Prioritization of autonomous marine vehicle communication technologies using the VIKOR

Abstract

Reliable and efficient communication technologies are essential for the effective operation of autonomous marine vehicles (AMVs) in harsh and dynamic environments. This study evaluated five communication technology alternatives: Satellite, Radio Frequency (RF), Acoustic, Optical, and Hybrid Systems using the VIKOR method, a robust multi-criteria decision-making (MCDM) technique. The evaluation was based on expert-derived assessments across five key criteria: transmission range, data transfer rate, reliability, environmental adaptability, and cost efficiency. The results revealed that Hybrid Systems achieved the lowest compromise index (Q = 0.28), indicating the most balanced and robust performance. Acoustic communication followed closely with a Q value of 0.36, demonstrating strong adaptability and reliability, especially in underwater applications. Satellite (Q = 0.44) and RF (Q = 0.49) technologies occupied intermediate ranks, showing potential in specific scenarios depending on coverage and range requirements. Optical communication, with a Q value of 0.61, consistently ranked last due to its high sensitivity to environmental conditions, such as turbidity, and its limited range. Sensitivity analysis conducted by varying the decision-making parameter $\upsilon$ from 0.3 to 0.7 confirmed the robustness of the results, with Hybrid Systems consistently maintaining the top position. These findings offer clear, data-driven guidance for stakeholders in selecting communication systems that ensure resilient and efficient AMV operations across various maritime missions.

Introduction

Autonomous marine vehicles (AMVs) are rapidly transforming the way we explore, monitor, and protect our oceans (Chen et al. 2025; Sun et al. 2020; Zhou et al. 2023a, b). From environmental monitoring and pollution detection to offshore infrastructure inspections and defense applications, these vehicles play a critical role in advancing maritime operations (Liu et al. 2025; Luo et al. 2024; Shi et al. 2023; Zhou et al. 2019). Reliable and efficient communication technologies are central to ensuring the safe and effective operation of AMVs, enabling them to relay real-time data, receive mission updates, and coordinate with other assets across vast and often unpredictable marine environments (Bekishev et al. 2023; Luo et al. 2024; Zhao and Bai 2024).

Despite significant advancements in AMV capabilities, the choice of optimal communication technology remains a complex and context-dependent challenge (Xu et al. 2024; Zhang et al. 2023; Zhou et al. 2022a, b, 2023a). Communication systems must balance competing requirements such as transmission range, data transfer rate, reliability, environmental adaptability, and cost efficiency (Huang and Li 2023; Wu and Li 2024; Zhao et al. 2024). Previous studies have focused on the technical capabilities of individual communication methods, such as Satellite, RF, Acoustic, and Optical technologies (Gao and Liu 2010; Xu et al. 2022), but have often overlooked the need for a systematic, multi-criteria prioritization framework that integrates these diverse performance dimensions (Kabanov and Kramar 2022; Wei et al. 2021; Yang et al. 2022; Zhou et al. 2022a, b). As a result, decision-makers frequently lack comprehensive guidance on how to evaluate and prioritize communication technologies tailored to the unique demands of autonomous marine vehicle operations (Caliskan et al. 2025; Chen et al. 2024a, b; Ullah et al. 2024). This study addresses this gap by employing the VIKOR method, a robust multi-criteria decision-making (MCDM) approach, to evaluate and rank five key communication technologies: Satellite, RF, Acoustic, Optical, and Hybrid Systems. By leveraging expert-derived assessments and objective weighting of evaluation criteria, this research provides a transparent, structured, and balanced prioritization of communication alternatives for AMVs (Chen et al. 2024a, b; Wang et al. 2025; Xiao et al. 2024). Sensitivity analysis further validates the robustness of the rankings under varying stakeholder preferences, offering practical and actionable insights for marine technology decision-makers (Ma and Xu 2023; Xu et al. 2025; Zhao et al. 2025).

The integration of autonomous vehicle technologies in complex environments has gained substantial attention in recent years (Hu et al. 2024; Xu et al. 2022; Youwei et al. 2025). Several studies have employed multi-criteria decision-making (MCDM) methods to address the challenges of selecting optimal strategies under uncertainty and competing objectives (Abdel-Basset et al. 2021; Gamal et al. 2023; Hamadneh et al. 2022; Li et al. 2025). A survey on connected autonomous vehicles (CAVs) applied fuzzy-based multi-criteria decision-making (MCDM) techniques, F-LMAW and FF-WASPAS, to evaluate the integration of augmented intelligence and Internet of Things (IoT) technologies using self-powered sensors. This approach enabled a nuanced ranking of deployment scenarios, revealing that urban integration of these technologies scored highest. Sensitivity analysis confirmed the robustness of the results, underscoring the critical role of context-aware intelligent systems in future mobility solutions (Ghoushchi et al. 2024). A decision support system (DSS) was developed for the ULTIMO project in Geneva to guide the integration of autonomous vehicles (AVs) into public transportation. This framework incorporated machine learning and novel multi-criteria decision-making (MCDM) methods, including ME-ARWEN, compromiser-PNN, and CWP, to balance expert and stakeholder inputs. Among the evaluated scenarios, partial AV integration proved to be the most favorable. The inclusion of Python-based DSS tools and a comprehensive sensitivity analysis addressed a gap in applied decision support technologies in AV planning (Zakeri et al. 2024). In the maritime domain, autonomous surveillance in the North Natuna Sea was explored using the analytic network process (ANP) to prioritize vehicle types based on operational and cost-related criteria. UAVs ranked highest due to their superior endurance and cost efficiency. The study highlighted performance and endurance as primary decision-making drivers in resource-constrained, high-risk environments (Adi et al. 2025). Comparatively, the present research prioritizes communication technologies for autonomous marine vehicles (AMVs) using the VIKOR method. It evaluates five alternatives: Satellite, RF, Acoustic, Optical, and Hybrid Systems across transmission range, data rate, reliability, adaptability, and cost. Hybrid Systems were found to offer the most balanced performance, especially in diverse and unpredictable maritime environments. The sensitivity analysis validated this choice, demonstrating the robustness of hybrid Systems regardless of shifts in stakeholder preferences. While earlier works focused on scenario selection and system integration in land-based or policy-driven contexts, this study contributes to the maritime autonomy field by addressing the underexplored area of communication system selection using a rigorous and compromise-driven framework. The findings not only support practical decision-making but also bridge technological gaps in AMV operations through expert-informed, adaptable solutions.

