Let δ n l be the sensing capability of the sensing unit u^l on sensor s_n and be defined as δ n l = { t m l | μ n , m * ν n l * τ m l = 1 ,   ∀   m }. Furthermore, let Δ_n be the sensing capability of sensor s_n, which is the union of the sensing capabilities of all sensing units equipped on s_n. That is, Δ n = ∪ ∀ ν n l = 1 δ n l. For the example shown in Figure 1, δ 1 2 = { t 1 2 } and δ 3 1 = { t 1 1 , t 2 1 }. In addition, Δ 1 = δ 1 1 ∪ δ 1 2 = { t 1 1 , t 1 2 } and Δ 3 = δ 3 1 ∪ δ 3 3 = { t 1 1 ,   t 2 1 ,   t 1 3 ,   t 2 3 }. Initially, a sensor, say s_n, will take Δ_n as its sensing responsibility. Let Γ_n denote the sensing responsibility of s_n. Thus, Γ_n is initialized to Δ_n.

In REFS, the setting of W_n solely depends on the remaining energy of the sensor. The more the energy remains, the shorter is the waiting time. As a result, the sensor with more energy will turn on more sensing units to sense the attributes of the targets. Let E n r stand for the remaining energy of s_n. W_n is set as follows: W n = ( 1 − E n r E ) * 𝒟 SAR .

At the beginning of every initial phase, each sensor, say s_n, will wait for a waiting time of its own (W_n) and overhear the decisions of the neighbors with smaller waiting time. While receiving the neighbors’ decision packets, the sensor will prune away from Γ_n those t m l indicated in the neighbors’ decision packets. Note that, in the neighbors’ decision packets, the next relay is also included. If s_n is indicated as a relay, s_n should turn on its communication unit and also need to find the next relay for itself in order to relay the sensed data for the neighbors.

After W_n expires, the remaining Γ_n is the sensing responsibility of s_n at this round. However, it is possible that all remaining energy of s_n is still not enough to support all sensing units indicated in Γ_n. As a result, s_n will orderly remove the sensing units whose sensing capability is the least. If S is a set, |S| means the cardinality of S. Therefore, the deletion of Γ_n can be formally represented as follows. Γ n = Γ n − { t m l ′ ,   ∀   m | l ′ = arg   min l ∈ L | { t m l ∈ Γ n ,   ∀   m } | } .

Finally, s_n enables the corresponding sensing units indicated in the remaining Γ_n to cover the attributes for the indicated targets.

Upon deciding to turn on the sensing units or being indicated as other’s relay, s_n will select a neighbor s_n′ to relay the sensed data to the sink. In general, the sensors closest to the sink are the best. Let nextRelay(n) denote the relay sensor for s_n. Therefore, in REFS, nextRelay(n) is set as below. nextRelay ( n ) = arg   min n ′ ∈ ℵ ( n )   d ( s n ′ ,   s 0 ) .

Note that the way to find the relay is the same as geographic routing. However, in geographic routing, a local minimum (dead end) problem will occur. Many approaches have been proposed to handle the problem [22, 23]. Therefore, the paper does not address the problem. In addition, if s_n does not have enough energy to enable any sensing unit, it will quit executing REFS and turn off all sensing units. The remaining energy of s_n will be left for communication only.

Finally, s_n announces its decision as well as the selected relay.

Overall, REFS is a simple scheme that can be easily implemented. Moreover, REFS incurs less control overhead. However, REFS only takes the sensor’s remaining energy and the neighbors’ decisions into account for making its own decision. The degree of contributions of the sensing units and the communication unit are not considered in REFS. Therefore, the sensor’s energy can not be efficiently utilized. As a result, the sensor applying REFS has a high possibility to enable more redundant sensing or communication units. Consequently, the network lifetime can not be prolonged effectively.

In EEFS, sensor s_n calculates W_n by the following information: (1) the sensing capabilities, (2) the critical sensing responsibilities, (3) the remaining energy of itself and its neighbors, as well as (4) the probability of being others’ relay. According to the sensing capabilities as well as the critical sensing responsibilities of itself and its neighbors, sensor s_n will rank its sensing priority among its neighbors. The sensor with higher sensing priority and lower probability of being others’ relay has shorter W_n so that the sensor can make its decision sooner. A sensor’s sensing priority and the probability of being others’ relay are regarded as its coverage contribution and connectivity contribution, respectively.

