Distributed Clustering in Ad-hoc Sensor Networks: A Hybrid, Energy-Efficient Approach

1. Distributed Clustering in Ad-hoc Sensor Networks: A Hybrid, Energy-Efficient Approach Ossama Younis and Sonia Fahmy Department of Computer Sciences, Purdue University Ossama Younis, Purdue University

2. Contributions • A new distributed clustering protocol for sensor networks that has the following properties: • Energy-efficient • Terminates rapidly • Considers cluster quality, e.g., load-balanced clusters or dense clusters • Has low message/processing overhead Ossama Younis, Purdue University

3. Sensor Networks • Application-specific, e.g., • Monitoring seismic activities • Surveying military fields • Reporting radiation levels at nuclear plants • Nodes are usually: • Densely deployed • Limited in processing, memory, and communication capabilities • Constrained in battery lifetime • Left unattended Ossama Younis, Purdue University

4. Goals • Scalability, data and state aggregation, robustness, and prolonged network lifetime What is network lifetime? Time until the first node dies Time until the last node dies • How to prolong the network lifetime? • Deploy redundant nodes • Apply energy-efficient protocols, e.g., • MAC layer protocols can reduce energy waste • Topology management can distribute energy consumption Ossama Younis, Purdue University

5. Topology management Cluster-based approach Cell-based approach observer Ossama Younis, Purdue University

6. Outline • System model and requirements • The Hybrid, Energy-Efficient, Distributed clustering protocol (HEED) • HEED properties • Evaluation • Related Work • Conclusion Ossama Younis, Purdue University

7. System Model • A set of n sensor nodes are dispersed uniformly and independently in a field • Sensor nodes are • Quasi-stationary • Unattended • Equally significant • Location un-aware • Homogeneous (similar capabilities) • Serving multiple observers • Possess a fixed number of transmission power levels Ossama Younis, Purdue University

8. Requirements • Our goal is to design a new clustering approach that has the following properties: • Completely distributed • Terminates in O(1) iterations • Has low message/processing overhead • Generates high energy, well-distributed cluster heads • Can provide other characteristics, such as balanced or dense clusters Ossama Younis, Purdue University

9. Approach (HEED) • We propose the Hybrid, Energy-Efficient, Distributed clustering approach (HEED) • Heed is hybrid: • Clustering is based on two parameters • HEED is distributed: • Every node only uses information from its 1-hop neighbors (within cluster range) • HEED is energy-efficient: • Elects cluster heads that are rich in residual energy • Re-clustering results in distributing energy consumption Ossama Younis, Purdue University

10. Cost definition node degree (for load balancing) AMRP: Average min. reachability power (for min. intra-cluster comm. energy) 1/node degree (for dense clusters) HEED - Parameters • Parameters for electing cluster heads • Primary parameter: residual energy (Er) • Secondary parameter: communication cost (used to break ties) i.e., maximize energy and minimize cost Ossama Younis, Purdue University

11. HEED – Algorithm at node v • Discover neighbors within cluster range • Compute the initial cluster head probability CHprob = f(Er/Emax) • Initialization • If v received some cluster head messages, choose one head with min cost • If v does not have a cluster head, elect to become a cluster head with CHprob . • CHprob = min(CHprob * 2, 1) • Repeat until CHprob reaches 1 • Main processing • If cluster head is found, join its cluster • Otherwise, elect to be cluster head • Finalization Ossama Younis, Purdue University

12. HEED - Example Discover neighbors (0.4,3) a10 (0.6,2) a13 (0.1,4) c2 a11 (0.2,2) (0.2,5) a7 a8 Compute CHprob and cost a12 (0.5,3) (0.2,3) c3 (0.2,3) a9 (0.8,4) (0.1,4) (0.1,2) Elect to become cluster head c1 a6 a5 (0.9,4) a14 (0.5,4) a2 c4 Resolve ties a4 (0.6,4) a3 (0.3,2) (0.7,5) (0.2,3) Select your cluster head (0.3,2) a1 Ossama Younis, Purdue University

13. HEED - Analysis • HEED has the following properties: • Completely distributed • Clustering terminates in O(1) iterations: • Message overhead: O(1) per node • Processing overhead: O(n) per node • Cluster heads are well distributed. Pr{two CHs are within the same cluster range}: (p = initial CHprob ) Ossama Younis, Purdue University

14. HEED – Inter-cluster communication • Lemma 1 (Blough and Santi’02): Assume n nodes are dispersed uniformly and independently in an area R=[0,L]2. If Rc2n=aL2lnL, for some a>0, Rc << L, and n>>1, then limn,N→∞E(number of empty cells) = 0, where a cell is an area • Lemma 2: There exists at least one cluster head a.a.s. in any area of size Ossama Younis, Purdue University

15. 2.7Rc CH2 2.7Rc Rt CH1 HEED – Inter-cluster communication • Theorem 1: Two cluster heads in two neighboring areas can communicate if • Theorem 2: HEED produces a connected multi-hop cluster head graph (structure) asymptotically almost surely Ossama Younis, Purdue University

16. Performance evaluation • 2000x2000 network field with 1000 nodes • Demonstrating HEED properties: fast termination, rich-energy cluster heads, and cluster quality Ossama Younis, Purdue University

17. Performance evaluation(cont’d) • Apply HEED to an application where nodes directly contact a far observer • Compare to multi-hop LEACH clustering • 100x100 network • Initial Er = 2 Joule • 1 round = 5 TDMA frames Ossama Younis, Purdue University

18. Related Work • Topology management protocols suffered from at least one of the following problems: • Dependence on location awareness (e.g., GAF) • Slow convergence (i.e., dependent on the network diameter) (e.g., DCA) • Energy efficiency was not the main goal of many protocols, e.g., Max-Min D-clustering • No focus on clustering quality, such as having cluster heads well-distributed in the network (e.g. LEACH) Ossama Younis, Purdue University

19. Conclusion • We have proposed HEED clustering • HEED is fast and has low overhead • HEED can provide other features, such as load-balancing • HEED is independent of: • Homogeneity of node dispersion in the field • Network density or diameter • Distribution of energy consumption among nodes • Proximity of querying observers • HEED can be extended to provide multi-level hierarchy Ossama Younis, Purdue University