## 4 A Routing Protocol for LLNs (RPL)
### RPL Objective Functions for Malicious LLNs (MLLNs)
[PERSON] and [PERSON] [121] proposed the Trust-based RPL Attacks Detection (TRAD) system, leveraging recurrent neural networks (RNNs) to enhance security. The system consists of four modules: (i) behaviour generation, (ii) sequence prediction, (iii) trust analysis, and (iv) attacker detection. The behaviour generation module generates behaviour profiles of sensor nodes during data transmission. The sequence prediction module predicts sequences of expected behaviour using the generated behaviour profiles. Trust analysis assesses the trust level of each node based on deviations from the expected behaviour during data traffic. In the attacker detection module, TRAD uses trust assessments to detect compromised nodes, specifically targeting two notorious RPL attacks: black-hole and grey-hole attacks. Although TRAD offers promising security enhancements, its main challenge lies in the training phase. This phase incurs significant computational overhead and must be conducted on edge sensor nodes, which adds to the complexity of implementation. [PERSON] et al. [122] proposed Packet Forwarding Indication (PFI), a trust-related metric that detects whether the selected parent has actually forwarded the packets. PFI can be defined as the probability that a packet will be successfully transmitted along a path without being dropped. This can be calculated as the number of successful forwarded packets divided by the total number of successful and unsuccessful forwarded packets for each node forming the path. The proposed metric, PFI, has been combined with the hop count metric, i.e., HCPFI, to select short paths and avoid malicious nodes. Experimental results show significant improvements in detecting malicious nodes; thus, better PDR and latency compared to OF0 [81]. Even though HCPFI takes into consideration both node and security metrics, it does not address the reliability issues caused by link quality or node energy. To overcome some of the limitations of the previous solution [122], [PERSON] et al. [123] proposed the Expected Frame Transmissions Packet Forwarding Indication (TXPFI) metric. TXPFI combines two routing metrics: (i) the PFI metric for node trust from [122] and (ii) the TX metric for link quality. The TX metric is similar to the ETX metric except that TX captures the expected number of frame transmissions instead of packets, including retransmissions needed to deliver data successfully. The author also proposed another combination of metrics for PFI with ETX and compared the performance of the two solutions, i.e., TXPFI and PFIETX, against each other. The obtained results prove TXPFI's efficiency in avoiding malicious nodes and/or unstable links, but PFIETX performs best with the lowest PLR at the expense of long paths. [PERSON] and [PERSON] [124] proposed a lightweight trust-based security architecture to select routing paths in RPL based on combined metrics. The metrics used are (i) the node trust to identify malicious nodes in the network and (ii) the average received signal strength indicator (ARSSI) for link quality. The node trust metric adopts a binary trust model that can avoid communication with malicious nodes and minimises the node trust computation at the same time. The simulation results showed better PDR performance than MRHOH [85] but with increased control overheads. Although [123; 124] address the reliability issues caused by malicious attacks and link quality, they do not consider the reliability issues caused by node metrics such as energy consumption or remaining energy. [PERSON] et al. [125] proposed another trust-based solution to secure RPL against PDAs without imposing extra control overheads. The proposed node trust metric provides a feedback-aware security system that computes a trust value for each node based on the forwarding behaviour of direct neighbours. The trust value is computed as the number of packets delivered through a neighbour node divided by the total number of packets sent to this neighbour node.