We analyze the performance of quantum key distribution (QKD) protocols that rely on discrete phase randomization (DPR). For many QKD protocols that rely on weak coherent pulses (WCPs), continuous phase randomization is assumed, which simplifies the security proofs for such protocols. However, it is challenging to achieve such a perfect phase randomization in practice. As an alternative, we can select a discrete set of global phase values for WCPs, but we need to redo the security analysis for such a source. While security proofs incorporating DPR have been established for several QKD protocols, they often rely on computationally intensive numerical optimizations. To address this issue, in this study, we derive analytical bounds on the secret key generation rate of BB84 and measurement-device-independent QKD protocols in the DPR setting. Our analytical bounds closely match the results obtained from more cumbersome numerical methods in the regions of interest.

Electromagnetic nano-networks operating in the THz band offer a promising solution for enabling communication among many nanoscale devices. However, the inherent limitations of nano-nodes, such as restricted energy and processing resources and short communication range, pose significant challenges for efficient data transmission. While prior research has explored Reinforcement Learning (RL) for optimising traffic routing in electromagnetic nano-networks, this paper proposes a novel approach that jointly optimises routing and sub-channel bandwidth allocation to minimise network energy consumption using RL. We leverage the Q-learning algorithm to develop a dynamic single-hop or multi-hop routing scheme that considers each node's location, energy storage capability, and the available sub-channel bandwidth. Our model formulates a reward function that balances these multiple objectives and enables the selection of optimal transmission policies for each nano-node. Our findings suggest carefully choosing the number of hops and increasing bandwidth in sub-channels can lead to substantial energy savings in nano-networks.

This study presents a novel power allocation optimisation strategy that does not rely on traditional power constraints. Unlike previous works in the literature, we introduce a ratio between the allocated and requested data rates and incorporate this ratio into the reward function of our deep reinforcement learning algorithm. The highest reward of 1 is achieved when the allocated and requested data rates are equal. Additionally, we jointly optimise power and numerology allocation, considering the users' delay and data rate requirements. Any numerology can be allocated to users as long as their requirements are satisfied. This approach enables users to be allocated optimum numerology and transmit power. By addressing the challenge posed by greedy users, our approach enhances the flexibility and performance of the power and numerology allocation process.

Currently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions. In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the networks’ control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle employing big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial efforts toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks.

In this paper, we study the impact of big data's volume and variety dimensions on energy efficient big data networks (EEBDN) by developing a mixed integer linear programming (MILP) model to encapsulate the distinctive features of these two dimensions. First, a progressive energy efficient edge, intermediate, and central processing technique is proposed to process big data's raw traffic by building processing nodes (PNs) in the network along the way from the sources to datacenters. Second, we validate the MILP operation by developing a heuristic that mimics, in real time, the behavior of the MILP for the volume dimension. Third, we test the energy efficiency limits of our green approach under several conditions where PNs are less energy efficient in terms of processing and communication compared to data centers. Fourth, we test the performance limits in our energy efficient approach by studying a “software matching” problem where different software packages are required to process big data. The results are then compared to the classical big data networks (CBDN) approach where big data is only processed inside centralized data centers. Our results revealed that up to 52% and 47% power saving can be achieved by the EEBDN approach compared to the CBDN approach, under the impact of volume and variety scenarios, respectively. Moreover, our results identify the limits of the progressive processing approach and in particular the conditions under which the CBDN centralized approach is more appropriate given certain PNs energy efficiency and software availability levels.

Cloud computing is expected to be a major factor that will dominate the future Internet service model. This paper summarizes our work on energy efficiency for cloud networks. We develop a framework for studying the energy efficiency of four cloud services in IP over WDM networks: cloud content delivery, storage as a service (StaaS), and virtual machines (VMS) placement for processing applications and infrastructure as a service (IaaS). Our approach is based on the co-optimization of both external network related factors such as whether to geographically centralize or distribute the clouds, the influence of users' demand distribution, content popularity, access frequency and renewable energy availability and internal capability factors such as the number of servers, switches and routers as well as the amount of storage demanded in each cloud. Our investigation of the different energy efficient approaches is backed with Mixed Integer Linear Programming (MILP) models and real time heuristics.

In this paper, we develop a Mixed Integer Linear Programming (MILP) model to study the impact of renewable energy availability, represented by wind farms, on the location of clouds and the content replication schemes of cloud content over IP/WDM networks. In our analysis, we assume that renewable energy is only available to power clouds while the IP/WDM network is powered by non-renewable energy. Our results show that popularity based replication in clouds is the most energy efficient content replication scheme when the clouds are powered only by non-renewable energy sources or when renewable energy availability is limited. With abundant renewable energy, a cloud with a full copy of the content can be built at each node. However, the model should achieve a trade-off between the transmission power losses to deliver renewable energy from wind farms to clouds and the non-renewable power consumption of the IP/WDM network. We discuss this trade-off and show how to optimize the transmission power losses of renewable energy while minimizing the non-renewable network power consumption.

In this paper, we evaluate the energy consumption of BitTorrent based peer to peer (P2P) content distribution systems in bypass IP/WDM core networks and compare it to client-server (C/S) systems. A Mixed Integer Linear Programming (MILP) model is developed to carry out the comparison. Our results for homogeneous peers with similar upload capacities show that the original BitTorrent, based on random peer selection, has comparable energy consumption to the C/S model. The results also reveal that the power-minimized BitTorrent model achieves 30% energy savings compared to the C/S model as it converges to locality. Furthermore, a heterogeneous BitTorrent system with two upload capacity classes is investigated and the results show a 50% reduction in energy consumption compared to a C/S model. For real-time implementation, we develop a simple heuristic based on the model insights. Comparable power savings are achieved with a reduction of only 13% in the download rate.