Abstract:
Wireless sensor networks (WSNs) consist of a large number of inexpensive, low-power,
sensors that can be placed in an ad hoc fashion to form a data gathering network. Subse-
quent to the sensor node deployment, the nodes will self-organize themselves to periodically
collect reliable information from the environment to a central location called base station
(BS). Once the nodes are deployed, upgrading and maintaining them is not practical. In
such a scenario, the main concern would be the optimal utilization of the sensor energy,
so that the entire sensor bed lasts as long as possible gathering useful information. Inter
node communication for network organization and information gathering requires the most
energy. Therefore, it is necessary to manage these activities in an energy e cient manner to
optimize the lifetime of the sensor network. This research focuses on nding energy e cient
methods of operating the sensor bed such that the lifetime is maximally extended.
Distributed clustering provides an e ective way for self-organizing the wireless sensor
networks for periodic data gathering applications. The research identi es the most positive
and negative aspects of the currently available distributed clustering algorithms. Based on
these ndings, the research proposes a new energy e cient distributed clustering algorithm
where the cluster heads (CHs) are selected based on relative residual energy level of sensors.
Further, the cluster boundary determination and cluster head role rotation is governed by
the cluster heads residual energy level. The algorithm favors more powerful nodes over the
weaker ones thus makes local energy balancing to prolong the lifetime of the entire sensor
network at a very low energy overhead. The proposed algorithm has realized near ideal
local energy balancing. The proposed algorithm is also extended to achieve global energy
balancing by introducing a mix strategy of communication (multi-hop and direct) from
cluster head to base station.
The research shows that the algorithm performance is in line with the desired objectives
using analytical proofs to back the simulation test results. Further, the research proposes
an analytical framework in determining the cluster distribution of the presented algorithm.
Subsequently, the framework was extended to other similar types of distributed clustering
algorithms. Finally, the research proposes an analytical technique in nding optimum al-
gorithm parameters such as the cluster head message broadcasting range and cluster head
role rotation.
