*Article* **EERS: Energy-E**ffi**cient Reference Node Selection Algorithm for Synchronization in Industrial Wireless Sensor Networks**

#### **Mahmoud Elsharief 1, Mohamed A. Abd El-Gawad 1, Haneul Ko 2 and Sangheon Pack 1,\***

Received: 30 June 2020; Accepted: 20 July 2020; Published: 23 July 2020

**Abstract:** Time synchronization is an essential issue in industrial wireless sensor networks (IWSNs). It assists perfect coordinated communications among the sensor nodes to preserve battery power. Generally, time synchronization in IWSNs has two major aspects of energy consumption and accuracy. In the literature, the energy consumption has not received much attention in contrast to the accuracy. In this paper, focusing on the energy consumption aspect, we introduce an energy-efficient reference node selection (EERS) algorithm for time synchronization in IWSNs. It selects and schedules a minimal sequence of connected reference nodes that are responsible for spreading timing messages. EERS achieves energy consumption synchronization by reducing the number of transmitted messages among the sensor nodes. To evaluate the performance of EERS, we conducted extensive experiments with Arduino Nano RF sensors and revealed that EERS achieves considerably fewer messages than previous techniques, robust time synchronization (R-Sync), fast scheduling and accurate drift compensation for time synchronization (FADS), and low power scheduling for time synchronization protocols (LPSS). In addition, simulation results for a large sensor network of 450 nodes demonstrate that EERS reduces the whole number of transmitted messages by 52%, 30%, and 13% compared to R-Sync, FADS, and LPSS, respectively.

**Keywords:** energy-efficiency; industrial wireless sensor networks; reference node selection; time synchronization

#### **1. Introduction**

Industrial wireless sensor networks (IWSNs) have been widely developed in the last decade. They play a significant role in many applications, e.g., industry, healthcare, agriculture, and smart metering systems. Currently, IWSNs perform as an essential foundation in the widespread industrial internet of things [1,2]. IWSNs typically consist of many sensor nodes that are spatially disseminated over a region of interest. Generally, sensor nodes are usually used to observe environmental conditions [3]. They are often battery-operated, and sometimes it is infeasible to recharge or replace batteries. Consequently, the lifetime of the battery becomes a crucial concern in the design of IWSNs [4,5]. To maximize the battery lifetime and conserve the battery power, a time-synchronous operation is regularly preferred. Most of the sensor nodes need time synchronization to coordinate wake up and sleep operations at an arranged time. Consequently, sensor nodes require trust and robust synchronization [5].

Time synchronization is considered as a critical part of the current industrial operation of sensor nodes. It offers a common reference time for whole sensor nodes in the network. Generally, sensor nodes are frequently prepared with hardware clock oscillator, which is relatively inexpensive, and inaccurate. In this way, it is much challenging to introduce precise synchronization for such sensor nodes [6,7]. With such sensor nodes, it is required to periodically conduct an accurate synchronization process to well-preserved time synchronization of networks for a long time [8]. Lack of synchronization in any sensor node can result in an imprecise wake-up time of the sensor node, which leads to a severe failure of network connectivity [9]. All sensor nodes, therefore, require regularly exchange timing messages in order to keep the whole network synchronized. Exchanging timing messages minimizes the time offsets that produced by the clock drift of each node in the network [7,10]. Frequent synchronization, however, dramatically raises energy consumption as the transmission of messages typically corresponds to the most significant portion of energy consumption [10].

In the literature, several conventional [11–14] and advanced [15–17] protocols for the synchronization of sensor nodes have been developed. The main aim of these protocols is to provide synchronization to all sensor nodes in networks. Most of these protocols were intended to focus on the accuracy of synchronization as it is very important with less concern of communication overhead. Besides, these protocols, due to frequent collisions, suffer from message loss. Over the past few years, a few synchronization protocols [5,6,18,19] have aimed to address the energy consumption issue. In general, however, the consideration gotten for the aspect of energy consumption is modestly lower contrasted to that for the synchronization accuracy [15]. We can conclude the energy consumption is a big challenge in the time synchronization of IWSNs. This challenge motivates us to introduce an energy-efficient reference node selection (EERS) algorithm for synchronization. It can substantially reduce the energy consumption by decreasing the number of transmitted messages among the nodes in the network. EERS minimizes the number of connected reference nodes and satisfies a minimum number of hops. Besides, EERS proposes a new method that avoids collisions completely among nodes during the time synchronization process. The contributions of EERS can be summarized as: (1) EERS significantly reduces the communication overhead (number of transmitting messages) and accelerates the reference scheduling, which can reduce energy consumption; (2) EERS employs a reference node scheduling method to resolve the collision problem and in turn to reduce power consumption; and (3) we implemented EERS in a real wireless sensor network with Arduino Nano RF sensors and conducted extensive large-scale simulations.

The remainder of this paper is organized as follows. We summarize the related works in Section 2. In Section 3, we elaborate on the system model and the operation of the EERS algorithm. In Section 4, we present the experimental and simulation results. The concluding remarks are finally given in Section 5.

#### **2. Related Work**

Time synchronization for sensor nodes has been considerably analyzed for decades. Within the literature, there have been many methods for clock synchronization that are aimed toward minimizing energy consumption via decreasing the communication overhead and increasing the accuracy. In this section, we focus on those works which are concerned with reducing the communication overhead by decreasing the number of transmitted messages.

Noh et al. proposed the pairwise broadcast synchronization (PBS) [20]. It utilizes the pairwise operation introduced in [21]. PBS reduces the energy consumption (number of transmitted messages) by employing an overhearing technique in a single hop domain. The authors extended their work by introducing the Multi-hop PBS called groupwise pair selection algorithm [22]. It consists of a pair selection methods and hierarchy forming. GPA, however, needs an additional pairwise operation that reduces the energy efficiency compared with its single-hop counterpart [7]. Selecting pairs of nodes in a large network can be very costly in terms of both computation time and energy.

Spanning tree-based energy-efficient time synchronization (STETS) [23] has been proposed for the industrial Internet of things (IIoT). It employs the sender-to-receiver protocol (SRP) as well as receiver-to-receiver protocol (RRP). Especially in a large-scale and densely connected network, it effectively decreases energy consumption by reducing the number of transmitted messages. STETS, however, cannot synchronize all the nodes in some cases. The robust time synchronization (R-sync) [6] for IIoT has been introduced to resolve the drawbacks of STETS. Similar to STETS, R-Sync utilizes SRP and RRP. It focuses on identifying and pulling back the isolated nodes to the network that have lost their synchronization. Although R-sync requires relatively fewer numbers of messages compared to the timing-sync protocol for sensor networks (TPSN) [14] and STETS, it does not have any technique to avoid collisions.

The energy-efficient coefficient exchange synchronization protocol (CESP) [19] was introduced by Gong et al. to address the excessive power consumption of the reference broadcast synchronization (RBS) [13]. CESP employs the synchronization coefficient to decrease the number of transmitted messages compared with RBS [10]. CESP, however, consumes relatively high power and does not have any technique to avoid collisions. Yildirim et al. proposed an adaptive value tracking synchronization (AVTS) [24] to resolve the drawback of quick flooding. AVTS provides a scalable synchronization and reduces memory overhead compared to the flooding time synchronization protocol (FTSP) [12] and flooding with clock speed agreement (FCSA) [25]. AVTS, however, uses the scheme of flooding messages which results in the low energy efficiency of the network.

The density table-based synchronization (DTSync) protocol [5] has been introduced. It utilizes the concept reference scheduling technique. Iteratively, the reference scheduling mechanism selects an ordered set of common nodes. These nodes are responsible for disseminating the timing messages in the entire network. Compared to hierarchy reference broadcast synchronization (HRTS) [11], DTSync requires fewer messages, which in turn minimize the energy consumption. Elsharief et al. introduced the fast scheduling and accurate drift compensation for time synchronization (FADS) [18]. Similar to DTSync, FADS employs a reference scheduling technique that organizes the message transmission among the sensor nodes. It improves the performance of the reference scheduling process by reducing the time consumption. FADS, however, during the reference scheduling process, produces a relatively large number of message broadcasts which causes excessive energy consumption [10].