The coverage contribution of a sensor reflects the ranking of the sensor among its neighbors in regard to the sensing capability. The ranking is processed as follows. If s_n is equipped with the sensing unit u^l, s_n will sort the sensing capability of δ n ′ l in an increasing order to a list 𝒧^l according to | δ n ′ l | ,   ∀   n ′   ∈ { n }   ∪   ℵ ( n ). If the sensing capabilities of two sensors are the same, the higher priority will be assigned to the one with more remaining energy. Otherwise, the sensor with a larger ID wins. Notice that only the sensor equipped with u^l is included in the ranking process. Let r n l be the order in 𝒧^l, which represents the priority of the sensing unit u^l of s_n among its neighbors. The larger the r n l is, the higher its priority is. It is worth mentioning that the reason to take a rank among the neighbors is to normalize the | δ n ′ l | for different u^l on s_n.

Take s₁ in Figure 1 as an example. Since ℵ(s₁) = {s₂, s₃}, only s₁, s₂, and s₃ are taken into account. Suppose E = 8, E 1 r = 6, E 2 r = 8, and E 3 r = 4. Since s₁ is equipped with u¹ and u², r 1 1 and r 1 2 as well as r max 1 and r max 2 are to be calculated, where r max l = | 𝒧 l |. Firstly, u¹ is considered. Obviously, | δ 1 1 | = 1, | δ 2 1 | = 1, and | δ 3 1 | = 2. Since | δ 1 1 | = | δ 2 1 | = 1, the remaining energy is taken into account. Therefore, the priority of δ 1 1 among s₁ and its neighbors is: δ 1 1 < δ 2 1 < δ 3 1. That is, 𝒧 1 = ( δ 1 1 ,   δ 2 1 ,   δ 3 1 ). Consequently, r 1 1 = 1 and r max 1 = 3. With regard to u², since s₂ and s₃ are not equipped with u², therefore, r 1 2 = 1 and r max 2 = 1.

Let ρ_n be the priority of s_n, which takes the priorities of all sensing units equipped on s_n into consideration. ρ_n is set as: ρ n = ∑ l | ν n l = 1   r n l r max l .For the above example, ρ 1 = 1 3 + 1 1 = 4 3.

Because the number of sensing units equipped on sensors is different, the coverage contribution of s_n, denoted ρ̄_n, is defined as the average priority of the sensing units equipped on s_n and represented as follows. (1) ρ ¯ n = ρ n ∑ l | ν n l = 1   ν n l = ∑ l | ν n l = 1   r n l r max l ∑ l | ν n l = 1   ν n l .Notice that 0 < ρ̄_n ≤ 1. With regard to the example in Figure 1, ρ ¯ 1 = 4 3 / 2 = 2 3.

The connectivity contribution of a sensor is defined as the probability of being others’ relay. Let σ_n be the connectivity contribution of s_n. σ_n is determined as follows.

It is hard for a sensor to find the best route to the sink merely by its local information, such as the one-hop neighbor information. Like REFS, the neighbor with the shortest distance to the sink is the best candidate to relay the sensed data for the sensor. Therefore, a sensor closest to the sink has a higher probability to serve as others’ relay. Based on the concept, the location of the relay for sensor s_n can only be located in the forwarding zone of s_n. The definition of the forwarding zone of s_n is defined as follows, where C(s, R) denotes a circle centered at s with the radius R. Figure 3 (a) also illustrates the forwarding zone of s_n.

Definition 3 (Forwarding Zone) Let the intersection points of two circles C(s₀, d(s₀, s_n)) and C(s_n, R_c) be z₁ and z₂. The forwarding zone of s_n, denoted 𝒡(s_n), is defined as the circular sector formed by two radii s n z 1 ¯ and s n z 2 ¯, and the arc z 1 z 2 ^.

Compared with s_n, the sensor located in the forwarding zone of s_n has a shorter to-sink-distance, which is defined as the distance from the sensor to the sink. On the contrary, the sensor located in the area C(s_n, R_c) − 𝒡(s_n) has a longer to-sink-distance than s_n. It implies that the sensor located in C(s_n, R_c) − 𝒡(s_n) may choose s_n as its relay. Consequently, the more the sensors are located in C(s_n, R_c) − 𝒡(s_n), the more likely s_n become others’ relay. Therefore, σ n = | { s n ′ | s n ′   ∈   ( C ( s n ,   R c ) − 𝒡 ( s n ) ) } | | ℵ ( n ) | .The higher the σ_n is, the more likely s_n become others’ relay. Similarly, consider the same example in Figure 1. Because s₂ and the sink, regarded as a neighbor of s₁, are located at 𝒡(s₁), σ 1 = 3 − 2 3 = 1 3.

