A sensor grid middleware for health care monitoring is proposed in this paper. The proposed middleware hides the heterogeneity between the sensors and the grid. Sensors are attached to the patient's body. The vital sign values collected by the sensors are transmitted to the grid through a mobile device. In the grid, the vital sign values are sent to an appropriate node which contains Software as Service (SaaS).The SaaS receives the vital sign values and analyze whether they exceed the normal value. In this case, the physician looking after the patient is sent an alert message. The physician recommends proper medication based on the patients report he receives.

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Advances in microelectromechanical systems (MEMS) and nanotechnology have enabled design of low power wireless sensor nodes capable of sensing different vital signs in our body. These nodes can communicate with each other to aggregate data and transmit vital parameters to a base station (BS). The data collected in the base station can be used to monitor health in real time. The patient wearing sensors may be mobile leading to aggregation of data from different BS for processing. Processing real time data is compute-intensive and telemedicine facilities may not have appropriate hardware to process the real time data effectively. To overcome this, sensor grid has been proposed in literature wherein sensor data is integrated to the grid for processing. This work proposes a scheduling algorithm to efficiently process telemedicine data in the grid. The proposed algorithm uses the popular swarm intelligence algorithm for scheduling to overcome the NP complete problem of grid scheduling. Results compared with other heuristic scheduling algorithms show the effectiveness of the proposed algorithm.

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