# A Comprehensive Review on Network Protocol Design for Autonomic Internet of Things
## 3 Review of Protocol Designs
### Protocol Designs for Routing
The simulation results have shown that this protocol has better performance than the Ad-hoc On-demand Distance Vector (AODV) protocol in terms of end-to-end delay, jitter, packet delivery ratio, and throughput. However, the weakness is that this protocol generates a lot of overhead packets in the network so that traffic in the network will increase. (4) Mixed-Integer Linear Programming  
[PERSON] et al. in [28] proposed the Mixed-Integer Linear Programming optimization framework for routing in WSN, where the optimal relay selection and power allocation are performed subject to signal-to-noise ratio constraints. The proposed framework determines whether direct transmission or cooperative transmission that will give the optimal result for a given configuration of nodes. If the cooperative transmission is the optimal one, the framework also can be used to obtain the best set of relaying nodes along with the corresponding optimal transmission power values. A mixed-integer optimization is used to solve the problem of optimal cooperative routing, which provides a low-complexity implementation for simple platforms in WSN. The proposed approach is validated through mathematical simulation. It is compared to other energy-efficient routings in WSN such as the minimum-power cooperative routing, the cooperation along the shortest path, and the SNR-constrained non-cooperative routing. The simulation result shows that the proposed approach outperforms the other algorithms in terms of power consumption and BER. (5) Multi-Population Firefly Algorithm  
[PERSON] and [PERSON] in [29] proposed the Multi-population Firefly Algorithm (MFA) for correlated data routing. The method is designed for underwater WSNs which usually have problems of low data delivery efficiency and high energy consumption. The algorithm uses different groups of fireflies, which conduct their optimization in the evolution in order to improve the convergence speed and solution precision. The aim is to improve the adaptability of building, selecting, and optimization of routing path considering the data correlation and their sampling rate. The data packets are merged during the process of routing path finding and redundant information is eliminated. The MFA algorithm is validated through simulation. The simulation results have shown that MFA achieves better performance than the existing Vector-Based Forwarding (VBF) and Distributed Underwater Clustering Scheme (DUCS) protocol in terms of packet delivery ratio, energy consumption, and network throughput. The limitation of this protocol is it is specifically designed and tested for underwater environment WSN only. (6) Optimal Gradient Routing  
[PERSON] and [PERSON] in [30] proposed the Energy-Efficient Routing using Optimal Gradient Routing (EEOGR) with on demand neighborhood information in WSN. The proposed protocol combines the on demand multi-hop information based multipath routing and the gradient-based network for achieving the optimal path, which reduces energy consumption of sensor nodes. It minimizes the number of hops for packet forwarding to the sink node, which gives a better solution for energy consumption and delay. It also reduces the message exchange overhead. The proposed routing protocol is validated through simulation. It provides the least deadline miss ratio, which is most suitable to real-time data delivery. Simulation resultsshow that the proposed routing protocol has achieved good performance with respect to the reduction in energy efficiency and deadline miss ratio. However, since EEOGRP uses more relaying neighborhood's information and shares large number of paths between a source and a destination, it needs a complex processing in the implementation. [PERSON] et al. in [31] proposed the Fuzzy Ant Colony Optimization Routing (FACOR) for WSNs. The method uses ant colony algorithm to find existing paths between the source and the destination. It is combined with fuzzy logic in order for the ants to make the best decision. The performance of FACOR is evaluated through simulation.