Recently, we proposed a low power scheduling for time synchronization protocols (LPSS) [10]. Compared to FADS, LPSS can significantly reduce the number of broadcasted messages and accelerates the reference scheduling process in a centralized manner, which in turn reduces the energy consumption. Besides, it provides a scheme to avoid collisions. LPSS, however, selects reference nodes randomly at each level which leads to the inefficient energy usage of the network.

#### **3. EERS Algorithm**

In this section, we first describe the system model for the EERS algorithm and detail the operation of the EERS algorithm.

#### *3.1. System Model*

In this paper, a wireless sensor network is designed as an undirect graph *G* = (*V*, *E*) consisting of a set of sensor nodes, *V* = {1, 2, ... ., ... , *N*}, in the network and a set of links, *E* ⊆ *V* × *V*, indicating the connection among the sensor nodes. That is, (*i*, *j*) ∈ *E* if node *i* and node *j* are located within the transmission range of each other [26]. In this paper, each node has a unique ID number and is supplied with a hardware clock oscillator. For simplicity, we assume that the network is stationary, and the transmission among nodes is reliable. Besides, it is assumed that all sensor nodes are identical (i.e., they have the same transmission range of *R* meter radius). Moreover, we assume that the sink node recognizes the position of every node in the network.

#### *3.2. Operation of EERS*

EERS is a greedy heuristic algorithm that attempts to minimize the communication overhead. It guarantees the coverage of all nodes and accelerates the reference node scheduling in a tree-based network topology. Additionally, to guarantee collision avoidance during the synchronization process, EERS assigns an exclusive scheduled time slot to each reference node.

*Sensors* **2020**, *20*, 4095

The pseudo-code of EERS is presented in Algorithms 1 and 2. Algorithm 1 is used by the sink node only. It describes the process of selecting reference nodes, *Re f Nodes*, and their scheduled time slots, *SchSlots*. On the other hand, Algorithm 2 is used by other nodes in the network, which shows the procedure of receiving the *Re f Nodes* and *SchSlots*.

In the beginning, in Algorithm 1, to guarantee the minimum hops starting from sink node to farthest node in the network, sink node has perfect awareness of the network topology and utilizes the breadth-first search (BFS) [27] algorithm to determine the level of every node in the network (Algorithm 1, Line 5). As a result of BFS, we have a tree-based network consisting of *L* levels. Each level, *k*, has a set of nodes, *Nk*. The optimal aim is to find the minimum number of connected reference nodes. In fact, finding a minimum number of reference nodes is a non-deterministic polynomial-time (NP)-hard problem [4,26]. Specifically, its time complexity is *O*(*m* × *m*!) ≤ *O*(*mm*), where *m* is the number of nodes in each level. In this article, our target is finding a simple approximation algorithm to find the minimum set of reference nodes, *Rk* ⊆ *N*k, that cover all nodes in the next level, *k* + 1. At the end of the initial step, the following steps are sequentially conducted.

*Step 1:* As the sink node, *S*, knows the position of each node, it determines the distance between each node in the level *k* and its neighbor nodes in the level *k* + 1 (Algorithm 1, Lines 7–9). The distance, *dik*, *j*k+1 , between node *ik* ∈ *N*k, and node *j*k+1 ∈ *N*k+1 is calculated as follows

$$d\_{\dot{\mathbf{i}}\_{\mathbf{k}\_\*} \dot{\mathbf{j}}\_{\mathbf{k}+1}} = \sqrt{(\mathbf{x}\_{\dot{\mathbf{i}}\_{\mathbf{k}}} - \mathbf{x}\_{\dot{\mathbf{j}}\_{\mathbf{k}+1}})^2 + (y\_{\dot{\mathbf{i}}\_{\mathbf{k}}} - y\_{\dot{\mathbf{j}}\_{\mathbf{k}+1}})^2} \tag{1}$$

where (*xik* , *yi*k ) and (*xjk*+1 , *yj*k+1 ) are the position of the nodes *ik*, and *jk*+1 , respectively. As a result of this process, EERS builds a set of the distances between *N*k and *N*k+1, *d N*k *N*k+1 .

*Step 2: S* selects the reference node, *R* using the equation (Algorithm 1, Line 11)

$$R = N\_k(\text{Max}(d\_{N\_{k+1}}^{N\_k})) \tag{2}$$

where the function *Max*(*d N*k *N*k+1 ) chooses a node that can cover the farthest node in the *k* + 1 level.

Then, EERS adds *R* to *Re f Nodes*. Next, *S* assigns a scheduled time slot to *R* and adds it to *SchSlots* (Algorithm 1, Lines 12 and 13).

*Step 3:* To avoid processing a node in *N*k+1 more than once, we mark it as a covered node (Algorithm 1, Line 14). Then, we update the *d N*k *N*k+1 by removing the distance between it and other nodes in *N*k.

*Step 4: S* increments the sequence number, *SeqN* (Algorithm 1, Line 19). *SeqN* is added to prevent sending the scheduling message many times. Next, *S* broadcasts the references scheduling message among all nodes in the networks (Algorithm 1, Line 20).

*Steps 1*–*4* in Algorithm 1 are repeated until no more reference node can be chosen in the network. We can conclude the time complexity of EERS is *LevelMax*−1 *k*=0 *O*( *N*k) ≈ *O*(N) where *LevelMax* is the highest level in the network.

Upon receiving the scheduling message (Algorithm 2), each node only retracts if the received sequence number is larger than the stored one (Algorithm 2, Line 2). Next, the node checks if its ID has been recorded in *Re f Nodes*l then, it keeps the corresponding time slot, *mySchslot* (Algorithm 2, Lines 5–7). Next, every reference node initiates the waiting timer (WT). The value of WT can be computed as

**Algorithm 2:** EERS pseudo-code for reference scheduling selection (except sink node).

**Initial** : *cnt* ← 0, *SeqN* ← 0 1. - **Upon receiving Scheduling Message** 2. **if** (*Received SeqN* > *SeqN*) **Then** 3. *SeqN* ← *Received SeqN* 4. **for** (*cnt* ← 0 *to length*(*Re f Nodes*))**do** 5. **if** (*NodeID* == *Re f Nodes*(*cnt*)) **Then** 6. *mySchsSlot* ← *SchSlots*(*cnt*); 7. **end if** 8. **if** (*Re f Nodes*(*cnt*) == *SenderID*) **Then** 9. *SchSlotSrcID* ← *SchSlots*(*cnt*) 10. **end if** 11. **end for** 12. *WaitingTime* ← ( *mySchSlot* − *SchSlotSrcID*) 13. Setup waiting timer *WT*(*WaitingTime*) 14. **end if** 15. - **Upon WT expires** 16. forward refernces scheduling message <*Re f Nodes*, *SchSlots*, *SeqN*. >

*WTvalue* = *mySchslot* − *SchslotSrcID* (3)

where *WTvalue* is the time that each reference should wait before forwarding the scheduling message, *SchslotSrcID* is the sender's time slot, and *mySchslot* is the current reference time slot (Algorithm 2, Line 12). As soon as WT expires, the current reference forwards the scheduling message to its neighbor nodes (Algorithm 2, Lines 15 and 16).

Figure 1 shows an example of EERS. Node *S* applies Algorithm 1 to select reference nodes (see Figure 1a) and assigns an exclusive scheduled time slot to each reference (see Figure 1b). Upon receiving the reference scheduling message, each node applies Algorithm 2 to verify if it has been selected as a reference node. For example, in Figure 1, it is assumed that node *F* is selected as a reference node and its assigned scheduled time slot is 4. As soon as it receives a packet form node *C*, it determines its waiting time equals to 2 (=4 (i.e., *F*'s *mySchSlot*) – 2 (i.e., *SchslotSrcID* of node *C*)). Then, *F* sets up its waiting timer and it forwards the received message to its neighbor nodes upon the WT is expired.

**Figure 1.** Wireless sensor network example. (**a**) A sensor network before applying EERS. (**b**) The EERS chooses the reference nodes (colored in black) and assigns schedule time slot to each reference.

#### **4. Experimental and Simulation Results**

In this section, we present experimental results achieved using a network of real hardware sensors. EERS was implemented in a C++ program and ported on an embedded processor in the hardware sensors. Next, we performed experiments with wireless networks composed of each hardware sensor running the C++ program. In addition, we compared our algorithm with the previous methods, FADS [18], LPSS [10], and R-Sync [6]. Note that the synchronization protocol in [5] was used for EERS to highlight the benefit of EERS.

