## 5 Collaboration of Multi-UAV Computing  
Recently, UAV computing has emerged as a viable solution for offering computing services to everything in smart and harsh environments. However, due to the restricted UAV computing capability and UAV energy limitation, it is difficult to satisfy the computation requirements of smart and harsh environments because each UAV works separately as an independent unit. Therefore, a multi-UAV computing collaboration is required. A collaborative multi-UAV-assisted MEC system was introduced in [137], combined with an MEC-enabled ground BS. Then, the challenge of minimizing delay for everything is investigated by optimizing the offloading decision and allocating communication and computation resources while meeting the energy restrictions of both UAVs and everything in smart environments. The authors of [79] also proposed an intelligent task offloading algorithm for UAV-empowered MEC services in which the UAVs offload computing tasks to fixed MEC servers. UAV BSs are clients in this system, served by MEC and cloud servers, and the task latency performance improvement was studied. Previous studies discussed UAV computing over a terrestrial MEC station [16; 138; 139; 140; 137; 138; 141; 142; 143; 144]. For example, the authors of [138] presented UAV trajectory control based on DRL in UAV computing, but task offloading and resource allocation were not considered. The authors of [145] investigated the combined resource allocation and UAV trajectory optimization in UAV computing to maximize UAV energy efficiency. Moreover, the authors of [140] investigated the issue of hierarchical reinforcement learning-based task scheduling in UAV computing. The authors of [141] presented a non-cooperative game for effective resource management in UAVcomputing to decrease the age of information. A Stackelberg game-based resource pricing and trade mechanism for a blockchain application was proposed in [142]. This was used to optimally allocate computing resources at UAVs and BSs. The authors of [16] discussed UAV computing issues for task execution time and energy reduction [16]. However, all previous studies [16; 138; 139; 140; 141; 142] focused on a single UAV computing. Meanwhile, the authors of [143] examined the multi-UAV scenario from trajectory planning. In multi-UAV computing, the aim is to optimize fairness among everything in smart environments. The authors of [144] proposed an energy-efficient communication and computation resource management in a two-stage mobile computing system aided by multi-UAVs. The authors postulated that the UAV compute a portion of users' offloaded computing activities while the remainder is transmitted to the MEC-enabled BS. An integrated MEC system of UAVs and ground vehicles is considered in [146] for providing communication, storage, and computing services to the IoT devices. In contrast to this work, the system in [102], as we discussed in Section 3, is also an integrated MEC system, however, it includes ground BSs as well in the MEC system, whereas the system in [146] does not. While the work reported in [143; 144] introduced multi-UAV computing, this work did not go into the problem of joint job offloading and communication and computing resource allocation to reduce overall latency for users. Furthermore, previous research has not taken into consideration UAV collaboration. In contrast, in multi-UAV computing connected to enabled terrestrial BS, the authors of [137] presented a combined task offloading and resource allocation issue that considers collaboration among UAVs computing. Furthermore, the authors in [147] used a multi-UAV task offloading for ground users to minimize energy consumption of the users through a joint optimization of UAV trajectory, task scheduling, and bit allocation. A dynamic programming-based bidding optimization method was used for the task scheduling optimization.