Kemal Ihsan Kilic, Samir Maity, Inkyung Sung, Peter Nielsen
Maritime Search and Rescue (MSAR) operations face significant challenges due to high uncertainty, dynamic conditions, and resource constraints. Additionally, rigid organizational structures and hierarchical human-centered communication frameworks, fail to adapt to the challenging conditions of maritime environments. This paper provides a comprehensive review of the integration of Artificial Intelligence (AI) into MSAR operations, highlighting how AI can transform these systems through enhanced decision-making, real-time adaptability, decentralized autonomy, and resource optimization. Through analysis and synthesis, we identified and categorized key challenges in traditional SAR frameworks, such as inherent environmental and structural challenges. We discussed AI-driven solutions that offer efficient, autonomous, resilient, and decentralized coordination. Our thematic and statistical analysis of existing literature reveals significant research gaps, particularly regarding the holistic integration of AI across all SAR stages toward a decentralized fully autonomous paradigm shift. The paper also considers the technological challenges for the integration and adaptation of AI in SAR. By envisioning fully autonomous, AI-driven MSAR operations, this study sets the stage for future research and practical innovations, aiming to improve effectiveness and efficiency in maritime rescue efforts. ⢠Comprehensive Literature Review of AI over MSAR. ⢠Identification of Gaps and Key Challenges in MSAR. ⢠Proposed AI-Driven Solutions Strategies for MSAR. ⢠Trends and Future Directions through AI in MSAR.
Umar Draz, Tariq Ali, Sana Yasin, Muhammad Hasanain Chaudary ¡ 7 authors
In IoT-based underwater acoustic sensor networks (IoT-UASNs), critical challenges, such as inadequate node authentication mechanisms, unpredictable network topology, and ineffective localized node identification significantly hinder network performance. These challenges adversely impact node localization, route requests (RREQs) reconstruction at the beacon level, and lead to increased routing overhead, end-to-end delays, and unreliable data forwarding, thereby compromising underwater communication in critical areas of interest. Achieving reliable and efficient data transmission to sonobuoys while maintaining an optimal packet delivery ratio for localized nodes remains a significant priority. However, existing communication schemes often overlook the importance of authentication mechanisms, such as blockchain, in ensuring secure and efficient localization. Although previous research has focused mainly on generic localization strategies, the role of beacon-based localization, a key component in the formation of underwater networks, has been largely neglected. Despite the potential integration of beacon nodes with emerging technologies, such as blockchain, uncertainties persist about their ability to ensure secure underwater localization. This article addresses the pressing need to enhance the localization framework in AODV-based underwater networks by improving data delivery, optimizing RREQ and RREP processes, and minimizing both end-to-end delays and localization errors, thus paving the way for more reliable and secure underwater communication systems. In order to address these challenges, we first introduce a hybrid underwater localization communication framework based on the use of blockchain technology along with the proposed scheme, which is geomatric distance-based communication-based localization routing (CGDBLR). To establish the performance of this framework, we employ various node configurations and speeds with comparative experiments with state-of-the-art techniques. Our results show significant performance improvements for multiple parameters when integrating the blockchain in this context.
The increasing pervasiveness of digital infrastructures, also extending into marine domains, makes Underwater Wireless Sensor Networks (UWSNs) an essential tool for the development of novel marine sustainability and monitoring paradigms. Applications in sensitive scenarios may require data encryption, non-repudiation, and provenance tracking. Moreover, the broadcast nature of the underwater acoustic channel makes the task of identifying and authenticating nodes of critical importance. To meet such requirements, we introduce AquaID, a protocol for resource-constrained hardware that leverages Distributed Ledger Technologies (DLTs) and Decentralised Identities. It guarantees confidentiality, authentication, and integrity using low bandwidth and CPU usage, while supporting high scalability and interoperability. We validate our solution in a threefold manner: via embedded board implementation, network simulation, and sea trials using commercially-available acoustic modems and underwater nodes. We also include a cost comparison among possible DLT choices. Results show AquaID to be robust to scaling, achieving low authentication delays and overhead, thus proving suitable even for large deployments.
