Author: Denis Avetisyan
This review explores how to protect sensitive data and maintain reliable control in unmanned aerial vehicle networks facing potential eavesdropping.
A systematic analysis of physical layer security techniques for sensing-communication-computing-control closed-loop systems, optimizing resource allocation to maximize performance and minimize information leakage.
The increasing reliance on wirelessly connected sensing, communication, computing, and control (SC3) systems introduces critical security vulnerabilities within closed-loop architectures. This paper, ‘Physical Layer Security for Sensing-Communication-Computing-Control Closed Loop: A Systematic Security Perspective’, addresses this challenge by refining physical layer security (PLS) to safeguard SC3 loops against eavesdropping. A novel closed-loop negentropy (CNE) metric is maximized subject to security constraints via joint optimization of transmit power, bandwidth, and computing capabilities, yielding a globally optimal solution derived through Karush-Kuhn-Tucker (KKT) conditions and monotonic optimization. By demonstrating performance gains over link-level designs, this work begs the question: how can we further leverage closed-loop structures to build inherently secure and resilient SC3 systems?
The Inevitable Fracture: Anticipating Control System Failure
Conventional control systems, designed for predictable operational landscapes, increasingly face challenges from rapidly changing environments and escalating cyber threats. These systems, often centralized and reliant on static infrastructure, exhibit limited adaptability when confronted with unexpected disturbances-such as natural disasters or intentional disruptions-leading to performance degradation and potential failures. Moreover, their inherent architectural limitations create single points of failure, making critical infrastructure-power grids, water treatment facilities, and transportation networks-particularly vulnerable to malicious attacks. The reliance on legacy communication protocols and a lack of robust security measures further exacerbate these risks, demanding a paradigm shift toward more resilient and secure control architectures capable of operating effectively in dynamic and contested environments.
A novel control system architecture is presented, built upon a non-terrestrial network (NTN) foundation and operating within a closed Sensing, Communication, Computing, and Control (SC3) loop to significantly improve system robustness. This integrated design moves beyond traditional, often siloed, approaches by tightly coupling data acquisition, transmission, processing, and actuation. The SC3 loop enables continuous monitoring and adaptation, allowing the system to dynamically respond to changing conditions and potential disruptions. By embedding intelligence throughout the network, rather than relying on centralized control, the system minimizes single points of failure and enhances resilience against both environmental uncertainties and malicious interference. This architecture represents a paradigm shift toward self-regulating and highly dependable control systems for critical infrastructure and beyond.
The proposed control system architecture strategically integrates unmanned aerial vehicles (UAVs) and edge information hubs (EIHs) to forge a remarkably resilient and adaptable infrastructure. UAVs function as mobile sensing and communication relays, extending the reach of the control network to areas difficult or dangerous to access, while also providing dynamic vantage points for improved situational awareness. These aerial platforms collaborate with geographically distributed EIHs, which serve as localized processing centers capable of analyzing data, executing control algorithms, and maintaining connectivity even in the face of communication disruptions. This distributed approach minimizes single points of failure and allows the system to rapidly reconfigure itself in response to changing conditions or potential threats, effectively creating a self-healing and highly robust control network capable of operating reliably in complex and unpredictable environments.
Establishing dependable long-range control necessitates overcoming the limitations of terrestrial communication infrastructure, particularly in geographically dispersed or disaster-affected areas. This system addresses this challenge by integrating a satellite link as a pivotal component, creating a resilient communication pathway independent of localized failures. The satellite connection ensures continuous command and data transmission between the central control system, unmanned aerial vehicles (UAVs), and edge information hubs (EIHs), even when traditional networks are compromised. This capability is crucial for maintaining operational control over critical infrastructure, enabling remote adjustments, and facilitating real-time responses to dynamic events, regardless of location or external disruptions. The inherent broadcast capabilities of satellite communication also enhance system scalability and reduce reliance on point-to-point connections, providing a robust and adaptable solution for wide-area control applications.
Balancing Revelation: The Geometry of Secure Information Exchange
The proposed CNE Maximization approach prioritizes maximizing the rate of information exchange within a closed-loop system, subject to defined security parameters. This framework directly addresses security vulnerabilities by establishing a quantifiable constraint on task-relevant information leakage. The methodology doesn’t eliminate information flow, but rather regulates it, ensuring that the amount of potentially compromised data remains below a pre-defined threshold while still enabling effective system operation. This is achieved by formulating the problem as an optimization that balances communication efficiency with the need to minimize exposure of sensitive data, effectively creating a closed-loop security constraint on information dissemination.
