Smart Wireless Sensor Networks Part 9 pptx

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 RELIABLE −TRANSMIT(CR, i, p i , P t x (i −1), F(i −1)) 1. P t x [i] ← P t x [i −1] · (1 − p i ) 2. if P t x [i] > CR 3. then F [i] ← f alse 4. else F [i] ← true 5. P t x [i] ← (1 − p i ) 6. cache data packets to a node n i Fig. 1. Active caching algorithm at i-th node, n i . 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 − p 1 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 false). 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 ’s buffer. Each node runs the algorithm of Figure 1 and the total active caching over a routing path is performed by the dynamic programming algorithm. Figure 2 shows an example of the active caching when seven sensor nodes are deployed sequentially and they have an average 5% packet loss rate and 80% C R. Every node satisfies 80% CR and data caching occurs at n 5 . When packet loss happens between a source node n 1 and the caching node n 5 , the caching node requests retransmission to the source node. When packet loss happens between the caching node and a destination node n 7 , the destination node requests retransmission to the caching node. 3. Analysis A packet loss rate occurs due to wireless link and contention errors. Since all the packets are destined to the sink node in wireless sensor networks, the contention error in links close to the sink node may increase. To model the packet loss rate at i-th hop, we assume the uniform link error p l and the contention error which is proportional to the square of transmission hop counts. p i = p l + αi 2 , (1) where α is the contention failure factor. Then the packet delivery rate during h hops from the s-th node is P tx (s,h) = s+h−1 ∏ i=s (1 − p i ). (2) Data caching occurs when P tx (s,h) is lower than CR. When the number of nodes N over a route and CR are given, the hop counts h from a caching node s and the number of caching nodes N c are obtained by the function in Figure 3. Φ represents a set of (s, h) tuples and the (s,h) tuples are used to compute the retransmission counts of lost packets. For example in Figure 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 sum of ψ (s,h) as E [C] = N c ∑ j=1 ψ(s j , h j ). (4) Because the retransmitted packets can also experience transmission failure, we should con- sider repeated retransmissions for ψ (s,h). Let Γ f (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 RELIABLE −TRANSMIT(CR, i, p i , P t x (i −1), F(i −1)) 1. P t x [i] ← P t x [i −1] · (1 − p i ) 2. if P t x [i] > CR 3. then F [i] ← f alse 4. else F [i] ← true 5. P t x [i] ← (1 − p i ) 6. cache data packets to a node n i Fig. 1. Active caching algorithm at i-th node, n i . 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 − p 1 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 false). 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 ’s buffer. Each node runs the algorithm of Figure 1 and the total active caching over a routing path is performed by the dynamic programming algorithm. Figure 2 shows an example of the active caching when seven sensor nodes are deployed sequentially and they have an average 5% packet loss rate and 80% C R. Every node satisfies 80% CR and data caching occurs at n 5 . When packet loss happens between a source node n 1 and the caching node n 5 , the caching node requests retransmission to the source node. When packet loss happens between the caching node and a destination node n 7 , the destination node requests retransmission to the caching node. 3. Analysis A packet loss rate occurs due to wireless link and contention errors. Since all the packets are destined to the sink node in wireless sensor networks, the contention error in links close to the sink node may increase. To model the packet loss rate at i-th hop, we assume the uniform link error p l and the contention error which is proportional to the square of transmission hop counts. p i = p l + αi 2 , (1) where α is the contention failure factor. Then the packet delivery rate during h hops from the s-th node is P tx (s,h) = s+h−1 ∏ i=s (1 − p i ). (2) Data caching occurs when P tx (s,h) is lower than CR. When the number of nodes N over a route and CR are given, the hop counts h from a caching node s and the number of caching nodes N c are obtained by the function in Figure 3. Φ represents a set of (s, h) tuples and the (s,h) tuples are used to compute the retransmission counts of lost packets. For example in Figure 