Transport Layer Security (TLS) protocol is a cryptographic protocol designed to secure communication over the internet. The TLS protocol has become a fundamental in secure communication, most commonly used for securing web browsing sessions. In this work, we investigate the TLSNotary protocol, which aim to enable the Client to obtain proof of provenance for data from TLS session, while getting as much as possible from the TLS security properties. To achieve such proofs without any Server-side adjustments or permissions, the power of secure multi-party computation (MPC) together with zero knowledge proofs is used to extend the standard TLS Protocol. To make the compliacted landscape of MPC as comprehensible as possible we first introduce the cryptographic primitives required to understand the TLSNotary protocol and go through standard TLS protocol. Finally, we look at the TLSNotary protocol in detail.
In some instances, intelligent, autonomous robotic systems (IARS) are transforming how the private and public sectors provide emergency services, deliver products, protect national interests, and accomplish their missions. Some futures, however, envision missions performed by cooperative teams of lARS-commonly described as swarms-making the purposeful design of swarms as information processing and communication systems an imperative. Furthermore, more optimal swarm performance depends on the degree to which its design fits its environment. In the context of organizational information processing and transactive memory theories, this paper explores the degree to which the centralization of decision-making and employment of distributed expertise may impact mission performance. Against the backdrop of collecting information on grey whale behaviors and migration routes, three swarm designs, starling, hive, and wolf-pack, are modeled and their simulated performance compared. The wolf-pack design, which is characterized by a largely decentralized decision-making structure and differentiated transactive memory system, out-performed the other designs based upon total work volumes and mission durations. Future studies should empirically investigate the impacts of cognitive slack and other swarm characteristics on mission accomplishment in a broader spectrum of scenarios.
Distributed ledger technologies, together with AI, smart systems and robotics could provide a scalable and robust platform for the smart underwater and surface marine infrastructures of the close future. They will be harbours or ports, marine farms or remote tourism facilities - only accessible through robotic avatars - for protected areas or for the elders. Autonomous Marine Vehicles, IoT networks, and humans will coexist in highly heterogeneous multivendor multiplatform environments where market transactions and complex administrative procedures will be ubiquitous. Some blockchains such as the Ethereum network are able to provide distributed scalable computing and trustable functionalities, capable of managing both technical interactions and market transactions among very diversified autonomous agents. For these reasons they seem to provide a valuable backbone for the smart marine infrastructure of the coming decades. In this paper we outline our research and innovation strategy and present our results showing the potential benefits of a subsidiary architecture integrating distributed ledger technologies with swarms of autonomous surface robots implemented by a Belief Space Planning approach.
The Internet of Underwater Things (IoUT) has become widely popular in the past decade as it has huge prospects for the economy due to its applicability in various use cases such as environmental monitoring, disaster management, localization, defense, underwater exploration, and so on. However, each of these use cases poses specific challenges with respect to security, privacy, transparency, and traceability, which can be addressed by the integration of blockchain with the IoUT. Blockchain is a Distributed Ledger Technology (DLT) that consists of series of blocks chained up in chronological order in a distributed network. In this paper, we present a first-of-its-kind survey on the integration of blockchain with the IoUT. This paper initially discusses the blockchain technology and the IoUT and points out the benefits of integrating blockchain technology with IoUT systems. An overview of various applications, the respective challenges, and the possible future directions of blockchain-enabled IoUT systems is also presented in this survey, and finally, the work sheds light on the critical aspects of IoUT systems and will enable researchers to address the challenges using blockchain technology.
Abstract Offshore wind farms will play a vital role in the global ambition of net zero energy generation. Future offshore wind farms will be larger and further from the coast, meaning that traditional humanâbased operations and maintenance approaches will become infeasible due to safety, cost, and skills shortages. The use of remotely operated or autonomous robotic assistants to undertake these activities provides an attractive alternative solution. This paper presents an autonomous multirobot system which is able to transport, deploy and retrieve a wind turbine blade inspection robot using an unmanned aerial vehicle (UAV). The proposed solution is a fully autonomous system including a robot deployment interface for deployment, a mechatronic linkâhook module (LHM) for retrieval, both installed on the underside of a UAV, a mechatronic onâload attaching module installed on the robotic payload and an intelligent global mission planner. The LHM is integrated with a 2âDOF hinge that can operate either passively or actively to reduce the swing motion of a slung load by approximately 30%. The mechatronic modules can be coupled and decoupled by special maneuvers of the UAV, and the intelligent global mission planner coordinates the operations of the UAV and the mechatronic modules for synchronous and seamless actions. For navigation in the vicinity of wind turbine blades, a visualâbased localization merged with the location knowledge from Global Navigation Satellite System has been developed. A proofâofâconcept system was field tested on a fullâsize decommissioned windâturbine blade. The results show that the experimental system is able to deploy and retrieve a robotic payload onto and from a wind turbine blade safely and robustly without the need for human intervention. The vicinity localization and navigation system have shown an accuracy of 0.65 and 0.44 m in the horizontal and vertical directions, respectively. Furthermore, this study shows the feasibility of systems toward autonomous inspection and maintenance of offshore windfarms.
