Future Directions

6.2 Future Directions

As future research directions, a few possibilities are summarized in the following:

Short term

Proof of correctness for Topology Aware and CEL algorithms.
Implementation and evaluation of a leader-based consensus algorithm, which could take advantage of the good leader connectivity offered both in Topology Aware and CEL.
Render publicly available the source code for the implementation of the Topology Aware and CEL algorithms, with the benchmarking environments, allowing, thus, other researchers to easily implement and evaluate their leader election algorithms through realistic experimentation.
Evaluation of the energy consumption of nodes running Topology Aware and CEL algorithms, on both wireless communication and computation. Since communication has a high energy consumption, extending the cross-layer approach by using existing data on different levels of the OSI model, should minimize the number of message exchanged in the algorithm, and, therefore, reduce the energy consumption of nodes. Preliminary works on the energy consumption are presented in Appendix A.1.
Furthermore, the choice of the eventual leader could be based on energy consumption. For instance, elect a leader with higher remaining battery, as it will probably exchange more messages than the other component nodes.

Mid-term

Implementation and evaluation of Topology Aware and CEL algorithms on real networks like the ones composed by devices such as Raspberry Pi.
A mechanism to reduce the size of messages exchanged, since larger message sizes also have an important impact on the collision rate. Such a reduction could be achieved by using compression algorithms, or by restricting the knowledge of the network to a limited number of hops, either locally for the centrality computation or for exchanged messages. Therefore, message exchanges would be smaller, and communication would be more reliable with less energy consumption.
Exploration of other centrality metrics, such as the one proposed by Kim et al. [KW13], the eigenvector used by Google PageRank algorithm [BP98], or an estimation of the closeness [Coh⁺14], taking advantage of network dynamics and reducing the number of required computation steps. Temporal centralities [Gha18] could also be considered.
A collaborative approach of nodes of the same component in order to exchange information about current centrality computation, aiming at reducing the number of required computation steps, redundant computation, and energy consumption. Examples, such as the algorithm of Wang et al. [WT15] or Lulli et al. [Lul⁺15], could be considered.

Table of Contents

1 Introduction
1.1 Contributions
1.1.1 Topology Aware Leader Election Algorithm for Dynamic Networks
1.1.2 Centrality-Based Eventual Leader Election in Dynamic Networks
1.2 Manuscript Organization
1.3 Publications
1.3.1 Articles in International Conferences
1.3.2 Articles in National Conferences
2 Background
2.1 Properties of Distributed Algorithms
2.2 Timing Models
2.3 Process Failures
2.4 Communication Channels
2.5 Failures of Communication Channels
2.6 Distributed Systems
2.6.1 Static Systems
2.6.2 Dynamic Systems
2.7 Centralities
2.8 Messages Dissemination
2.9 Leader Election
2.9.1 Classical Leader Election
2.9.2 Eventual Leader Election
2.10 Conclusion
3 Related Work
3.1 Classical Leader Election Algorithms
3.1.1 Static Systems
3.1.2 Dynamic Systems
3.2 Eventual Leader Election Algorithms
3.2.1 Static Systems
3.2.2 Dynamic Systems
3.3 Conclusion
4 Topology Aware Leader Election Algorithm for Dynamic Networks
4.1 System Model and Assumptions
4.1.1 Node states and failures
4.1.2 Communication graph
4.1.3 Channels
4.1.4 Membership and nodes identity
4.2 Topology Aware Leader Election Algorithm
4.2.1 Pseudo-code
4.2.2 Data structures, variables, and messages (lines 1 to 6)
4.2.3 Initialization (lines 7 to 11)
4.2.4 Periodic updates task (lines 12 to 16)
4.2.5 Connection (lines 20 to 23)
4.2.6 Disconnection (lines 24 to 27)
4.2.7 Knowledge reception (lines 28 to 38)
4.2.8 Updates reception (lines 39 to 53)
4.2.9 Pending updates (lines 54 to 65)
4.2.10 Leader election (lines 17 to 19)
4.2.11 Execution examples
4.3 Simulation Environment
4.3.1 Algorithms
4.3.2 Algorithms Settings
4.3.3 Mobility Models
4.4 Evaluation
4.4.1 Metrics
4.4.2 Instability
4.4.3 Number of messages sent per second
4.4.4 Path to the leader
4.4.5 Fault injection
4.5 Conclusion
5 Centrality-Based Eventual Leader Election in Dynamic Networks
5.1 System Model and Assumptions
5.1.1 Node states and failures
5.1.2 Communication graph
5.1.3 Channels
5.1.4 Membership and nodes identity
5.2 Centrality-Based Eventual Leader Election Algorithm
5.2.1 Pseudo-code
5.2.2 Data structures, messages, and variables (lines 1 to 4)
5.2.3 Initialization (lines 5 to 7)
5.2.4 Node connection (lines 8 to 17)
5.2.5 Node disconnection (lines 18 to 23)
5.2.6 Knowledge update (lines 24 to 34)
5.2.7 Neighbors update (lines 35 to 41)
5.2.8 Information propagation (lines 42 to 47)
5.2.9 Leader election (lines 48 to 52)
5.3 Simulation Environment
5.3.1 Algorithms Settings
5.3.2 Mobility Models
5.4 Evaluation
5.4.1 Metrics
5.4.2 Average number of messages sent per second per node
5.4.3 Average of the median path to the leader
5.4.4 Instability
5.4.5 Focusing on the 60 meters range over time
5.4.6 A comparative analysis with Topology Aware
5.5 Conclusion
6 Conclusion and Future Work
6.1 Contributions
6.2 Future Directions
A Appendix
A.1 Energy consumption per node
A.1.1 Simulation environment
A.1.2 Algorithms settings
A.1.3 Mobility Models
A.1.4 Metric
A.1.5 Performance Results