Smart Wireless Sensor Networks Part 9 pptx

A Reliable and Flexible Transmission Method in Wireless Sensor Networks 229A Reliable and Flexible Transmission Method in Wireless Sensor Networks Dae-Young Kim and Jinsung Cho 0 A Relia

A Reliable and Flexible Transmission Method in Wireless Sensor Networks 229

A Reliable and Flexible Transmission Method in Wireless Sensor Networks

Dae-Young Kim and Jinsung Cho

0

A Reliable and Flexible Transmission Method in Wireless Sensor Networks

Dae-Young Kim and Jinsung Cho

Kyung Hee University

S Korea

1 Introduction

Recent advances in wireless communication have enabled multifunctional tiny nodes to

con-struct a wireless network by themselves Akyildiz et al (2002) The network is called a

wire-less sensor network The tiny sensor nodes are densely deployed in a physical space They

monitor physical phenomena, deliver information, and cooperate with neighbor nodes

Aky-ildiz et al (2002); Culler et al (2004); Hac (2003); Zhao and Guibas (2004); Chong and Kumar

(2003) The communication systems in end-to-end data transmission of wireless sensor

net-works employ a recovery mechanism for lost data during data transmissions because reliable

data transmissions are required for various sensor network applications

Two types of retransmission have been proposed for the recovery, namely end-to-end loss

recovery (E2E) and hop-by-hop loss recovery (HBH) In these mechanisms, lost packets are

retransmitted from a source node or an intermediate node If a retransmit request for lost

packets is sent to a source node, the end-to-end delay may increase because channel error

accumulates exponentially over multi-hops Wan et al (2002) The well-known HBH

mecha-nisms are PSFQ Wan et al (2002) and RMST Stann & Heidemann (2003) PSFQ is based on

ACK message and RMST is on NACK message In HBH, when intermediate nodes cache data

packets into storage, retransmissions can be requested to an intermediate relay node to reduce

end-to-end delays Because sensor nodes have limited resources, however, it is difficult for all

sensor nodes to find sufficient space in their routing paths to cache data packets There is

therefore a tradeoff between end-to-end delays and memory requirements

Because data traffic on sensor networks requires a variety of levels of communication

reliabil-ity (CR) depending on the application, a loss recovery method to guarantee the desired CR

should be provided Traditional loss recovery mechanisms consider only 100% reliability In

this letter, we propose a flexible loss recovery mechanism to guarantee various CRs and we

discuss the tradeoff between end-to-end delays and memory requirements for various CRs.

The proposed method can be widely used for the design of wireless sensor networks that

require a variety of CRs.

2 A Reliable and Flexible Transmission Method in Wireless Sensor Networks:

Active Caching

As mentioned previously, E2E involves large end-to-end delays for 100% reliability because of

high packet loss during multi-hop transmissions To guarantee high reliability and minimal

13

Smart Wireless Sensor Networks230

Fig 2 An example of active caching

end-to-end delays, HBH caches data in every node over a routing path resulting in large

mem-ory requirements When only some nodes cache data on a routing path, there exists a tradeoff

between the end-to-end delays and the memory requirements For applications which do

not require 100% reliability, every node needs not cache data via HBH When a target CR is

given, we need a flexible method to guarantee the given CR while minimizing the memory

requirement In this section, we present such a method - active caching (AC)

The proposed scheme allows various CRs of application services It determines positions

where data caching occurs using a dynamic programming algorithm, which solves every

sub-problem just once and then saves its answer in a table to avoid the work of recomputing the

answer Cormen et al (2001) If there are holes in sequence numbers of received data, a caching

node recognizes packet loss Karl & Willig (2005) The caching node sends a NACK message

to a previous caching node along the path and the previous caching node retransmits lost

packets selectively

First, we define the problem and subproblems for the active caching as a dynamic

program-ming algorithm to guarantee an end-to-end reliable data transmission as:

Problem: P tx(H ) > CR.

Subproblem: P tx(h ) > CR, where h=1, 2,· · · , H.

The packet delivery rate P tx(H)during total hop counts H should be greater than the desired

communication reliability CR To do that, the packet delivery rate P tx(h)during hop counts h

in each hop should be greater than the CR The key idea for solving the problem is to cache

data packets if the probability of packet transmission does not satisfy the desired

communi-cation reliability By solving the subproblems, we can solve the entire problem

Figure 1 shows the proposed active caching algorithm for loss recovery Each node solves the

subproblem using the tables for the packet delivery rate P tx(i)until i-th hop and the caching flag of i-th node F(i) Both P tx(i −1)and F(i −1)of the tables are piggybacked in data packets

and they are delivered to the next node In a source node (i = 1), P tx(1) is 1− p1 as the

packet delivery rate at the 1st hop and F(1)is true Line 1-3: n i calculates P tx(i)using P tx(i −

1), where P tx(i) accumulates the packet delivery rate 1− p i of i-th hop while packets are transmitted After that, it compares P tx(i)with CR If P tx(i) satisfies the desired CR, n i is

not a caching node (F(i)is f alse) Line 4-6: If P tx(i)does not guarantee the desired CR, n i

becomes a caching node (F(i)is true) In this case, P tx(i)compensates for its packet delivery

rate as the reliability instead of accumulating P tx(i) and data packets are cached onto n i’sbuffer Each node runs the algorithm of Figure 1 and the total active caching over a routingpath is performed by the dynamic programming algorithm Figure 2 shows an example of theactive caching when seven sensor nodes are deployed sequentially and they have an average

5% packet loss rate and 80% CR Every node satisfies 80% CR and data caching occurs at n5

When packet loss happens between a source node n1and the caching node n5, the cachingnode requests retransmission to the source node When packet loss happens between the

caching node and a destination node n7, the destination node requests retransmission to thecaching node

