  The interconnection of resource-constrained and globally accessible things
with untrusted and unreliable Internet make them vulnerable to attacks
including data forging, false data injection, and packet drop that affects
applications with critical decision-making processes. For data trustworthiness,
reliance on provenance is considered to be an effective mechanism that tracks
both data acquisition and data transmission. However, provenance management for
sensor networks introduces several challenges, such as low energy, bandwidth
consumption, and efficient storage. This paper attempts to identify packet drop
(either maliciously or due to network disruptions) 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, a 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 the packet sequence number is enclosed as a routing entry in the
routing table of each participating node. Lossless in nature, both approaches
conserve the provenance size satisfying processing and storage requirements of
IoT devices. Finally, we evaluate the efficacy of the proposed scheme with
respect to provenance size, provenance generation time, and energy consumption.
