arXiv subjects

Dibakar Das

Publications and source records attributed to Dibakar Das.

At least 19 recordsLinked to original sources

arXiv2025

A Novel Integrated Architecture for Intent Based Approach and Zero Touch Networks

The transition to Sixth Generation (6G) networks presents challenges in managing quality of service (QoS) of diverse applications and achieving Service Level Agreements (SLAs) under varying network conditions. Hence, network management must be automated with the help of Machine Learning (ML) and Artificial Intelligence (AI) to achieve real-time requirements. Zero touch network (ZTN) is one of the frameworks to automate network management with mechanisms such as closed loop control to ensure that the goals are met perpetually. Intent- Based Networking (IBN) specifies the user intents with diverse network requirements or goals which are then translated into specific network configurations and actions. This paper presents a novel architecture for integrating IBN and ZTN to serve the intent goals. Users provides the intent in the form of natural language, e.g., English, which is then translated using natural language processing (NLP) techniques (e.g., retrieval augmented generation (RAG)) into Network Intent LanguagE (Nile). The Nile intent is then passed on to the BiLSTM and Q-learning based ZTN closed loop framework as a goal which maintains the intent under varying network conditions. Thus, the proposed architecture can work autonomously to ensure the network performance goal is met by just specifying the user intent in English. The integrated architecture is also implemented on a testbed using OpenAirInterface (OAI). Additionally, to evaluate the architecture, an optimization problem is formulated which evaluated with Monte Carlo simulations. Results demonstrate how ZTN can help achieve the bandwidth goals autonomously set by user intent. The simulation and the testbed results are compared and they show similar trend. Mean Opinion Score (MOS) for Quality of Experience (QoE) is also measured to indicate the user satisfaction of the intent.

cs.NI↗

arXiv2025

Digital Twin Assisted Proactive Management in Zero Touch Networks

The rapid expansion of cellular networks and rising demand for high-quality services require efficient and autonomous network management solutions. Zero Touch Network (ZTN) management has emerged as a key approach to automating network operations, minimizing manual intervention, and improving service reliability. Digital Twin (DT) creates a virtual representation of the physical network in realtime, allowing continuous monitoring, predictive analytics, and intelligent decision-making by simulating what-if scenarios. This paper integrates DT with ZTN proactive bandwidth management in end-to-end (E2E) next-generation networks. The integrated architecture applies Few-Shot Learning (FSL) to a memoryaugmented Bidirectional Long Short Term Memory (BiLSTM) model to predict a new network state to augment the known and trained states. Using Q-learning, it determines the optimal action (e.g. traffic shaping) under varying network conditions such that user Quality of Service (QoS) requirements are met. Three scenarios have been considered: 1) normal ZTN operation with closed-loop control, 2) a what-if scenario of DT, and 3) network state unknown to DT. The simulation results show that the network can adapt to underlying changing conditions. In addition, DT-assisted ZTN achieves better performance than the other techniques.

cs.NI↗

arXiv2025

Connecting the Unconnected -- Sentiment Analysis of Field Survey of Internet Connectivity in Emerging Economies

Internet has significantly improved the quality of citizens across the world. Though the internet coverage is quite high, 40% of global population do not have access to broadband internet. This paper presents an analysis of a field survey of population in some areas of Kathmandu, Nepal, an emerging economy. This survey was triggered by intermittent severe congestion of internet in certain areas of the city. People from three different areas were asked about their present experience of internet usage, its impact on their lives and their aspirations for the future. Survey pointed to high speed, low cost, reliable and secure internet as a major aspiration of the respondents. Based on their inputs, this paper presents a sentiment analysis as well as demographic information. Keys insights from this analysis shows that overall sentiment to most queries are positive. The variances of positive sentiments are high whereas those for negative ones are low. Also, some correlations and clusters are observed among the attributes though no dominant component exists in the data.

cs.CY↗

arXiv2025

Novel Closed Loop Control Mechanism for Zero Touch Networks using BiLSTM and Q-Learning

