Monitoring personal locations with a potentially untrusted server poses privacy threats to the monitored individuals. To this finish, we have a tendency to propose a privacy-preserving location monitoring system for wireless sensor networks. In our system, we have a tendency to design 2 in-network location anonymization algorithms, particularly, resource and quality-aware algorithms, that aim to enable the system to produce high-quality location monitoring services for system users, while preserving personal location privacy. Both algorithms rely on the well-established k-anonymity privacy concept, that's, an individual is indistinguishable among k persons, to enable trusted sensor nodes to supply the aggregate location information of monitored persons for our system. Each mixture location is in an exceedingly type of a monitored area A together with the amount of monitored persons residing during a, where A contains at least k persons. The resource-aware algorithm aims to minimize communication and computational price, whereas the standard-aware algorithm aims to maximize the accuracy of the aggregate locations by minimizing their monitored areas. To utilize the aggregate location information to provide location monitoring services, we use a spatial histogram approach that estimates the distribution of the monitored persons primarily based on the gathered mixture location data. Then, the estimated distribution is employed to produce location monitoring services through answering vary queries. We have a tendency to evaluate our system through simulated experiments. The results show that our system provides high-quality location monitoring services for system users and guarantees the situation privacy of the monitored persons.

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