A sensor with a higher coverage contribution can more efficiently cover the targets, that is, consume less energy but cover more targets. Therefore, the sensor with a higher coverage contribution shall make its decision sooner, implying a shorter W_n. On the other hand, a sensor with a higher connectivity contribution has a higher probability to relay. If the sensor is selected as a relay, a longer waiting time can let it make its decision later, to be likely to turn on its sensing units to cover the targets, and balance its energy consumption. Consequently, the sensor with a higher connectivity contribution shall wait for a longer time to make its decision.

As a result, a sensor with a higher coverage contribution and a lower connectivity contribution will have a shorter W_n so that the sensor can determine whether it should turn on its sensing and/or communication units. W_n is set as follows. (2) W n = 1 − ( α n   *   ( 1 − σ n ) + ( 1 − α n ) *   ρ ¯ n ) ] * 𝒟 SAR ,where α_n is the contribution tendency of s_n on coverage contribution or connectivity contribution. Basically, a sensor near the sink may have a higher probability to forward others the sensed data, because all the sensed data should be forwarded to the sink. The sensor near the sink shall have a higher connectivity tendency to relay the sensed data in order to keep the network connected. On the other hand, a sensor covering more targets shall have a higher coverage contribution tendency to sense the targets. Therefore, each sensor in the sensing field shall have different α_n to make the best use of its contribution tendency. In other words, in addition to the status of neighbors, the hardware difference and the location of a sensor should be considered to determine W_n. How to determine each sensors’ α_n will be shown in detail in next subsection.

In order to demonstrate how the coverage and connectivity contributions affects W_n, assume that the α_n of the sensors in the example in Figure 1 are the same and set to 0.5. That is, the locations and the energy model of the sensors in Figure 1 is assumed similar. According to Equation (2), W 1 = [ 1 − ( α 1 * ( 1 − σ 1 ) + ( 1 − α 1 ) * ρ ¯ 1 ) ] * 𝒟 SAR = [ 1 − ( 0.5 * ( 1 − 1 3 ) + ( 1 − 0.5 ) * 2 3 ) ] * 𝒟 SAR = 1 3 𝒟 SAR. Similarly, W₂ can be calculated as follows. Assume E 4 r = 7, ρ ¯ 2 = 8 3 / 2 = 3 4. On the other hand, because only the sink is located in 𝒡(s₂), σ 2 = 4 − 1 4 = 3 4. Therefore, W 2 = [ 1 − ( 0.5 * ( 1 − 3 4 ) + ( 1 − 0.5 ) * 3 4 ) ] * 𝒟 SAR = 1 2 𝒟 SAR. Clearly, s₁ will make its decision first, because W₁ < W₂. In Figure 1, both s₁ and s₂ are equipped with two kinds of sensing units that can sense the corresponding sensing attributes at t₁. Therefore, s₁ and s₂ have approximately the same coverage contribution. However, because the number of s₂’s neighbors is more than that of s₁, s₂ should make its decision later to ensure the network connectivity.

The tendency of a sensor toward coverage or connectivity is determined according to the locations of the targets, which are known by all sensors in advance. If the amount of sensed data to be relayed by a sensor is large, the sensor will take a higher priority in relaying the sensed data, instead of sensing the targets. Therefore, a relaying zone of a sensor is defined to estimate the amount of sensed data to be relayed by the sensor. Let 𝒭(s_n) denote the relaying zone of s_n. Definition 4 and Figure 3(b) give the formal definition and an illustration of 𝒭(s_n), respectively.

Definition 4 (Relaying Zone) Let 𝒭(s_n) denote the relaying zone of s_n. 𝒭 ( s n ) = ( 𝒜 − C ( s 0 ,   d ( s 0 ,   s n ) ) ) ∪ C ( s n ,   R c ) .

