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Efficient Active Clustering of Mobile Ad-Hoc Networks

Efficient Active Clustering of Mobile Ad-Hoc Networks. Damianos Gavalas, Grammati Pantziou, Charalampos Konstantopoulos, Basilis Mamalis. Presented by D. Gavalas. Department of Cultural Technology and Communication University of the Aegean Email: dgavalas@aegean.gr. Outline.

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Efficient Active Clustering of Mobile Ad-Hoc Networks

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  1. Efficient Active Clustering of Mobile Ad-Hoc Networks Damianos Gavalas, Grammati Pantziou, Charalampos Konstantopoulos, Basilis Mamalis Presented by D. Gavalas Department of Cultural Technology and Communication University of the Aegean Email: dgavalas@aegean.gr

  2. Outline • Introduction to Mobile Wireless Ad Hoc Networks • Differences in routing among conventional and Ad Hoc networks • Clustering in Ad Hoc Networks • Existing algorithms for Ad Hoc Networks clustering & their disadvantages • Adaptive Broadcast Period clustering algorithm • Simulation results • Conclusions

  3. Mobile Ad Hoc Networks • Formed by wireless hosts which may be mobile • Without (necessarily) using a pre-existing infrastructure • Routes between nodes may potentially contain multiple hops

  4. Mobile Ad Hoc Networks • Two mobile nodes are ‘linked’ if they are within transmission range of each other • May need to traverse multiple links to reach a destination

  5. Mobile Ad Hoc Networks (MANETs) • Mobility causes route changes

  6. Why Ad Hoc Networks ? • Ease of deployment • Speed of deployment • Decreased dependence on infrastructure

  7. Many Applications • All applications where a network infrastructure does not exist • Civilian environments • taxi cab network • meeting rooms, electronic conferences, e-classrooms • sports stadiums • boats, small aircraft • Personal area networking • cell phone, laptop, ear phone, wrist watch • Emergency operations • search-and-rescue • policing and fire fighting • Military environments • soldiers, tanks, planes

  8. Challenges • Limited wireless transmission range • Broadcast nature of the wireless medium • Packet losses due to transmission errors • Mobility-induced route changes • Mobility-induced packet losses • Battery constraints • Potentially frequent network partitions • Routing

  9. Why is Routing in MANET different ? • Host mobility • link failure/repair due to mobility may have different characteristics than those due to other causes • Rate of link failure/repair may be high when nodes move fast • New performance criteria may be used • route stability despite mobility • energy consumption • Difficult to assign hierarchical IP-like addresses (sub-networks which could share the same ‘domain name’ are not fixed)

  10. Unicast Routing Protocols • Many protocols have been proposed • Some have been invented specifically for MANET • Others are adapted from previously proposed protocols for wired networks • No single protocol works well in all environments • some attempts made to develop adaptive protocols

  11. Clustering in Ad Hoc Networks • A promising approach to ease routing process in MANETs is to is to build hierarchies among the nodes, such that the network topology can be abstracted • Logical grouping of mobile nodes in separate clusters • This process is commonly referred to as clustering • A mobile node in every cluster is elected as cluster head (CH) • CHs store routing tables • They also route incoming messages from their cluster members to neighboring clusters • Different clustering schemes may differ in • how clusters are determined • the way cluster head is chosen • duties assigned to the cluster head

  12. Clustering in Ad Hoc Networks S D Cluster Head Gateway S: Source Ordinary Node D: Destination

  13. Objectives for efficient clustering in Ad Hoc Networks • Cluster stability • Cluster formations should not frequently change over time (that causes exchange of significant volume of control messages) • Energy preservation • Mobile nodes are highly dependant to their battery power • Energy consumption should be balanced among mobile nodes • Minimal usage of network resources for cluster control • Network bandwidth should be available for data exchange, not control messages exchange

  14. Clustering Algorithms: Lowest ID (LID) • Mobile nodes are assigned unique IDs • Nodes transmit their ID (through a special ‘Hello’ message) in a given Broadcast Period • Each node ‘elects’ as cluster head the node with the lowest-ID in the neighborhood

