Wireless sensor
networks (WSNs) are networks of distributed autonomous devices that can sense
or monitor physical or environmental conditions cooperatively. WSNs face many
challenges, mainly caused by communication failures, storage and computational constraints
and limited power supply. Paradigms of Computational Intelligence (CI) have been
successfully used in recent years to address various challenges such as optimal
deployment, data aggregation and fusion, energy aware routing, task scheduling,
security, and localization.
CI provides adaptive
mechanisms that exhibit intelligent behaviour in complex and dynamic
environments like WSNs. CI brings about flexibility, autonomous behaviour, and robustness
against topology changes, communication failures and scenario changes. However,
WSN developers can make use of potential CI algorithms to overcome the challenges
in Wireless Sensor Network. The seminar includes some of the WSN challenges and
their solutions using CI paradigms.
A Wireless sensor
network is a network of distributed autonomous devices that can sense or
monitor physical or environmental conditions cooperatively. WSNs are used in numerous
applications such as environmental monitoring, habitat monitoring, prediction
and detection of natural calamities, medical monitoring and structural health
monitoring. WSNs consist of a large number of small, inexpensive, disposable
and autonomous sensor nodes that are generally deployed in an ad hoc manner in
vast geographical areas for remote operations. Sensor nodes are severely
constrained in terms of storage resources, computational capabilities,
communication bandwidth and power supply.
Typically, sensor
nodes are grouped in clusters, and each cluster has a node that acts as the
cluster head. All nodes forward their sensor data to the cluster head, which in
turn routes it to a specialized node called sink node (or base station) through
a multi-hop wireless communication. However, very often the sensor network is
rather small and consists of a single cluster with a
single base station .Other scenarios such as multiple base stations or mobile
nodes are also possible. Resource constraints and dynamic topology pose
technical challenges in network discovery, network control and routing,
collaborative information processing, querying, and tasking . CI combines
elements of learning, adaptation, evolution and fuzzy logic to create
intelligent machines. In addition to paradigms like neuro-computing, reinforcement
learning, evolutionary computing and fuzzy computing, CI encompasses techniques
that use swarm intelligence, artificial immune systems and hybrids of two or
more of the above.
Paradigms of CI have
found practical applications in areas such as product design, robotics,
intelligent control, biometrics and sensor networks. Researchers have
successfully used CI techniques to address many challenges in WSNs. However,
various research communities are developing these applications concurrently,
and a single overview thereofdoes not exist. Their aim is to bridge the gap
between CI approaches and applications, which provide the WSN researchers with
new ideas and incentives. A discussion on yet-unexplored challenges in WSNs,
and a projection on potential CI applications in WSN are presented with an
objective of encouraging researchers to use CI techniques in WSN applications.
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