(s, h)tuples are used to compute the retransmission counts of lost packets For example inFigure 2, Φ={(1, 4),(5, 2)}

Φ={( s j , h j)| j=1,· · · , N C } (3)

If the retransmission counts for h hops from a caching node s is given by ψ(s, h), the total

retransmission counts E[C]between a source node and a sink node are represented by the

Because the retransmitted packets can also experience transmission failure, we should

con-sider repeated retransmissions for ψ(s, h) Let Γf(j, s, h)indicate the number of transmitted

packets at the j-th retransmission Then ψ(s, h)can be represented as

A Reliable and Flexible Transmission Method in Wireless Sensor Networks 231

Fig 2 An example of active caching

end-to-end delays, HBH caches data in every node over a routing path resulting in large

mem-ory requirements When only some nodes cache data on a routing path, there exists a tradeoff

between the end-to-end delays and the memory requirements For applications which do

not require 100% reliability, every node needs not cache data via HBH When a target CR is

given, we need a flexible method to guarantee the given CR while minimizing the memory

requirement In this section, we present such a method - active caching (AC)

The proposed scheme allows various CRs of application services It determines positions

where data caching occurs using a dynamic programming algorithm, which solves every

sub-problem just once and then saves its answer in a table to avoid the work of recomputing the

answer Cormen et al (2001) If there are holes in sequence numbers of received data, a caching

node recognizes packet loss Karl & Willig (2005) The caching node sends a NACK message

to a previous caching node along the path and the previous caching node retransmits lost

packets selectively

First, we define the problem and subproblems for the active caching as a dynamic

program-ming algorithm to guarantee an end-to-end reliable data transmission as:

Problem: P tx(H ) > CR.

Subproblem: P tx(h ) > CR, where h=1, 2,· · · , H.

The packet delivery rate P tx(H)during total hop counts H should be greater than the desired

communication reliability CR To do that, the packet delivery rate P tx(h)during hop counts h

in each hop should be greater than the CR The key idea for solving the problem is to cache

data packets if the probability of packet transmission does not satisfy the desired

communi-cation reliability By solving the subproblems, we can solve the entire problem

Figure 1 shows the proposed active caching algorithm for loss recovery Each node solves the

subproblem using the tables for the packet delivery rate P tx(i)until i-th hop and the caching flag of i-th node F(i) Both P tx(i −1)and F(i −1)of the tables are piggybacked in data packets

and they are delivered to the next node In a source node (i = 1), P tx(1) is 1− p1 as the

packet delivery rate at the 1st hop and F(1)is true Line 1-3: n i calculates P tx(i)using P tx(i −

1), where P tx(i) accumulates the packet delivery rate 1− p i of i-th hop while packets are transmitted After that, it compares P tx(i)with CR If P tx(i) satisfies the desired CR, n iis

not a caching node (F(i)is f alse) Line 4-6: If P tx(i)does not guarantee the desired CR, n i

becomes a caching node (F(i)is true) In this case, P tx(i)compensates for its packet delivery

rate as the reliability instead of accumulating P tx(i)and data packets are cached onto n i’sbuffer Each node runs the algorithm of Figure 1 and the total active caching over a routingpath is performed by the dynamic programming algorithm Figure 2 shows an example of theactive caching when seven sensor nodes are deployed sequentially and they have an average

5% packet loss rate and 80% CR Every node satisfies 80% CR and data caching occurs at n5

When packet loss happens between a source node n1and the caching node n5, the cachingnode requests retransmission to the source node When packet loss happens between the

caching node and a destination node n7, the destination node requests retransmission to thecaching node

(s, h)tuples are used to compute the retransmission counts of lost packets For example inFigure 2, Φ={(1, 4),(5, 2)}

Φ={( s j , h j)| j=1,· · · , N C } (3)

If the retransmission counts for h hops from a caching node s is given by ψ(s, h), the total

retransmission counts E[C]between a source node and a sink node are represented by the

Because the retransmitted packets can also experience transmission failure, we should

con-sider repeated retransmissions for ψ(s, h) Let Γf(j, s, h)indicate the number of transmitted

packets at the j-th retransmission Then ψ(s, h)can be represented as

Smart Wireless Sensor Networks232

If we let Γs(k, s, h)be the number of successfully transmitted packets among k packets during

h hops from node s, Γ f(j, s, h)can be represented recursively as

Γf(j, s, h) =Γf(j − 1, s, h)−ΓsΓf(j − 1, s, h), s, h1, (6)where Γf(0, s, h) = K and K is the number of total packets which is generated in a source

node

The number of successfully transmitted packets Γs(k, s, h)can be calculated by the probability

of successful transmission of Bernoulli trials P s(k, m, s, h)as

Γs(k, s, h) =

k

∑

m=1 m · P s(k, m, s, h) (7)

If m data packets are transmitted successfully among k packets to deliver across h hops from a

caching node s, the probability of successful transmissions can be obtained by Bernoulli trials

The memory requirement B is defined as the caching rates of intermediate nodes including a

source node It is computed by N cand the number of relay nodes over a routing path:

E[B] = N c

1[x]is n, in case of n −0.5≤ x < n+0.5

Fig 4 Validation of our analysis (p=0.03).

A high E[C]indicates large end-to-end transmission delays and E[B]represents the memory

requirements of buffers on the data transmission routes Because both E[C]and E[B]can be

estimated by CR of traffic through Eq.(4) and Eq.(9), a flexible data transmission system can

be designed

4 Evaluation

In this section, we validate the analysis through simulations and compare the performance ofactive caching (AC) with that of E2E and HBH For the simulation, we assume 20 sensor nodesare deployed sequentially and the wireless channel has both link and contention error as de-

scribed in Section 3 The contention failure factor α is determined as 0.0001 by considering total hop counts So, p i in Eq.(1) ranges from 0.03 to 0.07 when p is 0.03 in our experiments.

