Radio Frequency Identification (RFID) and sensor networks are both wireless technologies that provide limitless future potentials. While the industry has witnessed rapid growth in developing and applying RFID technology, and the network research community has devoted tremendous efforts in sensor networks, these two communities would benefit greatly by learning from each other. In pursuing this effect, a project utilizing and integrating both technologies is described. The goal is to build an in-home elder healthcare system that monitors patients' medication in take. This would help addressing the challenge of a growing aging population.

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Cloud data centers consume an enormous amount of energy. Virtual Machine (VM) migration technology can be applied to reduce energy consumption by consolidating VMs onto the minimal number of servers and turn idle servers into powersaving modes. While most existing energy models consider mainly computing energy, an enhanced energy consumption model is formulated, which includes energy consumption for computation, for servers to switch from standby to active modes, and for communication during VM migrations. Next, two new dynamic VM migration algorithms are proposed. They apply a local regression method to predict potentially over-utilized servers, and the 0-1 knapsack dynamic programming to find the best-fit combination of VMs for migration. The time complexity of these algorithms is analyzed, which indicates that they are highly scalable. Performance is evaluated and compared with existing algorithms. The two new heuristics have significantly reduced the number of VM migration, the number of rebooted servers, and energy consumption. Furthermore, one of them has achieved the least overall SLA violations. We believe that the new energy formulation and the two new heuristics contribute significantly towards achieving green cloud computing.

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