Secure communication is essential for the Industrial Internet of Things (IIoT), but most IIoT devices cannot run conventional security models due to resource constraints. Multi-access mobile edge computing (MEC) brings computational resources closer to the network edge but struggles with scalability and flexibility in centralized security solutions. Distributed data processing on MEC servers also raises privacy and security concerns. This paper proposes a novel IIoT network security using federated blockchain (FB) and machine learning-based (ML) verification. MEC optimizes the FB model to ensure data integrity and confidentiality between the IIoT's local network cluster and external devices. Data within the local network cluster are secured with public-key cryptography. The ML-based verification model ensures legitimate key-pair updates and device joining in MEC-assisted IIoT. This approach outperforms conventional security solutions in scalability, data privacy, and adaptability to IIoT network changes. We present a detailed implementation and evaluate its performance using a realistic IoT testbed, showing improved network security while maintaining the performance and scalability of MEC-assisted IIoT systems.

Design complexities of trending UAVs and the operational harsh environments necessitates Control Law formulation utilizing intelligent techniques that are both robust, model-free and adaptable. In this research, an intelligent control architecture for an Unmanned Aerial Vehicle (UAV) having an unconventional inverted V-tail design, is presented. Due to unique design of the vehicle strong roll and yaw coupling exists, making the control of vehicle challenging. To handle UAV’s inherent control complexities, while keeping them computationally acceptable, a variant of distinct Deep Reinforcement Learning (DRL) algorithm, namely Deep Deterministic Policy Gradient (DDPG) is proposed. Conventional DDPG algorithm after being modified in its learning architecture becomes capable of intelligently handling the continuous state and control space domains besides controlling the platform in its entire flight regime. This stabilization and tracking controller for the UAV achieves the goal of an optimal flight path and exhibits satisfactory performance. The paper illustrates the application of modified DDPG algorithm towards the design, while the performance of the resulting controller is assessed in simulation using dynamic model of the vehicle. Nonlinear simulations were then performed to analyze UAV performance under different environmental and launch conditions. The effectiveness of the proposed strategy is further demonstrated by comparing the results with the linear controller for the same UAV whose feedback loop gains are optimized by employing technique of optimal control theory and another DRL method named Proximal Policy Optimization (PPO). Results indicate the significance of the proposed control architecture and its inherent capability to adapt dynamically to the changing environment, thereby making it of significance utility to airborne applications.

A multi-objective optimization method for exploring uncharted space is presented in the study. Robotics exploration personnel create a map of the immediate area using sensor data. It has been observed that a single optimization technique is typically used with a specific objective function to carry out the optimization for space exploration. The optimization process can be sped up and simplified using a mono-objective function, but the map accuracy and exploration depth suffer. This work offers an optimization technique with improved multi-objective functions in recognition of this crucial factor. This not only speeds up the search procedure but also improves the precision of the maps. The Reconfigured Whale Algorithm (rWO), the suggested framework, is based on a Whale Optimizer inspired by whales’ biological behavior. Initializing the whale population, also known as waypoints, is the first step. After being established in the initial stage/iterations, these waypoints are considered constant. The location update from the robot-catered non-dominated waypoints is the next phase. The algorithm optimizes the waypoints. Extensive simulations that simulate various scenarios and environments are used to assess the performance matrices properly. Following the identification of the algorithm’s advantages, the effectiveness of the outcomes is demonstrated by contrasting its performance with that of three popular optimization algorithms: the hybrid CME-Whale Optimizer (WO), the Coordinated Multi-Robot Exploration (CME), and the Arithmetic Optimizer (AO) integrated with CME. Findings show that the suggested approach significantly improves the optimization process by expanding the area that is investigated and speeding up the search process.