The sensor nodes employ AODV as a routing protocol Assuming a packet is 30 bytes and

the data rate is 250kbps, we perform the analysis and simulation by varying CR from 10% to 100% AC with CR from 0.1 to 1 is expressed as AC0.1 to AC1.

Figure 4 shows the results of the analysis and the simulation of the retransmission counts andthe memory requirements when a source transmits 40 packets The results of the analysisand the simulation show an average of 94% similarity Figure 4 also represents the tradeoff

as mentioned earlier The high CR requires a high memory requirement for reliability and it

decreases the retransmission counts When the memory requirement is the lowest, the mission counts are the highest and AC runs as E2E In short, we can design wireless sensor

retrans-networks that take the desired CR and memory requirements into consideration through the

proposed active caching

Figure 5 shows the performance comparison of E2E, HBH, and AC Because AC with thehighest memory requirement caches data to every intermediate node, it operates as HBH.When AC does not perform data caching, it operates as E2E That is, AC switches betweenHBH and E2E while showing the performance tradeoff between them In addition, it has a

tolerable end-to-end delay to minimize the memory requirement depending on CR In

Fig-ure 5, the end-to-end delays of E2E increase when the wireless channel has a high link errorrate However, the end-to-end delay of AC maintains similar values because AC increases the

memory requirements to ensure CR An evaluation has been performed for 10 and 50 nodes

A Reliable and Flexible Transmission Method in Wireless Sensor Networks 233

If we let Γs(k, s, h)be the number of successfully transmitted packets among k packets during

h hops from node s, Γ f(j, s, h)can be represented recursively as

Γf(j, s, h) =Γf(j − 1, s, h)−ΓsΓf(j − 1, s, h), s, h1, (6)where Γf(0, s, h) = K and K is the number of total packets which is generated in a source

node

The number of successfully transmitted packets Γs(k, s, h)can be calculated by the probability

of successful transmission of Bernoulli trials P s(k, m, s, h)as

Γs(k, s, h) =

k

∑

m=1 m · P s(k, m, s, h) (7)

If m data packets are transmitted successfully among k packets to deliver across h hops from a

caching node s, the probability of successful transmissions can be obtained by Bernoulli trials

The memory requirement B is defined as the caching rates of intermediate nodes including a

source node It is computed by N cand the number of relay nodes over a routing path:

E[B] = N c

1[x]is n, in case of n −0.5≤ x < n+0.5

Fig 4 Validation of our analysis (p=0.03).

A high E[C]indicates large end-to-end transmission delays and E[B]represents the memory

requirements of buffers on the data transmission routes Because both E[C]and E[B]can be

estimated by CR of traffic through Eq.(4) and Eq.(9), a flexible data transmission system can

be designed

4 Evaluation

In this section, we validate the analysis through simulations and compare the performance ofactive caching (AC) with that of E2E and HBH For the simulation, we assume 20 sensor nodesare deployed sequentially and the wireless channel has both link and contention error as de-

scribed in Section 3 The contention failure factor α is determined as 0.0001 by considering total hop counts So, p i in Eq.(1) ranges from 0.03 to 0.07 when p is 0.03 in our experiments.

The sensor nodes employ AODV as a routing protocol Assuming a packet is 30 bytes and

the data rate is 250kbps, we perform the analysis and simulation by varying CR from 10% to 100% AC with CR from 0.1 to 1 is expressed as AC0.1 to AC1.

Figure 4 shows the results of the analysis and the simulation of the retransmission counts andthe memory requirements when a source transmits 40 packets The results of the analysisand the simulation show an average of 94% similarity Figure 4 also represents the tradeoff

as mentioned earlier The high CR requires a high memory requirement for reliability and it

decreases the retransmission counts When the memory requirement is the lowest, the mission counts are the highest and AC runs as E2E In short, we can design wireless sensor

retrans-networks that take the desired CR and memory requirements into consideration through the

proposed active caching

Figure 5 shows the performance comparison of E2E, HBH, and AC Because AC with thehighest memory requirement caches data to every intermediate node, it operates as HBH.When AC does not perform data caching, it operates as E2E That is, AC switches betweenHBH and E2E while showing the performance tradeoff between them In addition, it has a

tolerable end-to-end delay to minimize the memory requirement depending on CR In

Fig-ure 5, the end-to-end delays of E2E increase when the wireless channel has a high link errorrate However, the end-to-end delay of AC maintains similar values because AC increases the

memory requirements to ensure CR An evaluation has been performed for 10 and 50 nodes

Smart Wireless Sensor Networks234

deployed over a route, and the results are similar to the case of 20 nodes These results have

been omitted due to the page limitation

Figure 6 shows the ratio of caching nodes over relay nodes Because the contention error

increases when the density of nodes increases, the ratio of caching nodes increases when the

number of sensor nodes increases

Fig 5 Performance comparison of E2E, HBH, and AC

Fig 6 The ratio of caching nodes

5 Conclusion

Wireless sensor networks transmit data through multiple hops End-to-end data transmission

must recover lost data for reliable data transmissions Active caching (AC) provides more

flexible end-to-end delays and memory requirements for a given reliability than the existing

recovery mechanisms (i.e., E2E, HBH) By using the proposed dynamic loss recovery with

active caching, a flexible end-to-end data transmission system can be designed

6 Acknowledgement

This research was supported by the MKE(The Ministry of Knowledge Economy), Korea, der the ITRC(Information Technology Research Center) support program supervised by theNIPA(National IT Industry Promotion Agency)" (NIPA-2010-(C1090-1021-0003))

un-7 References

Akyildiz, I F., Su, W., Sankarasubramaniam, Y., and Cayirci, E (2002) A survey on sensor

networks, IEEE Communications Magazine, Vol 40(No 8): pp 102–114, August 2002.