To show the performance of EERS for a large-scale network, we also conducted a simulation study and compared the performance of EERS with FADS, LPSS, and R-Sync. In our experimental and simulation studies, the number of transmitted messages was quantitatively analyzed, and the time of the whole procedure was measured, as they have a significant impact on energy consumption. Generally, the wireless network is a broadcast domain. For each transmission, therefore, there are multiple receptions [28]. For example, assume that we have a uniformly distributed network with *N* nodes, and each node has *B* surrounding nodes. The total energy consumption of the transceiver, *ET*, can be expressed by transmission energy (*ETX* = *A*.*PTX*.*TTX*), and reception energy (*ERX* = *A*.*B*.*PRX*.*TRX*) as defined by the following equation.

$$E\mathbf{r} = A(P\mathbf{r}X.T\mathbf{x} \mathbf{x} + B.P\mathbf{x}.T\mathbf{x} \mathbf{x})\tag{4}$$

where *A* is the number of transmitted messages, *B* is the number of received messages, *PTX* is the transmission power, *PRX* is the reception power, *TTX* is the time duration of the transmitted packet, and *TRX* is the time duration of the received packet. For the sake of simplicity, consider *PTX* ≈ *PTX* = *P*, the packet length, *L*, and data rate, *D*, are fixed. Hence, *TTX* = *TRX*= *L D* . We can, therefore, simplify Equation (4) as Equation (5).

$$E\_T = A \frac{P.L(B+1)}{D} \tag{5}$$

From Equation (5), it can be noted that the energy consumption is directly proportional to the number of transmitted messages. Besides, reducing the number of messages decreases the time it takes for the transmitter node to transmit all its messages. This in turn allows the node to spend less time in idle mode while processing the transmission procedure, and to switch to sleep earlier. Since the idle mode also consumes energy [19], minimizing idle time can further save energy. Considering the transceiver of Arduino Nano RF [29], we conducted a wireless network of 25 nodes. Each node in the network can communicate with each other. Therefore, the total energy consumption per each transmission is equal to 49×8×(33.9*m*+24×36.9*m*) 250*k* ≈ 1.44 *mJ*.

#### *4.1. Experiments Setting*

In general, choosing a sensor module is dependent on the application's requirements (computing power, power consumption, memory, dedicated range, data rate, etc.) and the budget. Here, in this paper, our target is spreading the timing messages over a multi-hop network for smart metering applications. We chose an Arduino Nano RF [29] to implement the hardware for sensor nodes. The hardware integrates an ATmega328P CPU core [30] and an NRF24L01 Rf transceiver on-chip [31]. Arduino Nano RF is a cheap module with acceptable memory, data rate, and transmission range. However, EERS can be implemented on other types of sensors modules. Table 1 provides the specification of the sensor board. A photo of the sensor board is shown in Figure 2a. We adopted a wireless sensor network of 25 nodes, as displayed in Figure 2b. We configured a network of the four-way grid topology illustrated in Figure 3 to test EERS over a multi-hop network. For simplicity of the testing, all the nodes were configured with a preconfigured set of neighbor nodes and the distances among them. The solid lines in Figure 3 indicate such neighbor nodes assigned to every node. The sensor nodes can communicate with each other if they have a common solid line. With this experiment, the performance of EERS could be evaluated under realistic conditions of congestion, transmission contention, and various packet collisions. Even though we did the tests using a simple network of small size in the laboratory, these tests demonstrated that EERS can scale to networks of a large scale with longer wireless range and can provide equally great execution.

**Table 1.** Specification of sensor module.

**Figure 2.** (**a**) Hardware platform (**b**) Set up of a network of 25 sensor nodes for the experiment.

**Figure 3.** Sensor node in 4-way grid topology.

#### *4.2. Experimental Results*

This section describes the evaluation of the EERS, LPSS, FADS, and R-sync protocols. First, we evaluate the reference node scheduling process. R-sync is excluded from this evaluation as it does not have any reference scheduling mechanism. In the reference node scheduling procedure, the key performance indicator is the number of transmitted messages and the consumed time of the whole process. Table 2 shows that EERS has considerably fewer messages than LPSS and FADS for the scheduling process. For the network topology of Figure 3, EERS sent only 11 messages, while LPSS sent 17 messages (around 1.5 times more) and FADS exchanged 24 messages (around two times more). Next, for time consumed by the reference node scheduling procedure, the experimental results show EERS is faster than that of LPSS and FADS. Table 2 also compares the processing time measured for EERS, LPSS, and FADS protocols. EERS can reduce the whole time of the process to around 35% and 76% compared to LPSS and FADS, respectively.

**Table 2.** Comparison of EERS, LPSS, FADS and R-sync.

Finally, we applied the synchronization protocol of [5] on top of the EERS. The experimental results show that EERS and FADS transmit fewer messages than other protocols for the synchronization process. Table 2 shows that EERS has considerably fewer messages than LPSS and R-Sync. EERS and FADS required around 27 messages, while LPSS exchanged 45 messages (around 1.66 times more) and R-Sync exchanged 51 messages (around 1.88 times more).

#### *4.3. Large-Scale Simulation Results*

Experimental results reveal that EERS improves energy consumption in a small test network. To further validate EERS, we developed a simulator that conducts the algorithms of EERS, LPSS, and FADS. Meanwhile, the simulation results of R-Sync were adopted from [6]. Simulations were developed in MATLAB using wireless networks of various sizes, and different transmission ranges. We presumed there are *N* sensor nodes which are randomly distributed in a square area of

1000 m × 1000 m. We also assumed that all nodes are identical and independent. Key simulation parameters are summarized in Table 3. In fact, we adopted most of the simulation parameters from [6]. We believe the simulation parameters is an appropriate choice to simulate realistic scenarios. By variation of the transmission range, we could simulate many types of sensor nodes which have different wireless ranges. For example, Arduino Nano RF has up to 100 m radius, which is located between 85 and 160 m (simulation range). On the other hand, changing the number of nodes in the network allowed us to demonstrate the behavior of our protocol in the sparse network (200 nodes) up to the dense network (450 nodes). We focused on the number of transmitted messages and the time of the process to show the performance of EERS. To reduce the impact of random errors, we ran 10,000 cycles for each experiment and obtained their average value. To illustrate the effect of the position of the sink node on the simulation results, two positions of sink node were tested: (1) sink node located at the center of the network; and (2) sink node located at the corner of the network. As expected, there is no big difference in the results due to the uniform distribution of the nodes. Therefore, to avoid redundancy, we only report the results in which the sink node was located at the center of the network.

**Table 3.** Simulation Parameters.

First, we evaluate the performance of all protocols (i.e., EERS, LPSS, FADS, and R-Sync) under transmission ranges from 85 to 160 m. Figure 4 shows the number of transmitted messages at the reference node scheduling process while Figure 5 shows the time of the whole process. For a network with the transmission range of 160 m, EERS, LPSS, and FADS generate 61, 71, and 380 messages, respectively. EERS outperforms LPSS and FADS by factors of 1.2 and 6.3, respectively. On the other hand, EER can decrease the time of the scheduling process to around 14% and 84% compared to LPSS and FADS, respectively, as shown in Figure 5. In the synchronization process. EERS has considerably fewer messages than LPSS, FADS, and R-Sync, as depicted in Figure 6. EERS outperforms LPSS, FADS, and R-Sync by factors of 1.17, 1.34, and 1.44, respectively. Next, we evaluate the performance of EERS, LPSS, FADS, and R-Sync over various network sizes from 200 to 450 nodes. Figure 7 shows the number of transmitted messages at the reference node scheduling process. For a large network with 450 nodes, EERS, LPSS, and FADS generate 157, 180, and 834 messages, respectively. EERS outperforms LPSS and FADS by factors of 1.15 and 5.3, respectively. On the other hand, EER can decrease the time of the scheduling process to around 13% and 81% compared to LPSS and FADS, respectively, as shown in Figure 8. In the synchronization process, for a large network with 450 nodes, EERS has considerably fewer messages than LPSS, FADS, and R-Sync, as depicted in Figure 9. EERS outperforms LPSS, FADS, and R-Sync by factors of 1.15, 1.4, and 2.1, respectively.