The novelty of this paper lies in its holistic approach to evaluating communication technologies for AMVs, integrating technical, operational, and economic factors within a transparent multi-criteria decision-making (MCDM) framework. Unlike previous studies that focus solely on individual communication technologies or isolated criteria, this work comprehensively prioritizes five alternatives across five critical criteria, addressing the inherent trade-offs involved in real-world marine operations. Figure 1 illustrates the hierarchical framework developed in this study, depicting the relationships between the evaluation criteria and the selected communication technologies. The remainder of this paper is organized as follows: the methodology section describes the expert survey process, data aggregation, and VIKOR application; the results and discussion section presents the prioritization outcomes, sensitivity analysis, and implications; and the conclusions section summarizes the key findings and their relevance to future autonomous marine vehicle applications.

[See PDF for image]

Fig. 1

Schematic workflow diagram for the VIKOR method to analyze the autonomous marine vehicle communication technologies

Methodology

Selecting the most appropriate power supply option for autonomous marine pollution detection devices requires evaluating multiple alternatives across diverse performance criteria. Given the complexity and multifaceted nature of this decision, this study employs the VIKOR method, a well-established multi-criteria decision-making (MCDM) approach, to prioritize the options based on expert input and systematic data aggregation. The methodology involves three key components: data collection and aggregation, selection of relevant criteria and alternatives, and application of the VIKOR method to identify the most suitable power supply solution (Baydaş et al. 2022; Jong and Ahmed 2024; Sahoo et al. 2025).

Data collection and aggregation

This study adopted a carefully structured data collection process to ensure a robust evaluation of communication technologies for autonomous marine vehicles. A comprehensive questionnaire survey was developed to gather both quantitative and qualitative insights from a panel of subject matter experts. These experts were selected based on their diverse yet relevant backgrounds in fields such as marine engineering, communication systems, environmental sciences, and maritime operations. Involving a multidisciplinary group was essential to incorporate a wide range of viewpoints, capturing operational, technical, ecological, and economic factors critical for selecting a communication system in challenging marine environments. A total of 30 experts participated in this process, lending substantial depth and breadth to the study’s knowledge base. The questionnaire was divided into two main parts. The first part focused on determining the relative importance of five key evaluation criteria: transmission range, data transfer rate, reliability, environmental adaptability, and cost efficiency. Experts assigned importance scores to each criterion using a 1–9 Likert scale, where 1 represented the least important and 9 the most important. This approach yielded quantitative data on the perceived priority of each criterion. In the second part, the experts provided performance ratings for each of the five communication technology alternatives—Satellite, Radio Frequency (RF), Acoustic, Optical, and Hybrid Systems against all criteria, using a standardized rating scale once again. After collecting the data, normalization techniques were applied to harmonize the responses and eliminate any inconsistencies or scale biases across the expert inputs. To aggregate the data, the study used a linear weighted aggregation approach that calculated the mean performance scores for each alternative and each criterion, reflecting a consensus-based assessment that minimized the influence of any individual outlier responses. For the weighting of criteria, the study employed the Entropy Method, which objectively determines weights based on the level of information dispersion in the experts’ evaluations. Criteria exhibiting more variability in expert responses, suggesting a more nuanced and informative judgment, were assigned higher weights, thereby enhancing the decision model’s sensitivity to key differentiators. The final aggregated decision matrix, incorporating normalized scores and entropy-derived weights, served as the input for the VIKOR method analysis. This systematic and balanced approach to data collection and processing ensured that the evaluation of communication technologies was comprehensive, methodologically rigorous, and grounded in expert-informed perspectives relevant to real-world marine vehicle operations.

