Connected-vehicle and roadside telemetry enable low-latency safety navigation, and traffic-optimisation services at the edge, but finegrained mobility streams (locations, speeds, events, and contexts) create high re-identification and linkage risk when accessed by multiple stakeholder domains. We present a Zero Touch Network and Service Management (ZSM) integrated, policy-driven data-collection service that operationalises mobility data governance through intent-based automation. Stakeholders submit high-level collection intents (purpose, fields, spatial/temporal granularity, latency, and utility targets); a policy engine evaluates and rewrites intents into compliant, effective intents; and a plan generator compiles them into executable data-collection pipelines deployable within a ZSM closed loop. Experiments on Beijing taxi mobility traces execute 87,500 DP-protected releases and achieve 1.26% relative error for Road Safety Authority (RSA) at ϵ = 8.0, while DP-Stochastic Gradient Descent (DP-SGD) risk scoring reaches 0.97 ± 0.03 test accuracy at ϵ = 0.5, with δ= 10-5.

The proliferation in the use of Online Social Networks has revolutionized information sharing and consumption, leading to the development of advanced techniques such as link prediction, recommendation systems, community detection, node classification, and network representation learning. However, the availability and quality of real-world datasets for testing these algorithms pose challenges. Synthetic signed datasets generated through signed network reconstruction models offer alternatives for algorithm testing and experimentation. This survey presents an overview of state-of-the-art signed network reconstruction modeling techniques, evaluates their performance through rigorous experimental analysis, explores real-world applications, discusses challenges and open research problems, and guides future research efforts in the field. By consolidating knowledge and providing insights into existing models, this survey contributes to advancing the understanding and improvement of signed network reconstruction modeling. Various research papers discussed in this survey along with publicly available links to their codes are available at: https://github.com/Aikta-Arya/Signed-Network-Reconstruction-Modeling .

Autonomous Vehicle (AV) navigation in dynamic environments is highly complex, with overtaking maneuvers adding complexity due to multiple sub-maneuvers and limited environmental perception. Recent advancements in Deep Reinforcement Learning (DRL) and sensors address a few challenges. Yet, the high dimensional sensor data and complex maneuvers slow RL agents in effectively perceiving the environment and performing the overtaking. Furthermore, the dynamic conditions and individual sensor data limit the RL agent's ability to localize and execute the precise sub-maneuvers. Thus, in this work, we propose TDRLO, a Deep Deterministic Policy Gradient (DDPG) based semi-Markov options acquired hierarchical reinforcement learning framework for AVs overtaking with LiDAR-RADAR fusion to tackle this issue. Our approach uses the option policy to control the sub-maneuvers execution and preprocesses sensor data for extracting crucial overtaking checkpoints and low dimensional environmental perception. Temporal Difference (TD) updates in the DDPG algorithm enhance episodic learning and incremental updates, while the option policy reduces the overtaking complexity. Moreover, the preprocessing of raw data and sensor fusion provides a better environmental perception and efficient overtaking in the CARLA simulator. We evaluated our approach using the National Highway Traffic Safety Administration (NHTSA) inspired overtaking pre-crash scenarios in CARLA. The result shows an average 20-30% improvement in average peak reward with stable critic values, alongside 100% completion rate, 10-30% least collision rate, and 20-40% more average speed compared to the baseline sate-of-the-art methods.

The proliferation in the use of social media and networking platforms has enhanced frequent interactions among individuals. The signed networks can be utilized to best perceive such complex relations. However, these real-world signed network datasets are scarce as the real-world signed datasets often involve dealing with sensitive information about individuals and their relationships. The ethical and privacy concerns of various social media platforms can further limit the availability of datasets for research and academic purposes. Furthermore, the availability of signed bipartite network datasets is even scarcer than signed network datasets. Existing signed network datasets also lack node/edge level attributes, yet recent trends in graph analytics have shown the significance of these features, for example, with GNNs, to advance benchmarking. To address such issues, we present three datasets in this paper: Reddit Posts (bipartite network with primary node level attributes and secondary level edge attributes), Bitcoin-OTC, and Bitcoin-Alpha datasets (signed networks with primary node and edge level attributes). Additionally, we explore potential research applications the dataset opens up across various domains. Various datasets discussed in this paper, along with already existing signed network datasets, are made publicly available using Signed Network Dataset Repository (SigNET Repo) at: https://aikta-arya.github.io/SigNET-/

