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TopA wireless sensor network (WSN) is a form of network that consists of randomly distributed devices/nodes in a known space. Each node is typically equipped with radio transceiver, power source (usually a battery), processor, memory and/or other wireless communication device such as GPS receiver. WSN was originally developed for military purposes in the battle field. For example in a rescue operation these sensors that can be dropped by an airplane prior the actual operation, can reduce the risk of the operations by having the rescue crew aware of the overall situation (Akkaya, 2005). However, the development of such networks has encouraged the healthcare, industrial, environmental and other industries to utilize this technology. The size of each sensor node varies from 1 foot squared box to the size of a golf ball.
There are few challenges that faces the routing protocols, and hence the network formation in WSN. 1) Although in some applications the nodes’ locations in Wireless Sensor Networks (WSN) is known and prefixed, however, the majority uses random distribution of nodes, which makes it difficult since the locations of the nodes are unknown. 2) The data flow from multiple nodes to a central base station. 3) Data redundancy since many nodes could sense the same phenomena and hence producing redundant data. 4) Last and most important of all, is the power constraint and the limitation of radio transmission and communications among the WSN nodes (Akkaya, 2005).
According to Akkaya (2005) power consumption in WSN is closely related to its architectural issues, that is 1) Network dynamics, 2) Network deployment, 3) Energy consideration 4) Data Delivery Model 5) Node Capabilities, and 6) Data aggregation. The WSN formation and setup has a great influence on power consumption and hence the network life time.
The main contribution of this paper is a new algorithm that significantly reduces the power consumption during the setup of a wireless sensor network, and hence prolongs the network life.
This solution relies on GPS technology to locate each node in the network. According to Sivaradje (2006) this may be a limitation especially in the urban areas were GPS signals are estimated to be around 15-40% less accurate due to magnetic disturbances, masking, unfavorable error propagation and other line of sight limitations. On the other hand WSN is normally applied in areas where human intervention is not probable; therefore the urban areas limitation is considered a major limitation.
Normally WSN nodes are distributed in an environment in which usual maintenance of the node is very difficult or highly undesirable, therefore the power source within the node is only and most valuable resource since it cannot be replaced. Hence keeping the network alive by using minimum resources is a big challenge (Pemmaraju, 2006). The transmission of data between WSN nodes consumes most of the node's power. One way to reduce this consumption is by grouping nodes into small groups within the transmission range of each node (cluster). Each cluster has a cluster-head that is usually at the center of the cluster radius and has the largest number of nodes within its transmission range (Guru, 2004). Figure 1 illustrates clustering in WSN.