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Classical tracking methods are not concerned with energy efficiency and require precise localisation. We addressed these in our previous work through HMTT (hierarchical Markov decision process for target tracking) that tracks single targets at location granularity. HMTT conserves energy by reducing the rate of sensing but preserves acceptable tracking accuracy through trajectory prediction. In this paper, HMTT is extended for the multiple targets case where the state of clusters could be affected by multiple incoming targets and where multiple updates are required at the lower level. The theoretical performance of HMTT in the multiple targets case is derived and simulations demonstrate its effectiveness against 2 other predictive tracking algorithms with up to 200% improvement.