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Observation of water level at various river sites could provide valuable insight about probable disaster in advance to initiate disaster management protocol as early as possible. We have developed an autonomous remote river water level monitoring network with event prediction algorithm at the server while maintaining a substantially low manufacturing cost. The WSN is comprised of several chosen sites based on their statistics with intelligent sensors for water level measurement. The sensors are autonomous in nature to account for any disturbance in node environment and also within the network. The real time data are transmitted to a remote server through GPRS for further processing. Server extracts information and simulates various real time parameters such as water level rise rate, time remaining to exceed the critical level for a particular site etc. A prediction algorithm running on the server side predicts the measured level values for each node over a period of time. A prototype system is implemented with six nodes at different points and has yielded satisfactory results.

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This paper presents a comparative performance analysis among different positive feedback based match line (ML) sensing scheme such as Mismatch dependent, Active feedback and Resistive feedback scheme. The comparison has been done based on parameters like search time, voltage margin, peak dynamic power and worst case energy consumption. Although most popular ML sensing scheme Current Race (CR) consumes more power than positive feedback based ML sensing scheme but performance comparison has also been shown between CR with each of three positive feedback based match line (ML) sensing scheme for equal search time. Finally, for search time Resistive feedback, for voltage margin and peak dynamic power consumption Mismatch dependent and for worse case energy consumption Active feedback scheme were found to be showing the best performance. For performance comparison the all the circuits is simulated using 130nm 1.2V CMOS logic.

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