Autonomous Vehicle (AV) driving involves complex maneuvers, with Obstacle Avoidance (OA) being one of the most challenging and safety critical tasks. While Reinforcement Learning (RL) has shown promise in achieving human like driving behavior, a single RL agent struggles to manage the multiple sub-maneuvers required for OA, particularly in safety critical scenarios. To address this, we propose an Option Policy inspired Hierarchical Deep Reinforcement Learning (OPDRL) framework that divides OA into sub tasks such as left lane change, straight driving, and right lane change. Each sub task is handled by a specialized RL agent, governed by a central master policy. This approach reduces training time, simplifies validation, and seamlessly incorporates traffic safety rules to ensure robust decision making in safety critical scenarios. The proposed method is validated using scenarios inspired by the National Highway Traffic Safety Administration (NHTSA) precrash scenarios in the CARLA simulator, demonstrating its effectiveness in handling OA maneuvers efficiently.

India's approval of COVID-19 vaccines such as Covishield and Covaxin, along with ongoing updates on health guidelines and boosters, underscores the importance of understanding vaccine efficacy and real-world vaccine effectiveness (VE). While efficacy indicates trial performance, effectiveness reflects actual impact across varied conditions. Precise VE estimation through mathematical modeling is crucial for optimizing vaccination strategies and resource distribution. In this study, we enhance the SEI${}_{3}$R${}_{2}$D${}_{2}$V model, segmented by reporting status, into the SE${}^{2}$I${}^{6}$R${}^{4}$D${}^{4}$V model. This model further subdivides infected, recovered, deceased, and exposed groups by vaccination status, improving VE analysis. This granularity is essential for comparing attack rates among vaccinated and unvaccinated populations, aligning closely with real-time data, and accounting for transmission variations. Our findings show that VE fluctuates, initially dropping to 41% due to transmission and variants but rising to 95% with higher vaccination, reducing transmission and severity. Using cohort analysis, our model estimates time-varying VE while factoring in distinct recovery and mortality rates by vaccination and reporting status, and also compares various VE definitions with findings from similar studies. We projected VE under scenarios of waning immunity, new variants, and age group impacts. Sensitivity analysis fine-tuned parameters to assess VE's dependence on vaccination coverage, transmission rates, and incubation periods. From March 2020 to April 2024, our SE${}^{2}$I${}^{6}$R${}^{4}$D${}^{4}$V model showed close alignment with real data, achieving a relative error of$0.87\boldsymbol{\times}10^{-2}$in infections, outperforming SEIRDVB's$3.53\boldsymbol{\times}10^{-2}$and SEI${}_{3}$R${}_{2}$D${}_{2}$V's$0.97\boldsymbol{\times}10^{-2}$. The model has also been validated across various countries, demonstrating its applicability in real-world contexts.

The plethora of Internet of Things (IoT) devices and their diversified requirements have opted to design security mechanisms that cover all major security requirements. Wireless Local Area Networks (WLANs) is the most common network domains where IoT devices are launched, particularly because of its easy availability. Security, in other words authentication however, remains to be a major constriction for IoT-WLAN deployments. Though there are IoT based authentication protocols prevailing, such protocols are either prone to threats such as perfect forward secrecy violations, insider with database access attack, traceability attack, stolen device attack, ephemeral secret leakage, or they consume excessive computational and communication resources that result in an unprecedented burden for the IoT system. This paper presents an Extensible Authentication Protocol (EAP) based mechanism for IoT devices deployed in a WLAN that addresses the above security issues and achieves cost-effectiveness. Validation follows an informal and formal approaches (using GNY and BAN logic, and Scyther verification tool) for the proposed protocol, demonstrating its robustness. Our performance analysis shows that the proposed protocol is lightweight and more secure in contrast to the state-of-the-art solutions. In addition, performance of the proposed protocol subjected to unknown attacks is investigated, which deduces that the proposed protocol has less overhead under unknown attacks than its competitors. A prototype of the protocol has been developed to demonstrate its feasibility and accuracy.