The aviation use of Unmanned Aerial Vehicles (UAVs) necessitates a strong control design. The key to designing a durable control system is a well-developed flight dynamics model. System-based model identification techniques provide a useful means for estimating UAV modal parameters thereby saving time and money. Although considerable research is performed on UAV flight dynamics analysis utilizing system identification techniques, limited work exists that compares various techniques of system identification for UAVs in an exhaustive manner. Moreover, the research contributions toward performance evaluation and comparison of system identification methods outputs are even more scarce, especially under varying environmental conditions. In this study, a comprehensive framework utilizing various linear and nonlinear estimation techniques estimates unknown UAV model dynamics. To analyze the effects of varying flight conditions, a detailed analysis is performed which includes a parametric sweep of environmental elements as input arguments to the estimation process. The comparison involves the estimation of key performance parameters such as residual analysis, final prediction error, and fit percentages. Through rigorous analysis, it is demonstrated that the proposed framework predicts system parameters under a variety of conditions, thereby confirming its validity. It has also been demonstrated that a parametric sweep of environmental conditions, can be utilized to improve the authenticity of models’ data learning ranges, and their response to the prediction parameters. This paper, to the best of our knowledge, provides an elaborate platform for researchers to carry out comprehensive model prediction under a wide range of environmental conditions.

Use of intelligent systems have marked a major breakthrough in the domain of aerial plat- forms. Under the ambit of Machine Learning (ML), Reinforcement Learning (RL) has started to address the inherent limitations of the conventional controllers and emerges as the most ac- tive, conceptually prudent and best suited machine learning category for autonomous control. In this paper paper, we present two innovating RL based control strategies namely ‘Reward Selective Dynamic Programming (RSDP)’ and ’Optimal Deep Deterministic Policy Gradient (O-DDPG)’ to control the dynamics of an unconventional UAV. Both the framework have been developed specifically to accommodate the continuous state and action domains of the aerial platform. Both the RL algorithms turned out to have satisfactory computational performance, and the agent was effectively trained for entire state and action space. The effectiveness of both the proposed strategies were verified through extensive 6-DOF simulations. Results indicate that both outperforms the classical control architecture primarily by eliminating the explicit requirement of gain scheduling for various equilibria during the trajectory. A perspective analysis was also carried out to perform a comparative analysis between both the proposed RL strategies.

Unmanned High Speed Aerial Vehicle (UHSAV) are an important tool for various applications applications. To ensure mission success, robust control system needs to be developed for these UHSAVs, which require well characterized dynamic system model. This paper aims on model estimation of an experimental UHSAV utilizing actual flight data. An elaborate estimation mechanism is proposed utilizing various model structures techniques such as Autoregressive Exogenous (ARX), Autoregressive Moving Average exogenous (ARMAX), Box Jenkin’s (BJ), Output Error (OE), and state space and non-linear Autoregressive Exogenous. A perspective analysis and comparison is made for identifying the salient aspects of each individual model structure. Model configuration with best characteristics is then identified based upon model quality parameters such as residual analysis, final prediction error and fit percentages. Extensive validation to evaluate the performance of developed model is then performed utilizing the actual flight dynamics data collected from different flights of the same UHSAV. Results indicate the viability of the model as the model can accurately predict the system performance at a wide range of operating conditions. Through this, to the very of our knowledge we present a comprehensive model prediction framework, which utilizes actual flight data instead of simulation work

In IT infrastructure, security is the first and foremost important factor to maintain a system’s confidentiality, integrity and availability. Compromising any of these factors can have disastrous effects on a system or a company. History has taught us the consequences of neglecting security needs. So, information security has become a topic of interest and various researchers are introducing different methodologies to secure the systems. Firewalls, Intrusion Detection Systems (IDSs), Endpoint Detection & Response (EDR), Managed Detection & Response (MDR), etc., are some famous security solutions that have enhanced the security of cyber-physical systems. Moreover, there are various advancements in cryptography and many new algorithms are being suggested by researchers. This work presents a novel approach to secure data communication within a network where the information can be vulnerable during transit. This work uses a Raspberry Pi in router creation, and a Virtual Private Network (VPN) is deployed over the router through a cloud link. AWS is being utilized in cloud knot. Thus, a secure gateway is formed in which the data is encrypted, and even the network admin or service provider cannot read the data. Advanced Encryption Standard (AES) has been used as an encryption algorithm in this work. The prototype is tested via IP and network traffic tests, and results verified that this router’s network traffic is secure over the internet.