Animals team up to collectively address challenges they could not overcome individually. Several species self-organize into large groups to leverage vital behaviors such as foraging, construction, or predator evasion. Ants, for instance, find shortest paths to food resources by depositing pheromones, bees indicate direction and distance to flower meadows through waggle dances in the hive, and fish display evasive maneuvers to escape predators. These three examples illustrate a collective problem-solving ability that leverages the cognition and actions of individually limited organisms. With the advancement of robotics and automation, engineered multi-agent systems have been inspired to achieve similarly high degrees of scalable, robust, and adaptable autonomy through decentralized and dynamic coordination. Scientists have demonstrated ground-based collective transport, construction, and self-assembly, in some cases with several hundred robots. Multiple aerial swarms fly complex maneuvers, some of them even with little external assistance. Small robot teams, although with limited autonomy, have been engaged to assist in search and rescue missions at sea, sample oceanic data, and find unknown deep-sea species. Overall however, robot swarms have been most successfully demonstrated in two-dimensional (2D) space or with partial assistance from central controllers and external tracking. In addition, many more demonstrations of self-organized collectives exist above-ground as opposed to the less explored underwater domain, which is particularly challenging because it often precludes traditional communication methods such as radio and GPS signals. Few underwater swarms exist and achieve limited coordination complexity and scale because they rely on explicit message passing. In this dissertation, I introduce a novel underwater robot collective, the Blueswarm, which realizes full 3D spatiotemporal coordination without any external assistance. Each Bluebot is equipped with four independently controllable fins and two wide-angle lens cameras for 3D locomotion and perception. The vision system is complemented by three LEDs, which encode information about direction, distance and heading, and facilitate implicit coordination among robots. In the bioinspired design process, I pursued simplicity in both hardware and software to enable real-time onboard multi-robot tracking for local decision making followed by swift action. Blueswarm is the first 3D underwater collective that uses only local implicit vision-based coordination to self-organize. Inspired by the dynamic and agile coordination of fish, I show that complex and dynamic 3D collective behaviors â synchrony, aggregation-dispersion, dynamic circle formation, search-capture, and escape â can be achieved by sensing minimal, noisy impressions of neighbors without any centralized intervention. To the best of my knowledge, this is the first significant demonstration of unsupervised and autonomous 3D collective coordination underwater. Accompanied by a custom simulator, the Blueswarm platform gives researchers a much-needed tool to systematically develop and test algorithms for self-organzied 3D collective behaviors in the laboratory. The results of this dissertation provide insights into the power of implicit coordination and advance the potential for future underwater robots that display collective capabilities on par with fish schools for applications such as environmental monitoring and search in coral reefs and coastal environments. In addition, the Bluebots are also well suited as an experimental testbed for investigating natural collective behaviors and biomimicry, for example, studying the energy savings for different formations in schooling fish or the performance landscape of aquatic propulsion with a diverse set of caudal fins.
Ahmed Al Guqhaiman, Oluwatobi Akanbi, Amer Aljaedi, C. Edward Chow
Underwater Wireless Sensor Networks (UWSNs) are liable to malicious attacks due to limited bandwidth, limited power, high propagation delay, path loss, and variable speed. The major differences between UWSNs and Terrestrial Wireless Sensor Networks (TWSNs) necessitate a new mechanism to secure UWSNs. The existing Media Access Control (MAC) and routing protocols have addressed the network performance of UWSNs, but are vulnerable to several attacks. The secure MAC and routing protocols must exist to detect Sybil, Blackhole, Wormhole, Hello Flooding, Acknowledgment Spoofing, Selective Forwarding, Sinkhole, and Exhaustion attacks. These attacks can disrupt or disable the network connection. Hence, these attacks can degrade the network performance and total loss can be catastrophic in some applications, like monitoring oil/gas spills. Several researchers have studied the security of UWSNs, but most of the works detect malicious attacks solely based on a certain predefined threshold. It is not optimal to detect malicious attacks after the threshold value is met. In this paper, we propose a multi-factor authentication model that is based on zero-knowledge proof to detect malicious activities and secure UWSNs from several attacks.