Driving an Autonomous Vehicle (AV) in dynamic traffic is a critical task, as the overtaking maneuver being considered one of the most complex due to involvement of several sub-maneuvers. Recent advances in Deep Reinforcement Learning (DRL) have resulted in AVs exhibiting exceptional performance in addressing overtaking-related challenges. However, the intricate nature of the overtaking presents difficulties for a RL agent to proficiently handle all its sub-maneuvers that include left lane change, right lane change and straight drive. Furthermore, the dynamic traffic restricts the RL agents to execute the sub-maneuvers at critical checkpoints involved in overtaking. To address this, we propose an approach inspired by semi-Markov options, called Dynamic Option Policy enabled Hierarchical Deep Reinforcement Learning (DOP-HDRL). This innovative approach allows the selection of sub-maneuver agents using a single dynamic option policy, while employing individual DRL agents specifically trained for each sub-maneuver to perform tasks during overtaking in dynamic environments. By breaking down overtaking maneuvers into several sub-maneuvers and controlling them using a single policy, the DOP-HDRL approach reduces training time and computational load compared to classical DRL agents. Moreover, DOP-HDRL easily integrates basic traffic safety rules into overtaking maneuvers to offer more robust solutions. The DOP-HDRL approach is rigorously evaluated through multiple overtaking and non-overtaking scenarios inspired by the National Highway Traffic Safety Administration (NHTSA) pre-crash scenarios in the CARLA simulator. On an average, the DOP-HDRL approach shows 100% completion rate, 14% least collision rate, 25% optimal clearance distance, and 7% more average speed compared to the state-of-the-art methods.

Privacy is one of the major concerns in the availability of large-scale real-world network datasets for various applications. Therefore, we require network models that can generate realistic synthetic networks of a very large scale that are capable of preserving patterns of given structural property in its evolution period. In this article, we proffer a novel network reconstruction model for signed networks, i.e., sequentially induced signed subnetwork reconstruction model (SISSRM), that is able to preserve the distribution of degrees, obsolescence, unbalanced, and balanced triangles, correlation among diverse triad types, spectral radius, degree correlation, and network balancedness during its growth process. SISSRM reproduces different structural properties of a given real-world signed network more accurately as compared to the considered state-of-the-art network models. The extensive experimentation on nine real-world empirical networks validates the significance of our proposed model in the generation of realistic synthetic signed networks.

For densely populated developing countries, such as India, where due to a lack of general and public awareness, limited data collection and compilation facilities, and inherent limitations of the available diagnostic test, accurate modeling of the pandemic is more challenging. Thus, a realistic model for predictions is required in order to formulate more effective strategic policies to control the COVID-19 pandemic using limited available resources. In this article, we propose a time-varying epidemiological model with two classes of compartments, reported and unreported, and consider influential latent factors, for example, undetectable infections, the false-negative rate of testing, testing hesitancy, vaccination efficacy, dual contact dynamics, and the possibility of reinfection in recovered as well as vaccinated individuals. For simulation purposes, we consider the COVID-19 data of India from March 13, 2020, to January 20, 2022. Furthermore, we provide a sensitivity analysis of various latent factors and predictions for the third wave in India. Simulated results suggest that India is able to control COVID-19 for the first time after the second wave, as observed from the trajectory of effective reproduction number. Moreover, for unseen or coming variants of virus for which vaccine efficacy is low, the available vaccine requires a high vaccination rate to control future waves.

In this article, we have employed the SEI3R2D2V model for our analysis. We conducted stability analysis for infection-free equilibrium (X′) and endemic equilibrium (X*). The obtained equilibrium points are globally asymptotically stable. Our findings reveal that the contact dynamics of the infected and uninfected populations primarily influence the dynamics of COVID-19. In managing COVID-19, it is crucial to ensure that the number of secondary infections (Rt) remains below the threshold (γ+ (1 −γ)/(αt)) which determines the growth or decline of the disease. Additionally, we conducted a sensitivity analysis of Rt to identify the key factors that significantly affect its value. It is observed that the recovery rate, transmission probability of the virus, contact rate of unreported infections, testing inaccuracy and hesitancy, vaccination rate, and its efficacy have the most substantial impact on the value ofRt. The influential parameters are categorized into two sets based on their effective controllability, allowing for the prioritization of intervention strategies that require fewer resources and are easier to manage, thereby optimizing efforts to control disease transmission.

Network modeling has been explored extensively by means of theoretical analysis as well as numerical simulations for Network Reconstruction (NR). The network reconstruction problem requires the estimation of the power-law exponent (γ) of a given input network. Thus, the effectiveness of the NR solution depends on the accuracy of the calculation of γ. In this article, we re-examine the degree distribution-based estimation of γ, which is not very accurate due to approximations. We propose X -distribution, which is more accurate than degree distribution. Various state-of-the-art network models, including CPM, NRM, RefOrCite2, BA, CDPAM, and DMS, are considered for simulation purposes, and simulated results support the proposed claim. Further, we apply X -distribution over several real-world networks to calculate their power-law exponents, which differ from those calculated using respective degree distributions. It is observed that X -distributions exhibit more linearity (straight line) on the log-log scale than degree distributions. Thus, X -distribution is more suitable for the evaluation of power-law exponent using linear fitting (on the log-log scale). The MATLAB implementation of power-law exponent (γ) calculation using X -distribution for different network models and the real-world datasets used in our experiments are available at https://github.com/Aikta-Arya/X-distribution-Retraceable-Power-Law-Exponent-of-Complex-Networks.git .

