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Designing effective Unmanned Aerial Vehicle(UAV)-assisted routing protocols is challenging due to changing topology, limited battery capacity, and the dynamic nature of communication environments. Current protocols prioritize optimizing individual network parameters, overlooking the necessity for a nuanced approach in scenarios with intermittent connectivity, fluctuating signal strength, and varying network densities, ultimately failing to address aerial network requirements comprehensively. This paper proposes a novel, Improved Q-learning-based Multi-hop Routing (IQMR) algorithm for optimal UAV-assisted communication systems. Using Q(\lambda) learning for routing decisions, IQMR substantially enhances energy efficiency and network data throughput. IQMR improves system resilience by prioritizing reliable connectivity and inter-UAV collision avoidance while integrating real-time network status information, all in the absence of predefined UAV path planning, thus ensuring dynamic adaptability to evolving network conditions. The results validate IQMR's adaptability to changing system conditions and superiority over the current techniques. IQMR showcases 36.35\% and 32.05\% improvements in energy efficiency and data throughput over the existing methods., Comment: arXiv admin note: substantial text overlap with arXiv:2308.16719

Emergency communication is vital for search and rescue operations following natural disasters. Unmanned Aerial Vehicles (UAVs) can significantly assist emergency communication by agile positioning, maintaining connectivity during rapid motion, and relaying critical disaster-related information to Ground Control Stations (GCS). Designing effective routing protocols for relaying crucial data in UAV networks is challenging due to dynamic topology, rapid mobility, and limited UAV resources. This paper presents a novel energy-constrained routing mechanism that ensures connectivity, inter-UAV collision avoidance, and network restoration post-UAV fragmentation while adapting without a predefined UAV path. The proposed method employs improved Q learning to optimize the next-hop node selection. Considering these factors, the paper proposes a novel, Improved Q-learning-based Multi-hop Routing (IQMR) protocol. Simulation results validate IQMRs adaptability to changing system conditions and superiority over QMR, QTAR, and QFANET in energy efficiency and data throughput. IQMR achieves energy consumption efficiency improvements of 32.27%, 36.35%, and 36.35% over QMR, Q-FANET, and QTAR, along with significantly higher data throughput enhancements of 53.3%, 80.35%, and 93.36% over Q-FANET, QMR, and QTAR., Comment: 13 pages

Quantum networks are of great interest of late which apply quantum mechanics to transfer information securely. One of the key properties which are exploited is entanglement to transfer information from one network node to another. Applications like quantum teleportation rely on the entanglement between the concerned nodes. Thus, efficient entanglement distribution among network nodes is of utmost importance. Several entanglement distribution methods have been proposed in the literature which primarily rely on attributes, such as, fidelities, link layer network topologies, proactive distribution, etc. This paper studies the centralities of the network when the link layer topology of entanglements (referred to as entangled graph) is driven by usage patterns of peer-to-peer connections between remote nodes (referred to as connection graph) with different characteristics. Three different distributions (uniform, gaussian, and power law) are considered for the connection graph where the two nodes are selected from the same distribution. For the entangled graph, both reactive and proactive entanglements are employed to form a random graph. Results show that the edge centralities (measured as usage frequencies of individual edges during entanglement distribution) of the entangled graph follow power law distributions whereas the growth in entanglements with connections and node centralities (degrees of nodes) are monomolecularly distributed for most of the scenarios. These findings will help in quantum resource management, e.g., quantum technology with high reliability and lower decoherence time may be allocated to edges with high centralities.

Multi-layer complex networks (MLCN) appears in various domains, such as, transportation, supply chains, etc. Failures in MLCN can lead to major disruptions in systems. Several research have focussed on different kinds of failures, such as, cascades, their reasons and ways to avoid them. This paper considers failures in a specific type of MLCN where the lower layer provides services to the higher layer without cross layer interaction, typical of a computer network. A three layer MLCN is constructed with the same set of nodes where each layer has different characteristics, the bottom most layer is Erdos-Renyi (ER) random graph with shortest path hop count among the nodes as gaussian, the middle layer is ER graph with higher number of edges from the previous, and the top most layer is scale free graph with even higher number of edges. Both edge and node failures are considered. Failures happen with decreasing order of centralities of edges and nodes in static batch mode and when the centralities change dynamically with progressive failures. Emergent pattern of three key parameters, namely, average shortest path length (ASPL), total shortest path count (TSPC) and total number of edges (TNE) for all the three layers after node or edge failures are studied. Extensive simulations show that all but one parameters show definite degrading patterns. Surprising, ASPL for the middle layer starts showing a chaotic behavior beyond a certain point for all types of failures.

This paper proposes a novel non-intrusive system failure prediction technique using available information from developers and minimal information from raw logs (rather than mining entire logs) but keeping the data entirely private with the data owners. A neural network based multi-class classifier is developed for failure prediction, using artificially generated anonymous data set, applying a combination of techniques, viz., genetic algorithm (steps), pattern repetition, etc., to train and test the network. The proposed mechanism completely decouples the data set used for training process from the actual data which is kept private. Moreover, multi-criteria decision making (MCDM) schemes are used to prioritize failures meeting business requirements. Results show high accuracy in failure prediction under different parameter configurations. On a broader context, any classification problem, beyond failure prediction, can be performed using the proposed mechanism with artificially generated data set without looking into the actual data as long as the input features can be translated to binary values (e.g. output from private binary classifiers) and can provide classification-as-a-service.