In the multiple fields covered by Artificial Intelligence (AI), robotic path planning is undoubtedly one of the issues that cover a wide range of research lines. This paper introduces recently developed Aquila Optimization algorithm specifically configured for Multi-Robot space exploration. The framework is a unique combination of both deterministic Coordinated Multi-robot Exploration (CME) and a swarm based Aquila Optimizer (AO), combinely known as Coordinated Multi-robot Exploration Aquila Optimizer (CME-AO). The proposed hybrid strategy also incorporates a novel parallel communication protocol, to improve multi-robot space exploration process while simultaneously minimizing both the computation complexity and time. This ensures acquisition of a optimal collision-free path in a barrier-filled environment via generating a finite map. The architecture starts by determining the cost and utility values of neighbouring cells around the robot using deterministic CME. Aquila Optimization technique is then incorporated to increase the overall solution accuracy. Algorithm validity and effectiveness was then validated utilizing different condition environment whose relative complexity was varied by varying parameters such as exploration space dimension and obstacle size, number and relative orientation. A perspective analysis is then performed to compare the performance of the proposed CME-AO algorithm with latest contemporary algorithms such as conventional CME and CME-WO (CME augmented Whale Optimizer). Results indicate efficacy of the proposed algorithm as it presents two distinct advantages a) enhanced map exploration in cluttered environment and b) significantly reduced computation complexity and execution time. This makes the suggested methodology particularly suitable for on-board utilization in an obstacle-cluttered environment, where other contemporary CME based techniques either fails (stuck locally) or takes longer exploration time.

This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithm's efficiency. The architecture, called the Multi-Agent Exploration-Parameterized Aquila Optimizer (MAE-PAO), starts by using deterministic MAE to assess the cost and utility values of nearby cells encircling the agents. A parameterized Aquila Optimizer is then used to further increase the exploration pace. The effectiveness of the proposed MAE-PAO methodology is verified through extended simulations in various environmental conditions. The algorithm viability is further evaluated by comparing the results with those of the contemporary CME-Aquila Optimizer (CME-AO) and the Whale Optimizer. The comparison adequately considers various performance parameters, such as the percentage of the map explored, the number of unsuccessful runs, and the time needed to explore the map. The comparisons are performed on numerous maps simulating different scenarios. A detailed statistical analysis is performed to check the efficacy of the algorithm. We conclude that the proposed algorithm's average rate of exploration does not deviate much compared to contemporary algorithms. The same idea is checked for exploration time. Thus, we conclude that the results obtained for the proposed MAE-PAO algorithm provide significant advantages in terms of enhanced map exploration with lower execution times and nearly no failed runs.

View Video Presentation: https://doi.org/10.2514/6.2023-1601.vid The paper presents a multi-objective optimization technique for the exploration of unknown space. In the context of robotics, exploration employees construction of the surrounding map using sensor information. Conventionally, for space exploration, the optimization is performed utilizing a single optimization technique with a particular objective function. Utilizing a mono objective function with a specific task of optimizing one particular aspect although simplifies and fasten the optimization process, but adversely effects the map accuracy and exploration depth. Realizing this vital aspect, this paper introduces an optimization technique with multi-objective functions which are simultaneously optimized. This not maximizes the search process but also increases the map accuracy. The proposed framework termed as Reinforced Whale Algorithm (RWO), is based on bio-inspired Whale Optimizer. It starts with the initialization of the whale's population, which are referred to as way-points. These way-points are assumed to be constant once they are set in the initial stage/iterations. The next step involves the position update from the non-dominated way-points catered by the robots. The waypoints are optimized by the algorithm. The performance matrices are carefully analyzed through extended simulations mimicking different conditions environment. After determining the isolated benefits of the algorithm, the results efficacy is then demon started by comparing its performance with three contemporary optimization algorithms namely Coordinated Multi-Robot Exploration (CME), and conventional Whale Optimizer (WO), and Arithmetic Optimizer (AO) integrated with CME. Results indicates that the proposed algorithm greatly enhance the optimization process by enhancing the explored area and reducing the search time.

This study introduces Adaptive Aquila Optimization Algorithm, designed particularly for Multi-Agent Space Exploration. By constructing a finite map, this enables the acquisition of a collision-free optimum mobility path in a barrier-filled environment. The proposed framework based upon Adaptive Aquila Optimizer (AAO) is a unique blend of deterministic Coordinated Multi-agent Exploration (CME) with a modified swarm-based technique namely Aquila optimizer (AO). The conventional Aquila optimization algorithm is dynamically modified to improve the rate of exploration by introducing stochastic parameters. The architecture, also known as the Coordinated Multi-robot Exploration Adaptive Aquila Optimizer (CME-AAO), begins by utilizing deterministic CME to determine the cost and utility values of neighbouring cells surrounding the agents. The rate of exploration is then further enhanced using adaptive Aquila optimization. The efficiency of the proposed CME-AAO algorithm was subsequently validated by performing wide range of simulations under various environmental conditions. The obtained output is then checked with contemporary algorithm CME-Aquila based algorithm. Results indicate that the proposed CME-AAO framework significantly improves map navigation in a congested environment in a comparatively shorter exploration time. This makes the proposed methodology ideal for use on-board in an dynamic environment, where conventional optimizer methods either fails or takes considerably longer duration to converge

