Cloud robotics, the fusion of cloud computing and robotics, defines a transformative era. Empowering robots with unprecedented cloud access enables intricate tasks but poses security challenges. Transmitting sensitive data demands robust security. Machine learning aids real-time threat detection; authentication, access control, data encryption, and storage best practices are explored. In conclusion, the chapter guides stakeholders in cloud robotics security by addressing critical issues, promoting best practices, and anticipating trends. Offering insights into data security and privacy, it equips professionals to harness the potential of cloud robotics while mitigating risks. This concise exploration, spanning the digital and physical realms, serves as a beacon in the dynamic field of cloud robotics security and privacy.
Top1. Introduction
The fusion of robotics with cloud computing has ushered in a new era of innovation, allowing robots to tap into the immense computational resources available in the cloud. Cloud robotics promises unprecedented capabilities, from autonomous navigation to complex problem-solving, transforming various sectors ranging from manufacturing and healthcare to service industries. Cloud computing, data storage in the cloud, and other internet-based technologies can be used to create new-generation robots that are more cost-effective, power-efficient, and have improved communication, storage, and mobility than existing robots and industrialization frameworks. Modern industries like business, medicine, space exploration, and many more use robots. Numerous examples demonstrate how dangerous it is to deploy technologies like the Internet of Things (IoT), cloud computing, big data, etc. in robotics, which also have negative effects on people and organizations as mentioned in (Jain, S., & Doriya, R. (2019)).
A networked robotic system is defined by a collection of robotic devices connected through both wired and wireless communication networks. These systems are typically categorized into teleoperated robots and multi-robot systems. In teleoperation scenarios, a human operator remotely controls or manipulates a robot by sending commands and receiving real-time measurements via the communication network. Examples include scenarios like operating planetary rovers and conducting remote medical surgeries. On the other hand, in multi-robot systems, a coordinated team of networked robots collaboratively completes tasks by exchanging sensory data and information through the communication network. Applications of multi-robot systems include cooperative robot manipulators, robot teams engaged in search and rescue operations, and clusters of microsatellites cooperating to form particular configurations. This interconnected and collaborative nature of networked robotic systems opens up diverse possibilities, from precision surgery to intricate search and rescue operations.
However, as the potential of cloud robotics is embraced, the profound implications it carries for security and privacy cannot be ignored. With robots becoming increasingly autonomous and interconnected, they generate and transmit a wealth of sensitive data, encompassing everything from environmental sensor readings to audio and video feeds. This data, while invaluable for decision-making and problem-solving, is also a potential goldmine for malicious actors seeking unauthorized access or exploitation. Therefore, addressing the multifaceted security and privacy challenges associated with cloud robotics comprehensively and proactively is paramount.
Deploying cloud robotics presents a significant challenge in terms of ensuring security and privacy. The collaboration and interaction of cloud robots with physically distant counterparts raise sensitive issues related to the protection of robotic data and activities. In scenarios where co-located robots communicate through wireless message broadcasts, it is crucial to safeguard these messages from eavesdropping or tampering by external robots or unauthorized devices.
Unlike the simplified model where all robots in a group share the same security privileges, real-world cloud robot groups comprise robots with diverse capabilities, functions, roles, and attributes, each requiring different levels of access privileges. Managing security in such a heterogeneous environment becomes a complex problem, particularly when considering the mobility of robots. Policies governing role assignment, attributes, authorization, and access privileges must accommodate dynamic changes in group membership.
The utmost priority is to prevent security breaches in cloud robots, as such breaches could result in irreversible disasters in the physical world. Regrettably, there is a scarcity of relevant research addressing the dynamic establishment of a secure cloud robot network. Key aspects, such as determining eligible robots for collaboration, specifying required resources and services, and defining access to these robotic services, remain unexplored. (Ko, H., Keoh, S. L., & Jin, J. (2017)) mentioned resolving various security issues, including authentication, membership management, privacy protection, and access control within the cloud robot network, is imperative to ensure a secure and robust deployment.