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Motivated by the need to judiciously allocate scarce sensing resources, and account for fusing information from multi-modal sensors, we develop a solutions methodology for maximizing the overall quality of information obtained subject to constraints on the energy utilized by a sensor network that is involved in the task of tracking multiple targets. Our methodology is based on integer programming, and explicitly allows for general fusion functions. We use an iterative Lagrangian relaxation technique to solve this problem where each iteration step involves solving for a Maximum Weight Independent Set (MWIS) of an appropriately constructed graph (which can be obtained in polynomial time for this problem). We apply our methodology to numerically study the problem of tracking targets moving over a period of time through a non-homogeneous, energy-constrained sensor field. In these applications, we study the QoI/energy tradeoffs for various modes of operation including the period for measurement updates.