelengths utilization. In our study, we assume nonblocking OCS communication for immediate access; therefore the number of required OCS wavelengths depends only on the number of nodes, while the number of required OTS wavelengths to obtain a desired QoS and latency level is independent from the number for OCS wavelengths. Simulation results show that within an OTS intra-MAN we achieve end-to-end (E2E) latency in submilliseconds scale, suitable for dynamic bursty traffic, while it is an decreasing function of the number of used OTS wavelengths.

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.

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.

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.

In this paper, we introduce an optical passive network architecture suitable for wavelength division multiplexing local area networks (LANs) which use a separate control wavelength. The data wavelengths are organized into several sets, while the access rights over them are distributedly determined aiming to totally expunge the packets collisions on the wavelengths and at destination. The proposed access algorithm pertains to the asynchronous transmission schemes. Thus, it is simple enough since it does not require any synchronization among the stations, providing high efficiency especially under high data rates (100 Gbps and beyond). The performance is evaluated through exhaustive analysis, whilst closed mathematical formulas provide the performance measures. The comparative study proves that the proposed wavelengths organization into sets strategy along with the access scheme significantly improves the performance. Especially, the throughput improvement is proven to be higher as the number of sets increases, and more than 100% even by organizing the wavelengths into only two sets, for diverse numbers of data wavelengths, data wavelengths sets and data packets size. Finally, the proposed study could be applied to optical passive single-hop LANs such as intra-rack data center networks or local institutional or enterprise networks.