Wireless Sensor Networks : Technology, Protocols, and Applications
Wireless Sensor Networks : Technology, Protocols, and Applications Wireless Sensor Networks : Technology, Protocols, and Applications
PERFORMANCE MODELING OF WSNs 293 The one-hop transmission from transmitter i to the receiver j is successful if (1) the distance between i and j is less than or equal to r: d i; j r; (2) for every other node, k, which is also receiving at the same time, d i;k > r; and (3) for every other node, l, which is transmitting at the same time, d l; j > r. Based on the definition above and the term l n;m , defined as the average transmission rate between node n and its generic receiver m, Chiasserini and Garetto [11.17] have constructed an interference model to calculate a successful transmission probability ðbÞ using l n;m as an input, under the assumption that the WSN employs a CSMA/CA mechanism with handshaking as in an IEEE 802.11 DCF. Also, if an IEEE 802.11 DCF or its variants is used as the MAC protocol, one may apply many existing performance analysis results directly (e.g., [11.19,11.20]) to obtain performance metrics such as throughput, delay, and collision probability, given the number of competing nodes. Routing Model Based on the energy model introduced earlier in the chapter, energy consumed for a generic route P ½EðPÞŠ can be computed as follows [11.17]: EðPÞ ¼ X i!P E i;np ðiÞðl i ; d i Þ ð11:5Þ where n p ðiÞ is the next hop of node i on path P. E i;np ðiÞðl; dÞ is the energy from node i to node n p ðiÞ. Assuming that the data size is l i bits and the distance between them is d i , the total energy consumed can be written using Eq. (11.3). The advantages of data-centric routing over address-centric routing in supporting data aggregation were found from analysis [11.21]. These results show that: 1. If the diameter of the set of source nodes ðXÞ is shorter than the minimal length of the shortest path from any source node to the sink ðD min Þ, the total number of transmissions under data-centric routing is smaller than with address-centric routing. Therefore, data-centric routing is more energy efficient. 2. The larger the distance between X and D min , the more energy is conserved by the data-centric routing. System Model Analysis of the overall performance of the sensor network is presented in [11.17], where a closed-loop model has been constructed to consider the sensor node model, MAC protocol, and routing policy all at the same time. This model consists of three submodels, as shown in Figure 11.5. The sensor node model was shown earlier in the chapter and in Figure 11.4. The interference model was also described earlier. The network model is used to model routing policy and to determine the average transmission rate between nodes, which is an input to the inference model. In [11.17], a simple routing policy is assumed as follows: When transmitting data to the next hop, a sensor node chooses the neighboring node that will result in the lowest energy consumption. The solution of the system model in Figure 11.5 has been obtained through fixed-point approximation [11.17], and
294 PERFORMANCE AND TRAFFIC MANAGEMENT Figure 11.5 Closed-loop model for the system [11.17]. system performance metrics such as average energy consumption and average delay have also been calculated in the following manner: 1. Construct a DTMC sensor node model, represented by the leftmost box for each sensor node i to get the stationary distribution p i and the probability that data are received in a time slot ða i Þ in node i. 2. Solve the network model using queuing network analysis to calculate the average data transmission rate between any pair of sensor nodes n and m in the network ðl n;m Þ as well as the average throughput for each sensor node. 3. Given l n;m as input to the interference model, compute the value of the probability that data are transmitted successfully in a time slot in node iðb i Þ. 4. b i is used as input to the sensor node model, iterating through steps 1 to 3. The worst relative error for two successive estimates of the sensor throughput is used as the stopping criterion. It is stated in [11.17] that 10 iterations result in an error below 0.0001. 11.5 CASE STUDY: SIMPLE COMPUTATION OF THE SYSTEM LIFE SPAN In this section we present a simple model to compute the system life span. The following assumptions are made: 1. All sensor nodes ðNÞ in the network organize a two-tiered topology. The sensor nodes in the lower layer are called leaf nodes. The sensor nodes in the high layer are called leader nodes. At the high layer, there are N 1 leader nodes forming a k-tree topology with h þ 1 levels (or h hops) from the sink, where each leader node in level i connects k child nodes in level i þ 1 to its parent node at layer i 1 (see Figure 11.6). Each leader node in the higher layer that
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PERFORMANCE MODELING OF WSNs 293<br />
The one-hop transmission from transmitter i to the receiver j is successful if (1) the<br />
distance between i <strong>and</strong> j is less than or equal to r: d i; j r; (2) for every other node,<br />
k, which is also receiving at the same time, d i;k > r; <strong>and</strong> (3) for every other node, l,<br />
which is transmitting at the same time, d l; j > r.<br />
Based on the definition above <strong>and</strong> the term l n;m , defined as the average transmission<br />
rate between node n <strong>and</strong> its generic receiver m, Chiasserini <strong>and</strong> Garetto [11.17]<br />
have constructed an interference model to calculate a successful transmission<br />
probability ðbÞ using l n;m as an input, under the assumption that the WSN employs<br />
a CSMA/CA mechanism with h<strong>and</strong>shaking as in an IEEE 802.11 DCF. Also, if<br />
an IEEE 802.11 DCF or its variants is used as the MAC protocol, one may apply<br />
many existing performance analysis results directly (e.g., [11.19,11.20]) to obtain<br />
performance metrics such as throughput, delay, <strong>and</strong> collision probability, given the<br />
number of competing nodes.<br />
Routing Model Based on the energy model introduced earlier in the chapter,<br />
energy consumed for a generic route P ½EðPÞŠ can be computed as follows [11.17]:<br />
EðPÞ ¼ X i!P<br />
E i;np ðiÞðl i ; d i Þ ð11:5Þ<br />
where n p ðiÞ is the next hop of node i on path P. E i;np ðiÞðl; dÞ is the energy from node i<br />
to node n p ðiÞ. Assuming that the data size is l i bits <strong>and</strong> the distance between them is<br />
d i , the total energy consumed can be written using Eq. (11.3).<br />
The advantages of data-centric routing over address-centric routing in supporting<br />
data aggregation were found from analysis [11.21]. These results show that:<br />
1. If the diameter of the set of source nodes ðXÞ is shorter than the minimal<br />
length of the shortest path from any source node to the sink ðD min Þ, the total<br />
number of transmissions under data-centric routing is smaller than with<br />
address-centric routing. Therefore, data-centric routing is more energy<br />
efficient.<br />
2. The larger the distance between X <strong>and</strong> D min , the more energy is conserved by<br />
the data-centric routing.<br />
System Model Analysis of the overall performance of the sensor network is<br />
presented in [11.17], where a closed-loop model has been constructed to consider<br />
the sensor node model, MAC protocol, <strong>and</strong> routing policy all at the same time. This<br />
model consists of three submodels, as shown in Figure 11.5. The sensor node model<br />
was shown earlier in the chapter <strong>and</strong> in Figure 11.4. The interference model was<br />
also described earlier. The network model is used to model routing policy <strong>and</strong> to<br />
determine the average transmission rate between nodes, which is an input to the<br />
inference model. In [11.17], a simple routing policy is assumed as follows: When<br />
transmitting data to the next hop, a sensor node chooses the neighboring node that<br />
will result in the lowest energy consumption. The solution of the system model in<br />
Figure 11.5 has been obtained through fixed-point approximation [11.17], <strong>and</strong>