It is possible that the sensed data from the targets located in 𝒭(s_n) may need s_n to relay to the sink. Therefore, the number of targets located in 𝒭(s_n) can be regarded as the amount of sensed data which needs s_n to relay to the sink. As a result, a sensor with a larger number of targets in 𝒭(s_n) shall have a higher connectivity contribution tendency. Formally, α_n of s_n is set as follows. α n = ɛ * | { t m | t m   ∈   𝒭 ( s n ) } | M ,where ɛ is a system adjustable parameter to reflect the hardware difference. If the energy cost of the communication unit is much higher than that of the sensing unit, even if the sensor has a higher priority in coverage contribution, the sensor should still pay more attention on the connectivity contribution. In contrast, if the energy cost of the sensing unit is higher than that of the communication unit, the sensor should put a higher weight on coverage contribution, regardless of the distance from the sensor to the sink. Therefore, ɛ is set high when the energy cost of the sensing unit is lower than that of the communication unit. Otherwise, ɛ is set low accordingly. Basically, ɛ is set between 0 and 1.

Notice that α_n is based on the hardware characteristic, e.g., the energy consumption models of sensing and communication units, and the environment characteristic, e.g., the locations of sensors. These characteristics have an influence on whether a sensor can spend its energy efficiently on sensing or communication. However, these characteristics of a sensor are unchanged after the sensor is deployed. Therefore, it is not sufficient for the sensor to efficiently use its energy. When determining W_n, a sensor also has to locally take the coverage and connectivity contributions among its neighbors and itself, as well as the remaining energy into consideration to meet the sensing requirements, to ensure the network connectivity, and to prolong the network lifetime.

The processes to be performed here are the same as those in Step 2 of REFS.

Upon the expiration of W_n, the remaining Γ_n is the sensing responsibility of s_n in this round. However, it is still possible for s_n to alleviate its burden via pruning out redundant sensing responsibilities. For example, for some neighbor of s_n, say s_n′, if Θ_n′ ≠ ∅, s_n′ has the responsibility to cover the sensing responsibilities indicated in Θ_n′. Suppose s_n′ turns on u^l, for some l, to cover t m l ( ∈   Θ n ′ ), for some m. If turning on the sensing unit u^l also covers the other targets, say t m ′ l, for some m′ (that is, t m ′ l   ∈   δ n ′ l) and t m ′ l   ∈   Γ n, therefore, t m ′ l can be pruned awat from Γ_n. As a result, Γ_n can be further improved.

In addition, it is possible to improve by pruning the inefficient sensing responsibilities of s_n if it is better to leave these responsibilities to the neighbors with higher sensing efficiency. As defined above, δ n l is the sensing capability of u n l. The more | δ n l | is, the more targets u n l can cover at a time. Therefore, | δ n l | can be regarded as the benefit of u n l if u n l is turned on. On the contrary, e l E n r can be regarded as the cost of u n l, where e^l and E n r are the energy consumption of u^l in sensing for a time unit and the remaining energy of s_n, respectively. The cost considers not only the energy consumption of u^l, but also takes the remaining energy of s_n into account in order to reflect the effect of the energy consumption of u^l on the remaining energy of s_n. Consequently, | δ n l | e l / E n r can be regarded as the benefit-cost ratio (BCR) of u n l. In addition to the BCR of u n l, the sensing efficiency of u n l on s_n should take the ratio of the remaining energy of s_n to the initial energy into consideration as well. Therefore, let BCR ( u n l ) and Eff(s_n, u^l) denote the BCR of u n l and the sensing efficiency of u^l on s_n, respectively. Accordingly, the sensing efficiency of u^l on s_n, Eff(s_n, u^l), is defined as below. (3) Eff ( s n ,   u l ) = ( 1 − α n ) *   BCR ( u n l ) *   E n r E ,     where     BCR ( u n l ) = | δ n l | e l E n r .

In designing Eff(s_n, u^l), the contribution tendency is also considered to differentiate the sensors nearby or far away from the sink with the same sensing unit to increase the sensing efficiency. To do so can further alleviate the sensing responsibility of s_n. As a result, if there exists a sensor, say s_n′, who has not sent out the DecAnn packet and whose sensing efficiency of u^l is better than that of u^l on s_n, then s_n will leave the sensing responsibilities covered by u^l to s_n′.

