Energy aware efficient geographic routing in lossy wireless Networks

Energy Aware Efficient Geographic Routing in Lossy Wireless Sensor Networks with

environmental Energy Supply

Prajwal Panchmahalkar Nishanth Reddy Kommidi

Wireless sensor networks are type of MANET’s

These networks monitor physical or environmental conditions, such as temperature, sound, vibration using the environmental energy supply.

Applications Environment monitoring Surveillance Military Emergency conditions Health application

Introduction

Applications of Wireless Sensor Networks

Environment monitoring

Sports and robotics

Applications of Wireless Sensor Networks

Healthcare

Military

Surveillance

This paper proposes a geographic routing with environmental energy supply using two protocols GREES-L, GREES-M

This protocol combines geographic routing and energy efficient routing techniques.

Considers lossy wireless channel conditions and renewal capability of environmental energy supply .

Energy constraints are the most crucial considerations of wireless sensing networks

The objective of this paper is minimizing the energy consumption and maximizing the network lifetime.

Geographic routing with environmental energy supply

This technique uses routing algorithms which maximize network lifetime and maximize the total number of successfully delivered messages.

These techniques assume that the nodes have a limited or fixed energy supply.

Drawback Did not take into account the node’s capability of extracting energy from the environment.

Energy Aware Ad Hoc Routing Technique

This technique the sender node needs to know the location of itself, one-hop neighbors, and destination.

C needs to know its immediate neighbors to forward to E Geographic forwarding needs a location service!

Geographic Ad Hoc Routing Technique

A

B

C

E

D

C

F

G

Energy harvesting is done to improve the lifetime and performance in the wireless sensor networks.

Energy Sources

Powered Sensor Networks

Environmental energy is a continued supply of energy which will allow the system to last forever.

There is an uncertainty associated with its availability and measurement , compared to the energy stored in the battery.

Difference between battery energy and environmental energy

Routing protocols that efficiently direct packets along low cost links.

Balance residual energy on the nodes with environmental energy supply

GREES routing techniques combine the progressive packet advancement towards the destination considering both the power aware and geographical routing techniques utilizing the environmental energy supply.

Geographic routing with environmental energy supply (GREES)

Describe battery status

A

C

D

B

Initial Battery Status before the Transmission at node B and node C

Harvest Rate: 2 units/secConsumption rate: 2units/relay


Eb -4

Eb -2


A

C

D

B



Eb

Node A transmits 5 packets to C as C has more residual energy

(Eb – 2) + (5x1) – (5x2) = Eb - 7

Eb - 7

(Eb – 4) + (5x2) = Eb


A

C

D

B



Eb - 7

Eb

Now node B has more residual Energy than B so A sends packets to B in next hop

Frame Delivery ratio (FDR) For example consider Frame Delivery ratio from node i to node j FDRij ,

◦ Periodic Time based event (T)◦ When j receives “Hello” packet (H)

Exponentially weighted moving average (EWMA) is used to calculate Link Quality Estimation.

Link quality Estimation

EWMA

“H” even occurs when j recieves a “hello” packet.

“T” event occurs at regular time intervals

EWMA

Nm -> known misses

Currentseg -> seq. no of current packet

Lastseg -> seq. no of last received

packet

lastHello -> time of the last hello

packet received

L -> misses fed into the estimation

algorithm

FDR -> Frame Delivery Ratio

ϒ -> tunable parameter

Ʈ -> Frequency of transmission

Ng->Guess on the number of missed

packets

This technique allows j to measure FDRij and i to measure FDRji

Each hello message sent by i contains the FDR measured by i from each of its neighbors.

Each neighbor then gets a FDR to i whenever it receives a probe from i. This FDR helps us to estimate the link quality and the efficiency of

network.

EWMA cont.

The cost for a node to send or receive data is a linear function Proportional to size of the packet Cost = C x Spkt + b

Energy Consumption Model

Energy harvested through sun light, air etc., High uncertainty and not homogenous at all nodes Mean of harvesting µi is considered with energy harvesting varying

between Pimin and Pimax

Note: Energy storage reservoirs can be used.

Energy Harvesting Model

GREES-L is a linear function that combines linear geographical advance efficiency and the energy availability.

Its considers packet advancement towards the destination along with link quality estimation and the energy achievable at the node.

GREES-L

GREES-L

i

Ni

D

GREES-L

Ni

CL(Ni,D) = 1 / (α.NPRO(i, Ni, D) + (1 – α).NE(Ni)

CL -> Cost when node i transmits the packet to the neighbor Ni towards the destination D

NPRO->Normalized progressive distance per data frame from i to Ni

NPRO(i, Ni, D) = PRO(i, Ni, D) / Max{PRO(i, Ni, D) }

PRO(i, Ni, D) = (dist(i, D) - dist(Ni, D)). FDRiNi. FDRNii

Where,

CL(Ni,D) = 1 / (α.NPRO(i, Ni, D) + (1 – α).NE(Ni)

NE(Ni) is normalized effective energy on node Ni

NE(Ni) = E(Ni)/ Max{E(Ni)}

E(Ni) = β.(µNi - ΨNi

).(tc – tl ) + Er (Ni)

µNi last received expected energy harvesting rate of Node Ni

ΨNi last received expected energy consuming rate of Node Ni

tc time when node i is forwarding the packet

tl time when last broadcasting hello message from node Ni is heard by i

GREES-L

To reduce the cost function (CL) - maximize the denominator It can be divided into two parts

◦ Progressive packet advancement towards destination◦ Estimated energy availability

If all the nodes have same energy harvesting rate and residual energy node I will transmit the packets to the neighbors with larger PRO towards the destination

If α = 1 GREES-L degrades to Geographic routing If α = β = 1 GREES- L degrades to Energy Aware only routing

GREES-L (cont.)

GREES-M uses multiplication to balance the geographical advance efficiency per packet transmission and the energy availability on receiving nodes.

Cost Function

CM(Ni,D) = (Eb(Ni). ηλNi)/(logŋ.(μNi + ϵ).PRO(i,Ni,D))

◦ Where λNi = Eb(Ni)- Er(Ni)/ Eb(Ni) {λNi= Fraction of energy used at node Ni}

The cost function here is an inverse function of energy harvesting rate and the geographical advancement towards the destination

Also an exponential function of nodal residual energy.

Note: Eb(Ni) in numerator doesn’t mean nodes with higher capacity have higher cost, because Eb(Ni) is embedded in ηλNi which is a cost metric

GRESS-M

GREES-L and GREES-M are the two energy aware geographic routing protocols which consider both the wireless lossy channels condition and energy constraints of the network, which were not taken into consideration by previous traditional techniques. Previous techniques considered either the network lifetime or in maximizing the successful delivery messages. Most of the energy to the network is through the environment hence, a lot of battery energy is conversed.

Also we suggest that a mobile charging station can be used which can distribute the harvested energy among the hosts where a uncertainty in energy harvesting and consumption is observed

Conclusion

Energy aware efficient geographic routing in lossy wireless Networks

Education

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