Abstract
A biomimetic distributed infection-immunity model (BDIIM), inspired by the immune mechanism of an infected organism, is proposed in order to achieve a high-efficiency wake-up control strategy based on multi-sensor fusion for target tracking. The resultant BDIIM consists of six sub-processes reflecting the infection-immunity mechanism: occurrence probabilities of direct-infection (DI) and cross-infection (CI), immunity/immune-deficiency of DI and CI, pathogen amount of DI and CI, immune cell production, immune memory, and pathogen accumulation under immunity state. Furthermore, a corresponding relationship between the BDIIM and sensor wake-up control is established to form the collaborative wake-up method. Finally, joint surveillance and target tracking are formulated in the simulation, in which we show that the energy cost and position tracking error are reduced to 50.8% and 78.9%, respectively. Effectiveness of the proposed BDIIM algorithm is shown, and this model is expected to have a significant role in guiding the performance improvement of multi-sensor networks.
Highlights
With the advantages of being low-cost, easy to implement, self-organizing, and highly reliable, multi-sensor fusion networks are widely used for such applications as non-cooperative tracking [1,2], forest fire detection [3], industrial process control [4], water quality detection [5], and machine health monitoring [6], and object state detection [7,8]
These sensor networks are composed of a group of cooperating low-power, low-precision, inexpensive sensor nodes equipped with limited transmission range transceivers, a small data processing unit, constrained memory, and limited available energy
In dynamic power management techniques [33], the energy consumed by the communication module (CM) cannot be ignored, which is why we presented a distributed infectious disease model (DIDM) in a prior work [34]
Summary
With the advantages of being low-cost, easy to implement, self-organizing, and highly reliable, multi-sensor fusion networks are widely used for such applications as non-cooperative tracking [1,2], forest fire detection [3], industrial process control [4], water quality detection [5], and machine health monitoring [6], and object state detection [7,8]. The most widely used technique for lifetime optimization of multi-sensor networks is sensor wake-up control (WC) [12], which is used to adaptively determine the “wake up” or “sleep” states of each node. 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 transformed into information on food location [9].
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