In EEFS, in addition to the neighbor’s to-sink-distance, the remaining energy of the neighbor is also taken into account for s_n to select its relay. Basically, the neighbor with more remaining energy and shorter to-sink-distance will be selected as a relay. However, it is possible that the neighbor with a longer to-sink-distance may be selected. This will result in a longer path to the sink. Therefore, in EEFS, the neighbors located in 𝒡(s_n) are considered as relay candidates, instead of all the neighbors. Formally, nextRelay(n) is set as below. nextRelay ( n ) = arg   min n ′ ∈ 𝒡 ( s n ) d ( s n ′ , s 0 ) max j ∈ 𝒡 ( s n )   d ( s j ,   s 0 ) * ( 1 − E n ′ r E ) ,where d ( s n ′ ,   s 0 ) max j ∈ 𝒡 ( s n )   d ( s j ,   s 0 ) and E n ′ r E are to normalize the to-sink-distance and the remaining energy of s_n′, respectively. Notice that the case of |𝒡(s_n)|= 0 is regarded as the local minimum problem and is not addressed in the paper.

s_n announces its decision by sending out a DecAnn packet.

As mentioned above, REFS has a significant drawback that a sensor may turn on too many redundant sensing units. It is because a sensor in REFS only considers its remaining energy and its neighbors’ decisions. However, EEFS can alleviate such a situation and make the best use of the sensing and communication units equipped on a sensor. In addition, the coverage and connectivity contributions are introduced in designing W_n of s_n. To do so can let the sensor make its decision sooner if it has a higher sensing capability and a lower probability to relay for others. The order of making a decision has a significant impact on the performance of both REFS and EEFS. Moreover, in EEFS, both the coverage and the connectivity contributions are taken into account. As a result, a sensor with a better coverage or connectivity contribution can make its best decision either in covering the target or in relaying the sensed data. Consequently, the network lifetime can be prolonged efficiently.

The complete REFS and EEFS algorithms are omitted due to the space limitation.

Figure 5 shows the snapshots of the remaining energy of each sensor in the last round of REFS and EEFS, respectively. The circles are the targets to be sensed and the gray squares represent the remaining energy of the corresponding sensor. The darker the gray color of the sensor is, the more remaining energy the sensor has. Figure 5 shows the snapshots of the remaining energy of the sensors in the last round of REFS and the corresponding round of EEFS both in scenarios 1 and 2. On average, the gray squares in Figures 5(b) and 5(d) are darker than those in Figures 5(a) and 5(c), whcih means that the remaining energy of the sensors in EEFS is higher than that in REFS. Moreover, in addition to the remaining energy, the number of remaining sensors (gray squares) in EEFS is also more than that in REFS, especially in scenario 2. These all imply that EEFS performs more efficiently than REFS.

To look into the detailed performance of REFS, EEFS, as well as the ILP solution, Figure 6 illustrates the energy consumption and the remaining energy in each round, which is randomly taken from some run, rather than the average of 10 runs. In addition to the settings of the scenarios 1 and 2 described above, the other settings are as follows. The numbers of sensors, targets, and attributes are 300, 10, and 3, respectively.

Figure 6(a) shows the energy cost of REFS, EEFS, and the ILP solution consumed in each round for some run in scenario 1. Obviously, the energy consumptions of REFS and EEFS fluctuate with time, while that of the ILP solution stays stable. This is because the sensing redundancy problem in the ILP solution is slight. Therefore, the energy consumption of the ILP solution in each round is very similar. On the contrary, REFS and EEFS are heuristic. The energy consumptions of REFS and EEFS in each round depend heavily on the number of sensing and communication units selected. Therefore, the energy consumptions of REFS and EEFS have a large variation among rounds. Moreover, since the sensing redundancy problem is much more serious in REFS, the energy consumption of REFS is always the worst and the network lifetime of REFS is the shortest. In addition, in EEFS, sensors consume less energy to perform the sensing tasks at the beginning because many redundant sensing units are removed. However, when the number of failed sensors increases, some sensing responsibilities of sensors may become the critical sensing responsibilities. Therefore, the sensing redundancy problem will be getting worse, which will result in a higher energy consumption of the sensors in a round.

On the other hand, although the energy consumption of EEFS is higher than that of the ILP solution in each round, the network lifetime of EEFS is still very close to that of the ILP solution, whose difference is smaller than eight rounds, as shown in Figure 6(b). Figures 6(c) and 6(d) illustrate the energy consumption and the remaining energy of REFS and EEFS in each round when the control and computation overheads are considered. The results are similar to those shown in Figures 6(a) and 6(b).

