POWER EFFICIENCY ROUTING ALGORITHMS OF WIRELESS SENSOR NETWORKS Presented by - S.ATCHUTHAN Supervised by – Prof Klaus Moissner

Major Achievements Investigated energy conservation algorithms for WSN and identify clustering is promising technique for energy saving Selected typical clustering algorithms for detailed investigation and comparison based on simulation Weaknesses of these algorithms were identified and a NEW algorithm was proposed, the improvement of which was proved through simulation and comparison to the selected algorithms

CONTENTS Introduction Energy efficiency Existing protocols Clustering LEACH DEEC HEED Proposed Algorithm Improvement Conclusion

INTRODUCTION What is a wireless sensor network ? - Data Acquisition network -Data Distribution network Applications of wireless sensor network - Environmental monitoring - Battle field surveillance - Transportation traffic monitoring

Architecture of a wireless sensor network

What happens in a monitoring sensor network

ENERGY EFFICIENCY What are the common problems in a wireless networking ? What is network lifetime ? Common problems- medium access control, routing, bandwidth allocation, power efficiency, security, life time network lifetime – Time taken until the first node or last node in the network dies Prolong the network lifetime – Minimize the power consumption from nodes How to prolong the network lifetime ?

ENERGY EFFICIENCY........ How energy is consumed in a sensor node ? Energy consumption – Transmitting or receiving data, processing query requests, forwarding data or queries to neighbour nodes, idle listening to the media, retransmission when packet collision and generating control packets Energy conservation – Major challenge How to conserve energy in a wireless sensor network ?

EXISTING PROTOCOLS LEACH LEACH-E SEP PEGASIS DEEC HEED TEEN APTEEN EAD GEAR GAF MECN

EXISTING PROTOCOLS......

CLUSTERING What is Clustering ? Promising technique for lifetime extension

CLUSTERING...... Choosing LEACH, DEEC and HEED for investigation LEACH – Basic clustering mechanism DEEC - Energy consideration for threshold HEED - Competitive method

LEACH Low Energy Adaptive Clustering Hierarchy Can apply for Single hop networks. Cluster head threshold T(n) = p/(1-p*(r mod(1/p))) if n ε G T(n) = 0 Otherwise G is the set consisting of nodes that have not been cluster heads during last (1/p) rounds n takes a value between 0 and 1 If n is less than threshold T(n), then that particular node would get the chance to become a cluster head at that particular round Node which becomes cluster head during round 0, would again get the chance to be a cluster head during next (1/p) rounds

LEACH......

LEACH...... Merits Global knowledge of the network is not necessary. Only two hops are needed to reach sink. Demerits Failure of the cluster head is a problem. Difficult to optimize the cluster head selection.

DEEC Distributed Energy – Efficient Clustering Algorithm Can apply for Single hop networks Cluster head threshold T(Si) = Pi /(1-Pi (r mod (1/Pi))) if Si ε G T( Si ) = 0 Otherwise Pi = Popt (1+a) Ei (r) / (1+a*m) Ē (r) if normal node Pi = Popt Ei (r) / (1+a*m) Ē (r) if advanced node Probability threshold based on the ration between residual energy of each node and the average energy of the network

DEEC....... Residual Energy Calculation ETx (l, d) = l*Eelec +l*єfs *d^2 if d < d0 ETx (l, d) = l*Eelec +l*єmp*d^4 if d >= d0 Average Energy Calculation Ē (r) = (1/N) * Etotal (1- r/R)

DEEC........ Merits Life time is prolonged than LEACH Demerits Global knowledge of the network is necessary

HEED Hybrid Energy Efficient Distributed Algorithm Multi hop routing protocol Clustering – Competitive mechanism

HEED....... Clustering parameters - Node residual energy - Node degree Probability to be a cluster head CHprob = Cprob *(Eresidual /Emax )

HEED...... Status of nodes - Tentative cluster head - Final cluster head - Uncovered Merits Large scalability Lifetime is prolonged than DEEC Demerits Clustering consumes much energy

PROPOSED ALGORITHM Clustering Energy: HEED >> DEEC DEEC Clustering for multi hop networks

PROPOSED ALGORITHM....... Pseudo code (DEEC Clustering for multi hop networks) For i = 1:1: n For j = 1:1: n If ( d( i, j) =< Tr ) join ( node j joins with cluster head i ); End K = rand (); If (DEEC clustering threshold probability < k) S ( i ). Cluster = TRUE;

PROPOSED ALGORITHM....... Long distance cause Energy loss Shortest path routing Obtaining maximum single hop distance (100 m)

PROPOSED ALGORITHM....... Pseudo code (Shortest path routing) While ((i =< n) & ((d.sink – d.cluster (i, sink)) >= 100)) While (j =< n) If (d.cluster (i, j) =< 100) Cluster (i) = cluster (j); Else If hop = hop +1; End End End

PROPOSED ALGORITHM....... Output

IMPROVEMENT Lifetime is prolonged

CONCLUSION Improvement - DEEC Clustering for multi hop networks - Shortest path routing (Based on Single hop maximum distance) Better combined of DEEC Clustering and Multi hop Twelve percent improvement of lifetime

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