The interconnection of resource-constrained and globally accessible things with unreliable Internet make them vulnerable to attack such as, but not limited to, data forging, false data injection, and packet drop. Such attacks may affect mission-critical applications which rely on sensor data for decision-making processes, hence, necessitates high assurance of trustworthy data. For the data trustworthiness, provenance is considered to be an effective mechanism that tracks both data acquisition and data transmission. However, provenance management for IoT networks is faced with several challenges such as low energy, bandwidth consumption, and efficient storage. This paper follows a bi-fold Provenance-enabled Packed Path Tracing (PPPT) approach 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). Firstly, ordered system-level 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 provenance size satisfying processing and storage requirements of IoT devices. The experimental results show that the provenance size remains constant (i.e., 2 bytes) irrespective of the number of hops or number of sent packets; which does not affect factors such as memory usage (additional RAM and ROM usage: 504 and 3874 bytes respectively), energy consumption, and processing efficiency for provenance generation time in comparison to RPL-only. Furthermore, our proposed provenance-enabled RPL (PPPT) outperforms the RPL-only approach from the perspective of added security such as data trustworthiness and features such as identification of malicious nodes and other disruptions in the network.

资源受限且可全局访问的事物与不可靠的Internet的互连使它们容易受到攻击，例如但不限于数据伪造，错误数据注入和数据包丢失。此类攻击可能会影响依赖关键数据的应用程序，这些应用程序依赖传感器数据进行决策流程，因此需要对可信赖数据进行高度保证。为了保证数据的可信度，出处被认为是跟踪数据采集和数据传输的有效机制。但是，物联网网络的物产管理面临着低能耗，带宽消耗和高效存储等诸多挑战。本文遵循的是双重来源的打包路径跟踪（PPPT）方法来识别数据包丢失（恶意或由于网络中断），并在低功率和有损网络（RPL）的路由协议中检测故障或行为不正常的节点。首先，有序的系统级来源信息将数据生成节点和转发节点封装在数据包中。其次，要密切监视丢弃的数据包，使用节点级的来源数据包序列号形式的数据包作为路由条目包含在每个参与节点的路由表中。本质上而言，这两种方法都是无损的，可以节省物产的大小，从而满足物联网设备的处理和存储要求。实验结果表明，与跳数或发送的数据包数量无关，出处大小保持不变（即2个字节）。与仅使用RPL相比，它不会影响诸如内存使用（额外的RAM和ROM使用：分别为504和3874字节），能源消耗以及出处生成时间的处理效率等因素。此外，