In distributed time series databases (TSDBs), time series data are typically partitioned by both series and time. These partitions are then allocated to shards, whose replicas determine the storage location, with the leader managing the write load. In Internet of Things (IoT) scenarios, clusters expand as the number of sensors continues to grow. A common approach to re-balancing storage is migrating existing partitions, yet it incurs additional overhead. Fortunately, Time to Live (TTL) is often implemented in time series databases to automatically unload expired data. As a result, dynamically expanding shards rather than migrating existing partitions can also restore storage balance. In addition, the cluster's fault tolerance depends on replica placement schemes, and an expanding cluster complicates this issue. Finally, the intensive write load in IoT scenarios requires balanced leader selection, which becomes difficult due to fault-tolerant placement schemes. To address these IoT challenges, this paper presents the migration-free data partitioning and allocation strategies, a storage-balanced replica placement algorithm with proven fault tolerance, and a write-balanced leader selection algorithm. Our proposals have been deployed in Apache IoTDB since version 1.3. Extensive evaluation of the system demonstrates its superiority in availability and performance.