As networks advance toward the Sixth Generation (6G), management of high-speed and ubiquitous connectivity poses major challenges in meeting diverse Service Level Agreements (SLAs). The Zero Touch Network (ZTN) framework has been proposed to automate and optimize network management tasks. It ensures SLAs are met effectively even during dynamic network conditions. Though, ZTN literature proposes closed-loop control, methods for implementing such a mechanism remain largely unexplored. This paper proposes a novel two-stage closedloop control for ZTN to optimize the network continuously. First, an XGBoosted Bidirectional Long Short Term Memory (BiLSTM) model is trained to predict the network state (in terms of bandwidth). In the second stage, the Q-learning algorithm selects actions based on the predicted network state to optimize Quality of Service (QoS) parameters. By selecting appropriate actions, it serves the applications perpetually within the available resource limits in a closed loop. Considering the scenario of network congestion, with available bandwidth as state and traffic shaping options as an action for mitigation, results show that the proposed closed-loop mechanism can adjust to changing network conditions. Simulation results show that the proposed mechanism achieves 95% accuracy in matching the actual network state by selecting the appropriate action based on the predicted state.

cs.NI↗

arXiv2025

Connectivity for AI enabled cities -- A field survey based study of emerging economies

The impact of Artificial Intelligence (AI) is transforming various aspects of urban life, including, governance, policy and planning, healthcare, sustainability, economics, entrepreneurship, etc. Although AI immense potential for positively impacting urban living, its success depends on overcoming significant challenges, particularly in telecommunications infrastructure. Smart city applications, such as, federated learning, Internet of Things (IoT), and online financial services, require reliable Quality of Service (QoS) from telecommunications networks to ensure effective information transfer. However, with over three billion people underserved or lacking access to internet, many of these AI-driven applications are at risk of either remaining underutilized or failing altogether. Furthermore, many IoT and video-based applications in densely populated urban areas require high-quality connectivity. This paper explores these issues, focusing on the challenges that need to be mitigated to make AI succeed in emerging countries, where more than 80% of the world population resides and urban migration grows. In this context, an overview of a case study conducted in Kathmandu, Nepal, highlights citizens' aspirations for affordable, high-quality internet-based services. The findings underscore the pressing need for advanced telecommunication networks to meet diverse user requirements while addressing investment and infrastructure gaps. This discussion provides insights into bridging the digital divide and enabling AI's transformative potential in urban areas.

cs.CY↗

arXiv2024

Robust control of Z-source inverter operated BLDC motor using Sliding Mode Control for Electric Vehicle applications

The rapid development and expansion of the EV market marked by the advent of third decade of the 21st century has improved the possibility of a sustainable automotive future. The present EV drivetrain run by BLDC motor has become increasingly complicated thus requiring efficient and accurate controls. The paper begins with discussing the problems in existing models, the research then focuses on increasing the robustness of the system towards disturbances and uncertainties by using Sliding Mode Control to control the ZSI, which has been chosen as the main power converter topology in place of VSI or CSI. The introduction of SMC has improved the performance of the drivetrain when applied with Vehicle dynamics over a Drive Cycle.

eess.SY↗

arXiv2024

A study on applications of various Energy Generation in pure Electric Vehicles: progress towards sustainability

The present work is an attempt to understand and review existing methods of energy generation in electric vehicles in the modern day context. Previous works in the field have proposed various mechanisms of energy generation that are very well adaptable to commercial scale uses and can be used as alternative power sourcing for electric vehicles having nil or very low environmental impact. The paper discusses strategies such as photovoltaic cell systems, regenerative braking, fuel cell, thermoelectric generators and micro wind-turbines with adequate propositions to select them on the basis of their suitability. The document also includes important formulas that can be used for individual modeling and designing. The paper emphasises on introducing the mechanisms that can be introduced as assistive mechanisms or secondary sources so that the range and other parameters are not compromised.

eess.SY↗

arXiv2024

Multi-class Classifier based Failure Prediction with Artificial and Anonymous Training for Data Privacy

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.

cs.AI↗

arXiv2024

Improved Q-learning based Multi-hop Routing for UAV-Assisted Communication

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(λ) 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.

cs.NI↗

arXiv2024

IoT-enabled Stability Chamber for the Pharmaceutical Industry

A stability chamber is essential for pharmaceutical facilities to test the stability and quality of products over time by exposing them to different environmental conditions. This paper introduces an IoT-enabled stability chamber designed for the pharmaceutical industry. We constructed four stability chambers by leveraging the existing infrastructure within a manufacturing facility. Each chamber is controlled using a state-of-the-art Proportional Integral Derivative (PID) system based on the Siemens S7-1200 PLC. The Siemens WinCC Runtime Advanced platform, compliant with FDA 21 CFR Part 11, was used for visualizing chamber data. Additionally, an Internet of Things (IoT) application was developed to remotely monitor sensor data through any client application. This research aims to enhance the performance of traditional stability chambers by integrating IoT functionalities, making them more cost-effective and user-friendly.