Due to the constant influx of multiple security attacks into the next generation of mobile communication technologies, the Third Generation Partnership Project (3GPP) has established authentication and key agreement protocol, 5-GAKA, to securely access the 5G communication services while maintaining the integrity of the underlying network. However, some recent findings pointed out that 5G-AKA has many drawbacks, including perfect forward secrecy violations, malicious Serving Network (SN) attacks, desynchronization attacks, privacy theft, stolen device, and denial of service (DoS) attacks when the user uses roaming mobile services. Considering the drawbacks of current 5G communication protocols and the necessity to facilitate additional security, a provably secure and lightweight protocol for 5G communication (PSLP-5G) is introduced. The PSLP-5G's security is guaranteed using the Scyther tool and Real-Or-Random (ROR) logic. Furthermore, performance comparisons are made to show how much lighter the PSLP-5G is than its counterparts. Additionally, the PSLP-5G's suitability for use in real-time applications is demonstrated by comparing the network performance of PSLP-5G and its counterparts using the Network Simulator tool NS3.

Modeling real-world signed networks is a challenging task due to the highly dynamic microlevel growth processes involved in it. The network reconstruction is a problem in which we define a model that not only captures the patterns followed by different structural and spectral properties of real-world networks but also minimizes numerical error. In this article, we define a simple yet an effective network generation mechanism that can learn model parameters efficiently and produces simulated networks having structural properties close to a given input real-world signed social network. In the proposed model, corresponding to each node, a characteristic function is defined that controls its dynamics and its link formation process. A family of exponential functions is suited well to include aging and local growth, including triangle formation of different types of balanced and unbalanced triangles, and produce a wide range of degree distributions. In the proposed model, two layers are modeled independently, and further superimposition of layers is applied to get the final simulated signed network. Apart from that, the experimental results manifest that our proposed model, signed network structural reconstruction model (SNSRM), is able to replicate the characteristic properties of the various real-world signed networks more closely compared to the state-of-the-art models.

Several symmetric and asymmetric encryption based authentication protocols have been developed for the wireless local area networks (WLANs). However, recent findings reveal that these protocols are either vulnerable to numerous attacks or computationally expensive. Considering the demerits of these protocols and the necessity to provide enhanced security, a lightweight extensible authentication protocol based authentication protocol for WLAN-connected Internet of Things devices is presented. We conduct an informal and formal security analysis to ensure robustness against the attacks. Furthermore, the empirical performance analysis and comparison show that the proposed protocol outperforms its counterparts, reducing computational, communication, storage costs, and energy consumption by up to 99%, 80%, 91.8%, and 98%, respectively. Simulation results of the protocol using the NS3 and its overhead under unknown attacks demonstrate that the proposed protocol performs better in all scenarios. A prototype implementation of the protocol has also been tested to evaluate its feasibility in real-time applications.

Triangle count is a frequently used network statistic, possessing high computational cost. Moreover, this task gets even more complex in the case of signed networks which consist of unbalanced and balanced triangles. In this work, we propose a fastIncrementalTriangleCounting (ITC) algorithm for counting all types of triangles, including balanced and unbalanced. The proposed algorithm updates the count of different types of triangles for newly added nodes and edges only instead of recalculating the same triangle multiple times for the entire network repeatedly. Thus, the proposed ITC algorithm also works for dynamic networks. The experimental results show that the proposed method is practically efficient having run time complexity of$O(m k_{{\max}})$, where$m$represents the number of edges and$k_{{\max}}$represents the maximum degree of the given signed network.

