Computation Offloading in Untrusted MEC-aided Mobile Blockchain IoT Systems

Abstract : Deploying a mobile edge computing (MEC) server in the mobile blockchain-enabled Internet of things (IoT) system is a promising approach to improve the system performance, however, it imposes a significant challenge on the trust of the MEC server. To address this problem, we first propose an untrusted MEC proof of work (PoW) scheme in mobile blockchain networks where plenty of nonce hash computing demands can be offloaded to the MEC server. Then, we design a nonce ordering algorithm for this scheme to provide fairer computing resource allocation for all mobile IoT devices/users. Specifically, we formulate the user’s nonce selection strategy as a non-cooperative game, where utilities of the individual user are maximized in the untrusted MEC-aided mobile blockchain networks. We also prove the existence of Nash equilibrium and analyze that the cooperation behavior is unsuitable for blockchain-enabled IoT devices by using the repeated game. Finally, we design the blockchain’s difficulty adjustment mechanism to ensure stable block times during a long period of time. Compared with the weighted round-robin algorithm, our proposed nonce ordering algorithm can provide fairer computation resources and optimal nonce selection strategies for all mobile users. Network stability is gained through the proposed blockchain’s difficulty adjustment mechanism. The analysis and optimization results provide valuable design insights for practical mobile blockchain IoT systems.

 EXISTING SYSTEM :

 ? Many existing and deployed sensing devices usually do not use the cellular network. ? There exists a great amount of sensing devices in the urban environment, the sensing devices in WSNs can sense the environmental data. ? However, the sensing devices exist shortcomings such as low computing power, limited storage capacity and limited battery power. ? Our scheme is better than the existing scheme. We can see that our performances on reward, packets and delay are better compared with the comparison schemes.

 DISADVANTAGE :

 ? To solve this problem, researchers have started to make efforts to place the computing close to mobile devices to reduce network latency. ? They formulate this problem as a global optimization by minimizing energy consumption while satisfying latency constraints. ? Then, an iterative algorithm based on successive convex approximation is proposed to solve this problem. ? As a solution to connected autonomous driving, AVE enlarges the benefit of vehicle-to-vehicle communication and solves the job assignment problem based on Ant Colony Optimization. ? It gives rise to the problem of seamless offloading service migration across edge nodes, i.e., migrating services from the current edge node to the nearest edge node based on the user’s location.

 PROPOSED SYSTEM :

 • Researchers proposed to adopt mobile vehicles to collect data sensed by the sensing devices distributed in the city. • Te mobile edge computing is proposed to provide computation services for edge devices. • In order to speed up the training process of the AC algorithm, asynchronous advantage actor-critic (A3C) algorithm is proposed. • A computation ofoading scheme through mobile vehicles (COTV) in IoT-edgecloud network is proposed in this paper, which solves the computation task ofloading problem for widely deployed sensing devices in the smart city. • In order to provide low processing delay for devices, MEC is proposed to improve the performance of the system.

 ADVANTAGE :

 ? These works show how to achieve performance improvement and energy saving of mobile devices by offloading computation to the cloud according to the network connectivity and resource provisioning. ? The network connectivity has a significant impact on the performance of offloading for both the latency and energy consumption. ? Offloading with WiFi is usually considered to have better performance than with 3G and 4G LTE, as WiFi has higher downlink and uplink in practice. ? Latency has a great impact on the game performance, especially for firstperson shooter games. ? Leveraging the power of edge computing by offloading compute-intensive tasks to edge nodes can significantly reduce the latency, distribute data traffic, and finally improve the performance.