A DRL-Driven Intelligent Joint Optimization Strategy for Computation Offloading and Resource Allocation in Ubiquitous Edge IoT Systems

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

“…The goal of resource allocation is addressed in approximately 10 % of the reviewed literature [ 99 , 103 , 105 , 111 , 116 , 121 ]. In addition, 48 % of the research work focuses on both task allocation and resource allocation issues [ 76 , 80 , 83 , 87 , 89 , 141 , 142 , 144 , 146 , 92 , 95 , 101 , 102 , 107 , [112] , [113] , [114] , [115] , [117] , [118] , [119] , [120] , [122] , [123] , [124] , [126] , [127] , [128] , [129] , [132] , [133] , [134] , [135] , [136] , 148 , 149 ]. The Distribution and overlay of IoT task offloading problems addressed in MEC are shown in Fig.…”

“…Provides high-speed data transmission and better performance for 4G networks. A small number of task offloading optimization mechanisms take into account resource allocation and task scheduling based on LTE network characteristics [ 83 , 120 ].…”

“…In Ref. [ 120 ], the researcher also studied the task offloading optimization problem in MEC networks involving UAVs. However, this study optimizes the cumulative policy gradient calculation step in policy update.…”

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

“…The multi‐agent DDPG (MADDPG) approach was also used to determine the resource allocation and offloading strategy. Yi et al 120 provided an DRL based intelligent joint optimization technique for CO and resource allocation that incorporates relay selection, offloading decisions, and resource allocation in order to decrease the latency and ECp of MUs in ubiquitous edge IoT systems. The authors developed the virtual backbone architecture to provide users with effective multi‐hop offload services through a connected dominating set (CDS).…”

“…A 31.25% of researchers have used the development environments such as Microsoft Visual Studio, 82,89 UBUNTU, 67,111 Anaconda(spyder), 81 MATLAB, 37,54,56,59,68,69,72,73,77,103,105,116,126,138,141,142,146,154,158,162 IBM Ilog Cplex optimization Studio 71 . A 20.00% of researchers used DL and ML based libraries such as TensorFlow, 46,47,62,63,65,66,110,144,151,161 Keras, 47 scipy, 45 sckit tool, 113 Pytorch, 111,123 TVM deep learning compiler, 120 LIBSVM 166 . Python, 40,62,63,65,81,109,110,144,151,161 C++, 99 and Java, 71,94 programming languages were considered by 15.00% of researchers.…”