This paper presents an overview of the potential obstacles and challenges related to research topics such as IoT, DfPL WSNs (Wireless Sensor Networks) in IoT and disaster management using WSNs. This review will analyse key aspects of deploying a DfPL WSN in IoT scenario for disaster management. In an IoT scenario the DfPL WSN is only collecting raw data that is forwarded to the Internet using a Compressed Sensing (CS) IoT framework or other solutions including data compression. Compressed Sensing (CS) refers to a method used to reduce the number of samples collected in an IoT WSN. Thus it is possible to create stand-alone applications that require fewer resources. There is no need to process the data in the WSN as this can be done in the Data Analysis Network, after the data is reconstructed. This will enable a reduced volume of data transmitted and lower power consumption for battery-operated nodes. The detection of people in a disaster scenario who are simply moving and not in the possession of a ‘tracking device’ is revolutionary. The aim here is to build upon our patent-pended technology in order to deliver a robust field-trial ready human detection system for disaster situations.