In practice, most of the optimization problems are non-linear requiring certain interactive solutions and approaches to model. In 5G Advanced and Beyond network slicing, mathematically modeling the users, type of service distributions and its adaptive SLAs are complex due to several dependencies. To facilitate the above, in this paper, we present novel Non-linear mathematical formulations and results that will form the base to achieve Optimized Scheduling., Comment: 4 pages, 5 figures

Network slicing is an effective 5G concept for improved resource utilization and service scalability tailored to users (UEs) requirements. According to the standardization, 5G system should support UEs through specification of its heterogenous requirements such as, high data rates, traffic density, latency, reliability, UE density, system efficiency and service availability. These requirements are specified as Service Level Agreements (SLAs) between UEs and network operators resulting in increased interest to develop novel challenging mechanisms for improved interference-free resource efficient performances. An emerging concept that enables such demanding SLA assurances is Open-Radio Access Network (O-RAN). In this paper, we study a novel resource scheduling problem for UE services conditioned on other services in network slicing. To improve system performance, we propose that UEs are connected across network slices to serve several applications. UEs of similar service classes (defined by SLAs) are grouped to form optimized slice-in-slice category(s) within network slice(s).We propose novel Predictive Dynamic Scaling Multi-UE service specific System Resource Optimized Scheduling (DMUSO) algorithm(s). Multiobjective multi-constraint optimization problems (MOP) are formed to learn the dynamic system resource allocation and throughput for UE services conditioned on new services entering the network slice. An epsilon-constraint line search algorithm is presented to estimate UE service bandwidth. Using the theoretical models, DMUSO forms optimal slice-in-slice categories and estimates the maximum dynamic additional slice-in-slice categories, throughput served across network slices. Finally, compared to state-of-the-art literatures, DMUSO guarantees UEs SLAs with 4.4 and 7.5 times performance gains, Comment: 6 pages, 4 figures, Submitted to IEEE

Open Radio Access Network (ORAN) Slicing for 5G and Beyond is an emerging architecture and feature that will facilitate challenging RAN Service Level Agreement (SLA) assurance targets. This could pave the way for operators to realize the benefits of network slicing efficiently. In this paper, we provide novel and detailed optimization results to achieve Slice-in-Slice Scheduling for 5G User Services in ORAN slicing., Comment: 4 pages, 2 figures

Emergency communication is extremely important to aid rescue and search operation in the aftermath of any disaster. In such scenario, Unmanned Aerial Vehicle (UAV) networks may be used to complement the damaged cellular networks over large areas. However, in such UAV networks, routing is a challenge, owing to high UAV mobility, intermittent link quality between UAVs, dynamic three dimensional (3D) UAV topology and resource constraints. Though several UAV routing approaches have been proposed, none of them so far have addressed inter UAV coverage, collision and routing in an integrated manner. In this paper, we consider a scenario where network of UAVs, operating at different heights from ground, with inter UAV coverage and collision constraints, are sent on a mission to collect disaster surveillance data and route it to Terrestrial Base Station via multi-hop UAV path. Analytical expressions for coverage probability (Pcov) and collision probability (Pcoll) are derived and minimum (Rmin) and maximum (Rmax) distance between UAVs are empirically calculated. We then propose a novel Multi-hop Opportunistic 3D Routing (MO3DR) algorithm with inter UAV coverage and collision constraints such that at every hop expected progress of data packet is maximized. The numerical results obtained from closed form mathematical modelling are validated through extensive simulation and their trade-off with variation in network parameters such as path loss component, trajectory divergence etc. are demonstrated. Finally, we obtain empirical optimality condition for inter UAV distance for the given application requirement., Comment: 11 pages, 14 figures, submitted to IEEE

Failures in optical network backbone can cause significant interruption in internet data traffic. Hence, it is very important to reduce such network outages. Prediction of such failures would be a step forward to avoid such disruption of internet services for users as well as operators. Several research proposals are available in the literature which are applications of data science and machine learning techniques. Most of the techniques rely on significant amount of real time data collection. Network devices are assumed to be equipped to collect data and these are then analysed by different algorithms to predict failures. Every network element which is already deployed in the field may not have these data gathering or analysis techniques designed into them initially. However, such mechanisms become necessary later when they are already deployed in the field. This paper proposes a Bayesian network based failure prediction of network nodes, e.g., routers etc., using very basic information from the log files of the devices and applying power law based data augmentation to complement for scarce real time information. Numerical results show that network node failure prediction can be performed with high accuracy using the proposed mechanism.

Failures in optical network backbone can lead to major disruption of internet data traffic. Hence, minimizing such failures is of paramount importance for the network operators. Even better, if the network failures can be predicted and preventive steps can be taken in advance to avoid any disruption in traffic. Various data driven and machine learning techniques have been proposed in literature for failure prediction. Most of these techniques need real time data from the networks and also need different monitors to measure key optical parameters. This means provision for failure prediction has to be available in network nodes, e.g., routers and network management systems. However, sometimes deployed networks do not have failure prediction built into their initial design but subsequently need arises for such mechanisms. For such systems, there are two key challenges. Firstly, statistics of failure distribution, data, etc., are not readily available. Secondly, major changes cannot be made to the network nodes which are already commercially deployed. This paper proposes a novel implementable non-intrusive failure prediction mechanism for deployed network nodes using information from log files of those devices. Numerical results show that the mechanism has near perfect accuracy in predicting failures of individual network nodes.

Quantum networks use quantum mechanics properties of entanglement and teleportation to transfer data from one node to another. Hence, it is necessary to have an efficient mechanism to distribute entanglement among quantum network nodes. Most of research on entanglement distribution apply current state of network and do not consider using historical data. This paper presents a novel way to quicken connection setup between two nodes using historical data and proactively distribute entanglement in quantum network. Results show, with quantum network size increase, the proposed approach improves success rate of connection establishments.