This paper introduces Aquila Optimization Algorithm specifically configured for Multi-Robot space exploration that can be utilized for a wide range of operations. The proposed strategy incorporates a novel parallel communication protocol, to improve multi-robot space exploration process while minimizing the computation complexity. This ensures acquisition of a collision-free optimal motion in a barrier-filled environment via generating a finite explored map. The framework is a unique combination of both deterministic Coordinated Multi-robot Exploration (CME) and a swarm based methodology, known as Aquila Optimizer (AO). Combinely known as Coordinated Multi-robot Exploration Aquila Optimizer (CME-AO). The architecture starts by determining the cost and utility values of neighbouring cells around the robot using deterministic CME. Aquila Optimization technique is then incorporated to increase the overall solution accuracy. Numerous simulations under different environmental conditions were then performed to validate the effectiveness of the proposed CME-AO algorithm. A perspective analysis was then performed by comparing the performance of the CME-AO algorithm with latest contemporary algorithms namely conventional CME, CME Arithmetic Optimization Algorithm (CME-AOA) and Frequency Modified Hybrid-whale Optimization Algorithm (FMH-WOA). The comparison duly accommodates all pertinent aspects such as

Introducing a novel structure for a narrowband combline bandpass filter with Chebyshev's response. This filter is comprised of two discrete physical resonator configurations: microstrip and suspended substrate stripline, both accommodated on a single-layer substrate. The in-band insertion loss is observed to be minimal in simulation, attributed to the inclusion of four high-Q stripline resonators, while the introduction of two microstrip resonators facilitates the attainment of transmission zeros. This strategic design augments selectivity and affords the possibility of integrating the filter seamlessly with other components within the system. The passband covers a frequency range of 2. 1 to 2. 7 GHz. Preliminary experimental results obtained from Advanced Design System (ADS) are provided to authenticate the design approach.

Innovative design intricacies of new generation of UAVs, necessitate formulation of control laws utilizing intelligent techniques which are independent of underlying dynamic model besides being robust to changing environment. In current research, a novel control architecture is presented for maximizing glide range of the UAV which bears an unconventional design. To handle the control complexities emerging due to the unique design of the UAV, a distinct RL technique named ’optimal dynamic programming’ is proposed which besides being computationally acceptable also effectively controls the entire flight regime of the UAV. The proposed methodology has been specifically modified to configure the problem in continuous state and control space domains. The efficacy of the results and performance characteristics, demonstrated the ability of the presented algorithm to dynamically adapt to the changing environment, thereby making it suitable for UAV applications. Nonlinear simulations performed under different environmental conditions demonstrated the effectiveness of the proposed methodology over the conventional classical approaches.

In this research, an intelligent control architecture for an experimental Unmanned Aerial Vehicle (UAV) bearing unconventional inverted V-tail design, is presented. To handle UAV's inherent control complexities, while keeping them computationally acceptable, a variant of distinct Deep Reinforcement Learning (DRL) algorithm, namely Deep Deterministic Policy Gradient (DDPG) is proposed. Conventional DDPG algorithm after being modified in its learning architecture becomes capable of intelligently handling the continuous state and control space domains besides controlling the platform in its entire flight regime. Nonlinear simulations were then performed to analyze UAV performance under different environmental and launch conditions. The effectiveness of the proposed strategy is further demonstrated by comparing the results with the linear controller for the same UAV whose feedback loop gains are optimized by employing technique of optimal control theory. Results indicate the significance of the proposed control architecture and its inherent capability to adapt dynamically to the changing environment, thereby making it of significant utility to airborne UAV applications.