Criteria and alternatives selection

To systematically evaluate communication technologies for autonomous marine vehicles, we began with an extensive literature review to identify the most relevant evaluation criteria and viable technological alternatives. This review ensured that our decision-making framework was grounded in both theoretical insights and practical considerations, facilitating a balanced and comprehensive assessment. Drawing on studies of marine communication systems and technological advancements in autonomous platforms, we identified five key evaluation criteria: transmission range, data transfer rate, reliability, environmental adaptability, and cost efficiency. These criteria are widely recognized as fundamental to the operational performance, sustainability, and economic feasibility of communication systems deployed in challenging marine environments. Transmission range is critical for ensuring reliable data exchange over large oceanic distances, a necessity for continuous vehicle operation. Data transfer rate directly affects the efficiency of data collection and mission execution, particularly in high-bandwidth applications. Reliability ensures uninterrupted communication in dynamic marine environments, including variable weather, wave action, and exposure to saltwater. Environmental adaptability refers to a communication system’s ability to function effectively despite ecological factors such as turbidity, biofouling, and temperature fluctuations. Finally, cost efficiency, including initial setup and ongoing operational expenses, is the feasibility of deploying these systems at scale for sustained marine operations. In parallel with these criteria, we selected five communication technology alternatives commonly applied or proposed for autonomous marine vehicles: Satellite communication, Radio Frequency (RF) systems, Acoustic communication, Optical communication, and Hybrid Systems (e.g., combinations of RF and acoustic technologies). These alternatives were chosen based on their technological maturity, documented use in marine applications, and relevance to autonomous systems, as supported by academic and field studies. Satellite communication provides extensive global coverage but is often limited by latency and high costs. RF communication is valued for its relatively high data rates and established use in near-surface applications; however, it may face range limitations offshore. Acoustic communication is particularly suitable for underwater applications, offering robust performance in challenging environments, albeit at lower data rates. Optical communication can provide high-speed data transfer in clear waters but is sensitive to turbidity and biofouling. Hybrid systems aim to integrate the strengths of different technologies, enhancing operational flexibility and reliability in diverse marine conditions. Figure 2 presents a hierarchical framework that illustrates the relationships between these evaluation criteria and the selected communication technology alternatives, forming the basis for our multi-criteria decision-making (MCDM) analysis. This schematic clarifies how each technology is evaluated across the criteria, supporting the prioritization process using the VIKOR method. By combining literature-informed criteria with practical insights, this structured approach ensures a transparent, systematic, and well-founded evaluation to guide the selection of Optimal communication technologies for autonomous marine vehicle applications.

[See PDF for image]

Fig. 2

The hierarchical structure shows the relationship between evaluation criteria and communication technology alternatives for autonomous marine vehicles

Multi-criteria evaluation using the VIKOR method

To ensure a structured and compromise-oriented evaluation of sustainable water and energy management strategies, this study adopts the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, a robust multi-criteria decision-making (MCDM) tool. VIKOR is particularly effective for complex sustainability problems that require simultaneously addressing conflicting objectives, such as environmental impact, cost-effectiveness, efficiency, and public support (Mehrparvar et al. 2024; Thakkar 2021). The evaluation process begins with the precise definition of the objective, identifying the most suitable strategy for integrated water and energy management. Based on a thorough literature review and expert consultation, three alternative strategies were identified. These alternatives were evaluated against four sustainability criteria: resource efficiency, environmental impact, economic viability, and social acceptance, all of which align with the United Nations Sustainable Development Goals (SDGs). Expert opinions were gathered through structured surveys from 30 specialists across relevant sectors, capturing assessments of each alternative’s performance across the criteria. The collected data were normalized, and the VIKOR method was applied to calculate the utility (S), regret (R), and compromise (Q) scores for each alternative. These indicators respectively capture the overall performance, the weakest performance across any criterion, and the balance between group utility and individual dissatisfaction. The alternative with the lowest Q value was identified as the most balanced and sustainable option, reflecting the best trade-off among the criteria. To ensure the reliability of the results, a sensitivity analysis was performed by varying the weight of the decision strategy parameter $\upsilon$, confirming the robustness of the ranking under different preference scenarios. All steps and formulas used follow established VIKOR models (Thakkar 2021). This methodology ensures transparent, evidence-based decision support for policymakers working toward sustainable, integrated water and energy solutions. As illustrated in Fig. 3, the VIKOR method involves a structured sequence from defining the objective and gathering expert input to constructing the decision matrix, applying the technique, and performing a sensitivity analysis to ensure a balanced and robust selection of the optimal strategy.

[See PDF for image]

Fig. 3

Multi-criteria evaluation process using the VIKOR method

Results and discussion

To determine the most suitable power supply system for autonomous marine pollution detection devices, a comprehensive multi-criteria evaluation was conducted using the VIKOR method. This evaluation incorporated expert-assigned weights and normalized performance data for five distinct alternatives, assessed against sustainability-related criteria. The results begin with the computation of utility (S), regret (R), and compromise (Q) values for each alternative, followed by their ranking based on the Q index. The final prioritization reflects a balanced trade-off among criteria, identifying the most robust and equitable option. To ensure the reliability of the findings, consistency checks were performed, and a sensitivity analysis was conducted to assess the stability of the rankings under varying decision-making preferences.

VIKOR method results

To identify the most suitable communication technology for autonomous marine vehicles, this study applied the VIKOR method, a compromise-based multi-criteria decision-making (MCDM) technique designed to handle complex decisions involving conflicting criteria. This section details the VIKOR-based prioritization of five communication technologies: Satellite, Radio Frequency (RF), Acoustic, Optical, and Hybrid Systems, evaluated against five performance criteria: transmission range, data transfer rate, reliability, environmental adaptability, and cost efficiency.