From the above results, it can be concluded that EERS consumes less energy than other techniques. At the same time, EERS does not lose the accuracy of the timing. Additionally, EERS is well-suited for those applications in which the position of each node is recognized, e.g., smart metering. Further, without losing its properties, EERS can handle high-density networks and therefore it can be considered as a highly scalable and adaptive protocol.

**Figure 4.** Required number of messages for reference node scheduling process (r = 85 m: 160 m, N = 240).

**Figure 5.** Required time for reference node scheduling process (r = 85 m: 160 m, N = 240).

**Figure 6.** Required number of messages for synchronization process (r = 85 m: 160 m, N = 240).

**Figure 7.** Required number of messages for reference node scheduling process (r = 85 m, N = 200: 450).

**Figure 8.** Required time for reference node scheduling process (r = 85 m, N = 200: 450).

**Figure 9.** Required number of messages for synchronization process (r = 85 m, N = 200: 450).

#### **5. Conclusions**

In this paper, we consider the recent synchronization protocols for wireless sensor networks and introduce the energy-efficient reference node selection algorithm for synchronization in IWSNs (EERS). EERS resolved major drawbacks of prior schemes (i.e., LPSS, FADS, and R-Sync) by introducing an efficient reference node scheduling process. EERS significantly reduced the number of messages in both scheduling and synchronization process, which leads to large savings in energy consumption. On the other hand, it accelerates the scheduling process. We conducted extensive simulations as well as the real measurement with hardware sensor nodes of the EERS algorithm. The experimental results show that EERS requires fewer messages than previous methods such as LPSS, FADS, and R-Sync.

As our future work, we are interested in combining deep learning techniques with EERS to find the optimal minimum set of reference nodes. The graph convolutional network will estimate the likelihood for each node in the graph to determine whether this node is part of the optimal solution [32]. Additionally, we plan to further improve EERS by allowing some of the reference nodes at the same level exchanging timing messages using the same scheduled time slot.

**Author Contributions:** M.E., M.A.A.E.-G. and S.P. created the idea of this research; M.E., H.K. and S.P. conceived the research methodology; M.E., M.A.A.E.-G. and H.K. performed the experiments and, simulation; and M.E., H.K. and S.P analyzed the data and wrote the article. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the National Research Foundation (NRF) of Korea Grant funded by the Korean Government (MSIP) (Nos. 2020R1A2C3006786 and 2019R1C1C1004352).

**Acknowledgments:** We gratefully appreciate the anonymous reviewers' variable reviews and comments.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**Kim de Wildt**

Transformation of Sacred Space: Function and Use of Religious Places in Germany (FOR 2733), University of Bonn, 53113 Bonn, Germany<EMAIL_ADDRESS>
Received: 29 August 2020; Accepted: 6 October 2020; Published: 10 October 2020

**Abstract:** The decrease in people who regularly celebrate liturgy in western Europe has led to the question of what to do with so-called obsolete church buildings. This question not only refers to whether or not a church building will be converted, reused or demolished, but also to the question of whether or not such a building needs to be deconsecrated, and if so, what does deconsecration of a church building actually entail? In this contribution, I will consider the role deconsecration rites play in the Roman Catholic church when a church building is taken out of liturgical use. In Roman Catholic liturgy, there are no prescribed, official deconsecration rites that are mandatory for a church building that is to be taken out of liturgical use. The actual deconsecration of a church building is, according to canon law, established by a decree that is issued by the responsible diocesan bishop. In the case of a church being taken out of liturgical use, however, there seems to be a shift from having a ritual void with regard to deconsecration rites, and also a focus on the "legitimate" way (in the sense of canon law) to deconsecrate a church building (object orientation), towards, in recent decades, paying more attention to a growing pastoral need (subject orientation) for deconsecration rites. These new ritual initiatives can be regarded as forms of pastoral care intended to help parishioners cope with the loss of their church building. I will show that different interpretations of canon law articles complicate straightforward answers to the question of which arguments are legitimate to deconsecrate a church. Furthermore, I will address the "ritual muddle", the mixture of the actual deconsecration act in the sense of canon law and deconsecration rites that, from the perspective of canon law, do not effect church deconsecration. I will also address the differentiation between desecration and deconsecration, address historical forms of deconsecration rites and pay attention to the making and unmaking of sacred space. Finally, I will focus on contemporary deconsecration rites against the background of the complex reality in which such rites are situated.

**Keywords:** deconsecration; desecration; consecration; profanation; church buildings; sacred space; church reuse; altar; Roman Catholic Church; canon law


**3.3 Passive tracking studies**

Four experiments using VR2 receiver were performed in Kromme Bay during the November 2003–2006 squid fishery closed seasons. In addition to the VR2 receiver arrays, the VRAP system was deployed in November 2005 and 2006.

#### **3.3.1 VR2 study**

426 Modern Telemetry

database for playback and analysis at a later date (Figure 4). A number of studies have shown the VRAP system to calculate transmitter position with an accuracy of 1 to 3 m (Bégout Anras et al., 1999; Klimeley et al., 2001; Zamora & Moreno-Amich, 2002 as cited in Jadot et al., 2006; Aitken et al., 2005), within the buoy triangle, with accuracy decreasing

Fig. 3. One of the three VRAP buoys deployed in Kromme Bay

equilateral triangular formation

Fig. 4. A single animal track, recorded by the VRAP Buoys, and played back using VRAP software. The smaller triangles in the diagram denote the position of the buoys in the

outside of the array.

Each year researchers searched for an active spawning aggregation. Diver observations confirmed the presence of egg beds, the footprint of these aggregations. VR2 receivers were then deployed 500 m apart, in a hexagonal array, on and around these egg beds. Initial range tests showed the receiving range of the VR2 receivers to be <500 m in Kromme Bay. It was therefore decided to deploy receivers 500 m apart to allow for an overlap in receiving ranges. In 2004, an additional VR2 receiver was deployed on a spawning site off Cape St Francis. The position of these arrays can be seen in Figure 5. Depending on the thermal conditions of the water column (Singh et al., 2009) the hexagonal configuration allowed an area of up to 1.28 km2 to be monitored. Each receiver was deployed 5 m above the seabed using a hollow-core polypropylene rope tensioned with a subsurface buoy. The mooring was anchored to the seabed with a 50 kg weight. During each study temperature data were collected using an array of Star-oddi Starmon mini underwater temperature recorders deployed at depths of 9, 14, 18, 21, and 24 m. This thermistor array (Figure 5) recorded temperature hourly. Hourly wind data, recorded at Port Elizabeth (Figure 1) airport, for 2003-2006 were obtained from the South African Weather Services. Wind data were filtered using an UNH Lanczos filter (weighted 73), and stick vector plots generated.

Fig. 5. The positions of the hexagonal VR2 receiver arrays (2003–2006) and the thermistor array overlaid on the bathymetry (contour lines).

#### **3.3.2 VRAP study**

VRAP buoys were deployed in the centre of the VR2 receiver arrays (Figure 6) in a 300 m equilateral triangle. This configuration allowed for optimal buoy performance. Each buoy was anchored to the seabed with two 50 kg weights. The hydrophone cable was run down the hollow-core polypropylene rope used to attach the buoy to the weights. The omnidirectional hydrophone was positioned approximately 5 m above the seabed.

The Use of Acoustic Telemetry in South African Squid Research (2003-2010) 429

Fig. 7. A chokka squid, *Loligo reynaudii*, caught on a jig

needles

Fig. 8. Tagging instrumentation (taken directly from Downey et al. (2010)): (a) the

attachment of hypodermic needles to an acoustic transmitter, (b) the specially designed tag applicator used to tag *L. reynaudii*, and (c) the placement of the acoustic transmitter within the mantle of the squid, on the ventral side, to avoid piercing organs with the hypodermic

Fig. 6. The positions of the triangular VRAP arrays (2005 & 2006) within the VR2 receiver arrays.

#### **3.3.3 Transmitter attachment**

A total of 45 squid and eight predators were tagged over the four experiments. The predators tagged included three ragged tooth sharks (*Carcharias taurus*), three shorttail stingrays (*Dasyatis brevicaudata*) and two smooth hound sharks (*Mustelus mustelus*). Details of the acoustic transmitters used are given in Table 1. For those animals that were tagged with transmitters without pressure sensors, only presence-absence data were collected. Transmitters with pressure sensors provided both depth and presence-absence data.

