## 4 Literature Review and Data Extraction
### Distributed Task Allocation
Due to the difficulty of computing the optimal allocation, a heuristic method was proposed to address the resource allocation subproblem, along with an approximation algorithm to handle the offloading choice subproblem. A recent study by [PERSON] et al. [45] proposed a novel approach for fault-tolerant scheduling in collaboration procedures carried out in edge IoT environments. In this work, a solution that combines a deep Q-learning network (DQN) and a PB mechanism to ensure process execution by choosing the best backup component of the workflow was presented. The job completion rate, server active time, and resource consumption reveal that the suggested technique outperforms conventional methods, according to the authors' comprehensive simulations. The study addresses the differences between the suggested methodology and earlier work while also discussing related fault-tolerance method studies. Overall, the suggested technique offers a fault-tolerant scheduling mechanism for multi-task processing on distributed edge-IoT systems that is more effective and dependable. A novel architecture for IoT networks that incorporates UAVs functioning as mobile fog nodes, termed hybrid-hierarchical spatial-aerial-terrestrial edge-centric (HZTEC) was presented by [PERSON] et al. [50]. While ensuring network dependability and outage probability, the suggested task allocation protocol (TOP) reduces energy consumption and maximizes the transmission rate of terminal nodes (TNs). When a UAV's energy falls below a set threshold, the protocol uses energy harvesting techniques to transmit energy to the UAV. The collected power and harvesting time are improved, and the suggested protocol outperforms the baseline procedure, according to the numbers. In order to enhance the functionality of fog-IoT networks, the study evaluates the literature on mobile fog nodes and suggests an energy model and a throughput model. The research also offers a thorough system model, explains how network latency, energy consumption, and UAV flight height are affected by one another, and suggests a problem for optimizing the flight altitude. Overall, the research offers a unique approach to the problem of mobile fog nodes in fog-IoT networks with a constrained energy budget. [PERSON] et al. [55] investigated whether the use of massive MIMO and non-orthogonal multiple access (NOMA) technology can improve the realization of quick fault elimination, by minimizing the data transmission time, and boosting the transmission rate. The computation of the acquired data can be offloaded to the MEC server. In order to accomplish the quick correction of errors in the intelligent distribution network and achieve stable operation, the massive MIMO-NOMA technology is thought to be applied to MEC. Recently, a work was presented by [PERSON] et al. [59] discussing a decentralized crowdsourcing system and a task assignment algorithm used to address the issues of task allocation. The authors offer a blockchain-based solution to address the problems with traditional, centralized crowdsourcing platforms, such as user privacy leaks and extra service charges, for multi-skill mobile crowdsourcing assignments. With the help of blockchain technology, the suggested system enables direct contact between the requester and the worker, and offers infrastructure services that are highly accessible, decentralized, and private. The system design allows mobile crowdsourcing projects with varied skill needs by including worker and task management contracts as well as a fair and transparent task allocation method. Additionally, the system has a solid mechanism for ensuring fairness and is economically viable with substantially less added consumption. Comparing the suggested system with current crowdsourcing models based on blockchain, the experimental findings reveal that the proposed system provides accurate task matching at a lower cost and greater job allocation rates.