## 1 Introduction
In [28], two distributed information fusion schemes are proposed in anti-submarine warfare applications. In [29], an energy-efficient adaptive overlapping clustering method is proposed for continuous monitoring applications. In [30], a strategy is proposed to track a moving target in an environment with obstacles. This cluster leader will wake up its members once a target is found. Therefore, the decisions regarding target detecting and tracking are dominated completely by a single node. This will inevitably lead to a number of false alarms. Recently, an artificial ant colony (AAC) approach was proposed to allow distributed sensor WC in WSN to accomplish the joint task of surveillance and target tracking, which in ants is then transformed into information on food location [9]. The communication, invalidation, and fusion of the target information are modeled as the processes of pheromone diffusion, loss, and accumulation, respectively. Compared with classical wake-up methods, a similar position error is obtained by the AAC method with much lower energy consumption, indicating that swarm intelligence is promising in dealing with the problem of distributed WC in WSNs. However, in the AAC method, the information is modeled as the pheromones that ants release, so whether the information comes from a sensing module (SM) or a communication module (CM) is not distinguishable. Therefore, as in classical wake-up methods, the AAC method is limited to optimizing the energy consumption of the SMs. However, in dynamic power management techniques [33], the energy consumed by the CM cannot be ignored, which is why we presented a distributed infectious disease model (DIDM) in a prior work [34]. With four sub-processes and six commonsense rules, the information from the SM and CM was modeled as the pathogen spread by direct infection and cross-infection, respectively. The simulation shows that the DIDM method is more effective than the AAC approach. On the other hand, the DIDM method placed most emphasis on the infection mechanism. For the immune mechanism, it only introduces a prior identical immunity repairing ratio \(0<q<1\), ignoring the variation tendency in the infectious process. However, in practice, with changes in infection, the biological immune capacity changed correspondingly. Therefore, it is reasonable to design the immune mechanism according to natural processes and dynamical behavior in the biological immune system. With such an immune mechanism, the model deficiency caused by the fixed immunity repairing ratio can be avoided. Motivated by this, a biomimetic distributed infection-immunity model (BDIIM) that includes three sub-processes related to the infection mechanism--the occurrence probabilities of direct-infection and cross-infection, the immunity/immune-deficiency of direct-infection and cross-infection, and the pathogen amount of direct-infection and cross-infection--is proposed. In addition, two sub-processes--immune cell production and immune memory--are added to depict the immune mechanism, and one sub-process--pathogen accumulation under immunity state--is revised. Furthermore, the relationship between sensor wake-up and the BDIIM is established to derive a collaborative wake-up method. A simulation of joint surveillance and target tracking is utilized to show the effectiveness of BDIIM. The rest of the paper is organized as follows: Section 2 establishes the BDIIM for node wake-up control and proposes a BDIIM model, and Section 3 establishes the correspondence between sensor wake-up and disease propagation. The proposed method is evaluated and analyzed by simulations in Section 4. Finally, Section 5 concludes the paper.