Md Ashraf Uddin, Andrew Stranieri, Iqbal Gondal, Venki Balasurbramanian
The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including underwater monitoring, where sensors are located at various depths, and data must be transmitted to surface base stations for storage and processing. Ensuring that data transmitted across hierarchical sensor networks are kept secure and private without high computational cost remains a challenge. In this paper, we propose a multilevel sensor monitoring architecture. Our proposal includes a layer-based architecture consisting of Fog and Cloud elements to process and store and process the Internet of Underwater Things (IoUT) data securely with customized Blockchain technology. The secure routing of IoUT data through the hierarchical topology ensures the legitimacy of data sources. A security and performance analysis was performed to show that the architecture can collect data from IoUT devices in the monitoring region efficiently and securely.
Modern ocean exploration and sensing approaches have been mainly based on Autonomous Underwater Vehicles (AUVs), Remotely Operated Vehicles (ROVs), and/or static Underwater Acoustic Sensor Networks (UASNs) deployments. Individual AUVs and ROVs represent a single point of failure in addition to being bulky and expensive as vehicles are usually full-featured and sophisticated. UASNs have traditionally been statically deployed. This limits their use to original deployment locations and renders them unsuitable for search tasks. Swarm Robotics (SR) are a natural, better alternative. Swarms possess superior features over a sophisticated AUV; they are smaller, cheaper, robust, reliable, and scalable by design and definition. They also have the sensing capabilities of UASNs and built-in active mobility.\nDesigning successful swarm missions in harsh aquatic environments is an involved task. We address this by analyzing the indispensable stages of a typical mission and carefully designing decentralized algorithms to achieve the desired per-stage goals. Important system and environmental parameters are taken into consideration to achieve the end goal: completing mission requirements while respecting time constraint, with best possible performance and minimum loss of agents. Special attention is given to target search, task identification and allocation, and mission-stage integration due to their importance. Identifying target location in an unbounded environment is challenging. Bandwidth limited and intermittent communication complicates the process further. Therefore, we develop global search algorithms that use minimal communication and utilize flocking to maintain cohesion. These algorithms have multiple advantages over traditional ones in terms of convergence time, omni-directionality, consideration of physical constraints, and being self-bounding. At the target, tasks are autonomously identified and allocated in a completely decentralized manner. Validation of the developed techniques is done through realistic simulations and analytical comparisons.\nOur main contributions are: 1) a general framework for underwater mission planning, 2) three novel global search algorithms for unbounded underwater environment, 3) an algorithm for initial self-organization, 4) an optimized same-position reorientation algorithm for use in certain mission stages, 5) three autonomous task allocation algorithms, 6) three local target search algorithms, 7) a measure of mission utility, and 8) the design of a human brain-inspired model to support learning and complete autonomy.
Many tasks requiring multiple autonomous underwater vehicles (AUVs) are simple, with static goals, of short duration, and require few AUVs, often of the same type. Simple coordination mechanisms that assign roles to AUVs before the mission are sufficient for these multi-AUV systems. However, for tasks that are complex and dynamic, of long duration (implying that AUVs will come and go during the mission), and that have many heterogeneous AUVs, organization of the system will not work. In addition, due to changes in the situation, the system will likely need to be reorganized during the mission. We are developing a distributed, context-aware self-organization/reorganization scheme for advanced multi-AUV systems. This is a two-level approach in which a meta-level organization first self-organizes, assesses the context, and uses contextual knowledge to design a task-level organization appropriate for the context that can then carry out the mission. We are extending our prior work by distributing both the context assessment process and the organization design process. The result will be a system that can self-organize efficiently and effectively for its context and that can reorganize appropriately as the context changes.