# A Comprehensive Review on Network Protocol Design for Autonomic Internet of Things
## 3 Review of Protocol Designs
### Protocol Designs for Routing  
Since WSN and IoT networks tend to have a large and complex topology, multi-hop communication can provide advantages in data delivery. Sending data from the source to the destination often cannot be done through direct communication between the two nodes, but should be assisted by intermediate nodes. In this case, an appropriate routing protocol is needed to arrange the best path from the source to the destination. The routing method will also determine the energy consumption for the data delivery. The more efficient the routing is, the more energy that can be saved to extend the lifetime of the network. Many routing protocols are investigated to improve the energy usage and QoS of the network in WSN and IoT. These routing protocols should be dynamic and adapt to the change in the topology and the position of the nodes in the network. They are expected to support the self-healing, self-organization, and self-optimization property of IoT network. (1) Multi Objective Dynamic Programming  
[PERSON] et al. in [25] proposed the Multi Objective Dynamic Programming (MODP) to improve the Simple Hybrid Routing Protocol (SHRP) in choosing the best route towards the sink. The simple hybrid routing protocol (SHRP) is an energy-saving protocol for WSN that uses four metrics, i.e., the residual energy level, the number of hops from the source to the sink, the quality of the physical connection, and the received signal strength indicator (RSSI). The proposed routing protocol allows simultaneous analysis of these four metrics and generates a pareto-optimal solution. A multi-objective procedure is applied to each member of the subset solution, where the higher result identifies the more optimal solution. The proposed routing protocol is validated through simulation. The result shows that it is superior in certain situations, in terms of time convergence and reliability. Furthermore, it promotes the simultaneous use of multiple metrics, while selecting the best route. However, it is a complex algorithm that still requires more processing time in the implementation. (2) Fixed-Tree-Relaxation and Iterative Distributed Algorithm  
[PERSON] and [PERSON] in [26] proposed the Fixed-Tree-Relaxation and Iterative Distributed Algorithm (FTRA-IDA) for WSNs. The method uses the problem of power-efficient distributed estimation of vector parameters related to localized phenomena so that both sensor selection and routing structure in a WSN are jointly optimized to obtain the best possible estimation performance at a given querying node, for a given total power budget. The study showed that the optimal routing structure is not a traditional shortest path tree problem, due to the interplay between the communication cost and the gain estimation when fusing measurements from different sensors. To find a better solution, Fixed-Tree Relaxation-Based Algorithm (FTRA) and Iterative Distributed Algorithm (IDA) are used to optimize the sensor selection and routing structure. The performance of FTRA-IDA is evaluated through simulation. The simulation results show that the algorithm provides a better trade-off between the overall power efficiency and estimation accuracy compared to the conventional sensor selection and fixed routing algorithms. However, FTRA-IDA is a complex algorithm that requires more processing time in its implementation. (3) Hybrid Ant Colony Optimization Routing  
[PERSON] et al. in [27] proposed the Hybrid Ant Colony Optimization Routing (HACOR) protocol. The algorithm of the protocol is based on swarm intelligence, inspired by the behavior of ants when looking for food. This HACOR has a reactive and proactive part. It sends agents to perform routing setup process, when there is a data packet to be sent to the destination. It also proactively builds the alternate routes. The HACOR protocol is validated through simulation.