Prajwal Panchmahalkar   Nishanth Reddy Kommidi
   Wireless sensor networks are type of MANET’s   These networks monitor physical or environmental conditions, such as  ...
Environment monitoring                         Sports and robotics
Healthcare     Military    Surveillance
   This paper proposes a geographic routing with environmental energy supply    using two protocols GREES-L, GREES-M   T...
   This technique uses routing algorithms which maximize network lifetime    and maximize the total number of successfull...
   This technique the sender node needs to know the location of itself, one-    hop neighbors, and destination.          ...
   Energy harvesting is done to improve the lifetime and performance in the    wireless sensor networks.   Energy Sources
 Environmental energy is a continued supply of energy which will allow the system to last  forever. There is an uncertai...
   Routing protocols that efficiently direct packets along low cost links.   Balance residual energy on the nodes with e...
Harvest Rate: 2 units/sec                                          Consumption rate:                                      ...
Harvest Rate: 2 units/sec                                          Consumption rate:                                      ...
Harvest Rate: 2 units/sec                                                 Consumption rate:                               ...
   Frame Delivery ratio (FDR)   For example consider Frame Delivery ratio from node i to node j FDRij ,    ◦ Periodic Ti...
“H” evenoccurs when jrecieves a“hello”packet. “T” event occurs at regular time intervals
Nm -> known missesCurrentseg -> seq. no of currentpacketLastseg -> seq. no of last receivedpacketlastHello -> time of the ...
   This technique allows j to measure FDRij and i to measure FDRji   Each hello message sent by i contains the FDR measu...
   The cost for a node to send or receive data is a linear function   Proportional to size of the packet   Cost = C x S...
   Energy harvested through sun light, air etc.,    High uncertainty and not homogenous at all nodes    Mean of harvest...
   GREES-L is a linear function that combines linear geographical advance    efficiency and the energy availability.   I...
Nii        D
CL(Ni,D) = 1 / (α.NPRO(i, Ni, D) + (1 – α).NE(Ni)                      Ni        CL -> Cost when node i transmits the pack...
   NE(Ni) is normalized effective energy on node Ni   NE(Ni) = E(Ni)/ Max{E(Ni)}   E(Ni) = β.(µNi - ΨNi).(tc – tl ) + E...
   To reduce the cost function (CL) - maximize the denominator   It can be divided into two parts    ◦ Progressive packe...
   GREES-M uses multiplication to balance the geographical advance    efficiency per packet transmission and the energy a...
   GREES-L and GREES-M are the two energy aware geographic routing    protocols which consider both the wireless lossy ch...
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Energy aware efficient geographic routing in lossy wireless Networks

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Energy aware efficient geographic routing in lossy wireless Networks

  1. 1. Prajwal Panchmahalkar Nishanth Reddy Kommidi
  2. 2.  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
  3. 3. Environment monitoring Sports and robotics
  4. 4. Healthcare Military Surveillance
  5. 5.  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.
  6. 6.  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.
  7. 7.  This technique the sender node needs to know the location of itself, one- hop neighbors, and destination. E A C G B F D C needs to know its immediate neighbors to forward to E Geographic forwarding needs a location service!
  8. 8.  Energy harvesting is done to improve the lifetime and performance in the wireless sensor networks. Energy Sources
  9. 9.  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.
  10. 10.  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.
  11. 11. Harvest Rate: 2 units/sec Consumption rate: 2units/relay B Eb -4 A D C Harvest Rate: 1 units/sec Consumption rate: Eb -2 2units/relayInitial Battery Status before the Transmission at node B andnode C
  12. 12. Harvest Rate: 2 units/sec Consumption rate: 2units/relay B Eb (Eb – 4) + (5x2) = Eb A D (Eb – 2) + (5x1) – (5x2) = Eb - 7 C Harvest Rate: 1 units/sec Consumption rate: Eb - 7 2units/relayNode A transmits 5 packets to C as C has more residual energy
  13. 13. Harvest Rate: 2 units/sec Consumption rate: 2units/relay B Eb A D C Harvest Rate: 1 units/sec Consumption rate: Eb - 7 2units/relayNow node B has more residual Energy than B so A sends packets to B innext hop
  14. 14.  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.
  15. 15. “H” evenoccurs when jrecieves a“hello”packet. “T” event occurs at regular time intervals
  16. 16. Nm -> known missesCurrentseg -> seq. no of currentpacketLastseg -> seq. no of last receivedpacketlastHello -> time of the last hellopacket receivedL -> misses fed into the estimationalgorithmFDR -> Frame Delivery Ratioϒ -> tunable parameterƮ -> Frequency of transmissionNg->Guess on the number ofmissed packets
  17. 17.  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.
  18. 18.  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
  19. 19.  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 PimaxNote: Energy storage reservoirs can be used.
  20. 20.  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.
  21. 21. Nii D
  22. 22. CL(Ni,D) = 1 / (α.NPRO(i, Ni, D) + (1 – α).NE(Ni) Ni CL -> Cost when node i transmits the packet to the neighbor Ni towards the destination DNPRO->Normalized progressive distance per data frame from i to Ni NPRO(i, Ni, D) = PRO(i, Ni, D) / Max{PRO(i, Ni, D) } Where, PRO(i, Ni, D) = (dist(i, D) - dist(Ni, D)). FDRiNi. FDRNii
  23. 23.  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
  24. 24.  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
  25. 25.  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
  26. 26.  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
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