# A Comprehensive Review on Network Protocol Design for Autonomic Internet of Things
## 4 Identification for Autonomic Properties
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Autonomic Properties Supported**} \\ \cline{3-8}  
Algorithms of PPKP-CBF, BICLP-IDID, and WSD-IDS protocols are designed for security purposes, as shown in Table 5, to find the best way to detect intruder or attacker. Hence they support the self-protection property for autonomic IoT network. PPKP-CBF is based on mathematical method, while BICLP-IDID and WSD-IDS are based on a bio-inspired algorithm. Energy harvesting can be the solution for the energy constraint and makes it possible for the continuous operation of an IoT system. It can be observed that the protocol designs to support this energy harvesting activity are mostly for the MAC layer. Other protocol designs that are for clustering and routing still focus on energy consumption reduction. In the future, the consideration of energy harvesting in clustering and routing activity may give more advantages to the WSN and IoT networks. For example, it is better to route in lower energy residual nodes, but soon recharged rather than to route in higher energy residual nodes that still require a long time to recharge or do not have an energy harvesting ability. Figure 4 shows the optimization methods used in network protocols. It can be observed that bio-inspired algorithms and advance mathematical programming are popularly used by researchers in recent studies. Furthermore, recent research protocols designed for security also use an bio-inspired algorithm. The meta-heuristic behavior of bio-inspired algorithms has become an advantage to solve the increasing complexity in WSN and IoT networks. In the future, more bio-inspired algorithms will be discovered with more improvements to help address the challenges in the WSN and IoT networks. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Autonomic Properties Supported**} \\ \cline{3-8}
**Name** & **Ref** & **Method** & **Self-Optimization** & **Self-Organization** & **Energy Consumption Reduction** & **Energy Harvesting** & **Self-Protection** \\ \hline \hline \multicolumn{8}{c}{Protocol Designs for Routing} \\ \hline MODP (2010) & [25] & Using multi objective dynamic programming for optimal routing & ✓ & & ✓ & \\ FTRA-IDA & [26] & Using fixed-tree-relaxation and iterative distributed & ✓ & & & \\
2013 (2013) & [27] & Using mixed-integer linear & ✓ & & ✓ & \\ MLP (2013) & [28] & Programming for optimal routing & ✓ & & ✓ & \\ MFA (2013) & [29] & Using algorithm inspired by firefly for optimal routing & ✓ & & ✓ & \\ EEOGR (2013) & [30] & Using multi-path and gradient-based network for optimal routing & ✓ & & ✓ & \\ FACOR (2014) & [31] & Using algorithm inspired by ants and fuzzy logic for optimal routing & & & \\ FACOR (2014) & [32] & Using mixed-integer linear & ✓ & & ✓ & \\ NDQIO (2015) & [32] & Using multi-path & ✓ & & ✓ & \\ CASER (2015) & [33] & Balancing security and efficiency in geography-based routing & ✓ & & ✓ & ✓ \\ Q- & [34] & Using Q-table reinforcement learning and Fuzzy-LEACH & ✓ & & \\ MORGSA (2019) & [35] & Using fractional gravitational search for optimal routing & ✓ & & \\ RPL-OF (2020) & [36] & Using low routing metric selection for optimal routing & ✓ & & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of Protocol Designs for Routing.