View Video Presentation: https://doi.org/10.2514/6.2022-1414.vid Space exploration refers to constructing a map with the aid of sensor data. This exploration is achieved utilizing a group of robots in an obstacle cluttered environment and distributing tasks amongst these robot(s). The robotic configuration is equipped with sensors to acquire data from the surroundings and to ensure collision-free motion. This paper presents a framework for the design of a Hybrid Stochastic Optimizer (HSO) for multi-robot space exploration. The proposed algorithm augments deterministic Coordinated Multi-Robot Exploration (CME) and stochastic Arithmetic Optimization (AO) techniques for maximizing the utility. The framework initially utilizes deterministic CME to ascertain the cost and utility values of adjacent cells around robot(s). The overall solution accuracy is then improved utilizing the Arithmetic Optimization algorithm. The proposed utilization of hybrid is interpreted that the algorithm starts with deterministic technique and continues off with stochastic method until the required improved solution with the desired accuracy is achieved. The effectiveness of the proposed Hybrid Stochastic Optimizer is then ascertained by training the multi-robotic framework in different complexity environmental conditions. The results efficacy is demonstrated by comparing the results of the proposed methodology with those achieved from conventional CME methods. Results indicate that the proposed HSO algorithm greatly enhanced the space exploration process as it gives enhanced area coverage apart from reduced search time.

This work offers an overview of the effective communication techniques for space exploration of ground, aerial, and underwater vehicles. We not only comprehensively summarize the trajectory planning, space exploration, optimization, and other challenges encountered but also present the possible directions for future work. Because a detailed study like this is uncommon in the literature, an attempt has been made to fill the gap for readers interested in path planning. This paper also includes optimization strategies that can be used to implement terrestrial, underwater, and airborne applications. This study addresses numerical, bio-inspired, and hybrid methodologies for each dimension described. Throughout this study, we endeavored to establish a centralized platform in which a wealth of research on autonomous vehicles (on the land and their trajectory optimizations), airborne vehicles, and underwater vehicles, is published.

View Video Presentation: https://doi.org/10.2514/6.2022-1416.vid Multi-robots configuration provides an effective means for space exploration in an obstacle cluttered environment. Recent progression in research involves a significant work in the field of robotics path planning. Advanced algorithms compute these trajectories utilizing two or more techniques. One such approach involves integration of deterministic Coordinated Multi-robot Exploration and meta-heuristic Whale Optimizer, collectively referred as stochastic method/exploration. This research presents an improved version of stochastic exploration namely Frequency Modified Hybrid Whale Optimization Algorithm (FMH-WOA), in which instead of using conventional whale algorithm, the stochastic parameters are dynamically tuned utilizing a frequency parameter. The frequency is adjusted to tune and optimize both the Exploitation and Exploration operators. Deterministic method derives the cost and utility determines the precedence of grid cell around robot. Stochastic optimizer then helps in improving the overall solution. The effectivity of the proposed FMH-WOA is validated under different environmental conditions. The results are then compared with conventional whale optimizer to demonstrate the improvements achieved in terms of enhanced area coverage in considerably less exploration time.

This study introduces Adaptive Aquila Optimization Algorithm, designed particularly for Multi Robot Search (MRS) of unknown space. The proposed framework based upon Adaptive Aquila Optimizer (AAO) is a unique blend of deterministic Coordinated Multiagent Exploration (CME) with a modified swarm-based technique namely Aquila optimizer (AO). The conventional Aquila optimization algorithm is dynamically modified to improve the rate of exploration by introducing stochastic parameters. The architecture, also known as the C oordinated M ulti-robot E xploration A daptive A quila O ptimizer (CME-AAO), begins by utilizing deterministic CME to determine the cost and utility values of neighbouring cells surrounding the agents. The rate of exploration is then further enhanced using adaptive Aquila optimization. The efficiency of the proposed CME-AAO algorithm was subsequently validated by performing wide range of simulations under various environmental conditions. The obtained output is then checked with contemporary algorithm CME-Aquila and CME-Whale Optimizer (CME-WOA) based algorithm. The comparison duly accommodates all pertinent aspects such as % area explored, number of failed runs, and time taken for map exploration for different environments. A statistical comparison with both CME-AO and CME-WOA is then carried out by performing multiple simulations under different environmental configurations. The mean and standard deviation of the (%) area explored and total time taken are then calculated. Results indicate that the proposed algorithm presents distinct advantages of enhanced map exploration in a considerably lesser execution time with almost no fail runs.