Recently, unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has emerged as a practical paradigm to enable low latency computing offloading for dispersed users in the fifth generation (5G) wireless networks. However, severe security and privacy concerns are associated with the open environment between the UAVs and edge computing nodes. In this paper, we address these challenges by integrating blockchain technology into UAV-enabled MEC networks. We present an innovative Delegated Proof of Stake (DPoS) consensus mechanism where the UAV is a primary node and verification nodes are edge computing nodes selected by the reputation mechanism. To enhance mobile users' quality of service (QoS), edge computing resources need to be allocated among UAV and verification nodes. Based on this, we propose the trading mechanism for resource pricing and allocation based on the two-stage Stackelberg differential game. Meanwhile, dynamic states of user demands and verification node reputations are modeled using differential equations as constraints of the objective function at various stages to simulate adaptive service requests for users and incentivize active participation for verification nodes. Simulation results prove the effectiveness of the proposed resource trading scheme and demonstrate the equilibrium and convergence status of resource pricing and allocation for edge computing.

The unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks exist security and privacy issues on task offloading, which can not be tackled by conventional security mechanisms. In this paper, we consider consortium blockchain combined with UAV-assisted MEC networks to overcome these challenges. An improved Delegated Proof of Stake (DPoS) consensus scheme is designed in which the UAVs act as light nodes to collect the tasks and verify their signatures from the ground users to form the initial block and then offload it to the ground blockchain nodes for final block generation. We ensure the trustworthiness of the ground blockchain nodes through voting by the reputation incentive mechanism. The two-stage the UAV and ground blockchain nodes by jointly optimizing the trajectory and communication resource allocation of the UAV and computing resource allocation of the ground blockchain nodes. Furthermore, we analyze the Stackelberg equilibrium by the backward induction and adopt the approximate convex algorithm (SCA) to approximate the non-convex problem solving. Simulation results demonstrate the effectiveness of the developed scheme for trusted management and superior delay.

This letter studies the task offloading in reputation-based consortium blockchain networks, where the tasks are transmitted to Edge Computing Servers (ECSs) due to limited resources. We propose a novel Delegated Proof of Stake (DPoS) consensus mechanism in which validation nodes (including active nodes and backup nodes) are voted based on their reputation. The incentive identifies the reputation and the consensus delay as two major factors determining the reward. A three-stage Stackelberg game is developed to jointly minimize cost of the users and maximize utilities of the master node and the validation nodes. We analyze the unique Stackelberg equilibrium exists in the proposed game by the backward induction. The simulation results demonstrate that the designed incentive is feasible for trust management, and the proposed consensus has lower delay and higher decentralization compared with the traditional DPoS.

With the development of the Internet of Things (IoT) technology, massive amounts of sensor data in applications such as fire monitoring need to be transmitted to edge servers for timely processing. However, there is an energy-hole phenomenon in transmitting data only through terrestrial multi-hop networks. In this study, we focus on the data collection task in an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) network, where a UAV is deployed as the mobile data collector for the ground sensor nodes (SNs) to ensure high information freshness. Meanwhile, the UAV is equipped with an edge server for data caching. We first establish a rigorous mathematical model in which the age of information (AoI) is used as a measure of information freshness, related to both the data collection time and the UAV’s flight time. Then a mixed-integer non-convex optimization problem is formulated to minimize the peak AoI of the collected data. To solve the problem efficiently, we propose an iterative two-step algorithm named the AoI-minimized association and trajectory planning (AoI-MATP) algorithm. In each iteration, the optimal SN–collection point (CP) associations and CP locations for the parameter $\epsilon$ are first obtained by the affinity propagation clustering algorithm. The optimal UAV trajectory is found using an improved elite genetic algorithm. Simulation results show that based on the optimized $\epsilon$, the AoI-MATP algorithm can achieve a balance between data collection time and flight time, reducing the peak AoI of the collected data.

A Game for Task Offloading in Reputation-Based Consortium Blockchain Networks