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Abstract

Energy-efficient pulse-coupled oscillators have recently gained significant research attention in wireless sensor networks, where the wireless sensor network applications mimic the firefly synchronization for attracting mating partners. As a result, it is more suitable and harder to identify demands in all applications. The pulse-coupled oscillator mechanism causing delay and uncharitable applications needs to reduce energy consumption to the smallest level. To avert this problem, this study proposes a new mechanism called random traveling wave pulse-coupled oscillator algorithm, which is a self-organizing technique for energy-efficient wireless sensor networks using the phase-locking traveling wave pulse-coupled oscillator and random method on anti-phase of the pulse-coupled oscillator model. This technique proposed in order to minimize the high power utilization in the network to get better data gathering of the sensor nodes during data transmission. The simulation results shown that the proposed random traveling wave pulse-coupled oscillator mechanism achieved up to 48% and 55% reduction in energy usage when increase the number of sensor nodes as well as the packet size of the transmitted data compared to traveling wave pulse-coupled oscillator and pulse-coupled oscillator methods. In addition, the mechanism improves the data gathering ratio by up to 70% and 68%, respectively. This is due to the developed technique helps to reduce the high consumed energy in the sensor network and increases the data collection throughout the transmission states in wireless sensor networks.

Keywords

Wireless sensor network pulse-coupled oscillators self-organizing energy-efficient synchronization

Introduction

The wireless sensor network (WSN) positions itself within the revolutionary network technologies as a credible member.^1–3 This is due to the enormous benefit of WSNs in diverse fields that affects human life. WSN applications can be classified into two categories, that is, tracking and monitoring.⁴ The main task of tracking applications is to track objects, animals, humans, and vehicles. However, monitoring applications focus on monitoring the environment, health, power, inventory, and process automation. According to Zulkifli et al.,⁵ the data obtained from the observed ecological phenomenon are only required to be occasionally sampled and transported to the base station. Therefore, it is imperative to create a smart mechanism that could further advance the performance of sensor nodes in reducing energy consumption, particularly among the process of transmission.^2,6,7

In this study, energy efficiency mechanism was proposed with the expectation of its benefits through the expanded number of sensor nodes and data packet size, specifically from the aspect of energy consumption and data gathering ratio. Moreover, the recommended random traveling wave pulse-coupled oscillator (RTWPCO) mechanism is able to spread the traffic load frequently and gradually over the sensor nodes during the process of transmission, which consequently enables the production of an improved network lifespan and allows the sensors to fail. On top of that, low energy consumption is possible through the use of multihop communication of each node with the sink that could be done by the proposed mechanism. Based on the above discussion, Figure 1 illustrates the energy-efficient taxonomy that encompasses two main mechanisms, which are biological-inspired network system and non-biological-inspired network system.

Figure 1.

Classification of the energy-efficient mechanism in WSNs.

The RTWPCO algorithms focused on improving energy consumption ratio and data gathering ratio to some sort of applications. Using this self-organizing strategy, the WSN develops energy consumption and data gathering, thus resulting into a prolonged network lifespan.

For the purpose of this study, a self-organized formula was established from the traveling wave pulse-coupled oscillator (TWPCO) mechanism developed by Al-Mekhlafi et al.⁸ through randomization-based mechanism. The main difference between RTWPCO and TWPCO is obvious because TWPCO ignored the high energy usage consumed during the synchronized transmission scheduling between sensor nodes in WSN. The proposed mechanism is not complicated and easy to operate compared to the other mechanism. Therefore, the aim of the proposed method is to reduce energy consumption and increase data gathering according to WSN conditions. This method will avoid packet collisions during data traffic between sender and receiver, resulting better data gathering, as well as minimized energy usage of sensors in transmission phase.

The flow of this article is described as follows: In section “Related works,” the previous works related to this study are thoroughly discussed. Section “Proposed algorithm” illustrates the suggested mechanism in detail. In section “RTWPCO mechanism design,” the purpose and major processes of the proposed method are described and discussed with details. Moving on, the performance assessment of the proposed method is presented in section “Performance evaluation of RTWPCO.” Finally, an overall summary of this study is provided in section “Conclusion.”

Related works

In this section, an extensive analysis of the previous works related to this study is presented together with the status of the recommended approach. In terms of energy efficiency, a self-organizing method is usually chosen in transmission scheduling in order to synchronize the time. Energy-efficient transmission scheduling is ensured in WSNs by explicit embedding energy minimization protocols into the underlying sensing model of the sensors, for example, minimizing the per packet energy consumption^9–11 or avoiding preventing high energy usage of any single node within the network. Another vital specification of WSNs revolves around a self-organizing capability which allows the sensor nodes to relocate their new neighbors (as a result of battery exhaustion or an immediate breakdown of some nodes in the network) in the case of dynamic network topology changes. Energy efficiency is the major focus of constructing any sensor nodes due to the inadequate and non-rechargeable power resource.¹²

According to the literature,^13–15 the traditional data collection mechanism suggests that a tree topology rooted at the sink is secured due to the many-to-one advantages of the monitoring environmental applications. Moreover, sleep timings and data packet transmission are separated to avoid high energy consumption and low data gathering. In these mechanisms, sensor nodes are expected to transport the control packets to secure the sensor network topology, to cooperate with the surrounding sensor nodes, or to prevent high energy consumption and low data gathering among the surrounding sensor nodes. In the case of self-organizing, sensor nodes must transport the supplemental control packets to re-establish the topology which could be energy- and time-consuming.

