## 2 Related Work  
In recent years, the task allocation methods within IUWSNs have garnered significant attention from researchers and scholars. As productive forces continue to advance and high technology becomes more prevalent, the mode of production is increasingly evolving towards comprehensive development driven by scientific and technological progress. Correspondingly, task allocation is gradually transitioning towards faster, simpler, and more efficient approaches. Task allocation now offers not only a way to maximize network revenue but also a potent optimization strategy to expedite convergence, circumvent local optima, and extend the overall network life cycle. In ref. [17], an offline discrete Particle Swarm Optimization algorithm (DPSO-TA) was introduced to address the reliable assignment problem in Internet of Things Wireless Sensor Networks (IoT WSN). This algorithm formulates a practical function to iteratively optimize task allocation solutions. It takes into account diverse objectives, including energy conservation, minimized task completion time, reduced fault rates, and prevention of signal interference. Additionally, it incorporates a load balancing mechanism to evenly distribute tasks across multiple hosts. However, in certain scenarios, the algorithm may become trapped in a local optimal solution, preventing the attainment of a global optimal solution. In ref. [18], a short-term architecture for sensor task time allocation has been developed. This architecture has led to an average enhancement of 50% in measurement accuracy, and there is a requirement to achieve a measurement completion rate surpassing 85%. However, it is important to note that the current model does not account for sensor lifespan. In ref. [19], a novel model was introduced with the aim of minimizing the overall energy consumption needed for task completion within wireless sensor networks (WSNs). This modeling framework employs 0-1 integer linear programming to optimize the problem. Nevertheless, it is worth noting that when the number of tasks becomes substantial, the implementation complexity can increase. In refs. [20; 21; 22], an investigation was conducted into the optimization of energy consumption through the examination of dynamic task allocation within virtual WSNs. However, these studies are still different from ours because they consider network level virtualization. In addition, numerous researchers employ heuristic algorithms--including the GA, ACO, SA, and BP neural network--to tackle the task allocation problem in wireless intelligent sensor networks. In ref. [23], the GA is improved to solve the target allocation problem in the target reconnaissance process, so that the improved algorithm has faster solution speed and better optimization ability. However, the improved GA needs to consider many factors, and its implementation is very complex. In ref. [24], the author introduced an enhanced version of the Ant Colony Optimization (ACO) and employed it to tackle the task assignment dilemma with the intent of enhancing global optimization search capabilities. The paper elaborates on the mathematical depiction and solving procedure of an ACO. Subsequently, the prowess of the ACO is showcased through an illustrative example. Nevertheless, the complexity of configuring the algorithm's parameters is worth noting. If these parameters are not properly set, there is a possibility of deviating from achieving optimal solutions. In paper [25], an optimization algorithm is introduced, utilizing the Simulated Annealing Genetic Algorithm (SAGA), designed to address the complexities of multi-objective and multi-sensor configurations. The algorithm proceeds in several steps: firstly, it quantifies the spatial information perception capability of sensors using the Analytic Hierarchy Process (AHP); secondly, it calculates the priority of the target; and lastly, the SAGA is employed for task assignment.