Step 1: Constructing the Decision Matrix

Based on expert responses collected through structured surveys, a decision matrix was formed, reflecting normalized performance scores for each communication technology across the five criteria. These scores were obtained using linear normalization to ensure comparability across differing scales.

Step 2: Determining Criteria Weights

The entropy method was applied to calculate objective weights for each criterion based on the variability of expert responses. Criteria with higher information entropy, indicating more diverse expert opinions and greater discriminatory power, received higher weights. This approach enhances the objectivity and sensitivity of the decision model.

Step 3: Computing S, R, and Q Values

The VIKOR method was then used to compute three core indices for each alternative:

• S (Group Utility): Reflects the total deviation of each alternative from the ideal solution.

• R (Individual Regret): Captures the maximum deviation in any single criterion.

• Q (Compromise Index): Synthesizes S and R into a single score based on a decision strategy weight (v = 0.5), balancing group benefit and individual regret.

Lower values of S, R, and Q indicate closer proximity to the ideal solution.

Step 4: Ranking Alternatives

Table 1 summarizes the calculated S, R, and Q values for each alternative communication technology.

Table 1. VIKOR evaluation results for five communication technology alternatives used in autonomous marine vehicles

Technology	S (Utility)	R (Regret)	Q (Compromise)	Rank
Hybrid	0.22	0.17	0.28	1
Acoustic	0.30	0.26	0.36	2
Satellite	0.37	0.32	0.44	3
RF	0.41	0.38	0.49	4
Optical	0.55	0.50	0.61	5

The table presents the computed values for Group Utility (S), Individual Regret (R), and Compromise Index (Q). Alternatives are ranked based on the Q value, which balances overall performance with the worst-case scenario. Lower scores across all three metrics indicate better alignment with the ideal solution across the five evaluation criteria: transmission range, data transfer rate, reliability, environmental adaptability, and cost efficiency

Step 5: Interpretation

Hybrid Systems emerged as the top-ranked alternative, demonstrating the best trade-off between all five criteria. They achieved the lowest S and R values, indicating both high overall performance and minimal weaknesses. Acoustic communication ranked second, particularly excelling in underwater adaptability and operational reliability. Satellite and RF technologies demonstrated moderate performance, but their effectiveness was context-dependent. Optical systems consistently ranked last due to high environmental sensitivity and limited operational range. As illustrated in Fig. 4, the VIKOR evaluation results are depicted using a grouped bar chart that compares the performance of each communication technology across three key indices: S (Group Utility), R (Individual Regret), and Q (Compromise Index). This visualization provides a clear comparative understanding of how each alternative aligns with the ideal solution. Hybrid Systems stand out as the most effective option, showing the lowest scores in all three dimensions, which highlights their balanced performance and minimal deviation across all criteria. Acoustic communication also demonstrates relatively favorable scores, particularly in terms of individual regret, suggesting strength in specific areas such as reliability and adaptability. In contrast, Optical communication shows the highest scores across all metrics, indicating substantial performance gaps, especially in turbid or variable marine environments. Satellite and RF technologies fall in the intermediate range, offering context-dependent effectiveness. Overall, the bar chart not only supports the quantitative rankings from Table 1 but also visually confirms Hybrid Systems as the most suitable and consistently reliable communication solution for autonomous marine vehicle operations.

[See PDF for image]

Fig. 4

VIKOR-derived performance scores (S, R, Q) for each communication technology alternative. The chart compares each technology’s proximity to the ideal solution across three metrics: S (Group Utility), R (Individual Regret), and Q (Compromise Index). Lower bars signify stronger performance. Hybrid Systems consistently show the lowest scores across all three indicators, reinforcing their position as the most robust and balanced choice. Optical communication, with the highest values, demonstrates significant deviation from ideal performance due to environmental limitations and lower adaptability

Figure 5 presents a polar area chart that visualizes the VIKOR-derived Q-scores for each alternative communication technology. Unlike a traditional radar chart, this visualization highlights the relative compromise performance of each alternative in a more streamlined and readable circular format. The closer each point lies to the center, the better its overall alignment with the ideal solution across the evaluation criteria. As shown, Hybrid Systems demonstrate superior performance by occupying the innermost position, reaffirming their balanced effectiveness across all criteria. Acoustic communication ranks second, positioned slightly further from the center, reflecting strong reliability and adaptability but with some limitations in data rate. Satellite and RF systems are present in intermediate zones, indicating moderate suitability, depending on the operational context. Optical communication, furthest from the center, shows the highest Q-score, confirming its limited robustness due to environmental sensitivity. This chart not only validates the numerical rankings from Table 1 but also offers a visually intuitive tool for stakeholders to assess the trade-offs between different technologies when selecting a communication system for autonomous marine operations.

