Existing wildlife tracking solutions typically use sensor nodes with specialised facilities, such as long-range radio, solar array of cells and Global Positioning System (GPS). This introduces additional manufacturing cost, increased energy and memory consumptions and increased sensor node weight. This paper proposes a novel Localization and Mobility Modelling (LMM) system, that can carry out wildlife tracking by merely using low-cost, lightweight sensor nodes and using short-range peer-to-peer communication facilities only, i.e. without the need for any specialised facilities. This is done by using two computationally simple operations, which are: (i) aggregated data collections from sensor nodes via peer-to-peer communications in a distributed manner, and (ii) estimation of sensor nodes’ movement traces using trilateration. The computational load placed on each sensor node is just that of data collection and aggregation, whereas movement traces estimation is carried out on a backend server, separated from the sensor nodes. In the design of the LMM system, we have: (i) carried out an empirical evaluation of different parameter value settings for data collection to develop a Multi-Zone Multi-Hierarchy (MZMH) communication structure, (ii) demonstrated a novel use of an Aggregation based Topology Learning (ATL) protocol for collecting sensor nodes’ topology data using peer-to-peer multi-hop communications, and (iii) used a novel Location Estimation (LE) method for estimating sensor nodes’ movement traces from the collected topology data. The evaluation results show that the LMM system can accurately estimate sensor nodes’ movement traces but with significantly less energy and memory costs, demonstrating its cost-efficiency as compared to the related wildlife tracking solutions.