Table 1. Details of acoustic transmitters used in the VR2 and VRAP studies

Squid were caught, using jigs (Figure 7), and tagged with V9 acoustic transmitters (Figure 8a). The modification of transmitters for attachment and the tagging process have been described in detail in Downey et al. (2010). Two-18-guage hypodermic needles were glued to the surface of each transmitter, to allow for attachment to the squid (Figure 8a). The length of the needles was dependent on the sex and size of the animal tagged. Hypodermic needles with a length of 17 mm were used for males and needles with a length of 14 mm for the smaller "sneaker" males and females. Each year squid were caught within the hexagonal array of VR2 receivers. Once the animals were removed from the water and their sex determined they were placed on a damp cloth (Figure 9a). Using an applicator specifically designed for this purpose (Figure 8b), a transmitter with the appropriate needles length was inserted into the mantle cavity (Figure 9a). A protective sheath covered the hypodermic needles during insertion (Figure 8b).

Fig. 6. The positions of the triangular VRAP arrays (2005 & 2006) within the VR2 receiver

A total of 45 squid and eight predators were tagged over the four experiments. The predators tagged included three ragged tooth sharks (*Carcharias taurus*), three shorttail stingrays (*Dasyatis brevicaudata*) and two smooth hound sharks (*Mustelus mustelus*). Details of the acoustic transmitters used are given in Table 1. For those animals that were tagged with transmitters without pressure sensors, only presence-absence data were collected.

> **Pressure sensor**

V9P-6L-S256 30 90 Yes 12 (*L. reynaudii*) 6 6 V16-5H-R04K 35 109 No 3 (*C. taurus*) Unknown V16-5H-R04K 35 109 No 1 (*D. brevicaudata*) Unknown V16-5H-R04K 35 109 No 1 (*M. mustelus*) Unknown

V9P-6L-S256 30 90 Yes 23 (*L. reynaudii*) 13 10 V9P-2H-S256 20 60 Yes 1 (*D. brevicaudata*) 1 V9P-2H-S256 20 60 Yes 1 (*M. mustelus*) 1

**Number of**

**animals tagged Male Female**

Transmitters with pressure sensors provided both depth and presence-absence data.

2003 V8SC-2H-R256 10 35 No 4 (*L. reynaudii*) 2 2

2006V9P-6L-S256 30 90 Yes 6 (*L. reynaudii*) 4 2 V9P-2H-S256 20 60 Yes 1 (*D. brevicaudata*) 1

Squid were caught, using jigs (Figure 7), and tagged with V9 acoustic transmitters (Figure 8a). The modification of transmitters for attachment and the tagging process have been described in detail in Downey et al. (2010). Two-18-guage hypodermic needles were glued to the surface of each transmitter, to allow for attachment to the squid (Figure 8a). The length of the needles was dependent on the sex and size of the animal tagged. Hypodermic needles with a length of 17 mm were used for males and needles with a length of 14 mm for the smaller "sneaker" males and females. Each year squid were caught within the hexagonal array of VR2 receivers. Once the animals were removed from the water and their sex determined they were placed on a damp cloth (Figure 9a). Using an applicator specifically designed for this purpose (Figure 8b), a transmitter with the appropriate needles length was inserted into the mantle cavity (Figure 9a). A protective sheath covered the hypodermic needles during insertion (Figure 8b).

Table 1. Details of acoustic transmitters used in the VR2 and VRAP studies

**Max offtime (s)**

**Min offtime (s)**

arrays.

2004

2005

**3.3.3 Transmitter attachment**

**Year Transmitter type**

Fig. 7. A chokka squid, *Loligo reynaudii*, caught on a jig

Fig. 8. Tagging instrumentation (taken directly from Downey et al. (2010)): (a) the attachment of hypodermic needles to an acoustic transmitter, (b) the specially designed tag applicator used to tag *L. reynaudii*, and (c) the placement of the acoustic transmitter within the mantle of the squid, on the ventral side, to avoid piercing organs with the hypodermic needles

The Use of Acoustic Telemetry in South African Squid Research (2003-2010) 431

transmitter detections at the spawning site, bottom temperature, and wind data against date and time. To determine significant differences in mean depth by day vs. night for male, female, and all squid combined, as well as mean depth for males vs. females by day and night, duplicate data, i.e. single detections recorded by more than one VR2 receiver, were removed and the total number of successfully detected transmissions for each sex per day and night calculated. The data for each sex were separated into depth categories, and the percentage of detections recorded in each depth category by day and night plotted. Twosample, two-tailed t-tests were used to identify significant differences. To analyse diurnal patterns at the spawning sites, the percentage of transmissions successfully detected per hour in a typical 24-h period were plotted, separately for males and females, using the data from which duplicates had been removed. The plots generated and the results of this

Fig. 10. A V16 pinger with a stainless steel trace attached to allow for external attachment. The analysis of the VR2 data showed three general presence–absence behaviours to be found at chokka squid spawning sites (Downey et al., 2010). They are, as given in Downey et al., (2010): (i) arrival at dawn and departure after dusk, (ii) a continuous and uninterrupted presence for a number of days, and (iii) a presence interrupted by frequent but short periods of absence. These authors also concluded that , in contrast to the findings of earlier studies, a core aggregation of squid occasionally remains on active spawning sites at night. At dawn, more squid arrive at the spawning site and the size of the aggregation increases, resulting in a dense aggregation by day. Shortly after dusk, spawning pairs break apart, and some squid leave the spawning site. Those squid remaining at a spawning site at night search for prey throughout the water column and in the benthos, whereas lone females deposit egg strands. The authors also found that movement between the spawning sites continues at night. Their VR2 study confirmed previous observations that the initial formation of spawning aggregations, before the deposition of the first egg strand, is

To investigate presence-absence of predators on the monitored spawning sites, the VR2 data was analysed per year. Signal detections from all tagged squid (grouped), the tagged

analysis are given in Downey et al., (2010).

triggered by upwelling.

The applicator was initially held sideways and once inserted was turned 90° and the protective sheath removed (Figure 8b). After pushing the hypodermic needles through the mantle (Figure 9b), nylon washers were pushed onto the ends of the needles (Figures 8c and 9c) followed by copper crimps (Figures 8c and 9d and e). The tagged squid was then placed in a bin containing seawater or held alongside the boat (Figure 9f), depending on sea conditions, to recover. Once normal fin-beating had resumed, the animal was released within the array of VR2 receivers.

Fig. 9. Attaching a transmitter to a squid (taken directly from Downey et al. (2010)): (a) a transmitter is inserted beneath the mantle using the applicator; (b) the apparatus is turned through 90°, the protective applicator sheath removed, and the hypodermic needles pushed through the mantle. (c) Nylon washers are pushed onto the ends of the hypodermic needles and (d) a metal cylinder slipped over each hypodermic needle, (e) the metal cylinders are crimped using long-nose pliers, and (f) the squid are held submerged alongside the boat until strong swimming ability is displayed (fin beating). Only then is the animal released on the capture site

Predators were tagged with V16 pingers (2004) and V9 sensor acoustic transmitters (2005 & 2006). The transmitters were modified for attachment by gluing a stainless steel trace (Figure 10) to the surface of the transmitter. Predators were either tagged by divers who used a Hawaiian sling (modified spear), to embed the stainless steel trace into the muscle alongside the fin, by wrapping the transmitter in bait and feeding it to the predator, or by surgical implantation. By using the feeding technique, the likelihood of transmitter loss due to merely falling off was avoided, however transmitters can be regurgitated. Surgical implantation, although more invasive, removes the possibility of transmitter loss.