Culler, D., Estrin, D., and Srivastava, M (2004) Guest editors’ introduction: Overview of

sensor networks IEEE Computer, Vol 37(No 8): pp 41–49, August 2004.

Hac, A (2003) Wireless sensor network designs, John Wiley & Sons, 2003.

Zhao, F and Guibas, L (2004) Wireless sensor networks: An information processing approach,

Morgan Kaufmann Publishers, 2004

Chong, C -Y and Kumar, S (2003) Sensor networks: Evolution, opprtunities, and challenges,

Proceedings of the IEEE, Vol 91(No 8): pp 1247-1256, August 2003.

Wan, C Y., Campbell, A T., and Krishnamurthy, L (2002) PSFQ: A reliable transport protocol

for wireless sensor networks, Proceedings of ACM International Workshop on Wireless Sensor Networks and Applications, pp 1-11, September 2002.

Stann, F and Heidemann, J (2003) RMST: Reliable data transport in sensor networks,

Pro-ceedings of IEEE International Workshop on Sensor Network Protocols and Applications,

pp 102-112, May 2003

Cormen, T H., Leiserson, C E., Rivest, R L., and Stein, C (2001) Introduction to Algorithms,

Vol 1, The MIT Press, 2001

Karl, H and Willig, A (2005) Protocols and architectures for wireless sensor networks, John Wiley

& Sons, 2005

A Reliable and Flexible Transmission Method in Wireless Sensor Networks 235

deployed over a route, and the results are similar to the case of 20 nodes These results have

been omitted due to the page limitation

Figure 6 shows the ratio of caching nodes over relay nodes Because the contention error

increases when the density of nodes increases, the ratio of caching nodes increases when the

number of sensor nodes increases

Fig 5 Performance comparison of E2E, HBH, and AC

Fig 6 The ratio of caching nodes

5 Conclusion

Wireless sensor networks transmit data through multiple hops End-to-end data transmission

must recover lost data for reliable data transmissions Active caching (AC) provides more

flexible end-to-end delays and memory requirements for a given reliability than the existing

recovery mechanisms (i.e., E2E, HBH) By using the proposed dynamic loss recovery with

active caching, a flexible end-to-end data transmission system can be designed

6 Acknowledgement

This research was supported by the MKE(The Ministry of Knowledge Economy), Korea, der the ITRC(Information Technology Research Center) support program supervised by theNIPA(National IT Industry Promotion Agency)" (NIPA-2010-(C1090-1021-0003))

un-7 References

Akyildiz, I F., Su, W., Sankarasubramaniam, Y., and Cayirci, E (2002) A survey on sensor

networks, IEEE Communications Magazine, Vol 40(No 8): pp 102–114, August 2002.

Culler, D., Estrin, D., and Srivastava, M (2004) Guest editors’ introduction: Overview of

sensor networks IEEE Computer, Vol 37(No 8): pp 41–49, August 2004.

Hac, A (2003) Wireless sensor network designs, John Wiley & Sons, 2003.

Zhao, F and Guibas, L (2004) Wireless sensor networks: An information processing approach,

Morgan Kaufmann Publishers, 2004

Chong, C -Y and Kumar, S (2003) Sensor networks: Evolution, opprtunities, and challenges,

Proceedings of the IEEE, Vol 91(No 8): pp 1247-1256, August 2003.

Wan, C Y., Campbell, A T., and Krishnamurthy, L (2002) PSFQ: A reliable transport protocol

for wireless sensor networks, Proceedings of ACM International Workshop on Wireless Sensor Networks and Applications, pp 1-11, September 2002.

Stann, F and Heidemann, J (2003) RMST: Reliable data transport in sensor networks,

Pro-ceedings of IEEE International Workshop on Sensor Network Protocols and Applications,

pp 102-112, May 2003

Cormen, T H., Leiserson, C E., Rivest, R L., and Stein, C (2001) Introduction to Algorithms,

Vol 1, The MIT Press, 2001

Karl, H and Willig, A (2005) Protocols and architectures for wireless sensor networks, John Wiley

& Sons, 2005

Performance Analysis of Binary Sensor-Based Cooperative Diversity Using Limited Feedback 237

Performance Analysis of Binary Sensor-Based Cooperative Diversity Using Limited Feedback

Ali EKŞİM and Mehmet E ÇELEBİ

X

Performance Analysis of Binary Sensor-Based

Cooperative Diversity Using Limited Feedback

Ali EKŞİM1 and Mehmet E ÇELEBİ2

Tubitak-BILGEM1, Istanbul Technical University2

Turkey1,2

1 Introduction

The most important advantage of wireless sensor networks (WSNs) is their ability to bridge

the gap between the physical and logical worlds by gathering certain useful information

from the physical world and communicating that information to more powerful logical

devices that can process it If the ability of the WSN is suitably harnessed, it is envisioned

that WSNs can reduce or eliminate the need for human involvement in information

gathering in certain civilian and military applications (He et al., 2004)

It is a common belief that in the near future, many WSNs will be deployed for a wide variety

of applications including monitoring and surveillance Each sensor is powered by battery

and is supposed to work for a relatively long time after deployment The total energy cost of

WSN includes all aspects of the sensor’s actions Transmission energy efficiency and

reliability becomes important because wireless transceivers usually consume a major

portion of battery energy (Akyildiz et al., 2002) This is true considering the severe channel

fading and node failure in hostile environment (Ng et al., 2005)

