In the Internet of Things (IoT), things can be connected to the Internet via IPv6 and 6LoWPAN networks. The interconnection of resource-constrained and globally accessible things with untrusted and unreliable Internet make things vulnerable to attacks including data forging, false data injection, packet drop and many more, resulting in an unreliable and untrustworthy data, especially for the applications with critical decision-making processes. To ensure the trustworthiness of data, reliance on provenance is considered to be an effective mechanism to keep track of both data acquisition and data transmission. However, provenance management for sensor networks introduces several challenging requirements, such as low energy, bandwidth consumption, and efficient storage. This paper attempts to identify packet drop (either maliciously or due to any other network disruption) and detect faulty or misbehaving nodes in the Routing Protocol for Low-Power and Lossy Networks (RPL) by following a bi-fold provenance-enabled packed path tracing (PPPT) approach. Firstly, the system-level ordered-provenance information encapsulates the data generating nodes and the forwarding nodes in the data packet. Secondly, to closely monitor the dropped packets, a node-level provenance in the form of packet sequence number is enclosed as a routing entry in the routing table of each participating node. Both ways conserve the provenance size satisfying processing and storage requirements of IoT devices. Furthermore, our approach is lossless in nature as it keeps track of routing nodes IDs along with the order traversed by the packet. We evaluate the proposed scheme with respect to provenance size, provenance generation time, and energy consumption.
View on arXiv