Clustering and routing

From Center for Integrated Circuits and Devices Research (CIDR)
Jump to navigation Jump to search
Figure 1. Clustering in Wireless Sensor Networks.

Routing protocols in wireless sensor networks (WSNs) are generally classified into three main categories according to network structure: flat, hierarchical, and location-based.[1][2] In flat routing protocols, all nodes in the network are typically assigned equal roles, where each node senses the environment and sends the sensed data to a sink or base station. In hierarchical routing protocols, nodes are grouped into clusters. Each cluster has its own cluster head and member nodes, as shown in Figure 1. The member nodes still play the same role of sensing the environment. They forward their sensed data to the respective cluster heads (instead of the base station) for aggregation, and the cluster heads are the ones responsible for transmitting the aggregated data to the base station. In comparison to flat routing protocols, hierarchical routing protocols offer several advantages, including higher energy efficiency, increased scalability, and increased robustness, all of which are advantageous for WSNs.[3]

Computational Intelligence Paradigms

Different computational intelligence (CI) paradigms that can be applied in solving clustering and routing in WSNs are shown in Table 1. Among these paradigms, reinforcement learning (RL) is the most appropriate for WSN applications as it has been proven to achieve optimal routing results in WSNs.[4] While it may need some time to learn the optimal routes, it is highly flexible to network topology changes. Moreover, it has low processing requirements, and low-cost or simple implementation. This makes RL suitable for energy-efficient routing at individual nodes, despite having medium memory requirements for keeping track of different possible actions and values. Thus, discussions on routing protocols with computational intelligence were focused on RL-based routing protocols.

Table 1. Different computational intelligence (CI) paradigms for solving clustering and routing in WSNs.[5]
CI Paradigms Computational Requirements Memory Requirements Flexibility Clustering & Routing
Neural Network medium medium low less appropriate
Fuzzy Logic medium medium high moderately appropriate
Evolutionary Algorithm medium high low less appropriate
Swarm Intelligence low medium high moderately appropriate
Reinforcement Learning low medium high most appropriate

Since routing protocols with intelligence improves network lifetime more than traditional approaches for WSN applications, we decided to implement RL-based protocols, and consider CLIQUE protocol[6] and the Deep Q-Network protocol by Kaur et al[7]. For our baseline protocol, we choose the LEACH protocol[8] as it is one of the pioneering clustering and routing approaches for WSNs.

References

  1. ↑ N. Al-Karaki and A. E. Kamal, “Routing techniques in wireless sensor networks: A survey,” IEEE Wireless Communications, vol. 11, pp. 6–28, 12 2004.
  2. ↑ N. A. Pantazis, S. A. Nikolidakis, and D. D. Vergados, “Energy-efficient routing protocols in wireless sensor networks: A survey,” IEEE Communications Surveys & Tutorials, vol. 15, pp. 551–591, 2013.
  3. ↑ Raghavendra V. Kulkarni, Anna Forster, and Ganesh Kumar Venayagamoorthy. “Computational Intelligence in Wireless Sensor Networks: A Survey”. In: IEEE Communications Surveys & Tutorials 13.1 (2011), pp. 68–96. doi: 10.1109/SURV.2011.040310.00002.
  4. ↑ Mohammad Abu Alsheikh et al. “Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications”. In: IEEE Communications Surveys & Tutorials 16.4 (2014), pp. 1996–2018. doi: 10.1109/COMST.2014.2320099.
  5. ↑ Raghavendra V. Kulkarni, Anna Forster, and Ganesh Kumar Venayagamoorthy. “Computational Intelligence in Wireless Sensor Networks: A Survey”. In: IEEE Communications Surveys & Tutorials 13.1 (2011), pp. 68–96. doi: 10.1109/SURV.2011.040310.00002.
  6. ↑ A. Förster and A. L. Murphy, “CLIQUE: Role-free clustering with Q-learning for wireless sensor networks,” in Proceedings of International Conference on Distributed Computing Systems, 2009, pp. 441–449.
  7. ↑ G. Kaur, P. Chanak, and M. Bhattacharya, “Energy-Efficient Intelligent Routing Scheme for IoT-Enabled WSNs,” IEEE Internet of Things Journal, vol. 8, no. 14, pp. 11440–11449, Jul 2021.
  8. ↑ W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,” IEEE Transactions on Wireless Communications, vol. 1, no. 4, pp. 660–670, Oct 2002.
Retrieved from "https://cidr.up-microlab.org/index.php?title=Clustering_and_routing&oldid=573"