A.1 Energy consumption per node

Some preliminary results about the energy consumption of nodes executing Centrality-based Eventual Leader (CEL) and Gómez-Calzado et al. algorithms (Chapter 5) are presented in the following.

A.1.1 Simulation environment

Experiments were conducted on the OMNeT++ [VH08]/INET [MVK19] environment described in Section 5.3, with the same configuration. However, the transmission range is fixed between 30 meters (instead of 20 meters) and 80 meters.

The power consumption consumer model for IEEE 802.11 used the following constant parameter values approximately based on a CC3220 transceiver:

Local computation (CPU) and mobility are not considered in the energy consumption model, neither the power transmission range fixed at each experiment. Each experiment lasts 30 simulated minutes.

A.1.2 Algorithms settings

The two versions of CEL, i.e. CEL-1 and CEL-0.7, and Gómez-Calzado et al. algorithm [Góm+13] are compared using the same settings has in Section 5.3.1.

A.1.3 Mobility Models

The two mobility models defined in Section 5.3.2 are used, i.e. Random Walk and Truncated Lévy Walk.

A.1.4 Metric

The energy consumption is computed using the radio power consumer model describe in A.1.1, where transmission, reception, or idle states consume energy from the node battery. At the end of each experience, the energy consumption per node for each algorithm is computed and measured in watts (W).

A.1.5 Performance Results

PIC

Figure A.1: Energy consumption per node (lower is better) Random Walk.

PIC

Figure A.2: Energy consumption per node (lower is better) Truncated Lévy Walk.

Overall, the energy consumption per node is correlated with the number of messages sent, presented in Section 5.4.2, as the power consumption consumer model considers only communication.

For the Random Walk mobility model in A.1, the power consumption of nodes in the Gómez-Calzado et al. algorithm decreases as the transmission range increases, since more nodes are in the same component and timeouts of leader messages are higher. The power consumption of both versions CEL algorithms increase as the transmission range increase, since movements in larger connected component lead to more messages sent. In lower transmission ranges, the energy consumption per node executing Gómez-Calzado et al. algorithm is higher than both CEL versions. For instance, at a transmission range of 30 meters, nodes executing Gómez-Calzado et al. algorithm consume on average 16.47W, while nodes in CEL-1 and CEL-0.7 consume on average 0.46W and 0.39W respectively. The probabilistic gossip version of CEL with ρ sets to 0.7 reduces the energy consumption compared to CEL-1, since less messages are sent as seen in Section 5.4.2, due to the self-pruning approach used in the TopologicalBroadcast method described in Section 5.2.8. Nodes in CEL-0.7 have an energy consumption of 6.41W on average at a transmission range of 80 meters, while nodes in CEL-1 and Gómez-Calzado et al. algorithms are up to 10.39W and 9.07W respectively.

A.2 shows a higher average energy consumption for nodes executing CEL-1 than Gómez-Calzado et al. in the Truncated Lévy Walk mobility model when the transmission range is larger than 65 meters. A similar observation can be made concerning the average number of messages sent on this mobility model as presented in Section 5.4.2. At a transmission range of 80 meters, nodes executing CEL-0.7 have an average energy consumption up to 14.27W, while nodes executing CEL-1 are up to 23.72W, and nodes in Gómez-Calzado et al. algorithm are up to 9.18W. These increase are due to flying nodes that induce many connections and disconnections among existing connected components. However, the energy consumption per node remains lower for both CEL versions than Gómez-Calzado et al. algorithm on smaller ranges such as 30 meters.

Note that this preliminary evaluation is a work in progress.


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