Transmission energy conservation in WSN has two aspects First, transmission protocols and

algorithms should have high energy efficiency Space-time coding and processing are helpful

for enhancing transmission energy efficiency and reliability (Li & Wu, 2003) In particular,

space-time block codes (STBCs) have attracted great attention because of their affordable linear

complexity (Alamouti, 1998; Tarokh et al., 1999) Among the numerous STBC schemes,

Alamouti’s STBC (Alamouti, 1998) is probably the most famous one due to its simplicity

However, space-time techniques are traditionally based on multiple transmit antennas

Due to insufficient antenna space, cost and hardware limitations, wireless sensors may not

be able to support multiple transmit antennas For the wireless sensors which have no

multiple transmit antennas, STBC may still be used with cooperative transmission schemes

(Li, 2005; Sendonaris, 2003a; Sendonaris, 2003b; Laneman & Wornell, 2003; Ohtsuki, 2006)

where multiple sensors work cooperatively to form a virtual antenna array Additional

performance improvement can be achieved if limited feedback is available at the

cooperating sensors Two techniques are generally used for limited feedback; Sensor (relay)

selection (SS) which selects n1 out of n active sensor for cooperation (n1 ≤ n) and Extended

Cooperative Balanced Space-Time Block Coding (ECBSTBC) which uses all active sensors

(Eksim & Celebi, 2009a; Eksim & Celebi, 2010a)

14

Smart Wireless Sensor Networks238

Another important aspect of transmission energy conservation is that energy consumption

rates in different parts of the WSN should be uniform or almost uniform so that the wireless

sensors have approximately same lifetime If the energy consumption rates are non-uniform,

some parts of the WSN may die much sooner than the others If these dying parts are

critical for the WSN, this situation may lead to early dysfunction of the network, thus

loosing Quality of Service (QoS), even if the other parts of the network still have a lot of

residual energy In the literature, this is called energy hole (Li & Mohapatra, 2007) problem

Although SS schemes prolong the network life in uniform wireless channels, due to nature

of the non-uniform wireless channels or location of the sensors, some of the sensors are

more frequently selected for cooperation, so, there may be little or no energy left for their

own use Then, the energy hole problem occurs For this problem not occurring in

non-uniform wireless channels, the ideal communication protocol should distribute

communication energy among the active sensors evenly without losing the QoS of the

communication

In (Ohtsuki, 2006), the performance of the statistical STBC cooperative diversity with

observation noise and quantization noise is analyzed In this work, the Alamouti`s code is

used which is the only orthogonal code which achieves full diversity and full rate for two

sensors, and the achievable diversity order is two when a single receive antenna is present at

the fusion center The use of the Alamouti`s code improves the bit error performance of the

system when more than two active sensors are present in the transmitting side The

achievable diversity order can be increased via limited feedback Since the limited feedback

is not used in (Ohtsuki, 2006), the issue of how much feedback from a fusion center

improves the performance when quantization and observation noise are present, is not

analyzed Additionally, the performance of binary sensors in non-uniform wireless channels

and the impact of the energy hole problem in non-uniform wireless channels are not well

investigated in the literature

In this chapter, we show how to improve the performance of the statistical STBC with

limited feedback The effect of quantization and observation noise is also included in the

analysis Moreover, we show that SS schemes cause an energy hole problem in non-uniform

wireless channels The ECBSTBC provides an improvement to this problem since this

scheme utilizes all available sensors to maintain equal power consumption among the

available sensors and meets QoS of the communication until the end of the network lifetime

This increases the energy efficiency of the communication protocol in non-uniform wireless

channels

In addition, not only the ECBSTBC but also the SS schemes are adversely affected by the

observation noise since it limits the bit error rate (BER) performance (Eksim &

with SS scheme (Eksim, 2010b) In this scheme, an active sensor does not cooperate with

other active sensors to transmit the observations if its observation is classified as “noisy” On

the other hand, the sensors cooperate with each other using the ECBSTBC when their

observation noise level is smaller than predefined threshold for transmission toward the

fusion center This hybrid technique yields improved performance at the fusion center

compared to solely using the ECBSTBC or the SS methods

In the following section, the system model is described, in the third section, the Extended

Cooperative Balanced Space-Time Block Codes (ECBSTBCs) are explained, in the fourth

section, a performance analysis presented, and in the last section, the results of the our work and the conclusion are given

The following notation used in this chapter: * denotes the conjugate operation; Re{.} and

Im{.} are the real and imaginary part of the argument, respectively The operator    rounds

to the smallest integer greater or equal than its argument

2 System Model

The wireless sensor network consists of one source, one fusion center and N sensors which

are located randomly and independently Figure 1-2 show the wireless sensor network and its analytical model, respectively All sensors are equipped with a single antenna and cannot communicate with each other All channels are assumed frequency flat Rayleigh fading channel where channel gains are circularly complex Gaussian random variables and statistically independent from each other The channels are quasi-static, namely, the fading coefficients remain constant over the duration of one frame and change independently in the

following frame h rid is the channel gain from the ith active sensor to the fusion center where i=1, 2, , n

The fusion center is assumed to have perfect knowledge of the sensor-fusion center channels This can be achieved via pilot tone training However, the fusion center has no knowledge of the accuracy of the sensor measurements, since knowledge of the measurements at the fusion center requires considerable protocol overhead Because of

energy efficiency, only n sensors are active Active sensors observe the environment Due to

the presence of the noise, the observation at each active sensor may be different The observed data are binary quantized and transmitted by BPSK

