Secure IoT Search Engine: Survey, Challenges Issues, Case Study, and Future Research Direction

Abstract

The Internet of Things (IoT) encompasses a near-incalculable collection of dispersed and embedded computing devices acting as sensors and actuators, generating data at an incredible scale. However, a lack of coherency and cross-compatibility in IoT deployments has lead to increasing redundancy and waste of resources. To combat this, various concepts have been proposed for an open IoT search engine (IoT-SE) that serves human and machine users. Invariably, the IoT-SE envisions distributed query retrieval to handle massive volumes of devices and data. Incorporating the massively heterogeneous protocols and properties of devices deployed, the search of such a system for timely and pertinent data is massively challenging, to provide useful knowledge and service for IoT systems. Moreover, enabling and maintaining security and privacy in an IoT-SE is likewise a prodigious task, as end users, IoT devices, and the search system itself, have different protocols and requirements. To this end, a study of security issues in IoT search is conducted to outline the challenges ahead, and a case study to resolve practical security vulnerabilities in an IoT-SE system is carried out. The pertinent issues of security in an IoT-SE system are reviewed. Particularly: 1) a taxonomy is detailed for IoT-SE security issues; 2) the vulnerabilities of machine learning (ML) models in the IoT-SE are considered; and 3) defensive mechanisms are presented for securing IoT Search. A case study is carried out to implement basic security features in the IoT search, addressing the risk of false queries through the design of ML-based solutions. Finally, a roadmap for future research is provided, including the security and privacy for IoT systems connected to the IoT-SE, distributed edge computing in IoT-SE, privacy-preserving data markets in IoT-SE, and distributed ML in IoT-SE.