Design methodology of a distributed multi-hop LoRa-based sensor network for monitoring extended areas

The paper addresses the problem of designing a distributed electronic communication network for monitoring extended territories based on LoRa technology. The relevance of the problem is determined by the need to ensure reliable monitoring of large areas under conditions of limited communication infrastructure, constrained energy resources of sensor nodes, and complex signal propagation environments. Existing approaches to the design of LoRa-based networks are analyzed, including the LoRaWAN architecture and multi-hop solutions for wireless sensor networks. It is shown that most known approaches do not consider the interdependence between sensing coverage parameters and network capacity constraints. A methodology for designing a distributed LoRa-based network is proposed, combining a probabilistic sensing model with a multi-hop data transmission model and channel load estimation. The sensing subsystem is described using a generalized probabilistic (Elfes-type) detection model, which allows transformation of a given detection probability into an equivalent sensing radius of a sensor node. The communication subsystem takes into account physical layer constraints, including the spreading factor, time-on-air, and transmission reliability over a single hop. As a result, analytical dependencies are obtained for determining the minimum number of sensor nodes, the maximum length of a multi-hop chain, and the maximum number of nodes in the network under limited channel capacity. It is shown that the use of a multi-echelon sensing structure increases the overall detection probability by combining results from multiple independent observations. At the same time, the required detection probability can be achieved through different combinations of the number of echelons and node density, enabling optimization of the network structure under system constraints. The scientific novelty lies in the improvement of the methodology for designing distributed sensor networks by integrating a probabilistic sensing model with a multi-hop data transmission model, as well as in establishing the influence of a multi-echelon sensing structure on the overall detection probability. The obtained results can be applied to the design of monitoring systems for extended objects, particularly in environments with limited infrastructure and increased energy efficiency requirements.