The following experiments are to observe the impacts of the number of sensors, in which the number of targets and the number of attributes are fixed at 10 and 3, the number of targets, in which the number of sensors and the number of attributes are fixed at 300 and 3, and the number of attributes, in which the number of sensors and the number of targets are fixed at 300 and 10, on the network lifetime with regard to REFS, EEFS, and the ILP solution. The results for scenarios 1 and 2 are shown in Figure 7.

Figures 7(a), 7(b) and 7 (c) illustrate the simulation results of scenario 1. Since the ILP solution becomes extraordinarily complicated when the numbers of attributes, targets or sensors increase, some results of the ILP solution are unavailable. Obviously, the network lifetime increases with the increase of the number of sensors. On the contrary, when the number of targets or attributes increases, the network lifetime decreases accordingly. However, the ILP solution always has the longest network lifetime, if available, whereas REFS is the worst in all cases. Nevertheless, the performance of EEFS is close to that of the ILP solution. From the practical viewpoint, EEFS is a promising alternative solution.

Figures 7(d), 7(e) and 7(f) respectively show the impacts of the number of sensors, targets, and attributes on network lifetime when the control and computation overheads are taken into consideration in scenario 2. Here, the communication overhead takes the messages exchanged among sensors into account, such as the sensor’s capabilities, decision announcement, etc.

Basically, Figures 7(d), 7(e) and 7(f) are similar to Figures 7(a), 7(b) and 7(c), respectively. As indicated in Figure 7(d), EEFS, REFS, and m-SU-CTC extend the network lifetime when the number of sensors increases. Among them, EEFS performs the best when a large number of sensors are deployed in the area of interest, even though EEFS needs extra control and computation overheads.

Figure 7(e) illustrates the impact of the number of targets on the network lifetime. It can be found that the network lifetime of m-SU-CTC rapidly decreases when the number of targets increases. However, the network lifetimes of EEFS and REFS decrease smoothly. That is because, with the aid of the proposed algorithms, the increase of the number of targets does not activate too many sensing units on sensors. The simulation result also indicates that m-SU-CTC is unsuitable for WHSNs when a large number of targets needs to be sensed.

According to Figure 7(f), the impact of the number of attributes on the network lifetime is not significant when the number of attributes is larger than 5. Basically, EEFS performs better than REFS and m-SU-CTC. However, the differences of the network lifetime among EEFS, REFS and m-SU-CTC are not significant, especially when the number of attributes is larger than 5. Since only 300 sensors are deployed in the area of interest and many kinds of attributes of the targets are needed to be sensed, a lot of sensors have to be activated to sense the required attributes. As a result, the effect of the policies to remove redundant or inefficient sensing responsibilities in the EEFS scheme is not obvious. Thus, the differences of the network lifetime between EEFS and REFS are not significant. The result is consistent with that in Figure 7(d), which indicates that the number of sensors to be deployed should be increased when the number of attributes increases.

It is worth noticing that m-SU-CTC performs the worst in all cases. It is because that m-SU-CTC is originally designed for the sensor equipped with only one sensing unit, which is unsuitable to be applied for WHSNs. Without good coordination among sensing units or sensors, m-SU-CTC will turn on many redundant sensing units or sensors, and thus, result in higher energy consumption.

Figure 8 shows the performance of REFS and EEFS in a comprehensive view combining the numbers of sensors and targets to observe their impact on the network lifetime. In the simulation, the number of sensors is varied from 100 to 600 with a step of 100, the number of targets is varied from 5 to 40 with a step of 5, and the number of attributes is fixed at 3.

In scenario 1, both REFS and EEFS have similar inclinations that the network lifetime increases with the increase of the number of sensors, but decreases with the increase of the number of targets, as shown in Figures 8(a) and 8(b). However, EEFS still shows a better performance than REFS. When the control and computation overheads are taken into consideration, both REFS and EEFS also have similar inclination that the network lifetime increases with the increase of the number of sensors, but decreases with the increase of the number of targets, as shown in Figures 8(c) and 8(d). Also, EEFS has better performance than REFS. In addition, when control and computation overheads are considered, the impact of the number of targets on the network lifetime is not significant. That is because when the number of targets increases, the energy consumption in sensing the required attributes does not increase substantially. It is shown again that, in practical view, when multiple attributes are required to be sensed, deploying a WHSN with multiple sensing units is a promising solution.