eess.SY↗

arXiv2023

Several Consequences of Optimality

Rationality is frequently associated with making the best possible decisions. It's widely acknowledged that humans, as rational beings, have limitations in their decision-making capabilities. Nevertheless, recent advancements in fields, such as, computing, science and technology, combined with the availability of vast amounts of data, have sparked optimism that these developments could potentially expand the boundaries of human bounded rationality through the augmentation of machine intelligence. In this paper, findings from a computational model demonstrated that when an increasing number of agents independently strive to achieve global optimality, facilitated by improved computing power, etc., they indirectly accelerated the occurrence of the "tragedy of the commons" by depleting shared resources at a faster rate. Further, as agents achieve optimality, there is a drop in information entropy among the solutions of the agents. Also, clear economic divide emerges among agents. Considering, two groups, one as producer and the other (the group agents searching for optimality) as consumer of the highest consumed resource, the consumers seem to gain more than the producers. Thus, bounded rationality could be seen as boon to sustainability.

cs.CY↗

arXiv2023

Energy Efficient UAV-Assisted Emergency Communication with Reliable Connectivity and Collision Avoidance

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.

cs.NI↗

arXiv2023

Network Centralities in Quantum Entanglement Distribution due to User Preferences

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.

quant-ph↗

arXiv2023

Consequences of Optimality

Rationality is often related to optimal decision making. Humans are known to be bounded rational agents. However, recent advances in computing, and other scientific and technical fields along with large amount of data have led to a feeling that this could result in extending the limits of bounded rationality in humans through augmented machine intelligence. In this paper, results from a computational model show that as more agents reach global optimality, faster with enhanced computing, etc., solving the same problem independently, this leads to accelerated "tragedy of the commons" due to quicker resource consumption. Thus, bounded rationality could be seen as blessing in disguise (providing diversity to solutions for the same problem) from sustainability standpoint.

cs.CY↗

arXiv2023

Economic Dynamics of Agents

Post-pandemic world has thrown up several challenges, such as, high inflation, low growth, high debt, collapse of economies, political instability, job losses, lowering of income in addition to damages caused natural disasters, more convincing attributed to climate change, apart from existing inequalities. Efforts are being made to mitigate these challenges at various levels. To the best of the knowledge of the author, most of the prior researches have focussed on specific scenarios, use cases, inter-relationships between couple of sectors and more so on optimal policies, such as, impact of carbon tax on individuals, interaction between taxes and welfare, etc. However, not much effort have been made to understand the actual impact on individual agents due to diverse policy changes and how agents cope with changing economic dynamics. This paper considers progressive deteriorating conditions of increase in expense, degrading environmental utility, increase in taxation, decrease in welfare and lowering of income with recourse to inherited properties, credits and return on investments, and tries to understand how the agents cope with the changing situations using an agent based model with matrices related to savings, credits, assets. Results indicate that collapse of agents' economic conditions can be quite fast, sudden and drastic for all income groups in most cases.

cs.CY↗

arXiv2022

Node and Edge Centrality based Failures in Multi-layer Complex Networks

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.

cs.SI↗

arXiv2022

Rationality in current era -- A recent survey

Rationality has been an intriguing topic for several decades. Even the scope of definition of rationality across different subjects varies. Several theories (e.g., game theory) initially evolved on the basis that agents (e.g., humans) are perfectly rational. One interpretation of perfect rationality is that agents always make the optimal decision which maximizes their expected utilities. However, subsequently this assumption was relaxed to include bounded rationality where agents have limitations in terms of computing resources and biases which prevents them to take the optimal decision. However, with recent advances in (quantum) computing, artificial intelligence (AI), science and technology etc., has led to the thought that perhaps the concept of rationality would be augmented with machine intelligence which will enable agents to take decision optimally with higher regularity. However, there are divergent views on this topic. The paper attempts to put forward a recent survey (last five years) of research on these divergent views. These viewsmay be grouped into three schools of thoughts. The first school is the one which is sceptical of progress of AI and believes that human intelligencewill always supersede machine intelligence. The second school of thought thinks that advent of AI and advances in computing will help in better understanding of bounded rationality. Third school of thought believes that bounds of bounded rationality will be extended by advances in AI and various other fields. This survey hopes to provide a starting point for further research.

cs.CY↗

arXiv2021

Connectivity and Collision Constrained Opportunistic Routing for Emergency Communication using UAV

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.

cs.NI↗