The limited energy in wireless sensor network has always been a key topic. Most previous studies usually regulate the network loading by periodically directing the data flow to different sinks in order to extend the overall life-time of network and enhance the network availability or data delivery. However, the time and place that an event occurred can not be estimated, thus regular information flow switching may not be able to balance the actual loading of network. Another, multiple paths to sinks for each node are established in the tolerance area defined by a pre-defined difference between the cost, i.e. hop counts away from sinks, of paths to different sinks such that each node in-between multiple sinks can distribute the forwarding burden among the paths to different sinks. Since sending data to a farther sink consumes much more overall energy of network, the gain of data delivery should be evaluated at the sacrifice of energy consumption. In this paper, a quantized model is proposed to evaluate the tradeoff between energy consumption and data delivery under non-uniformly events triggering in network. The tradeoff between network availability and energy consumption would be analyzed and the corresponding simulation results would be included.
Reducing energy consumption and prolonging lifetime of network to reduce the amount of packet loss are important issues in wireless sensor networks. Many researches derive the minimum hop path for each sensor to transmit its corresponding data to the sink. The sensors in the path forward the data. However, some common sensors in many forwarding paths will consume much more energy, and then they will die soon. Besides, the establishment and maintenance of the above routing need the whole information of the network, and this will consume more energy in gathering and synchronizing the locations of all sensors. In this paper, each sensor using the information of neighboring sensors derives the minimum hop path to the sink, and with the knowledge of the loading of its up-streaming sensors, it selects the minimum loaded sensor for its first sensor to transmit to. In this way, the loading of each sensor will be balanced. The above routing derives minimum balanced tree (MBT). This data structure will be adjusted locally while some sensors change their statuses in the network such that the control overhead needed to adjust is much less than to reconstruct all over again. Some results of simulated experimentations are shown in this paper.
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