EEDTO: An Energy-Efficient Dynamic Task Offloading Algorithm for Blockchain-Enabled IoT-Edge-Cloud Orchestrated Computing

TLDR

Compute‑intensive, delay‑sensitive mobile applications increasingly require abundant computational resources, prompting the offloading of tasks to nearby mobile‑edge computing (MEC) or scalable mobile cloud computing (MCC) servers, where MEC offers lower latency and MCC provides greater processing power. This work proposes a blockchain‑enabled IoT‑Edge‑Cloud architecture and introduces the EEDTO algorithm to dynamically select the optimal computing location—IoT device, MEC, or MCC—to jointly minimize energy consumption and task response time while preserving data integrity. Using Lyapunov optimization, the algorithm adaptively determines each task’s execution site in real time, balancing computation and communication costs without requiring future system information. Compared with existing offloading schemes, EEDTO delivers energy‑efficient decisions with reduced computational complexity.

Abstract

With the proliferation of compute-intensive and delay-sensitive mobile applications, large amounts of computational resources with stringent latency requirements are required on Internet-of-Things (IoT) devices. One promising solution is to offload complex computing tasks from IoT devices either to mobile-edge computing (MEC) or mobile cloud computing (MCC) servers. MEC servers are much closer to IoT devices and thus have lower latency, while MCC servers can provide flexible and scalable computing capability to support complicated applications. To address the tradeoff between limited computing capacity and high latency, and meanwhile, ensure the data integrity during the offloading process, we consider a blockchain scenario where edge computing and cloud computing can collaborate toward secure task offloading. We further propose a blockchain-enabled IoT-Edge-Cloud computing architecture that benefits both from MCC and MEC, where MEC servers offer lower latency computing services, while MCC servers provide stronger computation power. Moreover, we develop an energy-efficient dynamic task offloading (EEDTO) algorithm by choosing the optimal computing place in an online way, either on the IoT device, the MEC server or the MCC server with the goal of jointly minimizing the energy consumption and task response time. The Lyapunov optimization technique is applied to control computation and communication costs incurred by different types of applications and the dynamic changes of wireless environments. During the optimization, the best computing location for each task is chosen adaptively without requiring future system information as prior knowledge. Compared with previous offloading schemes with/without MEC and MCC cooperation, EEDTO can achieve energy-efficient offloading decisions with relatively lower computational complexity.