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 sum of ψ (s,h) as E [C] = N c ∑ j=1 ψ(s j , h j ). (4) Because the retransmitted packets can also experience transmission failure, we should con- sider repeated retransmissions for ψ (s,h). Let Γ f (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 CalcHopCounts(N, CR) 1. n ← 1, s ← 1, h ← 1, N c ← 0 2. Φ = φ 3. loop: n < N 4. if P t x (s,h) > CR 5. then n ← n + 1, h ← h + 1 //no caching 6. else h ← h −1 //caching 7. if (h = 0) 8. then h ← 1, n ← n + 1 9. add (s,h) to Φ, N c ← N c + 1 10. s ← n, h ← 1 11. end loop 12. if (h > 1) 13. then add (s, h −1) to Φ, N c ← N c + 1 Fig. 3. Function to obtain (s, h) tuples. ψ (s,h) = ∞ ∑ j=1  h ·Γ f (j, s, h) · P tx (s,h)  . (5) If we let Γ s (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 Γ f (j, s, h) = Γ f (j − 1, s, h) −  Γ s  Γ f (j − 1, s, h), s, h  1 , (6) where Γ f (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 Γ s (k,s,h) can be calculated by the probability of successful transmission of Bernoulli trials P s (k,m,s,h) as Γ s (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 as P s (k,m,s,h) =  k m  · P tx (s,h) m ·  1 − P tx (s,h)  k−m . (8) The memory requirement B is defined as the caching rates of intermediate nodes including a source node. It is computed by N c and the number of relay nodes over a routing path: E [B] = N c N −1 . (9) 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 of active caching (AC) with that of E2E and HBH. For the simulation, we assume 20 sensor nodes are 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 and the memory requirements when a source transmits 40 packets. The results of the analysis and 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 retrans- mission counts are the highest and AC runs as E2E. In short, we can design wireless sensor 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 the highest 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 between HBH 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 error rate. 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 CalcHopCounts(N, CR) 1. n ← 1, s ← 1, h ← 1, N c ← 0 2. Φ = φ 3. loop: n < N 4. if P t x (s,h) > CR 5. then n ← n + 1, h ← h + 1 //no caching 6. else h ← h −1 //caching 7. if (h = 0) 8. then h ← 1, n ← n + 1 9. add (s,h) to Φ, N c ← N c + 1 10. s ← n, h ← 1 11. end loop 12. if (h > 1) 13. then add (s, h −1) to Φ, N c ← N c + 1 Fig. 3. Function to obtain (s, h) tuples. ψ (s,h) = ∞ ∑ j=1  h ·Γ f (j, s, h) · P tx (s,h)  . (5) If we let Γ s (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 Γ f (j, s, h) = Γ f (j − 1, s, h) −  Γ s  Γ f (j − 1, s, h), s, h  1 , (6) where Γ f (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 Γ s (k,s,h) can be calculated by the probability of successful transmission of Bernoulli trials P s (k,m,s,h) as Γ s (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 as P s (k,m,s,h) =  k m  · P tx (s,h) m ·  1 − P tx (s,h)  k−m . (8) The memory requirement B is defined as the caching rates of intermediate nodes including a source node. It is computed by N c and the number of relay nodes over a routing path: E [B] = N c N −1 . (9) 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 of active caching (AC) with that of E2E and HBH. For the simulation, we assume 20 sensor nodes are 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 and the memory requirements when a source transmits 40 packets. The results of the analysis and 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 retrans- mission counts are the highest and AC runs as E2E. In short, we can design wireless sensor 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 the highest 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 between HBH 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 error rate. 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, un- der the ITRC(Information Technology Research Center) support program supervised by the NIPA(National IT Industry Promotion Agency)" (NIPA-2010-(C1090-1021-0003)) 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, un- der the ITRC(Information Technology Research Center) support program supervised by the NIPA(National IT Industry Promotion Agency)" (NIPA-2010-(C1090-1021-0003)) 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 EKŞİM 1 and Mehmet E. ÇELEBİ 2 Tubitak-BILGEM 1 , Istanbul Technical University 2 Turkey 1,2 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 n 1 out of n active sensor for cooperation (n 1 ≤ 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 & Celebi, 2010a). To improve upon this problem , we propose an ECBSTBC combined 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). [...]