基于NOMA的海洋物联网安全计算卸载

NOMA-based secure computation offloading in marine Internet of things

针对海洋物联网(M-Io T,marine Internet of things)中存在多种恶意窃听设备(ED,eavesdropping device),为了确保无人水面艇(USV,unmanned surface vehicle)向高空平台(HAP,high altitude platform)的安全计算卸载,利用非正交多址接入(NOMA,non-orthogonal multiple access)辅助传输机制,将一组空闲的USV充当干扰ED窃听的干扰USV(JU,jamming USV),与传输USV(TU,transmitting USV)形成NOMA集群。考虑能耗约束和安全传输等要求,以最小化系统最大任务处理时延为目标,对TU的卸载比率、传输功率、计算资源分配和NOMA集群选择进行联合优化。为了解决混合整数非凸优化问题,提出了深度确定性策略梯度(DDPG,deep deterministic policy gradient)和交叉熵结合的算法。仿真结果显示,所提算法有效地降低了最大系统任务处理时延,并且保证了系统的安全性。

Regarding the presence of multiple malicious eavesdropping device(ED)in the marine Internet of things(M-IoT),to ensure the secure computation offloading of unmanned surface vehicle(USV)to a high altitude platform(HAP),a non-orthogonal multiple access(NOMA)assisted transmission policy was employed,where a set of idle USV acted as jam‐ming USV(JU)that interfered with the eavesdropping of the ED and formed NOMA clusters with the transmitting USV(TU).Considering the requirements such as energy constraints and security transmission,the offloading ratio,transmitting power,computation resource allocation and NOMA cluster selection were jointly optimized with the objective of minimiz‐ing the maximum task processing latency.To solve the proposed mixed-integer non-convex optimization problem,an algo‐rithm combining deep deterministic policy gradient(DDPG)and cross-entropy was proposed.Simulation results show that the proposed algorithm can effectively reduce maximum task processing latency and ensure the security of the system.