Due to the constant penetration of various security attacks, it is highly important to secure the underlying communication networks between the IoT, Fog and Cloud in the next generation of mobile communication system (5G). Thus, secure authentication and key agreement protocol, namely 5G-AKA, has been proposed in the literature to safely and stably access the 5G mobile services. However, some recent findings reveal that 5G-AKA and its numerous versions based on symmetric or asymmetric encryption are either vulnerable to different attacks such as perfect forward secrecy violation, malicious Serving Network (SN), de-synchronization attack, privacy theft, stolen device, or are computationally intensive. Apart from that, these protocols use single-factor authentication. Considering the above demerits of these protocols and the necessity to provide enhanced security, we propose an Elliptic Curve-Cryptography (ECC)-based multi-factor 5G-AKA authentication protocol. It provides additional security and achieves cost-effectiveness in terms of computational, communication, storage costs and energy consumption. The formal security analysis using Real-Or-Random (ROR) logic has been done to confirm its security. Moreover, we evaluate the performance of the proposed protocol in terms of computational, communication, storage costs and energy consumption. The evaluation results show that the proposed protocol requires less cost than its counterparts, reducing computational cost by up to 57%, communication cost by up to 59%, storage cost by up to 52%, and energy consumption by up to 51%.

The growing usage of wireless devices has significantly increased the need for Wireless Local Area Network (WLAN) during the past two decades. However, security (most notably authentication) remains a major roadblock to WLAN adoption. Several authentication protocols exist for verifying a supplicant’s identity who attempts to connect his wireless device to an access point (AP) of an organization’s WLAN. Many of these protocols use the Extensible Authentication Protocol (EAP) framework. These protocols are either vulnerable to attacks such as violation of perfect forward secrecy, replay attack, synchronization attack, privileged insider attack, and identity theft or require high computational and communication costs. In this paper, a lightweight EAP-based authentication protocol for IEEE 802.11 WLAN is proposed that not only addresses the security issues in the existing WLAN authentication protocols but is also cost-effective. The security of the proposed protocol is verified using BAN logic and the Scyther tool. Our analysis shows that the proposed protocol is safe against all the above attacks and attacks defined in RFC-4017. A comparison of the computational and communication costs of the proposed protocol with other existing state-of-the-art protocols shows that the proposed protocol is lightweight than existing solutions.

We propose modeling signed networks by considering two layers in a social network for generation of positive and negative links where both the layers comprise of identical set of nodes. The growth process is modeled based on preferential attachment, formation of links probabilistically asserting structural balance of local groups, and internal growth which happens without addition of new nodes. We prove that the degree distribution of a generated network follows a power-law whose exponent depends on the largest eigenvalue of a matrix which governs the dynamics of growth of degrees of nodes with respect to positive and negative links. A computable formula for average degree and lower-bounds for the number of balanced and unbalanced triads of modelled networks are also obtained. A method for structural reconstruction of real signed networks is formulated through estimation the values of the model parameters to generate the network that can inherit different structural properties of the corresponding real network. Experimental results show that our model which we term as 2L-SNM can replicate properties of several real world signed networks much more robustly than competitive state-of-the-art techniques.

Complex systems have been successfully modeled as networks exhibiting the varying extent of randomness and nonrandomness. Network scientists contemplate randomness as one of the most desirable characteristics for real complex systems' efficient performance. However, the current methodologies for randomness (or nonrandomness) quantification are nontrivial. In this article, we empirically showcase severe limitations associated with the state-of-the-art graph spectral-based quantification approaches. Addressing these limitations led to the proposal of a novel spectrum-based methodology that leverages configuration models as a reference network to quantify the nonrandomness in a given candidate network. Besides, we derive mathematical formulations for demonstrating the dependence of nonrandomness on three structural properties: modularity, clustering, and the highest degree node's growth rate. We also introduce a novel graph signature (termed “cumulative spectral difference”) to visualize the nonrandomness in the network. Later, this article also discusses the relationship between the proposed nonrandomness measure and the diffusion affinity of networks. Toward the end, this article extensively discusses observations emerging from these signatures for both real-world and simulated networks.

We propose a parametric network generation model which we call network reconstruction model (NRM) for structural reconstruction of scale-free real networks with power-law exponent greater than 2 in the tail of its degree distribution. The reconstruction method for a real network is concerned with finding the optimal values of the model parameters by utilizing the powerlaw exponents of model network and the real network. The method is validated for certain real world networks. The usefulness of NRM in order to solve structural reconstruction problem is demonstrated by comparing its performance with some existing popular network generative models. We show that NRM can generate networks which follow edge-densification and densification power-law when the model parameters satisfy an inequality. Computable expressions of the expected number of triangles and expected diameter are obtained for model networks generated by NRM. Finally, we numerically establish that NRM can generate networks with shrinking diameter and modular structure when specific model parameters are chosen.