Research Article Mitigation Impact of Energy and Time Delay for Computation Offloading in an Industrial IoT Environment Using Levenshtein Distance Algorithm Ahsan Rafiq , 1 Ping Wang, 2 Min Wei , 2 Muhammad Saleh Ali Muthanna , 1 and Nteziriza Nkerabahizi Josbert 1 1 Department of Computer Science, Chongqing University of Posts and Telecommunication, Chongqing 400065, China 2 Department of Automation, Chongqing University of Posts and Telecommunication, Chongqing 400065, China Correspondence should be addressed to Min Wei; weimin@cqupt.edu.cn Received 24 November 2021; Accepted 12 January 2022; Published 12 February 2022 Academic Editor: Muhammad Arif Copyright©2022AhsanRafiqetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. DuetotheexplosivegrowthoftheInternetofthings(IoT)devicesandtheemergenceofdiversenewapplications,networktraffic volumeisgrowingexponentially.etraditionalcentralizednetworkarchitecturecannotfulfillIoTdevicesdemandbecauseofthe heavy network traffic in industrial IoT. Moreover, IoT devices have limited computational ability and battery power. Energy consumption and time delay problems during computation offloading are fundamental issues. A new architecture known as mobileedgecomputing(MEC)wasintroducedtoovercometheseissues,whichbringscloudservicesanditscontentstotheedge ofthenetwork.IoTdevicescanoffloadthedataforcomputationtothecloudserveroredgenodes.Differentschemeshavebeen proposedtoovercomethisproblemundermanyscenarios(i.e.,single-user,multiuser,andvehicularnetworks).Inthispaper,we proposedamodifieddelaymitigationLevenshteindistancealgorithm(MDML).Weconsideranindustrialscenariowithmultiple IoTdevices and multiple servers (edge nodes). Each edge node consists of one MEC server. e proposed algorithm solves the offloadingoptimizationproblemofenergyandmitigationoftimedelaywithmuchlowercomplexitywhilesignificantlyreducing offloadingtasks’executiontime.Itworksonthebasisofdynamicprogramming,wherewebreakdownacomplexprobleminto subproblems. Performance evaluation of our proposed algorithm shows that it can achieve satisfactory energy efficiency and mitigate time delay in the industrial IoT environment. 1. Introduction Smart industries called industry 4.0 are the future of the industrial revolution that will enable humanity to fulfill its manufacturing conditions with accuracy and efficiency in the industrial Internet of things (IoT) [1, 2]. e use of actuators, sensors, and IoT devices in the industrial IoT is increasing rapidly; devices communicate with each other to workefficiently[3].IndustrialIoTapplicationsthatconsume high computation need powerful computing capacity and deserve extraordinary energy consumption to process them locally on the devices [4]. e battery and computation capacity is limited for IoT devices. New emerging network architecture is mobile edge computing(MEC);theconceptofMECisonethatprovides userequipment(UE)applicationattheedgeofthenetwork withcloudcomputingaptitudeandInformationtechnology (IT) service environment [5, 6]. It can aggravate resource constraints at UEs by offloading computation tasks from UEstoMECandcanhelpthemtosecuretheuseofdifferent IoTapplications. In this revolutionary world of technology, IoTdevicesneedtoprovidehighperformancewithinashort time and less energy with wireless networks to share data andinformation.erefore,toresolvethisproblem,thebest way is to offload all the device tasks on the cloud or edge devices. e mobile edge computing application becomes Hindawi Security and Communication Networks Volume 2022, Article ID 6469380, 12 pages https://doi.org/10.1155/2022/6469380