2.1 Battery model

The Battery Model simulates the capacity and the lifetime of the sole energy source of the sensor In reality, the battery behavior highly depends on the constituent materials and modeling this behavior is a difficult task Present network simulation tools use linear model (Park et al., 2001) In the linear model, the battery behaves as a linear storage of current The maximum capacity of the battery is achieved regardless of what the discharge rate is The simple battery model allows user to see the efficiency of the user’s application by providing how much capacity is consumed by the user Knowing the current discharge of the battery and the total capacity in Ah (Ampere×Hour), one can compute the theoretical lifetime of the

battery using the equation, t = C bat /I, where t is the battery lifetime, C bat is the rated

maximum battery capacity in Ah, and I is the discharge current

In this model, sensor user having an initial amount of energy diminishes its value when a packet is sent or received In limited battery simulations, battery counter is added (Lim et al., 2005; Buttyan & Hubaux, 2003) It represents the battery power which is left to the sensors When a sensor`s battery is consumed, further cooperation requests will not be accepted In addition, many short range wireless networks generally consume the available energy for receiving which is approximately 2/3rd of the energy for transmitting (Lal et al., 2005)

Performance Analysis of Binary Sensor-Based Cooperative Diversity Using Limited Feedback 239

Another important aspect of transmission energy conservation is that energy consumption

rates in different parts of the WSN should be uniform or almost uniform so that the wireless

sensors have approximately same lifetime If the energy consumption rates are non-uniform,

some parts of the WSN may die much sooner than the others If these dying parts are

critical for the WSN, this situation may lead to early dysfunction of the network, thus

loosing Quality of Service (QoS), even if the other parts of the network still have a lot of

residual energy In the literature, this is called energy hole (Li & Mohapatra, 2007) problem

Although SS schemes prolong the network life in uniform wireless channels, due to nature

of the non-uniform wireless channels or location of the sensors, some of the sensors are

more frequently selected for cooperation, so, there may be little or no energy left for their

own use Then, the energy hole problem occurs For this problem not occurring in

non-uniform wireless channels, the ideal communication protocol should distribute

communication energy among the active sensors evenly without losing the QoS of the

communication

In (Ohtsuki, 2006), the performance of the statistical STBC cooperative diversity with

observation noise and quantization noise is analyzed In this work, the Alamouti`s code is

used which is the only orthogonal code which achieves full diversity and full rate for two

sensors, and the achievable diversity order is two when a single receive antenna is present at

the fusion center The use of the Alamouti`s code improves the bit error performance of the

system when more than two active sensors are present in the transmitting side The

achievable diversity order can be increased via limited feedback Since the limited feedback

is not used in (Ohtsuki, 2006), the issue of how much feedback from a fusion center

improves the performance when quantization and observation noise are present, is not

analyzed Additionally, the performance of binary sensors in non-uniform wireless channels

and the impact of the energy hole problem in non-uniform wireless channels are not well

investigated in the literature

In this chapter, we show how to improve the performance of the statistical STBC with

limited feedback The effect of quantization and observation noise is also included in the

analysis Moreover, we show that SS schemes cause an energy hole problem in non-uniform

wireless channels The ECBSTBC provides an improvement to this problem since this

scheme utilizes all available sensors to maintain equal power consumption among the

available sensors and meets QoS of the communication until the end of the network lifetime

This increases the energy efficiency of the communication protocol in non-uniform wireless

channels

In addition, not only the ECBSTBC but also the SS schemes are adversely affected by the

observation noise since it limits the bit error rate (BER) performance (Eksim &

with SS scheme (Eksim, 2010b) In this scheme, an active sensor does not cooperate with

other active sensors to transmit the observations if its observation is classified as “noisy” On

the other hand, the sensors cooperate with each other using the ECBSTBC when their

observation noise level is smaller than predefined threshold for transmission toward the

fusion center This hybrid technique yields improved performance at the fusion center

compared to solely using the ECBSTBC or the SS methods

In the following section, the system model is described, in the third section, the Extended

Cooperative Balanced Space-Time Block Codes (ECBSTBCs) are explained, in the fourth

section, a performance analysis presented, and in the last section, the results of the our work and the conclusion are given

The following notation used in this chapter: * denotes the conjugate operation; Re{.} and

Im{.} are the real and imaginary part of the argument, respectively The operator    rounds

to the smallest integer greater or equal than its argument

2 System Model

The wireless sensor network consists of one source, one fusion center and N sensors which

are located randomly and independently Figure 1-2 show the wireless sensor network and its analytical model, respectively All sensors are equipped with a single antenna and cannot communicate with each other All channels are assumed frequency flat Rayleigh fading channel where channel gains are circularly complex Gaussian random variables and statistically independent from each other The channels are quasi-static, namely, the fading coefficients remain constant over the duration of one frame and change independently in the

following frame h rid is the channel gain from the ith active sensor to the fusion center where i=1, 2, , n

The fusion center is assumed to have perfect knowledge of the sensor-fusion center channels This can be achieved via pilot tone training However, the fusion center has no knowledge of the accuracy of the sensor measurements, since knowledge of the measurements at the fusion center requires considerable protocol overhead Because of

energy efficiency, only n sensors are active Active sensors observe the environment Due to

the presence of the noise, the observation at each active sensor may be different The observed data are binary quantized and transmitted by BPSK

2.1 Battery model

The Battery Model simulates the capacity and the lifetime of the sole energy source of the sensor In reality, the battery behavior highly depends on the constituent materials and modeling this behavior is a difficult task Present network simulation tools use linear model (Park et al., 2001) In the linear model, the battery behaves as a linear storage of current The maximum capacity of the battery is achieved regardless of what the discharge rate is The simple battery model allows user to see the efficiency of the user’s application by providing how much capacity is consumed by the user Knowing the current discharge of the battery and the total capacity in Ah (Ampere×Hour), one can compute the theoretical lifetime of the

battery using the equation, t = C bat /I, where t is the battery lifetime, C bat is the rated

maximum battery capacity in Ah, and I is the discharge current

In this model, sensor user having an initial amount of energy diminishes its value when a packet is sent or received In limited battery simulations, battery counter is added (Lim et al., 2005; Buttyan & Hubaux, 2003) It represents the battery power which is left to the sensors When a sensor`s battery is consumed, further cooperation requests will not be accepted In addition, many short range wireless networks generally consume the available energy for receiving which is approximately 2/3rd of the energy for transmitting (Lal et al., 2005)

