Energy-Efficient Edge Offloading in Heterogeneous Industrial IoT Networks for Factory of Future

Abstract

The ultra-reliable and low latency communication (URLLC) and massive machine type communication (mMTC) in 5G are envisioned to support intelligent automation in the heterogeneous Factory of Future (FoF) networks, and Mobile-edge computing (MEC) is considered to be a promising system for enabling real-time task processing at the edge of the network. In the future factory, production machines, and environmental monitoring devices will be endowed with the wireless connecting for mobility. These devices are deployed for running complicated real-time tasks. To make such mission-critical tasks being processed in time, parts of the tasks should be completed with the assistance of the edge server or even the cloud. In this work, we jointly investigate the partial task offloading, computation, and communication (licensed and unlicensed) resource allocation problem in the trade-off between overall power consumption and quality of service (QoS) satisfaction. A 2-tier MEC-cloud framework is provided, wherein the IoT mobile devices (MDs) are able to partition the tasks into segments and offload them to the MEC and the cloud server. Considering the limits of communication and computation resources, we proposed a mechanism call 5G and NR-U opportunity-cost-based offloading algorithm (5G/NR-U OCBOA) to optimize resource allocation. Within the mechanism, there are two proposed algorithms, 5G OCBOA is for the licensed-only case, and NR-U OCBOA dedicates on unlicensed one. We iteratively perform the two algorithms to get the final solution. The simulation results show that our low-complexity algorithms almost outperform the other benchmark greedy algorithms. The proposed algorithm is up to 59.3% MD blocking probability less, up to 58.7% power saving gain, and up to 47.6% more QoS gain.INDEX TERMS URLLC, mMTC, factory of future (FoF), multi-access edge computing (MEC), the industrial IoT (IIoT), 5G NR-U.

A comprehensive review on internet of things task offloading in multi-access edge computing

“…Over 40 % of the literature surveyed in this survey focused on task allocation as the primary problem-solving goal [ 73 , 74 , 77 , 79 , 81 , [84] , [85] , [86] , 88 , [138] , [139] , [140] , 143 , 145 , [90] , [91] , [147] , 94 , [96] , [97] , [98] , 100 , 104 , 106 , [108] , [109] , [110] , 125 , 130 , 131 , 137 ]. The goal of resource allocation is addressed in approximately 10 % of the reviewed literature [ 99 , 103 , 105 , 111 , 116 , 121 ].…”

“…An enhanced version of the opportunity cost-based offloading algorithm is proposed in Ref. [ 91 ]. This algorithm supports offloading computing tasks to MEC and cloud platforms and makes full use of the characteristics of the 5G communication environment to optimize task offloading.…”

“… Ref. Problems addressed Technique Network Objective Evaluation tools Baseline Advantages Limitations [ 56 ] Task assignment Probability distributions Undefined Minimize computational time Python based implementation CPLEX Global knowledge utilization Ignore energy consumption [ 90 ] Task assignment Greedy Undefined Maximize deadline satisfaction ratio Python based implementation NECS, Detour Joint optimization of deadlines and energy consumption Not suitable for large networks [ 91 ] Task assignment, resource allocation Greedy 5G Maximize utility, Minimize energy consumption Testbed UGA, DFA Tradeoff between utility and energy consumption Parallel task offloading of a single TD is not supported [ 92 ] Task assignment Greedy Wifi Maximize total utility GT-ITM ILP, classic greedy algorithm Support cloudlets Not suitable for large-scale networks [ 93 ] Task assignment Greedy X2 link Maximize cost while the latency of tasks is guaranteed NS-3 MC-RDA, MC-UNA, MC-GA Reduce the redundant data …”

A resource management survey for mission-critical and time-critical applications in multiaccess edge computing

“…In addition, as MEC may possess heterogeneous Radio Access Technologies (RATs), it raises the interesting problem of choosing the right RAT to serve a device at a given time for a given task. For instance, Hsu et al [72] consider the licensed 5G and the unlicensed NR-U. Indeed, each RAT has its own characteristic, like coverage, mobility support, data rate and so on.…”

A joint optimization scheme of task caching and offloading for smart factories

“…The Clear5G project initiated by the European Union is aimed at solving technical difficulties in the future engineering environment and future wireless networks. The project clearly pointed out that various applications in smart factories proposed higher requirements for communication technology (such as network delay, reliability, and large-scale connectivity) [7]. These higher requirements cannot be satisfied by any known wired or wireless network communication technology.…”