Efficient and secure communication within the CNE Maximization framework is directly dependent on strategic Bandwidth Allocation and Power Allocation. Bandwidth allocation determines the rate at which information can be transmitted, influencing the speed of the closed-loop feedback. Simultaneously, Power Allocation controls the transmission range and signal strength, impacting both communication reliability and the potential for eavesdropping. Optimizing these two factors involves balancing the need for high data throughput with the constraints imposed by security requirements – specifically, minimizing information leakage to unauthorized parties. Insufficient bandwidth limits the responsiveness of the closed loop, while excessive transmission power increases the risk of interception. Therefore, a co-design approach to bandwidth and power allocation is crucial for achieving both performance and security goals within the system.
Implementation of the CNE Maximization approach leverages advanced optimization techniques to manage computational complexity. Specifically, Monotone Optimization (MO) theory and Polyblock Outer Approximation are employed to solve the resulting optimization problem. These methods yield an algorithmic complexity of O(log_2(1/\epsilon)) when applied to the superior channel case, where ε represents the desired accuracy. This logarithmic complexity ensures scalability as the required precision increases, making the approach practical for real-time implementation in resource-constrained environments. The combination of MO theory and Polyblock Outer Approximation provides a computationally efficient solution for maximizing information flow under security constraints.
The optimization problem central to CNE maximization is solved using Karush-Kuhn-Tucker (KKT) conditions, a set of necessary conditions for optimality in nonlinear programming. These conditions establish criteria that must be met at a solution for it to be considered optimal, encompassing stationarity, primal feasibility, and dual feasibility. Specifically, the Lagrangian function is constructed to incorporate both the objective function and the constraints, and its gradient with respect to the optimization variables must be zero at the optimal solution. Furthermore, the constraints themselves must be satisfied, and the associated dual variables (Lagrange multipliers) must be non-negative. Satisfying these KKT conditions guarantees a local optimum, providing a rigorous framework for determining the optimal bandwidth and power allocation strategies while adhering to the security constraints imposed by the CNE maximization approach.
The Shadow of Observation: Quantifying Eavesdropping Risk
The introduction of an eavesdropper into a closed-loop control system presents a substantial risk to system integrity and operational performance. An attacker capable of intercepting communication signals can gain knowledge of system states and control actions, potentially enabling malicious interference or manipulation. This compromised information can lead to inaccurate feedback, destabilizing the control loop and reducing the overall Closed-Loop Performance – measured by metrics such as settling time, overshoot, and steady-state error. Furthermore, the compromised data can be used to reconstruct sensitive system parameters, facilitating more sophisticated attacks and potentially leading to complete system failure or unintended behavior. The severity of the threat is directly proportional to the amount of information leaked to the eavesdropper and the criticality of the controlled process.
The CNE Maximization framework addresses security vulnerabilities by integrating a Closed-Loop Security Constraint directly into the optimization process. This constraint functions by mathematically limiting the amount of information an eavesdropper can reliably decode from the transmitted signal. Specifically, the framework minimizes the mutual information between the transmitted signal and the signal received by the eavesdropper, thereby reducing the potential for unauthorized access to control data. This is achieved through adjustments to transmission parameters, such as power allocation and coding schemes, all while maintaining desired control system performance. The constraint ensures that security is not treated as an afterthought but is considered concurrently with communication reliability and energy efficiency during the optimization of the entire control loop.
The efficacy of mitigating eavesdropping threats within the CNE Maximization framework is fundamentally determined by the Channel Gain, a metric representing the signal strength and quality between the transmitter and receiver. A higher Channel Gain indicates a stronger, clearer signal, reducing the information leakage to an eavesdropper and thus enhancing security. Conversely, a lower Channel Gain, potentially caused by distance, interference, or obstructions, weakens the signal, increasing the potential for successful eavesdropping and degrading Closed-Loop Performance. The framework’s ability to limit leaked information is directly proportional to the Channel Gain; therefore, maintaining a strong and reliable signal is crucial for effective security and optimal system operation. H = \frac{P_{rx}}{P_{tx}}, where H represents the Channel Gain, P_{rx} is the received power, and P_{tx} is the transmitted power.
Physical Layer Security (PLS) techniques enhance system security by exploiting the inherent randomness of the physical communication channel, rather than relying solely on computational complexity. These techniques, such as artificial noise transmission and secure beamforming, introduce controlled interference to degrade the eavesdropper’s received signal while minimally impacting the legitimate receiver. By minimizing the information leaked to an eavesdropper, PLS directly improves Closed-Loop Performance metrics such as stability margins and tracking accuracy. Specifically, reducing the Channel Gain experienced by the eavesdropper – effectively lowering the Signal-to-Interference-plus-Noise Ratio (SINR) at the eavesdropper’s receiver – limits their ability to accurately reconstruct the control signal and disrupt the closed loop. This results in a quantifiable improvement in system robustness against eavesdropping attacks and a corresponding increase in achievable control performance.
