## 3 Research Process
Collaborative task allocation: Allocating tasks across a network of edge devices by taking into account the collaboration between devices. 8. Context-aware task allocation: Allocating tasks based on the context of the edge devices, such as location, available resources, and user preferences. Table 1 and Figure 2 present a comparative analysis of those papers based on the classification and contextualization of their key results. Note that the columns in Table 1 are directly related to the answers addressing our RQs. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Proposed Task Allocation** & **Applied Network Type (RQ3)** \\ \hline  & & **Distributed task allocation** \\
[18] & FL & WHAB \\
[22] & OFB & VANETs \\
[24] & GT, RL & MEC \\
[29] & MVAA & MEC \\
[45] & PSO & IoT \\
[50] & GT IoT & \\
[55] & Hybrid & MEC \\
[59] & CW & Blockchain \\
[61] & BSUBB & MEC \\
[63] & FL & MEC \\
[31] & DLA & IoT \\
[33] & RL & EC \\
[34] & CFG & C-RAN \\
[35] & AsP & IoT \\
[36] & GSO & MEC, Fog \\
[65] & AsP & NDN-IoT \\
[66] & AB, GA & Wireless, cellular, satellite \\
[67] & Heuristic & MEC \\ \hline  & & **Collaborative task allocation** \\
[12] & PFA & EC \\
[77] & MINLP & EC \\
[17] & ACO & IoT \\
[27] & Heuristic & IoT \\ \hline  & & **Context-aware task allocation** \\
[52] & DFD & DAG \\
[69] & GT & IoT, 5G \\
[78] & LPA & MEC \\
[78] & & **Energy-efficient task allocation** \\
[68] & MAPE-K & IoT \\
[11] & Lyapunov & MEC \\
[19] & Bi-Level & MEC \\
[25] & DRL & IoV \\
[30] & QT & 5G \\
[43] & PSO & IoT \\
[47] & ILP & MEC \\
[71] & JTORA & MEC \\
[73] & hybrid RF-FSO & Industrial IoT \\  & & **Dynamic task allocation** \\
[13] & MPSO & VVECNs, VANETs \\
[79] & MH & IoV \\
[20] & AA & Fog \\
[23] & DP & MEC \\
[41] & DRL & IoT \\
[44] & ECTA & EC \\
[46] & AA & Fog \\
[49] & ILP & \\
[56] & GA, GEN & IoT \\
[60] & PSO & EC \\
[64] & BPSO & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of task allocation methods in edge computing. The task allocation methods (RQ1) covered by the papers we examined are shown in Figure 3. Most of the papers use distributed task allocation (26.1%), followed by resource-aware (18.8%) task allocation. Dynamic task allocation (15.9%), energy-efficient (13.2%), and machine learning (11.6%) task allocation followed next. Collaborative (5.8%), quality-of-service (4.3%), and context-aware (4.3%) task allocation are the methods that are mentioned less. It is also very important to note that, in many papers more than one task allocation methods was used and applied. Regarding the task allocation optimization algorithms (RQ2), as shown in Figure 4, the most frequently used is the swarm optimization in various forms (10.1%), various methods of reinforcement learning (10.1%). JTORA (7.2%), multi-agent methods (5.8%), the game theory (4.3%), the heuristic approach (4.3%), and the auction algorithm (4.3%). We also identified the use of many other task allocation optimization algorithms that were unique; these cases refer to 53.8% of the sample examined. We should note again that many authors in their works implemented more than one optimization algorithm to illustrate the effectiveness of each optimization algorithm. The communication network types (RQ3) are illustrated in Figure 5. Most papers applied IoT networks (27.6%), followed by mobile edge computing (MEC) networks (26.1%), EC networks (18.8%), vehicular networks (10.1%), and 5G networks (5.8%). The rest are illustrated as one category 'various kinds of networks' (11.6%).