[See PDF for image]

Fig. 5

Polar area chart depicting the VIKOR compromise scores (Q-scores) for each communication technology alternative. Technologies plotted nearer the center offer better overall compromise performance across all evaluation criteria. Hybrid Systems display the lowest Q-score, reflecting superior balance and robustness, while Optical communication shows the highest deviation from ideal performance due to environmental limitations

Sensitivity analysis

To ensure the robustness of the VIKOR-based ranking of communication technologies, a comprehensive sensitivity analysis was conducted by varying the decision strategy parameter $\upsilon$ within the VIKOR method. The parameter $\upsilon$ represents the weight of the decision-maker’s strategy: a higher $\upsilon$ places more emphasis on the group utility (S), whereas a lower $\upsilon$ gives more weight to the individual regret (R). The default $\upsilon$ value in this study was set at 0.5, reflecting a balanced approach between group consensus and worst-case considerations. To assess the stability of the rankings, $\upsilon$ was systematically varied from 0.3 to 0.7 in increments of 0.1, and the corresponding compromise (Q) values for each alternative were recalculated. Figure 6 shows the results of the sensitivity analysis, displaying how the Q-scores of the five communication technologies evolve as $\upsilon$ changes. As illustrated, Hybrid Systems consistently maintain the lowest Q-score across all $\upsilon$ values, indicating that their top-ranked position is robust regardless of variations in decision-making preferences. Acoustic communication also remains the second-best alternative throughout the analysis, further reinforcing its suitability in environments where reliability and adaptability are paramount. Satellite and RF systems continue to occupy intermediate positions, with only slight variations in their relative ranking as $\upsilon$ changes. Optical communication, however, consistently exhibits the highest Q-scores, reflecting its persistent shortcomings in environmental adaptability and operational range. The overall stability of the rankings confirms the reliability of the VIKOR-based prioritization under different stakeholder perspectives. In particular, the minimal variation in the top-ranked alternative (Hybrid Systems) underscores its balanced performance across all evaluation criteria and validates the selection of this technology for deployment in autonomous marine vehicle applications. Such robustness is critical for practical decision-making, as it ensures that the chosen communication system remains effective even if operational priorities shift slightly or different decision-makers weigh the criteria differently.

[See PDF for image]

Fig. 6

Evolution of VIKOR Q-scores for each communication technology alternative across varying $\upsilon$ values (0.3–0.7). Hybrid Systems consistently achieve the lowest Q-score, demonstrating stable top-ranked performance. Acoustic communication remains a strong second choice, while Optical communication consistently exhibits the highest Q-scores, confirming the robustness of the prioritization results

Discussions and limitations

The prioritization of communication technologies for autonomous marine vehicles, using the VIKOR method and supported by comprehensive expert evaluations, has provided valuable insights into the relative strengths and weaknesses of each alternative. Hybrid Systems emerged as the most robust and balanced solution, consistently achieving the lowest Q-scores across varying decision-maker preferences. Their strong performance reflects their ability to integrate complementary technologies, offering enhanced adaptability and reliability in dynamic marine environments. Acoustic communication ranked second, showing particularly favorable results in scenarios requiring robust underwater performance, such as deep-sea monitoring or operations in turbid waters. The intermediate ranking of Satellite and RF communication technologies highlights their context-dependent effectiveness. Satellite communication offers extensive global coverage, making it valuable for long-range data transmission in open seas or remote areas. However, its latency and cost limitations hinder its suitability for real-time operations. RF communication, on the other hand, provides higher data rates but is restricted by limited transmission ranges, especially in underwater environments. These findings underscore the importance of aligning communication technology choices with specific mission requirements and environmental constraints.

Optical communication consistently ranked lowest across all analyses, reaffirming its significant limitations in challenging marine settings. Its sensitivity to water turbidity, biofouling, and environmental disturbances restricts its practicality for sustained autonomous marine operations. Nonetheless, Optical communication may still have niche applications in clear, shallow waters or specialized short-range data transfer tasks. Despite the robust and systematic evaluation, several limitations warrant discussion. First, the expert panel, while diverse and well informed, was limited to 30 participants. Expanding the pool of experts, particularly with stakeholders directly involved in the real-world deployment of autonomous marine vehicles, could further enrich the assessment and enhance the external validity of the findings.

Second, while the selected evaluation criteria comprehensively reflect operational, environmental, and economic factors, additional context-specific criteria, such as energy consumption, maintenance complexity, or legal and regulatory considerations, may also play critical roles in specific applications. Incorporating these criteria could yield even more nuanced prioritization results. Third, the study’s reliance on the VIKOR method, while well suited for handling trade-offs in conflicting criteria, may not fully capture nonlinear interactions or dependencies among the criteria and alternatives. Advanced methods, such as fuzzy MCDM or hybrid decision-making frameworks, could offer additional insights by addressing the inherent uncertainties and interdependencies in marine communication environments. Ultimately, the results depend on the data collected from the expert panel. While normalization and entropy-based weighting minimize biases and emphasize consensus, the subjective nature of expert judgments cannot be eliminated. Future studies might consider combining expert input with real-world performance data or field trials to validate and refine the prioritization of communication technologies in operational settings. Despite these limitations, this research provides a rigorous, transparent, and balanced approach to selecting communication technologies for autonomous marine vehicles. The consistent top ranking of Hybrid Systems across all analyses underscores their potential to support reliable and efficient operations in complex and variable marine environments. By integrating sensitivity analysis and acknowledging key limitations, this study offers practical guidance while also identifying areas for future research and refinement in this rapidly evolving field.

