Integrated Cognitive Sensing and AI-Enabled Communications for Autonomous Systems
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".
Deadline for manuscript submissions: closed (25 February 2025) | Viewed by 6309
Special Issue Editors
Interests: cognitive dynamic systems; adaptive and self-aware video processing, tracking and recognition; generative models and inference schemes based on hierarchical dynamic Bayesian networks, software and cognitive radio
Interests: cognitive and AI-enabled radios; wireless communications; UAV communications; V2X; self-awareness, dynamic Bayesian networks, active inference and artificial intelligence
Special Issue Information
Dear Colleagues,
Autonomous systems (AS) are recognized as cognitive and intelligent systems that function without human intervention and are underpinned by the latest advances in Artificial Intelligence (AI) for realizing brain-inspired systems. For AS to reach high levels of autonomy, several functional requirements must be fulfilled, such as environmental awareness, self-awareness and network connectivity. Autonomous connected vehicles and intelligent radios, among others, are emerging AS applications that have the potential to reshape future human society. Environmental awareness relies on detecting vehicles, pedestrians, licensed users, or malicious users and accurately estimating the distance, velocity and radio signals of the targets (users) involved in the system. Self-awareness is concerned with an agent's knowledge about itself and its ability to interpret the surrounding environment according to the knowledge encoded in its internal models, and to adapt its behaviour according to the detected environmental changes. Connectivity is crucial for the cognitive entities (e.g., vehicles) involved in the system to exchange information individually and make intelligent decisions that are collaboratively supported by reliable communications. There are two different aspects concerned with environmental awareness, self-awareness and connectivity: sensing and communication. Nowadays, we have solutions to formulate those components separately in an AS. Thus, the current trend is to present an integrated solution in which the ability of an agent to perceive the external environment, represent itself and communicate are formulated in an integrated way within an AS.
Consequently, the integration of sensing and communication has emerged very recently as a revolutionary element of the sixth generation (6G) that could potentially help enable adaptive learning and intelligent decision-making in future AS applications, such as the internet of things (IoT), robotics, drones, and vehicle to everything (V2X). The combination of sensing and communication allows vehicles to perceive their surroundings better, predict manoeuvres from nearby users and make intelligent decisions, thus, paving the way toward safer physical interactions in applications involving humans and machines. In addition, information extracted through sensing can significantly improve connected agents' performance in collaborating to reach a specific task in a dynamic environment. For example, a drone seeks to land on a moving carrier vehicle to be charged; a delivery robot aims to refill a smart container once it is detected as empty. Furthermore, information obtained from sensing (or communication) should be fed with semantic meaning to interpret from the learnt linkage between the physical coordinates (or signals) and a specific task, e.g., transmitting from locations with the best channel conditions.
This Special Issue seeks to gather novel contributions from researchers focusing on integrating cognitive sensing and AI-enabled communications in AS. We solicit original research papers or review articles covering topics of interest that include, but are not limited to:
- Multimodal perception for AS
- Machine learning and deep learning of dynamic representations for AS
- Integrated framework to formulate environmental awareness, self-awareness and connectivity in AS
- Sensor fusion in AS
- Anomaly detection in AS
- Incremental learning for AS
- Reinforcement learning for decision making in AS
- Active inference for Bayesian decision making in AS
- Simultaneous imaging, mapping and localization in AS
- AI techniques for radio environment awareness in autonomous vehicular networks
- Reinforcement learning for network decision making in autonomous vehicular networks
- Joint design of AI-based sensing and communication in autonomous vehicular networks
- Position-aided radio signal prediction in autonomous vehicular networks
- Position-aided traffic prediction for resource allocation in autonomous vehicular networks
- Protection against jamming and spoofing in autonomous vehicular networks
Prof. Dr. Carlo S. Regazzoni
Dr. Ali Krayani
Guest Editors
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Keywords
- autonomous systems
- environmental awareness
- self-awareness
- sixth generation (6G)
- internet of things
- robotics
- drones
- vehicle to everything (V2X)
- AI-enabled communications
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