Low Energy Adaptive Clustering Hierarchy with Deterministic Cluster-Head Selection

1. Low Energy Adaptive Clustering Hierarchy with Deterministic Cluster-Head Selection M. J. Handy, M. Haase, D. Timmermann Institute of Applied Microelectronics and Computer Science University of Rostock

2. Outline • Introduction / Motivation • sensor networks, lifetime, communication models • Problem Formulation • cluster-head selection, LEACH algorithm • Contribution • improved CH-selection algorithm, definition of sensor network lifetime • Simulations • simulation tool, simulation set-up, results

3. Introduction Where is the spot of leakage? • - Only the sandbags know • Useful application of wireless microsensor networks • Equip each sandbag with a moisture sensor • Collect and evaluate data How do sensors collaborateefficiently?

4. Introduction • Efficient collaboration of sensors means: • Ensure connectivity • Efficient role assignment • Collect only significant data • Decrease latency • Save energy Our Goal: Extendnetwork lifetime

5. Introduction How to increase sensor lifetime? Increase energy supply Reduce energy consumption • Energy density is the problem • Battery capacity increases only by 30-50 % in 5 years • Compare with Moore‘s Law • Micro-sensors vs. macro-batteries? • Hardware issue(e.g. circuit design) • Software issue • Applications / OS • Algorithms • Protocols

6. Communication Models - Direct transmission [1] - Multihop transmission - Clustering [1] [1] Heinzelman, Chandrakasan `01

7. Cluster-Based Communication A Simple Algorithm The problem: Select j cluster-heads of N nodes without communication among the nodes • The simplest solution: • Each node determines a random number x between 0 and 1 • If x < j / N node becomes cluster-head ...it‘s good, but: Cluster-heads dissipate much more energy than non cluster-heads! How to distribute energy consumption?

8. LEACH Communication Protocol Low-Energy Adaptive Clustering Hierarchy • Cluster-based communication protocol for sensor networks, developed at MIT • Adaptive, self-configuring cluster formation • - The operation of LEACH is divided into rounds • - During each round a different set of nodes are cluster-heads • Each node n determines a random number x between 0 and 1 • If x < T(n) node becomes cluster-head for current round

9. Cluster-Head Selection LEACH Algorithm P= cluster-head probability (j/N) r = number of the current round G = set of nodes not been cluster-heads in the last 1/P rounds Every node becomes cluster-head exactly once within 1/P rounds

10. Cluster-Head Selection LEACH Algorithm P= cluster-head probability (j/N) r = number of the current round G = set of nodes not been cluster-heads in the last 1/P rounds Every node becomes cluster-head exactly once within 1/P rounds Drawback: Selection of cluster-heads is completely stochastic!

11. Cluster-Head Selection, Our Approach I Basic Idea: Include the remaining energy level En_current= current energy of node n En_max= initial energy of node n • Simulations showed: • + longer network lifetime • After a certain number of rounds the network is stuck, although there are still nodes alive • The reason: T(n) is too low since the remaining nodes have very low energy level

12. Cluster-Head Selection, Our Approach II Idea: Increase T(n) when network is stuck rs = number of rounds a node has not been cluster-head (reset to 0 when a node becomes cluster-head) • T(n) is increased when the network is stuck • Possible deadlock of the network is solved Significant longer network lifetime

13. Lifetime of Microsensor Networks Introducing 3 New Metrics • First Node Dies (FND) • Network quality decreases considerably as soon as one node dies • Half of the Nodes Alive (HNA) • The loss of a single or few nodes does not diminish the QOS of the network • Last Node Dies (LND) • Estimated value for overall lifetime of thenetwork

14. Simulations Simulation Tool • YANASim (Yet Another Network Analyzing and Simulation Tool) • Simulates energy consumption of microsensor networks • Uses Clustering, Multihop and Direct Transmission • Visualisation of simulation results • Platform independent (Java)

15. Simulations Energy Model Transmit: Receive: k = message length d = distance λ = path-loss index

16. Simulations Simulation Results (1) Simulation Setup: Nodes: 200 Area: 200m*200m Base Station Pos.: (100,300)m Initial Energy / Node: 1 J Message Length: 200 bit CH-Probability: 0.05 Path-Loss (intra-cluster): 2 Path-Loss (to BS): 2.5 30 % longer lifetime for FND, 20 % for HNA

17. Simulations Simulation Results (2) Simulation Setup: Nodes: 200 Area: 200m*200m Base Station Pos.: (100,500)m Initial Energy / Node: 1 J Message Length: 200 bit CH-Probability: 0.05 Path-Loss (intra-cluster): 2 Path-Loss (to BS): 2.5 25 % longer lifetime for FND, 18 % for HNA

18. Contribution / Conclusions • Improvement of LEACH‘s cluster-head selection algorithm • 30 % increase of lifetime of sensor networks • Only local information is necessary for cluster-head selection • Communication with the base station or an arbiter node is not necessary • Three new lifetime metrics FNA, HNA, and LND • Use of metrics depends on application.