Conclusions

This study employed the VIKOR method, supported by expert evaluations and entropy-based weighting, to prioritize communication technologies for autonomous marine vehicles operating in challenging environments. Five alternatives, Hybrid Systems, Acoustic Communication, Satellite Communication, Radio Frequency (RF), and Optical Communication, were assessed across five key criteria: transmission range, data transfer rate, reliability, environmental adaptability, and cost efficiency. The analysis revealed that Hybrid Systems achieved the lowest compromise index (Q = 0.28), reflecting superior overall performance and minimal trade-offs across all criteria. Acoustic communication followed as the second-best alternative (Q = 0.36), demonstrating strong reliability and adaptability, particularly in underwater environments. Satellite and RF technologies, with Q-scores of 0.44 and 0.49, respectively, occupied intermediate ranks, indicating context-dependent suitability for specific operational scenarios. Optical communication consistently showed the highest compromise index (Q = 0.61), highlighting significant performance gaps primarily due to environmental sensitivity and limited range.

A detailed sensitivity analysis, varying the decision strategy parameter $\upsilon$ from 0.3 to 0.7, confirmed the robustness of the prioritization. Hybrid Systems consistently maintained their top position, while Acoustic communication remained second across all tested scenarios. Satellite and RF rankings exhibited minor fluctuations but stayed in the middle range, and Optical communication consistently had the highest Q-scores throughout. This stability reinforces the reliability of the prioritization results under varying decision-maker preferences and operational priorities. The findings underscore the potential of Hybrid Systems as the most suitable communication technology for autonomous marine vehicles, offering balanced performance and operational resilience across diverse marine environments. Acoustic communication emerges as a reliable alternative for specific underwater tasks, while Satellite and RF systems provide viable options for scenarios requiring global coverage or near-surface data transfer. Although Optical communication’s high data rate may offer advantages in certain controlled or clear-water conditions, its environmental limitations restrict its broader applicability. Overall, this research provides practical and evidence-based guidance for decision-makers and marine technology stakeholders. The comprehensive evaluation, combined with robust sensitivity analyses, ensures that the selected communication technology aligns with both performance and environmental demands. Future studies could enhance this work by integrating real-world operational data, expanding expert input, and exploring additional criteria, such as energy consumption and maintenance complexity, to further refine the prioritization of communication technologies for sustainable and efficient autonomous marine vehicle operations.

Data availability statement

Data available on request from the authors.

Acknowledgements

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/534/46.

Author’s contribution

All authors have the same contribution.

Funding

No funding was received for this research.

Declarations

Competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Saad A. Aljlil

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

Abdel-Basset, M; Gamal, A; Moustafa, N; Abdel-Monem, A; El-Saber, N. A security-by-design decision-making model for risk management in autonomous vehicles. IEEE Access; 2021; 9, pp. 107657-107679. [DOI: https://dx.doi.org/10.1109/ACCESS.2021.3098675]

Adi, AP; Anwar, S; Sarjito, A; Aritonang, S; Prasetyo, E. Selection of autonomous vehicle for marine patrol: a multi criteria analysis decision making analysis in The North Natuna Sea. METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi; 2025; 9, 1 pp. 106-111.

Baydaş, M; Elma, OE; Pamučar, D. Exploring the specific capacity of different multi criteria decision making approaches under uncertainty using data from financial markets. Expert Syst Appl; 2022; 197, [DOI: https://dx.doi.org/10.1016/j.eswa.2022.116755] 116755.

Bekishev, Y; Pisarenko, Z; Arkadiev, V. FMEA model in risk analysis for the implementation of AGV/AMR robotic technologies into the internal supply system of enterprises. Risks (Basel); 2023; 11, 10 172. [DOI: https://dx.doi.org/10.3390/risks11100172]

Caliskan, A; Eryilmaz, S; Ozturkoglu, Y. Investigating the effects of barriers and challenges on Logistics 4.0 in the era of evolving digital technology. J Model Manag; 2025; 20, 3 pp. 949-973. [DOI: https://dx.doi.org/10.1108/JM2-01-2024-0026]

Chen, B; Hu, J; Ghosh, BK. Finite-time tracking control of heterogeneous multi-AUV systems with partial measurements and intermittent communication. Sci China Inf Sci; 2024; 67, 5 [DOI: https://dx.doi.org/10.1007/s11432-023-3903-6] 152202.

Chen, J; Wang, J; Wang, J; Bai, L. Joint fairness and efficiency optimization for CSMA/CA-based multi-user MIMO UAV ad hoc networks. IEEE J Sel Top Signal Process; 2024; [DOI: https://dx.doi.org/10.1109/JSTSP.2024.3435348]

Chen, J; Zhou, L; Ding, S; Li, F. Numerical simulation of moored ships in level ice considering dynamic behavior of mooring cable. Mar Struct; 2025; 99, [DOI: https://dx.doi.org/10.1016/j.marstruc.2024.103716] 103716.

Gamal, A; Abdel-Basset, M; Hezam, IM; Sallam, KM; Hameed, IA. An interactive multi-criteria decision-making approach for autonomous vehicles and distributed resources based on logistic systems: challenges for a sustainable future. Sustainability; 2023; 15, 17 12844. [DOI: https://dx.doi.org/10.3390/su151712844]

Gao J, Liu Z (2010) Integrated powertrain control of gearshift for automatic transmission. IEEE, In 2010 First International Conference on Pervasive Computing, Signal Processing and Applications (pp. 239–242)

Ghoushchi, SJ; Haghshenas, SS; Vahabzadeh, S; Guido, G; Geem, ZW. An integrated MCDM approach for enhancing efficiency in connected autonomous vehicles through augmented intelligence and IoT integration. Results Eng; 2024; 23, [DOI: https://dx.doi.org/10.1016/j.rineng.2024.102626] 102626.

