  Routing Protocol for Low Power and Lossy Networks (RPL) is the de-facto
routing standard in IoT networks. It enables nodes to collaborate and
autonomously build ad-hoc networks modeled by tree-like destination-oriented
direct acyclic graphs (DODAG). Despite its widespread usage in industry and
healthcare domains, RPL is susceptible to insider attacks. Although the
state-of-the-art RPL ensures that only authenticated nodes participate in
DODAG, such hard security measures are still inadequate to prevent insider
threats. This entails a need to integrate soft security mechanisms to support
decision-making. This paper proposes iTRPL, an intelligent and behavior-based
framework that incorporates trust to segregate honest and malicious nodes
within a DODAG. It also leverages multi-agent reinforcement learning (MARL) to
make autonomous decisions concerning the DODAG. The framework enables a parent
node to compute the trust for its child and decide if the latter can join the
DODAG. It tracks the behavior of the child node, updates the trust, computes
the rewards (or penalties), and shares with the root. The root aggregates the
rewards/penalties of all nodes, computes the overall return, and decides via
its $\epsilon$-Greedy MARL module if the DODAG will be retained or modified for
the future. A simulation-based performance evaluation demonstrates that iTRPL
learns to make optimal decisions with time.