### **3.3.4 VR2 data analysis**

To correct time-drift of individual VR2 receiver clocks, VR2 data files were time-corrected using a program created by Dale Webber of Vemco. The VR2 data was analysed separately for each year. To measure spawning intensity the number of hours each squid was present on the spawning site, expressed as a percentage of the total number of hours of passive tracking, was plotted. The presence-absence of individual squid was determined by plotting

The applicator was initially held sideways and once inserted was turned 90° and the protective sheath removed (Figure 8b). After pushing the hypodermic needles through the mantle (Figure 9b), nylon washers were pushed onto the ends of the needles (Figures 8c and 9c) followed by copper crimps (Figures 8c and 9d and e). The tagged squid was then placed in a bin containing seawater or held alongside the boat (Figure 9f), depending on sea conditions, to recover. Once normal fin-beating had resumed, the animal was released within the array of

Fig. 9. Attaching a transmitter to a squid (taken directly from Downey et al. (2010)): (a) a transmitter is inserted beneath the mantle using the applicator; (b) the apparatus is turned through 90°, the protective applicator sheath removed, and the hypodermic needles pushed through the mantle. (c) Nylon washers are pushed onto the ends of the hypodermic needles and (d) a metal cylinder slipped over each hypodermic needle, (e) the metal cylinders are crimped using long-nose pliers, and (f) the squid are held submerged alongside the boat until strong swimming ability is displayed (fin beating). Only then is the animal released on

Predators were tagged with V16 pingers (2004) and V9 sensor acoustic transmitters (2005 & 2006). The transmitters were modified for attachment by gluing a stainless steel trace (Figure 10) to the surface of the transmitter. Predators were either tagged by divers who used a Hawaiian sling (modified spear), to embed the stainless steel trace into the muscle alongside the fin, by wrapping the transmitter in bait and feeding it to the predator, or by surgical implantation. By using the feeding technique, the likelihood of transmitter loss due to merely falling off was avoided, however transmitters can be regurgitated. Surgical

To correct time-drift of individual VR2 receiver clocks, VR2 data files were time-corrected using a program created by Dale Webber of Vemco. The VR2 data was analysed separately for each year. To measure spawning intensity the number of hours each squid was present on the spawning site, expressed as a percentage of the total number of hours of passive tracking, was plotted. The presence-absence of individual squid was determined by plotting

implantation, although more invasive, removes the possibility of transmitter loss.

VR2 receivers.

the capture site

**3.3.4 VR2 data analysis**

transmitter detections at the spawning site, bottom temperature, and wind data against date and time. To determine significant differences in mean depth by day vs. night for male, female, and all squid combined, as well as mean depth for males vs. females by day and night, duplicate data, i.e. single detections recorded by more than one VR2 receiver, were removed and the total number of successfully detected transmissions for each sex per day and night calculated. The data for each sex were separated into depth categories, and the percentage of detections recorded in each depth category by day and night plotted. Twosample, two-tailed t-tests were used to identify significant differences. To analyse diurnal patterns at the spawning sites, the percentage of transmissions successfully detected per hour in a typical 24-h period were plotted, separately for males and females, using the data from which duplicates had been removed. The plots generated and the results of this analysis are given in Downey et al., (2010).

Fig. 10. A V16 pinger with a stainless steel trace attached to allow for external attachment.

The analysis of the VR2 data showed three general presence–absence behaviours to be found at chokka squid spawning sites (Downey et al., 2010). They are, as given in Downey et al., (2010): (i) arrival at dawn and departure after dusk, (ii) a continuous and uninterrupted presence for a number of days, and (iii) a presence interrupted by frequent but short periods of absence. These authors also concluded that , in contrast to the findings of earlier studies, a core aggregation of squid occasionally remains on active spawning sites at night. At dawn, more squid arrive at the spawning site and the size of the aggregation increases, resulting in a dense aggregation by day. Shortly after dusk, spawning pairs break apart, and some squid leave the spawning site. Those squid remaining at a spawning site at night search for prey throughout the water column and in the benthos, whereas lone females deposit egg strands. The authors also found that movement between the spawning sites continues at night. Their VR2 study confirmed previous observations that the initial formation of spawning aggregations, before the deposition of the first egg strand, is triggered by upwelling.

To investigate presence-absence of predators on the monitored spawning sites, the VR2 data was analysed per year. Signal detections from all tagged squid (grouped), the tagged

The Use of Acoustic Telemetry in South African Squid Research (2003-2010) 433

Active or manual tracking involves monitoring the movement of acoustically tagged animals from a vessel. South African researchers made use of the VR100 system for active

The manual tracking study discussed here made use of a VH110 directional hydrophone and a VR100 receiver. This general purpose, splash-resistant receiver is designed for tracking animals from vessels. The hydrophone is held in the water, either manually or by attachment to the side of the boat. The hydrophone detects transmitter signals and the VR100 records the ID Code, date, time, other received information (depth/temperature) and GPS location of the detections. This information can then be downloaded to a computer for

As part of a project investigating deep spawning (71-130 m) in *Loligo reynaudii*, a phenomenon researchers as yet know very little about, the movement of squid on the deep spawning grounds was monitored using the above-mentioned manual tracking system. As it is difficult to find and identify active spawning aggregations deeper than 60 m, using the two fixed telemetry systems previously described would not be feasible. This study was

Using the jigging fishing method (Figure 7), squid at depths >60 m can only be caught at night, using powerful lights to attract them to the surface. For the manual tracking study, squid were caught from an 8 m inflatable boat anchored next to a chokka boat. The two boats were close enough for the chokka boat lights to attract squid to the area around the smaller boat. Two squid were caught in this manner, on separate nights, and tagged with V9TP-6L continuous sensor transmitters. Details of the transmitters used are given in Table 2. Animals were tracked (Figure 11) from the time of tagging to shortly after sunrise. The tagging method and instrumentation used was the same as that described for the VR2 and

> **Pressure sensor**

V9TP-6L 450 1050 Yes Yes 63 Male V9TP-6L 450 1050 Yes Yes 75 Sneaker

The VR100 data was manually examined, using Microsoft Excel, for erroneous depth and/or temperature data. Erroneous data were identified by their large difference from previous and successive values, whereas these were similar. Those data entries containing errors

**Temperature sensor**

**Frequency**

**(kHz) Sex**

male

conducted during the November 2010 squid fishery closed season.

**Max period (ms)**

Table 2. Details of acoustic transmitters used in the VR100 tracking study

**Min period (ms)**

**4. Active tracking telemetry system**

tracking.

**4.1 VR100 receiver**

viewing or analysis.

**4.2 Active tracking studies**

**4.2.1 Tagging of animals**

VRAP studies.

2010

**Year Transmitter type**

**4.2.2 VR100 data analysis**

predators (individually) and surface and bottom temperatures were plotted. The position of predators in the water column, in relation to squid, was analyzed by plotting all squid depth data (grouped), predator depth data (individually) and surface and bottom temperatures. Plots were generated only for those days predators were present.

The results of the predator study are as yet unpublished. This study, however, showed predators moved to and from the spawning sites a number of times, despite the continual presence of squid. The presence of predators on the spawning sites appeared to be strongly linked to surface temperature. When temperatures were stable at ~18 °C, predators remained on the spawning sites for long periods. When surface temperatures increased, predators either moved to the surface and left the spawning site shortly thereafter or immediately moved off.

#### **3.3.5 VRAP data analysis**

Invalid positional fixes were identified by their large distance from previous and successive fixes, whereas these were close in proximity. For each squid monitored by the VRAP system daily plots, separating day vs. night movement, were generated using Arcview GIS software. This allowed analysis of horizontal movement at the individual level as well as the identification of patterns in movement. Similarly depth over time was plotted for each individual. Depth data recorded by the VRAP system was not analyzed in great detail as the analysis of the VR2 receiver depth data was fairly comprehensive. The distance between two consecutive points, when the time between consecutive detections was less than 10 minutes, was used to calculate swimming speed. The distance (d) between two consecutive locations was calculated in Microsoft Excel using Equasion 1:

> d=acos(cos(radians(90-Latitude1)).cos(radians(90-Latitude2))+ sin(radians(90-Latitude1)).sin(radians(90-Latitude2)). (1) cos(radians(Longitude1-Longitude2))).R

The value 6371 km was used for the radius of the earth (R). This formulae made use of latitudes and longitudes in decimal degrees. Swimming speed was calculated by dividing the distance between two consecutive detections by the number of seconds taken to move between the two points (m.s-1). Average swimming speeds were then calculated. As these results are as yet unpublished and data is still being analysed, only the initial analysis and findings are reported here.

At night males appeared to move around the spawning site, covering a larger surface area, compared to females. This was possibly due to the males' main nocturnal activity being feeding, whereas females often continue to deposit eggs, using stored spermatophores for fertilization. On occasion however, males would also spend a number of hours in one specific area of the site, possibly resting. Both sexes spent time concentrated in one area for a number of hours during the day. Average swimming speed for males at night was calculated as 0.25 m.s-1, compared to 0.22 m.s-1 for females. These slight differences are possibly a result of the different nocturnal activities. Average swimming speed for males during the day (0.21 m.s-1) was slower than that calculated for females (0.24 m.s-1). The 1993/1994 telemetry studies (Sauer et al., 1997) also reported males to swim more slowly than females when part of a spawning aggregation. The swimming speeds reported by these authors were however, slower than those observed in this study (0.18 m.s-1 for females and 0.14 m.s-1 for males). No predators were detected by the VRAP system.