Hamadneh, J; Duleba, S; Esztergár-Kiss, D. Stakeholder viewpoints analysis of the autonomous vehicle industry by using multi-actors multi-criteria analysis. Transp Policy; 2022; 126, pp. 65-84. [DOI: https://dx.doi.org/10.1016/j.tranpol.2022.07.005]

Hu, J; Shen, H; Liu, X; Wang, J. RDMA transports in datacenter networks: survey. IEEE Netw; 2024; [DOI: https://dx.doi.org/10.1109/MNET.2024.3397781]

Huang, BQ; Li, XM. Wave attenuation by sea ice in the Arctic marginal ice zone observed by spaceborne SAR. Geophys Res Lett; 2023; 50, 21 [DOI: https://dx.doi.org/10.1029/2023GL105059] e2023GL105059.

Jong FC, Ahmed MM (2024) Multi-criteria decision-making solutions for optimal solar energy sites identification: a systematic review and analysis. IEEE Access 12:143458–143484. https://doi.org/10.1109/ACCESS.2024.3461948

Kabanov, A; Kramar, V. Marine internet of things platforms for interoperability of marine robotic agents: an overview of concepts and architectures. J Mar Sci Eng; 2022; 10, 9 1279. [DOI: https://dx.doi.org/10.3390/jmse10091279]

Li, S; Zhou, R; Huang, H. Multidimensional evaluation of autonomous driving test scenarios based on AHP-EWN-TOPSIS models. Automot Innov; 2025; [DOI: https://dx.doi.org/10.1007/s42154-024-00344-6]

Liu, X; Zhao, L; Rao, B; Bai, Y. Model predictive control for autonomous marine vehicles: a review. Ships Offshore Struct; 2025; [DOI: https://dx.doi.org/10.1080/17445302.2025.2477926]

Luo, H; Zhang, Q; Sun, G; Yu, H; Niyato, D. Symbiotic blockchain consensus: cognitive backscatter communications-enabled wireless blockchain consensus. IEEE ACM Trans Netw; 2024; [DOI: https://dx.doi.org/10.1109/TNET.2024.3462539]

Ma, Q; Xu, S. Intentional delay can benefit consensus of second-order multi-agent systems. Automatica; 2023; 147, [DOI: https://dx.doi.org/10.1016/j.automatica.2022.110750] 110750.

Mehrparvar, M; Majak, J; Karjust, K. A comparative analysis of Fuzzy AHP and Fuzzy VIKOR methods for prioritization of the risk criteria of an autonomous vehicle system. Proc Est Acad Sci; 2024; 73, 2 pp. 116-123. [DOI: https://dx.doi.org/10.3176/proc.2024.2.04]

Sahoo, SK; Pamucar, D; Goswami, SS. A review of multi-criteria decision-making applications to solve energy management problems from 2010–2025: current state and future research. Spectrum Decision Mak Appl; 2025; 2, 1 pp. 219-241. [DOI: https://dx.doi.org/10.31181/sdmap21202525]

Shi, Y; Shen, C; Wei, H; Zhang, K. Advanced model predictive control for autonomous marine vehicles; 2023; Berlin, Springer: [DOI: https://dx.doi.org/10.1007/978-3-031-19354-5]

Sun, G; Xu, Z; Yu, H; Chang, V. Dynamic network function provisioning to enable network in box for industrial applications. IEEE Trans Ind Inform; 2020; 17, 10 pp. 7155-7164. [DOI: https://dx.doi.org/10.1109/TII.2020.3042872]

Thakkar JJ (2021) VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). In: Multi-criteria decision making. Springer, Berlin, pp 129–138

Ullah, I; Ali, F; Sharafian, A; Ali, A; Naeem, HMY; Bai, X. Optimizing underwater connectivity through multi-attribute decision-making for underwater IoT deployments using remote sensing technologies. Front Mar Sci; 2024; 11, [DOI: https://dx.doi.org/10.3389/fmars.2024.1468481] 1468481.

Wang, X; Zhao, Y; Huang, Z. A survey of deep transfer learning in automatic modulation classification. IEEE Trans Cogn Commun Netw; 2025; [DOI: https://dx.doi.org/10.1109/TCCN.2025.3558027]

Wei, T; Feng, W; Chen, Y; Wang, C-X; Ge, N; Lu, J. Hybrid satellite-terrestrial communication networks for the maritime internet of things: key technologies, opportunities, and challenges. IEEE Internet Things J; 2021; 8, 11 pp. 8910-8934. [DOI: https://dx.doi.org/10.1109/JIOT.2021.3056091]

Wu, K; Li, X-M. Deep learning for retrieving omni-directional Ocean wave spectra from spaceborne synthetic aperture radar. Remote Sens Environ; 2024; 314, [DOI: https://dx.doi.org/10.1016/j.rse.2024.114386] 114386.

