## 1 Introduction  
Modern concepts of communication networks include such collective robotic components as autonomous vehicles (or Internet of Vehicles, IoV) [1; 2], logistics [3], industrial machinery [4], and Internet of Things (IoT) [5; 6]. As autonomous agents are based on traditional microprocessors and embedded platforms [7], then attacks of eavesdroppers are quite possible and efficient distant monitoring plays critical role. Discussions of possible methods for distant data verification has included special network protocols for mobile agents [8; 9; 10], quantum cryptography [11; 12], schemes of distributed computing [13], blockchain (BC) methods [14; 15; 16], position-based cryptography [17], and non-binary k-valued discrete logic [18]. Interest to blockchain technologies for network agents can be attributed to the problem of low availability of trusted network nodes for mass robotics, as trusted systems for communication networks discussed e.g., in [19] need special equipment, personnel, and intensive monitoring procedures. At the same time, the integration of fiber-optics and wireless quantum key distribution (QKD) lines [12; 20] in communication networks provides new possibilities to raise security, although QKD is a complicated and expensive tool, which has its specific vulnerabilities and can be suppressed by intensive optical noise. QKD schemes and quantum random number generators (QRNG) [21] are the high quality sources of one-time quasi-random keys, which provide the base for various data protection schemes. That is why they are also expected to raise the security level of autonomous robotic systems based on artificial intellect (AI) methods [22], which are strongly associated with the concept of collectives of intellectual agents or multi-agent systems (MAS) [23]. In Figure 1 a local network of hardware agents 0, 1,... is a part of nodes of the communication network, and can include mobile and static agents. As autonomous agents should be able to work without permanent personnel control due to the imitation of human abilities by means of AI algorithms [19], various verification procedures are quite actual for them, including schemes based on BC [24; 25; 26]. However, data protection of internal subsystems in autonomous agents is a separate problem, as an agent, see Figure 1 to the left, may include subsystems with different throughput, software and hardware complexity [27]. As a result, hardware platform for autonomous agents can be very complicated [27; 28], and model debugging and data verification is the difficult problem.  
For the position-based cryptography task [17] and agents following the given route, verification procedures [29] should combine authentication, identification and secret coding. It is necessary to check identity and access rights of the mobile agent by verification of parols, pin-codes, licenses, digital images, and prehistory of carried out agent's tasks, what involves either the design of trusted check-points [29] or needs the estimation of trust models [30; 31]. Besides this, schemes based on BC principles [32; 33; 34] were already designed for the collective verification of data in energy power networks, as static network agents of dishonest users can declare false data.