Abstract
Wireless sensor and robot networks (WSRNs) often work in complex and dangerous environments that are subject to many constraints. For obtaining a better monitoring performance, it is necessary to deploy different types of sensors for various complex environments and constraints. The traditional event-driven deployment algorithm is only applicable to a single type of monitoring scenario, so cannot effectively adapt to different types of monitoring scenarios at the same time. In this paper, a multi-constrained event-driven deployment model is proposed based on the maximum entropy function, which transforms the complex event-driven deployment problem into two continuously differentiable single-objective sub-problems. Then, a collaborative neural network (CONN) event-driven deployment algorithm is proposed based on neural network methods. The CONN event-driven deployment algorithm effectively solves the problem that it is difficult to obtain a large amount of sensor data and environmental information in a complex and dangerous monitoring environment. Unlike traditional deployment methods, the CONN algorithm can adaptively provide an optimal deployment solution for a variety of complex monitoring environments. This greatly reduces the time and cost involved in adapting to different monitoring environments. Finally, a large number of experiments verify the performance of the CONN algorithm, which can be adapted to a variety of complex application scenarios.
Highlights
Event-driven deployment is a new research area in wireless sensor and robot networks (WSRNs) [1,2,3,4]
By using collaborative neural networks, the event-driven deployment algorithm can be adapted to different application scenarios to meet a variety of monitoring requirements; For the first time, a multi-constrained event-driven deployment model based on the maximum entropy function is proposed
The rest of this paper is arranged as follows: related works are described in Section 2; the compound event-driven deployment model is proposed in Section 3; the compound event-driven deployment algorithm based on the collaborative neural network strategy is proposed in Section 4 and evaluated in Section 5; and Section 6 concludes this paper
Summary
Event-driven deployment is a new research area in wireless sensor and robot networks (WSRNs) [1,2,3,4]. By using collaborative neural networks, the event-driven deployment algorithm can be adapted to different application scenarios to meet a variety of monitoring requirements; For the first time, a multi-constrained event-driven deployment model based on the maximum entropy function is proposed. This model transforms the complex event-driven deployment problem into two single-objective sub-problems. Both of the sub-problems are continuously differentiable This model overcomes the limitation that the objective function of the traditional min–max method is non-differential; It is the first time that the difficulty of obtaining a large amount of sensor data and environmental information in a complex and dangerous monitoring environment is effectively overcome. The rest of this paper is arranged as follows: related works are described in Section 2; the compound event-driven deployment model is proposed in Section 3; the compound event-driven deployment algorithm based on the collaborative neural network strategy is proposed in Section 4 and evaluated in Section 5; and Section 6 concludes this paper
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