**4. Signal processing for Doppler audio anti-aliasing**

The direction separation system of the foregoing section is developed further, and the Doppler audio technology exceeding the Nyquist frequency is examined. Some directionseparation systems for a Doppler audio that is interlocked with the baseline-shift of a spectrum image are investigated. First, section 4.1 explains a problem peculiar to the Doppler audio corresponding to the Doppler display processing. In section 4.2 we defined the target performance of anti-aliasing Doppler audio processing selected three kinds of signal-processing systems. In section 4.3 the various systems of the modulation/demodulation system, the FFT/IFFT system and the complex IIR Filter system are explained. Next, in section 4.4 the signal-processing algorithms are compared with the target performances. It was confirmed that the complex IIR band-pass filter system has an excellent response and a low calculation load. Finally, in section 4.5 using the blood-flow data collected from Doppler phantom, we performed functional and performance analyses by simulation shown in Fig. 22.


**Meet the editor**

Ibeh Bartholomew, PhD, is currently the acting director/ head of Medical Biotechnology Department, National Biotechnology Development Agency, Abuja, Nigeria. He holds a doctoral degree in Medical Biochemistry/ Immunochemistry. He has obtained several grants and awards. Dr. Ibeh has served as an external examiner and supervised over 30 university students on various

HIV research topics. Though his work on HIV serodiscordant infection in blacks is widely acknowledged, his current research interest is on development of efficient immunodeficient mouse models using CRISPR/Cas9-mediated genome engineering technique. He is a member of the International Union of Biochemistry and Molecular Biology and the Nigerian Society of Biochemistry and Molecular Biology.


**2. General aspects on COVID-19 diagnosis**

Among the biomarkers analyzed, one of the most important findings was the raised C-reactive protein (CRP) level of 41.4 mg/L (standard at 0–6 mg/L) whose circulating concentrations rise in response to inflammation. Followed by the higher CRP, the biochemistry cardiac panel also showed raised myocardial infarction markers, such as lactic dehydrogenase (LDH), aspartate aminotransferase (AST), and creatine kinase (CK). Taken together, these biomarkers strongly indicated a severe acute inflammatory phase with the ongoing cardiac effect to be controlled. Mild lymphopenia with less than 9 × 10e5 cells per mL with normal platelet counts completes the first patient's overview [2].

Pulmonary function and lung aspects were investigated in this patient in order to do the etiological diagnosis. Hypoxemia was observed according to oxygen levels of 67 mm Hg. Lungs' aspect was set by chest radiographs on day 1 of hospital admission, which was the 6th day of disease progression. The images showed abnormal features with focal and patchy consolidation in both lungs, beyond air-space shadowing such as ground-glass like opacities [2].

Image examination of the chest shows a consolidation pattern at computedtomography (CT) scans: bilateral focal consolidation, lobar consolidation, and patchy consolidation, especially in the lower lung. Five days after admission, at the 11th day of disease progression, a chest radiograph revealed a bilateral diffuse patchy and fuzzy shadow [2].

With the advancement of the pandemic, what was seen as a respiratory disease became a more complex disease and new studies were set up to list other complications and associated risk factors.

#### **3. Potential impact of thrombotic complications on COVID-19 prognosis**

Many factors can contribute to increasing the risk for severe COVID-19, in some cases followed to death. The main comorbidities described are high age, obesity, diabetes, and hypertension. Beyond the inflammation and impaired coagulation, focal damage in some tissues/organs is also related to the COVID-19 spectrum, such as liver, kidney, and heart [3].

Thrombotic complications seem to emerge as an important issue in patients infected with COVID-19. Preliminary reports on COVID-19 patients' clinical and laboratory findings include thrombocytopenia, elevated D-dimer, prolonged prothrombin time, and disseminated intravascular coagulation.

In the course of the COVID-19 studies, a clear association with coagulation dysfunction was pointed in many cases. Intra-alveolar clots were prominent findings in COVID-19 patients who developed severe respiratory disease. The same findings have been described in both clinical and animal model studies. Apparently, an impaired response in the prothrombotic pathway is in charge of diffuse alveolar hemorrhage since it is related to overt clot formation [4].

In the recent publication "Should COVID-19 be branded to Viral Thrombotic Fever?," the authors intended to frame COVID-19 in more clinical terminology, making an analogy to Viral Hemorrhagic Fever (VHF). In this article, the authors reported: "We found irrefutable evidence in the current literature that COVID-19 is the first viral disease that can be marketed as a viral thrombotic fever" [5]. Although this is a very categorical statement, considering the small number of studies exclusively dedicated to the characterization of COVID-19 as a thrombotic fever, it is very important to consider this designation. Categorizing COVID-19 as a febrile variant of thromboembolism adds a series of procedures to be adopted in

patients' care. This approach can advance the treatment adequacy by many steps, making it as more personalized as possible.


**Author details**

Muhammad Mubeen Masud and Benjamin D. Braaten

North Dakota State University, Fargo, U.S.A.

### **References**

[4] Amin Y., Batao S., Hallstedt J., Prokkola S., Tenhunen H., Zhen L.-R. Design and characterization of efficient flexible UHF RFID tag antennas: proceedings of 3rd Eu‐ ropean Conference on Antennas and Propagation, March 2009, Berlin, Germany.

propagation, co-planar waveguides and interdigital capacitor loaded co-planar waveguides have been introduced and summarized. From these sections, the ZOR-RFID antenna for pas‐ sive UHF RFID tags is presented. The operating principle behind the ZOR-RFID antenna is the use of interdigital capacitors along the length of the antenna to support wave propaga‐ tion. Furthermore, the capacitive input impedance of the passive RFID ASIC attached to the port of the antenna supports propagation in a manner similar to the interdigital capacitors. This allows the ASIC to still harvest power and communicate while supporting wave propa‐ gation. Measurements show that a predicted 7.6 m read range is possible with this new an‐ tenna design. This read range is comparable to existing commercially available passive UHF

There are several different avenues of future work possible. The first topic of interest is to reduce the overall size of the ZOR RFID prototype antenna. This could be done by using res‐ onator elements instead of the interdigital capacitors. Further development on printing the ZOR-RFID antenna on flexible substrates would be of great interest. Maybe the investigation of paper, LCP and Kapton substrates could be performed. Extending this work to develop a multi-band antenna would also be possible. This would allow this antenna design to be

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[3] Calabrese C., Marrocco G. Meander-slot antennas for sensor-RFID tags. IEEE Anten‐

RFID tags with similar overall sizes.

146 Radio Frequency Identification from System to Applications

**7. Future work**

used in multiple countries.

Muhammad Mubeen Masud and Benjamin D. Braaten

North Dakota State University, Fargo, U.S.A.

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**Chapter 8**

**Advancements and Prospects of Forward Directional**

In the current age of rapid technological progress and development, radio frequency identification (RFID) technology has found many applications in various areas such as supply chain, warehouse, and retail store management. Alike mobile communications, the high data performance and compact profile are becoming obvious expectations of the users of handheld RFID devices. In this regard, directional antennas have a bright

This chapter presents a comprehensive review of RFID technology concerning the pros‐ pects of directional especially forward directional antennas and propagation for multiband operation. The technical considerations of directional antenna parameters are also discussed in details in order to provide a complete realization of the parameters in pragmatic approach to the directional antenna designing process, which primarily in‐ cludes scattering parameters and radiation characteristics. The antenna literature is also critically overviewed to identify the possible solutions of the directional antennas to uti‐ lize in single and multi-band handheld RFID reader operation. However, it has been seen that these techniques can be combined to enhance the directional antennas with wider bandwidths and higher gain. Last but not least, the possibilities of forward-direc‐ tional antennas which spectacularly use the surface wave for the radiation will be ex‐ plored, and the difference with the conventional directional antennas with them will be

> © 2013 Toaha Mobashsher and W. Aldhaheri; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is

© 2013 Toaha Mobashsher and W. Aldhaheri; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

prospect for a more user friendly experience even in the rugged environments.

