Research Area:  Wireless Sensor Networks

Abstract:

Wireless Sensor Networks (WSNs) has been an active area of research since last two decades. A wireless sensor network is consisting of huge number of small, inexpensive and resource constrained devices called sensing nodes. These sensor nodes are densely deployed to measure a given physical phenomenon. Currently, there exist enormous applications of WSNs in various domains, such as health care (e.g. patient monitoring and assistance), environmental monitoring (e.g. weather forecast, forest fire detection), natural disasters (e.g. flood detection and monitoring, earth quick), military (e.g. military equipment monitoring/surveillance, battle field management), business (e.g. products monitoring and tracking) and smart homes (e.g. monitoring and controlling home appliances). Note that in all above mentioned applications of WSNs, the organizations / enterprises or even individuals take critical decisions on the basis of gathered data from WSNs. We advocate that if the gathered data is accurate and trustworthy, the decisions taken based on that data might be correct, otherwise it will guide the organizations/enterprises in erroneous direction. Since, in WSNs the sensor nodes are interconnected with each other through some wireless channel and use most of the time multi hop ad hoc topology, it makes them vulnerable to various security threats and one of the less addressed security threat of WSNs is the data provenance. It represents one of the key issues in examining the trustworthiness of sensor-s data. Thus, the focus of this paper is to provide a detailed review of the state-of-the-art data provenance schemes for wireless sensor networks (WSNs). This paper could serve as a beginning guide for the researchers who just started to explore the domain of data provenance for WSNs.

Keywords:  
data provenance
wireless sensor network
trustworthiness of data

Author(s) Name:  Ariz Muhammad Brohi; Yasir Arfat Malkani; Muhammad Saleem Chandio

Publisher name:  IEEE

DOI:  10.1109/ICOMET.2018.8346374

Volume Information:  

Paper Link:   https://ieeexplore.ieee.org/abstract/document/8346374