Smart Wireless Sensor Networks240

Fig 1 Wireless sensor network

ˆs

Fig 2 Analitical model of wireless sensor network

2.2 Channel model

We assume that all parallel wireless channels are independent but they have statistically

uniform paths with have identical means and variances (Cetinkaya, 2007) That is to say that

the sensors-fusion center channels have equal variance and mean This is not true for

realistic scenarios, since some of the parallel channels have non-uniform statistical

properties (Cetinkaya, 2007) In the non-uniform wireless channel simulations, the parallel

channels may contain “better” or “worse” channels When the ith active sensor-fusion center

channel`s variance is much higher than the jth active sensor-fusion center channel`s variance

2 2

rid rjd

(  where j=1, ,n and j≠i), this channel can be considered as “better” channel On the

contrary, when the ith sensor-fusion center channel`s variance is much lower than the jth

sensor-fusion center channel`s variance 2 2

rid rjd

(  where j=1, ,n and j≠i), this channel can

be called as “worse” channel (Ibrahim et al., 2008)

3 Extended Cooperative Balanced Space-Time Block Codes

The ECBSTBCs can be obtained from an OSTBC multiplied by an extension matrix Since Alamouti`s code is the only orthogonal code with rate one and minimum delay, the ECBSTBCs can be obtained as an extension of the Alamouti`s code (Alamouti, 1998) as

where a=e j2πm/q , q is the extension level and m=0, 1,…q-1 The columns and rows of C1 denote

symbols transmitted from three active sensors in two signaling intervals, respectively C1 is obtained from the Alamouti code using Equation (1) where

In this fashion, arbitrary number of the ECBSTBCs can be generated by increasing the

extension level For that reason, the fusion center needs n+d feedback bits (n≥3) to select any

possible ECBSTBCs where dn2 log 2q1 (Eksim & Celebi, 2009b; Eksim, 2010b) n-2

feedback bits are needed to achieve full diversity as in Cooperative Balanced Space-Time Block

Codes (CBSTBC) (Eksim & Celebi, 2007) The rest of the d+2 feedback bits provide additional

coding gain

The ECBSTBCs can be used in WSN The ECBSTBC contains two phases: Measurement and cooperation There are many measurement and cooperation phases respectively within a frame Additionally, each frame includes an initialization phase In the initialization phase, which occurs at the beginning of the each frame, the fusion center informs the active sensors about which ECBSTBC would be utilized within the frame using feedback channel The selected code is fixed over one frame In the measurement phase, each cooperating sensor makes two consecutive observation and binary quantization The observation at each sensor

is assumed to be Gaussian random variable with mean ±m and variance σ2 In the

cooperation phase of the ECBSTBCs, the fusion center receives the signal, r D,

N

Here h rd is the channel coefficient vector that contains path gains from the sensors to the

fusion center, n D is additive white Gaussian noise vector whose components are complex zero-mean with variance 2

D

 , P is the average total transmit power of the active sensors and

C is the ECBSTBC matrix

Performance Analysis of Binary Sensor-Based Cooperative Diversity Using Limited Feedback 241

Fig 1 Wireless sensor network

ˆs

Fig 2 Analitical model of wireless sensor network

2.2 Channel model

We assume that all parallel wireless channels are independent but they have statistically

uniform paths with have identical means and variances (Cetinkaya, 2007) That is to say that

the sensors-fusion center channels have equal variance and mean This is not true for

realistic scenarios, since some of the parallel channels have non-uniform statistical

properties (Cetinkaya, 2007) In the non-uniform wireless channel simulations, the parallel

channels may contain “better” or “worse” channels When the ith active sensor-fusion center

channel`s variance is much higher than the jth active sensor-fusion center channel`s variance

2 2

rid rjd

(  where j=1, ,n and j≠i), this channel can be considered as “better” channel On the

contrary, when the ith sensor-fusion center channel`s variance is much lower than the jth

sensor-fusion center channel`s variance 2 2

rid rjd

(  where j=1, ,n and j≠i), this channel can

be called as “worse” channel (Ibrahim et al., 2008)

3 Extended Cooperative Balanced Space-Time Block Codes

The ECBSTBCs can be obtained from an OSTBC multiplied by an extension matrix Since Alamouti`s code is the only orthogonal code with rate one and minimum delay, the ECBSTBCs can be obtained as an extension of the Alamouti`s code (Alamouti, 1998) as

where a=e j2πm/q , q is the extension level and m=0, 1,…q-1 The columns and rows of C1 denote

symbols transmitted from three active sensors in two signaling intervals, respectively C1 is obtained from the Alamouti code using Equation (1) where

In this fashion, arbitrary number of the ECBSTBCs can be generated by increasing the

extension level For that reason, the fusion center needs n+d feedback bits (n≥3) to select any

possible ECBSTBCs where dn2 log 2q1 (Eksim & Celebi, 2009b; Eksim, 2010b) n-2

feedback bits are needed to achieve full diversity as in Cooperative Balanced Space-Time Block

Codes (CBSTBC) (Eksim & Celebi, 2007) The rest of the d+2 feedback bits provide additional

coding gain

The ECBSTBCs can be used in WSN The ECBSTBC contains two phases: Measurement and cooperation There are many measurement and cooperation phases respectively within a frame Additionally, each frame includes an initialization phase In the initialization phase, which occurs at the beginning of the each frame, the fusion center informs the active sensors about which ECBSTBC would be utilized within the frame using feedback channel The selected code is fixed over one frame In the measurement phase, each cooperating sensor makes two consecutive observation and binary quantization The observation at each sensor

is assumed to be Gaussian random variable with mean ±m and variance σ2 In the

cooperation phase of the ECBSTBCs, the fusion center receives the signal, r D,

N

Here h rd is the channel coefficient vector that contains path gains from the sensors to the

fusion center, n D is additive white Gaussian noise vector whose components are complex zero-mean with variance 2

