# Conceptual Framework for Future WSN-MAC Protocol to Achieve Energy Consumption Enhancement
## 3 State-of-the-Art Approaches for MAC-Protocols Optimization
### Heuristic-Based Approaches  
In the work of [44], the authors integrated WSN scheduling with deployment. After using a meta-heuristic algorithm: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and a heuristic algorithm for deployment that considers the degree of coverage, they have proposed a heuristic scheduling algorithm based on grouping the sensors and considering the residual energy in each sensor. In the work of [45], a multi-channel scheduling algorithm was proposed. For this purpose, the authors formulated Integer Linear Programming (ILP) optimization. In addition, they proposed heuristic algorithm based on Langford subset generation. The algorithm was attributed to a contention-free, multi-RF channel, and was heuristic. Their heuristic algorithm was claimed to give a lower execution time than ILP with a comparable performance. In addition, the authors used CSMA/CA in a typical way without any model in order to change the back-timer. In the work of [46], heuristic-based scheduling using a greedy approach was proposed with consideration of the aspect of energy harvesting. More specifically, the network was considered to contain nodes with energy harvesting capability; however, the batteries were imperfect, leaking was possible, and nodes were heterogeneous. This work was pioneering in the networks of the MAC scheduling of harvest use and store (HUS). However, the optimization solutions provided were subject to local minima because of their greedy nature. In the work of [47], a mathematical model for solving the converge cast problem in WSN was presented. The model considers the case application of target monitoring and aims to minimize scheduling time-slots. It is solved using pricing problems in a round-robin fashion due to the NP-hard nature of the optimization. The authors of this work \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference Paper** & **Approach** & **Type** & **Contribution** & **Results** \\ \hline
[40] & Game theory based & Synch-TMDA & Game-based & Reduces the energy-efficient TDMA & energy \\  & ETDMA & & (G-ETDMA) for & consumption to maximize the \\  & & & & intra-cluster WSN & lifetime of the sensor network \\ \hline
[41] & Game theory & & Optimizes the sleep & \\  & distributed & & interval between & \\  & & & consecutive wake-ups by & \\  & Game theory & & & reducing idle- & \\  & distributed & & listening time through & \\  & approach & & & a dynamic duty-cycling & \\  & & & technique & \\ \hline
[42] & Game theory & & & Proposes a generalized & \\  & framework & & optimization framework to & \\  & & & map the cost of each player & \\  & & & onto protocol-specific & \\  & & & MAC parameters. & \\ \hline
[43] & Multilayer DS-MAC & & & The use of multilayer & \\  & using game theory & & nodes with distributed & \\  & optimization approach & & MAC (DS-MAC) where the & \\  & & & listening time of the nodes & \\  & & & is controlled based on & \\  & & & neighboring & \\  & & & communication. Route loss & \\  & & & constraints are addressed & \\  & & & using game theory & \\  & & & optimization while & \\  & & & selecting routes towards & \\  & & & base stations (BS) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._reported two issues: real time concern and a lack of physical layer issue consideration such as fading and shadowing. The WSN scheduling approach that depends on the network virtualization concept is proposed in [48]. It separates networks into profiles, with each profile indicating a group of nodes with a similar channel demand nature or characteristics. To put it another way, two types of profiles have been proposed: periodic and bursts. The mainframe was then separated into a contention-free frame and a contention access frame, each carrying a set of guaranteed time-slots, according to IEEE802.15.4. Then, to enhance each profile's utility, an optimization process was carried out. To complete the optimization, the method takes a greedy approach.