Evaluating the agility of adaptive command and control networks from a cyber complex adaptive systems perspective

Tran, Huy; Domerçant, Jean Charles; Mavris, Dimitri N.

doi:10.1177/1548512915592517

Cited by 13 publications

(7 citation statements)

References 51 publications

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“…Our results and findings support the gaps identified in Haghnevis's work by generally extending their mathematical modeling approach for engineered CASs to an agent‐based modeling and simulation approach 59 . Other agent‐based modeling and simulation research reported similar success in establishing a framework for evaluating CASs against different criteria, and this paper extends that research by including a structure within our framework to acquire goals from all stakeholders using KAOS in addition to only mission‐oriented stakeholders 60 . Lastly, this research contributes to the CAS body of knowledge for strengthening processes and means for realizing general and theoretical assertions 61 …”

Section: Discussionsupporting

confidence: 79%

A stakeholder framework for evaluating the ‐ilities of autonomous behaviors in complex adaptive systems

Douglas

Mazzuchi

Sarkani

2020

Systems Engineering

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Complex adaptive systems (CAS) exhibit emergent behaviors caused by the nonlinear actions between individual components within the system and their environment. These emergent behaviors are difficult to predict and measure making it very challenging to initially design from the start. Developing CASs, such as an autonomous swarming unmanned system, is a relevant key task today for solving hard problems with limited resources in a demanding economy. Swarming unmanned systems are valued by their unique capabilities, but how do stakeholders ensure their needs are considered during development? Certain stakeholders have interests that go beyond the initial delivery of the system, yet developers seem to focus efforts on designing autonomous behaviors to satisfy immediate mission needs. To close this gap, we propose a stakeholder focused framework for evaluating CASs over a full system life cycle for swarming unmanned systems. A goal‐oriented requirements engineering modeling methodology, Knowledge Acquisition in AutOmated Specification, is used to map requirements between stakeholder goals and autonomous behaviors. Behaviors are analyzed in an agent‐based simulation using NetLogo for quantifying stakeholder‐driven performance qualities (‐ilities). A case study was performed for a swarm of unmanned surface vessels called wave gliders to evaluate performance over the entire system life cycle.

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Section: Discussionsupporting

confidence: 79%

A stakeholder framework for evaluating the ‐ilities of autonomous behaviors in complex adaptive systems

Douglas

Mazzuchi

Sarkani

2020

Systems Engineering

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“…The N = 20 case denotes undamaged UAVs, and y(t) hovers around the theoretical value 1570, aligning with the actual scenario. Thus, the simulation results depicted in Figure 11 validate our proposed model and performance metric are suitable for surveillance tasks compared with reference [3,4,9,33,39]. In Figure 11, the N = 18 case is relatively closer to the undisturbed event (N = 20).…”

Section: Experiments Resultsmentioning

confidence: 58%

“…During the mission, each UAV collaborates with other UAVs through multihop wireless communication, continuously sharing information to successfully accomplish the surveillance mission. It also considers the possibility of the UAVs being attacked by the enemy with a probability [39]. To establish the initial topology of the swarm system, a scale-free network with a preferential attachment algorithm is utilized [9,33], with m 0 = 2 and m = 2.…”

Section: Experiments Descriptionmentioning

confidence: 99%

A Co-Adaptation Method for Resilience Rebound in Unmanned Aerial Vehicle Swarms in Surveillance Missions

Wei,

Zhang,

Zhang

2023

Drones

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An unmanned aerial vehicle (UAV) swarm is a fast-moving system where self-adaption is necessary when conducting a mission. The major causative factors of mission failures are inevitable disruptive events and uncertain threats. Given the unexpected disturbances of events and threats, it is important to study how a UAV swarm responds and enable the swarm to enhance resilience and alleviate negative influences. Cooperative adaptation must be established between the swarm’s structure and dynamics, such as communication links and UAV states. Thus, based on previous structural adaptation and dynamic adaptation models, we provide a co-adaptation model for UAV swarms that combines a swarm’s structural characteristics with its dynamic characteristics. The improved model can deal with malicious events and contribute to a rebound in the swarm’s performance. Based on the proposed co-adaptation model, an improved resilience metric revealing the discrepancy between the minimum performance and the standard performance is proposed. The results from our simulation experiments show that the surveillance performance of a UAV swarm bounces back to its initial state after disruptions happen in co-adaptation cases. This metric demonstrates that our model can contribute towards the swarm’s overall systemic resiliency by withstanding and resisting unpredictable threats and disruptions. The model and metric proposed in this article can help identify best practices in improving swarm resilience.

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“…25, No. 4, 2023 manner [30], prioritizing the rewiring of drones with a higher number of neighbors. In this paper, we adopt the initial swarm topology with scale-free networks used in [28,29].…”

Section: Mission Background and Experiments Settingsmentioning

confidence: 99%

Research on resilience model of UAV swarm based on complex network dynamics

Wei,

Zhang,

Zhang

2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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The resilience of UAV swarms mainly revolves around ensuring stable and uninterrupted operations. Malicious attacks can implement the adverse impacts of potential threats through swarm communication links. In this context, the SIS (Susceptible → Infected → Susceptible) method is suitable for describing the information transmission within UAV swarms. An enhanced resilience model of the UAV swarm is proposed in this study, which incorporates the factors of self-dynamics, dynamics of topology, dynamics of information transmission, and SIS into the complex network model. The model proposed in this paper has the capability to effectively capture changes in the network topology as well as the dynamics of the system. The average number of susceptible drones is utilized as the metric to evaluate the resilience of the swarm. Furthermore, an experiment is conducted where a UAV swarm successfully carries out a surveillance mission. The proposed model not only enables the support of mission planning but also facilitates the design enhancements of UAV swarms.

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Evaluating the agility of adaptive command and control networks from a cyber complex adaptive systems perspective

Cited by 13 publications

References 51 publications

A stakeholder framework for evaluating the ‐ilities of autonomous behaviors in complex adaptive systems

A stakeholder framework for evaluating the ‐ilities of autonomous behaviors in complex adaptive systems

A Co-Adaptation Method for Resilience Rebound in Unmanned Aerial Vehicle Swarms in Surveillance Missions

Research on resilience model of UAV swarm based on complex network dynamics

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