D

 , P is the average total transmit power of the active sensors and

C is the ECBSTBC matrix

Smart Wireless Sensor Networks242

3.1 Three active sensors

Due to energy efficiency, when three sensors are active in the wireless environment, then,

C1, C2 and C3 are available ECBSTBC matrices These matrices are

Here a is the coefficient as defined previously The fusion center selects the ECBSTBC C j,

j=1,2,3 and the feedback bit a that gives the maximum coding gain In this case, two bits of

feedback is needed to select the ECBSTBC matrices and k bit of is needed to select the

feedback bit a where k log2q

The decoding of the ECBSTBCs is similar to CBSTBCs (Eksim & Celebi, 2007) Assume that

the C1 matrix gives maximum coding gain The received signals at fusion center are given as

Here r is the observed data which includes observation and quantization noise by the ith ri j,

active sensor at the jth symbol interval Here η1 and η2 are noise at the fusion center The

fusion center estimates s1 and s2 by linear processing

where φ1 and φ2 are the noise terms which include both observation and quantization noise

at the active sensors and the noise at the fusion center The contribution of the

2 3 1 3 1 2

2max Re ah h r d r d ,Re ah h r d r d ,Re ah h r d r d term in Equation (8) will always be

positive and the gain will be greater than the sum of the magnitude squares of all path gains



h r d h r d h r d If the observation noise is very low, then, the diversity order

approaches to 3 It can be easily shown that the diversity order of the ECBSTBC approaches

to n if n sensors are active when the observation noise is very low A proof can be found in

sensor selection (SS n:1) (Eksim & Celebi, 2009a; Eksim & Celebi, 2010a) To maximize

active sensors and then the selected sensors transmit the received signals using the Alamouti scheme (Gore & Paulraj, 2002) In the simulations, the best active sensor pair which has the best instantaneous sensor-fusion center channel pair is selected This is called as the sensor

selection with Alamouti (SS n:2) (Eksim & Celebi, 2009a; Eksim & Celebi, 2010a)

The bit error probabilities of the ECBSTBC, SS, SS with Alamouti and statistical STBC cooperative diversity (Ohtsuki, 2006) are evaluated by computer simulations A frame of 100 symbols is used For meaningful comparison, the total transmission power and bandwidth are fixed, namely, the power is divided equally among cooperative active sensors Each

active sensor is assumed to observe either of two events H 0 and H 1 with equal probability

The observation at each sensor is assumed to be Gaussian random variable with mean ±m

and variance σ2 The noisy observation is quantized by the active sensors independently Then, the quantized observation is transmitted according to selected transmission scheme

m/  =2

m/  =3

m/  =4

No error m/  =1

Fig 3 The BER of three active sensors

Performance Analysis of Binary Sensor-Based Cooperative Diversity Using Limited Feedback 243

3.1 Three active sensors

Due to energy efficiency, when three sensors are active in the wireless environment, then,

C1, C2 and C3 are available ECBSTBC matrices These matrices are

Here a is the coefficient as defined previously The fusion center selects the ECBSTBC C j,

j=1,2,3 and the feedback bit a that gives the maximum coding gain In this case, two bits of

feedback is needed to select the ECBSTBC matrices and k bit of is needed to select the

feedback bit a where k log2q

The decoding of the ECBSTBCs is similar to CBSTBCs (Eksim & Celebi, 2007) Assume that

the C1 matrix gives maximum coding gain The received signals at fusion center are given as

Here r is the observed data which includes observation and quantization noise by the ith ri j,

active sensor at the jth symbol interval Here η1 and η2 are noise at the fusion center The

fusion center estimates s1 and s2 by linear processing

where φ1 and φ2 are the noise terms which include both observation and quantization noise

at the active sensors and the noise at the fusion center The contribution of the

2 3 1 3 1 2

2max Re ah h r d r d ,Re ah h r d r d ,Re ah h r d r d term in Equation (8) will always be

positive and the gain will be greater than the sum of the magnitude squares of all path gains



h r d h r d h r d If the observation noise is very low, then, the diversity order

approaches to 3 It can be easily shown that the diversity order of the ECBSTBC approaches

to n if n sensors are active when the observation noise is very low A proof can be found in

sensor selection (SS n:1) (Eksim & Celebi, 2009a; Eksim & Celebi, 2010a) To maximize

active sensors and then the selected sensors transmit the received signals using the Alamouti scheme (Gore & Paulraj, 2002) In the simulations, the best active sensor pair which has the best instantaneous sensor-fusion center channel pair is selected This is called as the sensor

selection with Alamouti (SS n:2) (Eksim & Celebi, 2009a; Eksim & Celebi, 2010a)

The bit error probabilities of the ECBSTBC, SS, SS with Alamouti and statistical STBC cooperative diversity (Ohtsuki, 2006) are evaluated by computer simulations A frame of 100 symbols is used For meaningful comparison, the total transmission power and bandwidth are fixed, namely, the power is divided equally among cooperative active sensors Each

active sensor is assumed to observe either of two events H 0 and H 1 with equal probability

The observation at each sensor is assumed to be Gaussian random variable with mean ±m

and variance σ2 The noisy observation is quantized by the active sensors independently Then, the quantized observation is transmitted according to selected transmission scheme

m/  =2

m/  =3

m/  =4

No error m/  =1

Fig 3 The BER of three active sensors