## 2 Related Work
[PERSON] [26] reported a study of LoRaWAN uplink traffic, in which the packet delivery rate decreased exponentially with the increase in the number of end nodes in the network. [PERSON] et al. [27] presented an estimation of the throughput of the LoRa technology taking into account the broadcast time of the packet transmission. As a result, the maximum number of end nodes that could communicate with the gateway could be determined. [PERSON] et al. [28] proposed an efficient coordinate-based indexing mechanism to solve the fast lookup problem, using a superposition jump to minimize the index lookup delay. [PERSON] et al. [29] considered a dense network deployment of IoT devices and propose a time scheduling algorithm and a distance spreading factor algorithm to reduce the probability of collisions, thus achieving higher throughput and lower transmission power. [PERSON] et al. [30] proposed a deployment strategy for 6 G IoT environments utilizing the machine learning algorithms particle swarm optimization (PSO) and k-means clustering (KMC), and considering processing, transmission, and backhaul communication to improve the transmission power. All of the above studies considered the system operational efficiency without taking into account factors such as fairness, user friendliness, and user budget related to QoS. [PERSON] and [PERSON] [13] considered a system with one macro BS and several micro BSs and solved the transmission power allocation problem using a subgradient-based non-cooperative game model and solved the dual decision-making problem of request offloading and computational resource scheduling using MO-NSGA. The system operation cost was minimized and QoS improved by solving both PA and JRORS in the same network system. However, a non-cooperative game model usually lacks global coordination, and each device only focuses on maximizing its own interest, which may lead to the degradation of the overall system performance. Meanwhile, the system involves the edge system cost when considering the JRORS problem but does not consider the cost of cloud servers, and the overall cost of system operation is not sufficiently involved. Many heuristic algorithms are suitable for solving the edge cloud computing scheduling problem. [PERSON] [3] proposed a nature-inspired meta-heuristic scheduler smart ant colony optimization (SACO) task offloading algorithm for offloading IoT sensor application tasks in a foggy environment. [PERSON] et al. [31] proposed particle swarm optimization (EETSPSO) for fog computing for energy efficient task scheduling. [PERSON] et al. [32] used ant colony optimization (ACO) and the genetic algorithm (GA) to maximize the system utility and to meet various quality requirements of latency sensitive and computationally intensive applications for mobile users. Referring to the above related works, in this paper, we focus on taking into account the whole process of task scheduling when solving PA and JRORS problems. In order to improve QoS, the pricing and task completion of cloud servers are added to the JRORS problem, which ensure the system efficiency and consider the budget constraints and fairness of users. Meanwhile, we propose AGDOA and BSSA to better solve the PA and JRORS problems, respectively. In addition, we introduce a scheduling dominance degree (SDD) to measure the fairness of the algorithm.