Intelligent Security Management and Control in the IoT. Mohamed-Aymen Chalouf
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in terms of latency, we can see in Figure 2.12 that after an exploration stage, the revenue improves very significantly. This recompense is clearly higher than for the adaptive controller, which shows a reduced and very variable recompense (Figure 2.11). In fact, the average of the recompense in TD3 is in the order of 13.91%, while the adaptive controller shows a recompense in the order of 3.6%. This recompense reflects the fact that under TD3, the average number of terminals attempting access gets closer to the optimum. This result, perhaps also shown in Figure 2.14, shows that the number of attempts with TD3 is closer still to the optimum which is equal to 15.49. In fact, the average number of attempts using the adaptive controller is equal to 30.12 (Figure 2.13), while it is equal to 19.6 for our approach.
Figure 2.11. The average recompense with the adaptive controller
Figure 2.12. The average recompense with the controller using TD3
We can note that in Figure 2.13 the adaptive technique does not make it possible to control correctly the number of attempts. In fact, we very often reach numbers significantly higher than the optimum. This triggers many collisions at access and new access attempts. We also see that the number of abandonments remains relatively significant compared to the TD3 controller (Figure 2.14). The latter, after the exploration stage, succeeds in significantly reducing the number of abandonments, which demonstrates the effectiveness of the proposed approach.
Figure 2.13. Access attempts (blue) and abandonments (red) with the adaptive controller. For a color version of this figure, see www.iste.co.uk/chalouf/intelligent.zip
Figure 2.14. Access attempts (blue) and abandonments (red) with the controller using TD3. For a color version of this figure, see www.iste.co.uk/chalouf/intelligent.zip
It should be noted that having recourse to our approach based on reinforcement learning, we have an improvement in performance with each access attempt. The limit, however, is found in the estimation errors, which lead to errors in calculating the recompense, hence the importance of having precise estimators.
2.7. Conclusion
In this chapter, we proposed a mechanism to control congestion of the access network, which is considered one of the most critical problems for IoT objects. We have proposed tackling congestion at its root by effectively managing random accesses from these devices thanks to use of the ACB mechanism.
The proposed access control mechanism is different from conventional methods, which generally rely on simple heuristics. Indeed, the proposed technique relies on recent advances in deep reinforcement learning, through use of the TD3 algorithm. The proposed approach has, in addition, the advantage of learning from its environment and could therefore enable it to adapt to variation of the access schema.
The simulation results make it possible to show the superiority of the proposed approach, which succeeds in maintaining a number of access attempts close to the optimum, despite the absence of exact information on the number of access attempts. This work also makes it possible to show the potential of using learning techniques in environments where the state cannot be known with precision.
In the context of our future work, we envisage improving estimation of the number of attempts using learning techniques.
2.8. References
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