  15. Clustering Algorithms: Lowest ID (LID) 13 8 1 6 1 6 2 14 7 15 4 12 3 9 11 5 11 10 Cluster Head Ordinary Node

  16. Clustering Algorithms: Highest Degree (HD) • Clustering based on location information • the node with the largest number of neighbors is elected as CH 13 8 8 1 6 2 14 7 15 15 4 12 3 3 9 11 5 10 Cluster Head Ordinary Node

  17. Clustering Algorithms: Vote-Based Clustering (VC) • CH elections are based not exclusively on location but also on the battery power level of mobile nodes • nodes with high degree (large number of neighbors) and sufficient battery power are elected as CHs

  18. Disadvantages of Existing Clustering Algorithms • LID: CHs election is biased in favor of nodes with low IDs • these nodes are likely to serve as CHs for long time and their energy supply rapidly depletes • HD and VC methods imply cluster instability • losing contact of a single node (due to node movement), may cause failure of the current CH to be re-elected • A CH may end up dominating so many nodes that its computational, bandwidth and battery resources will rapidly exhaust • Common disadvantage of LID, HD, VC: cluster formation is based on the periodic broadcast of ‘Hello’ messages • In relatively static MANETs (e.g. e-classrooms), this ‘storm’ of control messages only verifies that cluster structure should remain unchanged

  19. Adaptive Broadcast Period (ABP) clustering algorithm: objectives • A quick method for cluster formation is needed • required speed should not be achieved at the expense of instable cluster configurations • we extend VC algorithm so as to avoid frequent CH ‘re-elections’ • Balanced distribution of energy consumption • Cluster sizes should be controlled • not too large neither too small clusters • For relatively static MANET topologies, broadcast period should be dynamically adapted to avoid unnecessary control message exchanges

  20. ABP algorithm: cluster formation • We introduce the concept of “cluster head competence” (CHC) which represents the competence of a MH to undertake the role of a CH • Format of ‘Hello’ message: • MH_ID: the ID of the mobile node • CH_ID: the ID of the mobile node’ s CH • CHC: cluster head competence value of the mobile node • BP (Broadcast Period): used to adapt the broadcast period within a particular cluster

  21. ABP algorithm: cluster formation • Cluster head competence (CHC) values are calculated according to: • CHC = (c1 × d + c2 × b) – p • c1, c2: weighted coefficients of node degree and battery availability, respectively (c1 + c2 = 1) • d: Number of neighbors (degree of MH) • b: Remaining battery lifetime (percentage of remaining over full battery power) • p: ‘handover’ penalty coefficient (used to avoid frequent CH re-elections) • Mobile nodes with maximum CHC value in the neighborhood (maximum degree and battery availability) become CHs

  22. ABP execution example 5 13 8 1 6 2 14 7 15 4 12 10 3 9 11

  23. ABP execution example

  24. ABP execution example 5 13 8 1 6 2 14 7 15 4 12 10 3 9 11 5 13 8 1 6 1 6 2 14 7 15 4 12 10 3 9 11 11 Cluster Head Ordinary Node

  25. Dynamically Adaptive Broadcast Period for control messages exchange • A principal objective of ABP algorithm is to reduce the number of control messages within the MANET • The broadcast period (BP) should adapt on mobility pattern of mobile nodes • For highly mobile nodes, BP is shortened • For relatively static MANETs (e.g. e-classrooms) BP is lengthened, relaxing the MANET from unnecessary control messages

  26. Measuring mobility rate • Mobility rate is measured by CHs by keeping record of their neighbors at the end of every BP • Mobility rate: The sum of nodes removed and added between successive BPs 5 5 12 12 BP #2 BP #1 9 8 9 8 9 1 1 2 3 4 12 4 2 3 5 5 14 8 14 8 BP #4 3 BP #3 1 1 3 2 2 12 12

  27. Simulation results: number of control messages

  28. Simulation results: cluster stability

  29. Simulation results: balancing energy consumption distribution

  30. Conclusions • A novel algorithm with the following strengths: • clustering procedure is completed quickly (within three ‘Hello’ cycles) • both location and battery power metrics are taken into account in clustering process • derived cluster formations exhibit enhanced stability by preventing unnecessary CH re-elections • for relatively static network topologies, control traffic volume is minimized • Slightly increased control (‘Hello’) packet size

  31. Thank you! Questions?

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