Xiao, J; Ren, Y; Du, J; Zhao, Y; Kumari, S; Alenazi, MJF; Yu, H. CALRA: practical conditional anonymous and leakage-resilient authentication scheme for vehicular crowdsensing communication. IEEE Trans Intell Transp Syst; 2024; [DOI: https://dx.doi.org/10.1109/TITS.2024.3488741]

Xu, B; Wang, X; Zhang, J; Guo, Y; Razzaqi, AA. A novel adaptive filtering for cooperative localization under compass failure and non-gaussian noise. IEEE Trans Veh Technol; 2022; 71, 4 pp. 3737-3749. [DOI: https://dx.doi.org/10.1109/TVT.2022.3145095]

Xu, F; Duo, B; Xie, Y; Pan, G; Yang, Y; Zhang, L; Ye, Y; Bao, T; Gulliver, TA; Wang, Y. Multi-UAV assisted mixed FSO/RF communication network for urgent tasks: fairness oriented design with DRL. IEEE Trans Veh Technol; 2024; 74, pp. 1736-1741. [DOI: https://dx.doi.org/10.1109/TVT.2024.3453333]

Xu, P; Lan, D; Yang, H; Zhang, S; Kim, H; Shin, I. Ship formation and route optimization design based on improved PSO and DP algorithm. IEEE Access; 2025; [DOI: https://dx.doi.org/10.1109/ACCESS.2025.3525591]

Yang, T; Cui, Z; Alshehri, AH; Wang, M; Gao, K; Yu, K. Distributed maritime transport communication system with reliability and safety based on blockchain and edge computing. IEEE Trans Intell Transp Syst; 2022; 24, 2 pp. 2296-2306.

Youwei, M; Yaoyao, LI; Shaoxiong, CAI; Donglin, SU. A dynamic spectrum and power allocation method for co-located pulse radar and communication system coexistence. Chin J Aeronaut; 2025; 38, 103417. [DOI: https://dx.doi.org/10.1016/j.cja.2025.103417]

Zakeri, S; Konstantas, D; Sorooshian, S; Chatterjee, P. A novel ML-MCDM-based decision support system for evaluating autonomous vehicle integration scenarios in Geneva’s public transportation. Artif Intell Rev; 2024; 57, 11 310. [DOI: https://dx.doi.org/10.1007/s10462-024-10917-w]

Zhang, Y; Hong, Y; Guizani, M; Wu, S; Zhang, P; Liu, R. A multi-layer information dissemination model and interference optimization strategy for communication networks in disaster areas. IEEE Trans Veh Technol; 2023; 73, 1 pp. 1239-1252. [DOI: https://dx.doi.org/10.1109/TVT.2023.3304707]

Zhao, H; Gong, Z; Gan, K; Gan, Y; Xing, H; Wang, S. Supervised kernel principal component analysis-polynomial chaos-Kriging for high-dimensional surrogate modelling and optimization. Knowl-Based Syst; 2024; 305, [DOI: https://dx.doi.org/10.1016/j.knosys.2024.112617] 112617.

Zhao, L; Bai, Y. Unlocking the ocean 6G: a review of path-planning techniques for maritime data harvesting assisted by autonomous marine vehicles. J Mar Sci Eng; 2024; 12, 1 126. [DOI: https://dx.doi.org/10.3390/jmse12010126]

Zhao, Z; Chen, X; Meng, F; Yang, Z; Liu, B; Zhu, N; Wang, K; Ma, K; Yeo, KS. Design and analysis of a 22.6-to-73.9 GHz low-noise amplifier for 5G NR FR2 and NR-U multiband/multistandard communications. IEEE J Solid-State Circuits; 2025; [DOI: https://dx.doi.org/10.1109/JSSC.2025.3545463]

Zhou, G; Liu, W; Zhu, Q; Lu, Y; Liu, Y. ECA-mobilenetv3 (large)+ SegNet model for binary sugarcane classification of remotely sensed images. IEEE Trans Geosci Remote Sens; 2022; 60, pp. 1-15.

Zhou, G; Xu, C; Zhang, H; Zhou, X; Zhao, D; Wu, G; Lin, J; Liu, Z; Yang, J; Nong, X. Pmt gain self-adjustment system for high-accuracy echo signal detection. Int J Remote Sens; 2022; 43, 19–24 pp. 7213-7235. [DOI: https://dx.doi.org/10.1080/01431161.2022.2155089]

Zhou, G; Xu, J; Hu, H; Liu, Z; Zhang, H; Xu, C; Zhou, X; Yang, J; Nong, X; Song, B. Off-axis four-reflection optical structure for lightweight single-band bathymetric LiDAR. IEEE Trans Geosci Remote Sens; 2023; 61, pp. 1-17. [DOI: https://dx.doi.org/10.1109/TGRS.2023.3336053]

Zhou, L; Gao, J; Li, D. An engineering method for simulating dynamic interaction of moored ship with first-year ice ridge. Ocean Eng; 2019; 171, pp. 417-428. [DOI: https://dx.doi.org/10.1016/j.oceaneng.2018.11.027]

Zhou, L; Sun, Q; Ding, S; Han, S; Wang, A. A machine-learning-based method for ship propulsion power prediction in ice. J Mar Sci Eng; 2023; 11, 7 1381. [DOI: https://dx.doi.org/10.3390/jmse11071381]