<div>IoT devices (wireless sensors, actuators, computer devices) produce
large volume and variety of data and the data</div>


<div>produced by the IoT devices are transient. In order to overcome the
problem of traditional IoT architecture where</div>


<div>data is sent to the cloud for processing, an emerging technology known
as fog computing is proposed recently.</div>


<div>Fog computing brings storage, computing and control near to the end
devices. Fog computing complements the</div>


<div>cloud and provide services to the IoT devices. Hence, data used by the
IoT devices must be cached at the fog nodes</div>


<div>in order to reduce the bandwidth utilization and latency. This chapter
discusses the utility of data caching at the</div>


<div>fog nodes. Further, various machine learning techniques can be used to
reduce the latency by caching the data</div>


<div>near to the IoT devices by predicting their future demands. Therefore,
this chapter also discusses various machine</div>


<div>learning techniques that can be used to extract the accurate data and
predict future requests of IoT devices.</div>