Reducing Energy Consumption in Human- centric Wireless Sensor Networks The 2012 IEEE International Conference on Systems, Man, and Cybernetics October 14-17, 2012, COEX, Seoul, Korea Roc Meseguer 1, Carlos Molina 2, Sergio F. Ochoa 3, Rodrigo Santos 4 1 Universitat Politècnica de Catalunya, Barcelona, Spain 2 Universitat Rovira i Virgili, Tarragona, Spain 3 Universidad de Chile, Santiago, Chile 4 Universidad Nacional del Sur, Bahia Blanca, Argentina

Motivation Potentiality OLSRp Conclusions & Future Work OLSR Outline

Motivation

Human-Centric Wireless Sensor Networks (HWSN) oppnet that uses mobile devices to build a mesh Human-Centric Wireless Sensor Networks (HWSN) oppnet that uses mobile devices to build a mesh

Human-centric Sensor Wireless Networks: – Need for maintaining network topology – Control messages consume network resources Proactive link state routing protocols: – Each node has a topology map – Periodically broadcast routing information to neighbors Motivation … but when the number of nodes is high …

… can overload the network!!!

OLSR OLSR: Control Traffic and Energy Traffic and energy do NOT scale !!! OLSR is one of the most intensive energy-consumers OLSR is one of the most intensive energy-consumers

… can we increase scalability of routing protocols for Human-centric Wireless Sensor Networks? …

Data per query × Queries per second →constant – For routing protocols: D = Size of packets Q = Number of packets per second sent to the network We focus on Q: – Reducing transmitted packets – Without adding complexity to network management HOW? OLSR DQ principle PREDICTING MESSAGES !!!!

– Called OLSRp – Predicts duplicated topology-update messages – Reduce messages transmitted through the network – Saves computational processing and energy – Independent of the OLSR configuration – Self-adapts to network changes. We propose a mechanism for increasing scalability of HWSN based on link state proactive routing protocols

Potentiality

NS-2 & NS-3 Grid topology, D = 100, 200, … 500 m b Wi-Fi cards, Tx rate 1Mbps Node mobility: Static, 0.1, 1, 5, 10 m/s Friis Prop. Model ICMP traffic OLSR control messages: – HELLO=2s – TC=5s OLSR Experimental Setup

OLSR TC vs HELLO OLSR: Messages distribution Ratio of TC messages is significant for low density of nodes

OLSR Control Information Repetition Number of nodes does not affect repetition

Density of nodes slightly affects repetition OLSR Control Information Repetition

Repetition is mainly affected by mobility OLSR Control Information Repetition

OLSR Control Information Repetition Repetition still being significant for high node speeds

OLSRp

Prevent MPRs from transmitting duplicated TC throughout the network: OLSR OLSRp: Basis – Last-value predictor placed in every node of the network – MPRs predicts when they have a new TC to transmit – The other network nodes predict and reuse the same TC – 100% accuracy: If predicted TC ≠ new TC  MPR sends the new TC – HELLO messages for validation The topology have changed and the new TC must be sent The MPR is inactive and we must deactivate the predictor

Upper Levels Lower Levels OLSR Input OLSR Output Wifi Input Wifi Output TC Wifi  TC OLSR if MPR: TC OLSR  TC Wifi OLSR OLSRp: Layers Upper Levels Lower Levels OLSR Input OLSR Output OLSRp Input OLSRp Output Wifi Input Wifi Output if (TC[n]=TC[n-1]): TC OLSRp  TC OLSR else: TC Wifi  TC OLSR if MPR  if(TC[n]=TC[n-1]): TC OLSRp else: TC OLSR  TC Wifi

OLSR OLSRp: Basis – Each node keeps a table whose dimensions depends on the number of nodes – Each entry records info about a specific node: The The list of the MPRs (O.A.) that announce the node in their TCs and the current state of the node (A or I). (HELLO messages received). A predictor state indicator for MPR nodes (On or Off): – On when at least one of the TC that contains information about the MPR is active – Off when the node is inactive in all the announcing TC messages (new TC message will be sent)

NS-2 Physical area of 200m X 200m 25 stationary nodes & 275 mobile nodes Nodes are randomly deployed (11 simulations) All nodes assume IPhone 4 features Mobile nodes assume: random mobility and walking speed (0.7m/s) Wifi Channel assumes Friis Propagation loss model OLSR control messages: HELLO=2s & TC=5s Data traffic assumes UDP packets transmitted every second OLSR Experimental Setup

OLSR OLSRp: Benefits Reduction in energy consumption

OLSR OLSRp: Benefits Reduction in control traffic & CPU processing

Conclusions & Future Work

OLSR Conclusions & Future Work Conclusions: – OLSRp has similar performance than standard OLSR – Can dynamically self-adapt to topology changes – Reduces network congestion – Saves computer processing and energy consumption Future Work: – Further evaluation of OLSRp performance – Assessment in real-world testbeds – Application in other routing protocols

Questions? Thanks for Your Attention The 2012 IEEE International Conference on Systems, Man, and Cybernetics October 14-17, 2012, COEX, Seoul, Korea

Questions? The 2012 IEEE International Conference on Systems, Man, and Cybernetics October 14-17, 2012, COEX, Seoul, Korea

ANEXOS

OLSR OLSRp: Example B B B B E E

OLSR OLSRp: Example B B B B E E NODE D TABLE

OLSR OLSRp: Example B B B B E E NODE D TABLE X X X X X X X X

OLSR OLSRp: Example B B B B E E NODE D TABLE X X X X X X X X

OLSR OLSRp: Example B B B B E E NODE D TABLE X X X X X X X X

OLSR OLSRp: Other Results

OLSR OLSRp: Other Results

OLSR OLSRp: Other Results