Routing Protocols to Maximize Battery Efficiency R.R. Rao University of California, San Diego C.F. Chiasserini Politecnico di Torino rao@cwc.ucsd.edu

Outline Life-time of wireless ad-hoc networks Battery behavior: recovery and rate-capacity effects The BEE (Battery Energy Efficient) routing protocol Simulation results rao@cwc.ucsd.edu

Life-time of Ad-Hoc Networks Network of largely battery-powered nodes with dynamic topology Objective: Maximize network life-time; i.e., the time elapsed until the first node in the network runs out of battery (Tassiulas, Infocom 2000) S=source; D=destination; R=relay rao@cwc.ucsd.edu

Battery Behavior Recovery Effect: Battery discharged for short time intervals followed by idle periods (pulsed discharge) is able to deliver a greater energy than under constant current discharge. During idle times battery recovers the charge lost while delivering current pulses Rate-capacity effect: if the requested current exceeds the rated current specification of the battery, the battery delivers a smaller amount of energy rao@cwc.ucsd.edu

Electrochemical Model of a Li-ion cell Results obtained by using a program developed by Newman et al. (UC Berkeley) Time discretized into time intervals 1 ms long Bernoulli driven discharge: At each time interval a pulse of current density I is drawn off with probability p (discharge rate), with probability 1–p the cell recovers Cut-off potential equal to 2.8V (voltage value at which the battery is considered discharged) rao@cwc.ucsd.edu

Specific Energy vs. Discharge Rate rao@cwc.ucsd.edu

Summarizing…. Pulsed current discharge outperforms constant current discharge Battery capacity can be improved by using a bursty discharge pattern due to charge recovery effects that take place during idle periods Given a certain value of current drawn off the battery, higher current pulses degrade battery performance even if the percentage of higher pulses is relatively small rao@cwc.ucsd.edu

Assumptions N: set of all nodes in the network S: set of source nodes generating data traffic D: set of destination nodes; nodes in S can direct their traffic to any of the nodes in D using other nodes as relays Time discretized into intervals corresponding to the packet transmission time with unit duration Nodes transmission range limited to r. Ri ( i  N) is the set of nodes whose distance from node i is  r rao@cwc.ucsd.edu

Assumptions Energy consumed for each data transmission by the generic node i to node j: eij={ (dij/r )4 if j  Ri  else Energy consumed by node j (j  Ri) to receive data from node i is 10 times lower than the transmission energy eij Transmission energy discretized into few levels ranging from emin to emax rao@cwc.ucsd.edu

Battery Status Evolution At the generic node i: Energy accrue due to recovery effect modeled as a function G(li,ei) where li is the transmission rate of node i and ei is the average energy necessary to i to transmit a packet Whenever an energy transmission eij > emin is needed, the energy loss is augmented by F(eij emin) Thus, bi={ bi + G(li,ei) if node i is idle bi  [eij + F(eij emin)] else rao@cwc.ucsd.edu

The BEE Routing Algorithm Given the generic source s and destination g, each route rsgk is assigned the cost function Fk=Slij  rsg [X(li)eij + pij] minirsg bi k k lij: link between nodes i and j belonging to route rsgk X(li): weighting function that emphasizes the energy loss of the source node. X(li)=Ali , for i=s, with A a proper constant > 1, and X(li)=1 otherwise pij: energy penalty due to the rate-capacity effect; pij=max{0,eij ei} rao@cwc.ucsd.edu

Route Selection Fm= minrsg Fk Whenever s has a block of data to transmit to g, the cost function is evaluated for all the possible loop-free routes and it is selected a route, rsgm, such that Fm= minrsg Fk If the set of routes is large, the algorithm complexity can be reduced by choosing c routes independently and uniformly at random. The cost function is computed over c routes only, and the selected path is the one among the c routes which minimizes the cost function k

Performance Metrics Mean delay of the transmitted packets from the time instant when a packet is generated at the source node to the time instant when it is delivered to the destination Network life-time: the time elapsed from the time instant when all the nodes have a fully charged battery to the time instant when the first node in the network runs out of battery rao@cwc.ucsd.edu

Simulation Scenario Network scenario: N={0,…,14}, S={0,1,2,3,4}, D={1,14}; initial battery status=1.0; li=0.4 for i=0,...,2 and li=0.3 for i=3, 4 Results derived by simulating several network topologies randomly generated and then averaging over the obtained values Results obtained through the BEE algorithm are compared to the performance of the MTE (Minimum Transmission Energy) scheme rao@cwc.ucsd.edu

Results - Network Life-time rao@cwc.ucsd.edu

Results - Mean Packet Delay rao@cwc.ucsd.edu

Conclusion Routing protocol for wireless ad-hoc network that efficiently exploits battery capacity: it selects a route with low transmission energy to avoid the rate-capacity effect it distributes the traffic load in a manner that nodes with low battery can benefit of recovery effect The BEE protocol can be adapted to either a slow or a fast varying network topology by changing the parameter c (and thus reducing its complexity) Excellent performance in terms of both network life-time and packet delay rao@cwc.ucsd.edu