# Distributed Reliable and Efficient Transmission Task Assignment for WSNs
## 2 Related Work  
Many research results on distributed task assignments for WSNs have been achieved, although most methods aim solely at maximizing the network lifetime [1; 2]. [PERSON] et al. propose an optimal online task assignment algorithm DOOTA (Distributed Optimal On-line Task Allocation) that is suitable for multi-partition scheduling by taking into consideration the energy cost of communication, calculation, sensing, and sleep activities [1]. To solve the problem of task allocation on the Internet of Things-based applications, considering special functions and design features, [PERSON] et al. proposed a new task group and a virtual object-based framework and then adopted a meta-heuristic solving method [2] by modeling the problem as a single-objective optimization problem with the primary goal of minimizing energy consumption. In addition to considering the extension of the network lifetime, part of the work also takes into account the quality of service (QoS) requirements [3; 4]. Using fuzzy inference systems, [PERSON] et al. proposed a method for discovering multi-criteria resource discovery in the context of distributed systems [4]. The main disadvantage of this is that the computational overhead increases rapidly as more requests and resources are invoked. Several researchers have applied game theory to WSNs in order to solve task allocation problems [5; 6]. To solve the selfish behavior of some nodes, some have adopted the cooperative enforcement games strategy [7]. Despite that, the QoS requirements they satisfy are mainly focused on minimizing completion time, load balancing, data sampling rate, and accuracy, among others. Among the requirements to be considered, there is no research work on reliability requirements. Alternatively, some researchers have addressed the problem of distributed task allocation in WSNs from cloud-based architectures [8; 9]. Though, due to the distance between the cloud platform and the user, the group perception has higher waiting times. Based on the self-organizing characteristics of WSNs, [PERSON] et al. divided the implementation techniques of distributed task allocation into two types [10]: one is based on reinforcement learning [11], and another is a collaboration based on nodes--the local interaction between nodes in a collaborative manner to achieve a self-organizing task assignment. For example, based on the auction method [9; 12] and the distributed task allocation based on the negotiation method [13], the two methods can achieve optimal results since they are obtained through negotiation between the two parties, which differs from other methods in that they use only a specific algorithm or a specific set of algorithms to obtain results. It happens that substantial communication overhead cannot be avoided during the negotiation process, so the auction-based and negotiation-based approaches are not suitable for resource-constrained WSNs. The task assignment based on auction and negotiation has a significant communication load in a typical distributed task allocation due to the frequent interaction between nodes and unsuitable resource-limited WSNs. Moreover, the task allocation based on reinforcement learning also faces the problem of longer convergence time. In research that utilizes intelligent algorithms to solve the optimization goals of task assignment, such as particle swarm optimization, it is easy to fall into the optimal local problem. [PERSON] et al. proposed a globally optimized task allocation algorithm based on an ant colony algorithm that requires a global pheromone matrix to obtain the optimal solution [14]. However, the authors missed mentioning how to construct a three-dimensional (3D) path pheromone storage space and the cost for the construction of this 3D space. The research status presented above prompted the authors to investigate the distributed reliable task allocation problem in WSNs in order to develop an optimal global solution for reliable and efficient task allocation problems at a minimal cost.