**Antennas for Compact Handheld RFID Readers**

Ahmed Toaha Mobashsher and

Additional information is available at the end of the chapter

properly cited.

Rabah W. Aldhaheri

**1. Introduction**

discussed.

http://dx.doi.org/10.5772/53283


*2.7. Statistical Analysis*

We analyzed the data obtained using the SPSS statistical software package. We subjected the experimental data to a one-way analysis of variance (one-way ANOVA) to identify differences between multiple groups. To compare the differences between groups, we used Duncan's multiple-range test. A significant difference was indicated when *p* < 0.05.

#### **3. Results**

#### *3.1. Effects of SPE on Food Intake, Water Intake, Body Weight, and Organ Weight of SAMP8 Mice*

Tables 1 and 2 show the effects of feeding 3-month-old SAMP8 mice with SPE in terms of changes in body weight, food intake, and water intake for male and female animals, respectively. For groups of both sexes, we found no significant changes in body weight, food intake, and water intake after the administration of SPE. Tables 3 and 4 show weight changes in the brain, heart, liver, spleen, lung, and kidney of male and female mice, respectively. We found no significant changes in organ weights in the animal groups that received SPE treatment.

**Table 1.** Effects of SPE treatment on body weight, food intake, and water intake in male SAMP8 mice.

Group A—control group, Group B—low-dose SPE (61.5 mg/kg BW/day), Group C—medium-dose SPE (123 mg/kg BW/day), and Group D—high-dose SPE (184.5 mg/kg BW/day). Data are expressed as the mean ± SEM and analyzed by one-way ANOVA.

Group A—control group, Group B—low dose SPE (61.5 mg/kg BW/day), Group C—medium dose SPE (123 mg/kg BW/day), and Group D—high dose SPE (184.5 mg/kg BW/day). Data are expressed as the mean ± SEM and analyzed by one-way ANOVA.

Group A—control group, Group B—low-dose SPE (61.5 mg/kg BW/day), Group C—medium-dose SPE (123 mg/kg BW/day), and Group D—high-dose SPE (184.5 mg/kg BW/day). Data are expressed as the mean ± SEM and analyzed by one-way ANOVA.

**Table 4.** Effects of SPE treatment on organ weights of male SAMP8 mice.

Group A—control group, Group B—low-dose SPE (61.5 mg/kg BW/day), Group C—medium-dose SPE (123 mg/kg BW/day), and Group D—high-dose SPE (184.5 mg/kg BW/day). Data are expressed as the mean ± SEM and analyzed by one-way ANOVA.

#### *3.2. Effect of SPE on Aging Index of Senescence-Accelerated Mice*

Tables 5 and 6 show the effects of SPE administration on aging index assessment scores after 10 weeks for male and female mice, respectively. The skin glossiness, spinal curvature, and total aging index assessment scores were significantly lower for mice in the treatment groups (*p* < 0.05) compared with animals in the control group. SPE treatment produced the same effects in both male and female mice.

**Table 5.** Effects of SPE administration on aging index assessment scores of male SAMP8 mice.

Group A—control group, Group B—low-dose SPE (61.5 mg/kg BW/day), Group C—medium-dose SPE (123 mg/kg BW/day), and Group D—high-dose SPE (184.5 mg/kg BW/day). Data are expressed as the mean ± SEM and analyzed by one-way ANOVA. Groups with different letters indicate significant differences among each group (*p* < 0.05).

**Table 6.** Effects of SPE administration on aging index assessment scores of female SAMP8 mice.

Group A—control group, Group B—low-dose SPE (61.5 mg/kg BW/day), Group C—medium-dose SPE (123 mg/kg BW/day), and Group D—high-dose SPE (184.5 mg/kg BW/day). Data are expressed as the mean ± SEM and analyzed by one-way ANOVA. Groups with different letters indicate significant differences among each group (*p* < 0.05).


**7. Naringenin based benzoxazine for flame retardant applications**

Kan Zhang et al., synthesized [80] a fully bio-based benzoxazine monomer (NAR-fa) using Naringenin, furfurylamine and paraformaldehyde (**Figure 22**). Thermal properties of the resulting co-polymeric thermosets were maintained or slightly enhanced, while those related to flame retardancy improved to about 38 and 51% for THR and HRC, respectively (**Table 5**).

**Figure 23** displays the graphic representation of the HRR as a function of temperature, where the maximum HRR is detected at 455°C. From **Figure 23**, it was calculated a HRC and THR of 31.9 J/gK and 6.6 KJ/g, respectively. The HRC value for poly(NAR-fa) is significantly lower than those reported for PBz's with high

**Figure 22.** *Synthesis of fully bio-based naringenin-benzoxazine monomer (NAR-fa).*

*Microscale combustion calorimetric (MCC) analysis of poly(NAR-fa). (a) heat release rate, and (b) total heat release as a function of the temperature [80]. (Copyright 2020, reproduced permission from Royal Society of Chemistry).*

furfurylamine as amine precursor both derived from bio-sources. The well-

heat formulation, which prolongs the shelf life of the Bz resin. Besides, the benzopyrone and furan rings with carbon–carbon double bonds in the API-fa are proficient of making further cross-linking networks, which are anticipated to considerably increase the mechanical, thermal and flame resistant properties of the resulting PBz thermosets. Remarkably, the thermal possessions of the API-fa based PBz achieved in this work are considerably higher than those of recently reported PBz's. The resulting polymerized PBz thermoset developed from API-fa indicates the high glass transition temperature of 376°C, a higher char residue of 66%, with

an extremely lower heat release capacity of 20.2 J/gK (**Table 5**).

**Figure 20.**

**Figure 21.**

**56**

*from Royal Society of Chemistry).*

*Synthesis of apigenin-based bis-benzoxazine (API-fa).*

*Flame Retardant and Thermally Insulating Polymers*

structured intra-molecular hydrogen bond in API-fa is probable to be a stable latent

Thus, the LOI values obtained for poly(API-fa)-1 and poly(API-fa)-2 are as high as 42.7 and 43.9, respectively (**Table 5**). From the MCC analysis of poly(API-fa)-1

*Microscale combustion calorimetric (MCC) analysis of poly(API-fa)-1 and poly(API-fa)-2. (a) heat release rate, and (b) total heat release as a function of the temperature [79]. (Copyright 2020, reproduced permission* flame resistance properties attained upon polymerization of ortho-imide and ortho-amide functionalized benzoxazine monomers. These results show that poly (NAR-fa) can indeed be classified as non-ignitable given that its HRC is lower than 100 J/gK.


**4. Conclusions**

Phantoms with horizontal graphite absorbers were used to examine the efficacy of full-ring, diffuse, and point illumination PAT imaging using a ring US transducer. The full-ring illumination was

able to provide a more uniform fluence irrespective of the vertical depth of the imaged cross-section, while the point source and diffused-beam resulted in a high fluence at the point of entry, which diminished with vertical depth. In addition, the preliminary results indicate that illuminating an object 10–15 mm below the imaged cross-section of interest might be optimal for this system since it avoids large surface reflections and provides better coverage to the cross-section.

**Author Contributions:** N.A. and S.S.A. contributed to: conceptualization, methodology, formal analysis, investigation, data curation, writing—original draft preparation, writing—review and editing, and visualization. A.P. developed the software used for creating the PAT back-projection images. M.M. was involved in conceptualization, methodology, formal analysis, investigation, writing, the review and editing, supervision, and project administration.

**Funding:** This project was supported by Department of Defense (Breast Cancer Research program, Award number (W81XWH-18-1-0039). Naser Alijabbari (NA) was further supported by Office of the Vice President for Research (OVPR) offers the Faculty Competition for Postdoctoral Fellows.

**Acknowledgments:** The authors acknowledge Wayne State University for their financial support and the Barbara Ann Karmanos Cancer Institute. Also, we would like to acknowledge Neb Duric from Karmanos Cancer Institute for his guidance and assistance with using the UST system. MM was supported by Department of Defense (Breast Cancer Research program, Award number (W81XWH-18-1-0039), University Research Grant from Wayne State University Provost office, Technology Development Incubator Award through Wayne State University, and pilot research funding from Barbara Ann Karmanos Cancer Institute.

**Conflicts of Interest:** The authors have no relevant financial interests in the manuscript and no other potential conflicts of interest to disclose.