This article investigates a hierarchical aerial computing system, where both high-altitude platforms (HAPs) and unmanned aerial vehicles (UAVs) provision computation services for ground devices (GDs). Different from the existing works which ignored UAV task offloading to HAPs and suffered long transmission delay between HAPs and GDs, in our system, UAVs are responsible for collecting the tasks generated by GDs. Considering limited resources and constrained coverage, UAVs need to cooperatively allocate their resources (including spectrum, caching, and computing) to GDs. After collecting GD tasks, UAVs are allowed to offload part of these tasks to the HAP, in order to minimize task processing delay and then better satisfy GD delay requirement. Our objective is to maximize the amount of computed tasks while satisfying tasks’ heterogeneous Quality-of-Service (QoS) requirements through the joint optimization of UAV resource allocation and task offloading. To this end, a joint optimization problem is first formulated as a partially observable Markov decision process (POMDP) under the constraints of available resources, UAV energy, and collision avoidance. Then, we design a multiagent proximal policy optimization (MAPPO)-based algorithm to solve the optimization problem. By introducing the centralized training with decentralized execution framework, UAVs acting as agents can cooperatively make decisions on GDs association, resource allocation, and task offloading according to their local observations. In addition, state normalization and action mask are also adopted to improve training efficiency. Experimental results verify the efficiency of the proposed algorithm and the system performance is also analyzed by the numerical results.

This work proposes Practical Byzantine Fault Tolerance (PBFT) ordering service needed for block formation in permissioned blockchain environments. Contrary to current PBFT implementations that only provide a single point of entry to the ordering service, we allow each ordering node to act as an entry point that proposes and conducts the consensus process of including new record in the distributed ledger. To ensure atomicity of record insertion in distributed ledger, we have developed a bandwidth reservation protocol that uses a modification of CSMA/CA protocol to regulate access to the broadcast medium formed by the P2P network of TCP connections between orderers. We have modeled record insertion service time in a cluster where ordering nodes have random position within Cartesian coordinate system. We have also modeled total request access time to the ledger which includes waiting time in the orderer's queue and record insertion time. These models are used to evaluate system performance under variable request rate ordering service, variable number of nodes and variable physical cluster dimensions. Our results show the interaction between decreased request waiting time in orderer's queue and increased contention among orderers when the number of orderers increases for the given total request arrival rate. This interaction is also investigated for two different physical cluster sizes which affect record insertion time. The interplay of request rate, number of orderers and physical cluster size determines system capacity expressed in total request rate. Our model can be used to make the trade-off between the required system capacity, number of orderers, and physical cluster dimensions under constraints on Byzantine fault rate.

The Unmanned Aerial Vehicles (UAVs) play a significant role to alleviate the negative impacts of disasters by providing essential assistance to the rescue and evacuation operations in the affected areas. Then, the reliability of UAV connections and the accuracy of exchanged information are critical parameters. In this paper, we propose networking and security architecture for disaster surveillance UAV system. The networking scheme involves a two-tier cluster network based on IEEE 802.11ah, which can provide traffic isolation between the tiers. The security scheme guarantees the accuracy and availability of the collected information from the disaster area applying fingerprint features and data redundancy techniques; the proposed scheme also utilizes the lightweight Ring-Learning with Errors (Ring-LWE) crypto-system to assure the confidentiality of the transmitted data with low overhead.

This paper investigates a dual-unmanned aerial vehicle (UAV) aided communication system to improve the security of the communication between ground devices and UAVs. Different from the existing works which ignored ground devices mobility and just considered one-way communication security between ground devices and UAVs, we allow the devices to be mobile and consider bi-directional ground-UAV communication security. Specifically, one UAV server communicates with mobile ground devices, and the other UAV jammer is invoked to confuse eavesdroppers. Our objective is to maximize the worst-case average secrecy rate by the joint optimization of UAV trajectory and sender transmit power. To achieve it, we first formulate the worst-case average secrecy rate maximization problem as a constrained Markov decision process (CMDP) under the constraints of UAV flight space, flight speed, energy capacity, anti-collision, and peak transmit power. Then, we design a Deep Deterministic Policy Gradient (DDPG) based algorithm to solve the CMDP. Experiment results demonstrate that our joint optimization scheme can enhance the communication security in terms of the secrecy rate in both UAV-to-ground (U2G) case and ground-to-UAV (G2U) case. Besides, it is observed that UAV trajectory and sender transmit power have different impacts on the communication security in U2G case and G2U case.