Designing low power sensor networks has been the general goal of design engineers, scientist and end users. It is desired to have a wireless sensor network (WSN) that will run on little power (if possible, none at all) thereby saving cost, and the inconveniences of having to replace batteries in some difficult to access areas of usage. Previous researches on WSN energy models have focused less on the aggregate transceiver energy consumption models as compared to studies on other components of the node, hence a large portion of energy in a WSN still get depleted through data transmission. By studying the energy consumption map of the transceiver of a WSN node in different states and within state transitions, we propose in this paper the energy consumption model of the transceiver unit of a typical sensor node and the transceiver design parameters that significantly influences this energy consumption. The contribution of this paper is an innovative energy consumption model based on simple finite automata which reveals the relationship between the aggregate energy consumption and important power parameters that characterize the energy consumption map of the transceiver in a WSN; an ideal tool to design low power WSN
Abstract:In this paper we design and implemented a dynamic and smart wireless mesh sensor network for aquaculture and water quality management applications. This system utilizes the Waspmote embedded systems platform developed by Libelium, mesh networking transceivers from Digi International and smart sensors from UNISM to implement a novel smart Wireless Mesh Sensor network -Aquamesh with multiple gateways of different technologies (Zigbee, GPRS and WIFI). The system is designed to continuously monitor aqua-environmental parameters and then initiate an alert or early warning to system user when certain thresholds are exceeded. The data generated from this system is stored locally on the gateway or sent to a remote web server. Data on the local database or remote web server can be accessed with smart mobile phones or personal computers. The experimental results show that the system presented in this paper is feasible to implement and present results consistent with traditional aqua-quality monitoring systems. This system will find application in the monitoring of marine and wetlands environments like fish ponds, coastal water pollution monitoring systems, effluent and sewage treatment plants, offshore oil and gas drilling facilities.
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