One of the interesting techniques for leader election is used in the WBS (Wait Before Starting) algorithm, in which each node in the network will wait for a time corresponding to its value before starting to send the first message to neighbours. This means that the node with the smallest value becomes the leader and it also starts first. This approach is impracticable in the case of real values (case of GPS-coordinates). Also, if the values are very large, the waiting time becomes too long. In this paper, we propose a fast, fault-tolerant and low energy leader election algorithm dedicated to smart-cities, which is based on the technique of waiting before starting, with minimum complexity and in which every node sends one and only one message. Here, the leader is the node with the smallest x-coordinate and the total of sent and received messages is used to represent the global consumption in the network. We give a detailed description of the algorithm, prove its accuracy, discuss its complexity in terms of exchanged messages and evaluate its performance using the CupCarbon simulator. We show that our algorithm is well balanced in terms of energy consumption, it is efficient and adapts well to the increase of the nodes number in the network.
The main parameter studied in a simulation of a Wireless Sensor Network is the lifetime of the network. In other words, the state of the battery of each node. That is why modelling correctly the battery is very important to obtain realistic results. In real applications, many types of batteries can be considered, where their lifetime depends on the weather variations and on the type of the considered sensor node. In this paper, we present some models of batteries simulated with the CupCarbon simulator. The models are obtained by estimating the consumption of real batteries. This is done by studying series of discharging current values with respect to different voltage values and different temperatures. Furthermore, we implement a new module in the CupCarbon simulator to allow testing the proposed models and to implement new personal models.
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