The Inevitable Decay: Charting a Path Beyond Current Limitations
This research establishes the practical viability of a novel control system architecture founded on Non-Terrestrial Networks (NTNs) and optimized through Core Network Efficiency (CNE) maximization. By strategically allocating resources and prioritizing network pathways, the system achieves both robust security and efficient operation, even under challenging conditions. The demonstrated framework effectively mitigates potential vulnerabilities inherent in traditional control systems while simultaneously enhancing data transmission speeds and reducing latency – critical factors for real-time applications. This approach moves beyond conventional centralized control, leveraging the distributed nature of NTNs to create a more resilient and adaptable system capable of maintaining functionality even in the face of disruptions or attacks, paving the way for dependable control in diverse and demanding environments.
Investigations are now shifting towards broadening the applicability of this control framework to encompass more intricate operational landscapes. Future studies will prioritize adapting the system for deployment within multi-agent environments, where numerous independent entities must coordinate and collaborate under secure control. Simultaneously, research will address the challenges posed by dynamic environments – those characterized by unpredictable changes and disturbances – requiring the control system to exhibit enhanced adaptability and resilience. Successfully integrating these advancements will necessitate developing algorithms capable of managing increased computational complexity and maintaining robust performance even amidst environmental uncertainty, ultimately paving the way for deployment in real-world applications demanding sophisticated, responsive control.
The incorporation of machine learning offers significant potential to enhance the resilience of networked control systems. Current systems often react to security breaches or resource limitations; however, predictive algorithms can anticipate these challenges before they manifest. By analyzing historical data and real-time network conditions, machine learning models can forecast potential vulnerabilities and proactively adjust security protocols. Furthermore, adaptive resource allocation, guided by these predictive insights, allows the system to dynamically distribute bandwidth and computational power to critical components, ensuring continued operation even under stress. This shift from reactive to proactive control promises not only improved security but also optimized performance and increased robustness in complex and unpredictable environments, paving the way for more reliable automation in fields ranging from robotics to critical infrastructure.
The development of resilient control systems stands to benefit significantly from this research, promising advancements across diverse fields. Reliable operation in challenging conditions is paramount for applications like robotics, where autonomous navigation and manipulation require unwavering control; infrastructure management, demanding consistent performance of critical systems such as power grids and transportation networks; and environmental monitoring, necessitating dependable data collection from remote or hazardous locations. By bolstering the robustness of these control systems, this work paves the way for more efficient, safer, and sustainable solutions in a world increasingly reliant on automated and interconnected technologies, ultimately enhancing the dependability of essential services and facilitating proactive responses to unforeseen events.
The pursuit of optimized resource allocation within this SC3 closed-loop system echoes a fundamental truth about complex systems: control is, at best, a temporary illusion. This study, by attempting to maximize performance while minimizing information leakage, doesn’t so much establish security as navigate the inevitable entropy. As John McCarthy observed, “It is better to deal with reality than with the ideal.” The paper’s focus on the secrecy rate, and the trade-offs inherent in balancing communication with computational demands, illustrates this perfectly. Every dependency introduced-every bandwidth allocation, every computing resource assigned-is a promise made to the past, a constraint on future flexibility, and a potential avenue for disruption. The system will, eventually, start fixing itself-or failing in unpredictable ways-regardless of initial optimization efforts.
The Horizon Recedes
This work, focused on securing the feedback loop in a sensing-communication-computing-control system, inevitably reveals the limitations of attempting to define security. Optimizing secrecy rates, allocating bandwidth-these are merely tactical responses to a fundamentally stochastic environment. The system doesn’t become secure; it becomes more predictably vulnerable. Monitoring is the art of fearing consciously, and each optimized parameter is a prophecy of the next, unforeseen attack surface. The pursuit of perfect secrecy is a distraction from the inevitability of revelation.
Future investigations must abandon the premise of control. The true challenge lies not in preventing eavesdropping, but in designing systems that degrade gracefully under observation. Consider architectures where information leakage is not a failure, but a feature – a form of distributed consensus or even a necessary component of adaptation. The notion of a ‘closed loop’ implies a boundary that does not exist; signals bleed, intentions are inferred, and the environment responds.
True resilience begins where certainty ends. Rather than striving for impenetrable defenses, the field should prioritize the development of self-healing, polymorphic systems – those capable of reconfiguring themselves in the face of compromise. The objective isn’t to build a fortress, but to cultivate an ecosystem capable of absorbing, adapting to, and even benefiting from, the constant pressures of an adversarial landscape.
Original article: https://arxiv.org/pdf/2603.00943.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2026-03-04 03:55