... cooperation diversity part II: Implementation aspects and performance analysis IEEE Trans Commun., Vol 51, No 11, pp 193 9- 194 8 Tarokh, V ; Jafarkhani, H & Calderbank, A.R ( 199 9) Space-time block codes from orthogonal designs IEEE Transactions on Information Theory, Vol 45, No 5, pp 14561467 Time Synchronization in Wireless Sensor Networks 253 15 X Time Synchronization in Wireless Sensor Networks Jonggoo... for transmitting (Lal et al., 2005) 240 Smart Wireless Sensor Networks Fig 1 Wireless sensor network ˆ s 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... Çayrc, E (2002) A survey on sensor networks IEEE Commun Mag., Vol 40, No 8, pp 102-114 Alamouti, S.M ( 199 8) A simple transmit diversity technique for wireless communications IEEE J Select Areas Commun., Vol 16, No 8, pp 1451-1458 Buttyan, L & Hubaux, J.P (2003) Stimulating cooperation in self-organizing mobile ad hoc networks Mobile Networks and Applications, Vol 8, No 5, pp 5 79- 592 Cetinkaya, C (2007)... metric in energy constrained wireless sensor networks, Proceedings, IEEE Global Telecommunications Conference, 446-452 Laneman, J.N & Wornell, G.W (2003) Distributed space-time coded protocols for exploiting cooperative diversity in wireless networks IEEE Transactions on Information Theory, Vol 49, No 10, pp 2415-2425 Li, X., ve Wu, N.E., (2003) Power efficient wireless sensor networks with distributed... Commun., Vol E 89- B, No 3, pp 97 0 -97 3 Park, S., Savvides, A., ve Srivastava, M.B., (2001) Simulating networks of wireless sensors, Proceedings, Winter Simulation Conference, 1330-1338 Proakis, J.G., (2001) Digital communications, McGraw-Hill, 4th Edition Sendonaris, A ; Erkip, E & Aazhang, B (2003a) User cooperation diversity part I: System description IEEE Trans Commun., Vol 51, No 11, pp 192 7- 193 8 Sendonaris,... contains path gains from the sensors to the fusion center, nD is additive white Gaussian noise vector whose components are complex 2 zero-mean with variance  D , P is the average total transmit power of the active sensors and C is the ECBSTBC matrix 242 Smart Wireless Sensor Networks 3.1 Three active sensors Due to energy efficiency, when three sensors are active in the wireless environment, then,... (A.7), we obtain the BER upper bound at the fusion center (A.7) 250 Smart Wireless Sensor Networks 3   3 Pb      SNR  3    (A.8) Above equation can be expanded to arbitrary number of active sensors, thus, the BER upper bound for n active sensors is given as n   n Pb      SNR  n     (A .9) From Equation (A .9) , the diversity is n when the value of m/σ is high 7 References Akyildiz,... of three active sensors No error 8 10 SNR [dB] 12 14 16 18 244 Smart Wireless Sensor Networks In Figure 3, the bit-error probability curves are shown for three active sensors It is assumed that the ratio between the mean and the standard deviation of the observation in each active sensor (m/σ) is in the range of 1 and 4, and for comparison purposes no observation noise in each active sensor is also... Celebi, M.E (2009a) Extended cooperative balanced space-time block coding for increased efficiency in wireless sensor networks (Work in Progress), Proceedings, Networking 20 09, 456-467 Eksim, A & Celebi, M.E (2009b) Extended balanced space-time block coding for wireless communications IET Signal Processing, Vol 3, No 6, pp 476-484 Eksim, A & Celebi, M.E (2010a) Performance improvement of binary sensor based... devised by Mills (Mills, 199 1) Nodes could also be equipped with a global positioning system (GPS) to synchronize them (Hofmann-Wellenhof et al 199 7; Mannermaa et al 199 9) It is used to provide network-wide agreement among a large group of nodes in the Internet NTP works well synchronizing the computers on the Internet, but is not designed with the energy and computation limitations of sensor nodes in mind . Wireless Sensor Networks 2 29 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. 2005). Smart Wireless Sensor Networks2 40 Fig. 1. Wireless sensor network ˆ s Fig. 2. Analitical model of wireless sensor network 2.2 Channel model We assume that all parallel wireless. of the active sensors and C is the ECBSTBC matrix. Smart Wireless Sensor Networks2 42 3.1 Three active sensors Due to energy efficiency, when three sensors are active in the wireless environment,