Various researchers in wireless communication have established different types of energy-efficient pulse-coupled oscillator (PCO)-based models. The PCO model is possible to be adopted in establishing WSNs.^{7,8,12,16–21} Similar to sensor nodes, the energy efficiency of PCO models demonstrates decentralization performance as well as inadequate individual processing potentials. In fact, communication is greatly confined.

According to Taniguchi et al.,¹² a self-organizing mechanism that depends on phase-locking in PCO was first created. The purpose of this particular mechanism was to circulate the data of the sender node to the sink node inside WSN in order to prevent deafness problem. Following it, a simple randomization-based mechanism and a desynchronization-based mechanism were developed by focusing on the anti-phase provided in the PCO model. The purpose of creating both mechanisms was to solve the issue of an unnecessary contact among sensors and at the same time sustain the same hop count attained by means of the energy efficiency ratio and the data gathering ratio. However, the desynchronization-based mechanism is appropriate to be applied to WSN, as it requires a high level of data collection and efficiency and clarity of mechanism instead of the data gathering ratio.

In Phung et al.,²² a multichannel protocol for high-bandwidth WSNs was introduced by incorporating the time-division multiple access (TDMA) to the frequency-division multiple access (FDMA). In this case, the aim was to solve the issues of deafness and packet collision. In other words, it aimed to meet the demand of scheduling of transmissions. Therefore, it was augmented by choosing learning for collaborated scheduling and routing in each node, which is in agreement with the service quality (i.e. packet delivery ratio, end-to-end latency, and energy waste factor). As expected, this study managed to accomplish the predicted conditions without any complications, which include a minimal latency and a packet loss. Furthermore, it was reported that the developed protocol could enhance the operations related to an end-to-end delivery rate and an end-to-end latency, including making the energy efficiency possible compared to other protocols.

Leidenfrost and Elmenreich²³ proposed a self-organizing scheme based on the biological features of firefly. Moreover, this method utilizes the PCO model to synchronously emit light flashes to attract mating partners, as a result the timing of light flashes will distribute in a given time window without affecting the quality of the synchronization. In their study, no dedicated synchronization node was required and thus there was no single point of failure. Moreover, the additional rate calibration mechanism allows a longer resynchronization interval, and the use of cheap oscillators with high drift rates is usually featured in low-cost nodes. It is also possible to achieve a synchronization precision which is lower than 1 ms.

Núñez et al.²⁴ proposed the implementation of pulse-coupled synchronization in an acoustic event detection system that aimed to locate the source of acoustic events by means of an acoustic capable WSN. The distributed localization with pulse-coupled synchronization over a pure-broadcasting infrastructure-free ad hoc network seems to be the ideal configuration to solve the acoustic source localization problem.

Al-Mekhlafi et al.⁸ presented a TWPCO using a self-organizing mechanism for energy-efficient WSNs. This proposed method neglected packet collisions as a result of synchronized transmission scheduling between sensor nodes and recorded high energy consumption ratio and fewer data gathering ratio.

Kamimura and Tomita²⁵ suggested a novel self-organizing network coordination framework (SoNCF) for WSNs for information-gathering applications in large-scale ad hoc wireless networks. The framework supports numerous nodes’ P2P communication with a decentralized time-division technique for transmissions using a simplified PCO model. The proposed framework demonstrated the feasibility of SoNCF using software simulations by comparing the SoNCF and the traditional carrier-sense multiple access with collision avoidance (CSMA/CA) method. A simulation of the extended method successfully transferred data from 59 nodes in a mesh network to a base node with no packet loss.

Based on the above review, the previous works assisted this study to achieve a better understanding as well as to enable communication phases, acquisition, and network synchronization to be differentiated. However, the proposed mechanisms that have been reviewed above are not appropriate to be applied to WSNs due to the fact that it must adhere to the topology.

Proposed algorithm

Radio communications of sensor nodes contribute to the rising energy costs on the sensor node, especially in the active mode (receive and transmit). As a matter of fact, the energy consumed in transmitting and receiving modes are larger compared to sleep and idle modes that they are assumed to be negligible in this proposed framework. A number of different sensor network applications will result in various demands and specifications, which cause it to be more complicated in having identical requirements for all applications. For instance, the adoption of the TWPCO in the energy efficiency process is very energy-consuming, thus making it inapplicable for other applications.⁸ Alternatively, the RTWPCO is preferred because it does not absorb a lot of energy, thus making it possible to maximize the lifespan of the whole network. According to the literature,^13–15 referring to the traditional mechanism of collecting data, a tree topology that is rooted at the sink is not changed as a result of many-to-one features of the monitoring applications. Moreover, the data packet transmission and the sleep timings are being separated for the purpose of avoiding radio interference and high power. In this case, sensor nodes are required to assign control packets to sustain the network topology and integrate themselves with the surrounding sensor nodes, or avoid any interference and high power among the surrounding sensor nodes. In the case of a network topology change, sensor nodes have to transport extra control packets for the purpose of strengthening the topology because it requires more energy and time.

The prime mechanism behind the self-organizing algorithm is to utilize the parameter information from the application requirements to construct a conclusion about the suitable value of the offset $τ_{i}$ . The provided information that points to the kindliness of the application to the energy-efficient delay as an instance can be used to tune some variables, for example, the value of offset $τ_{i}$ among the sensor nodes. Adjusting the offset $τ_{i}$ value according to the application requirements in energy efficiency has a significant impact on the data gathering ratio and power consumption ratio at each sensor node. To evaluate the proposed RTWPCO mechanism, we have integrated the self-organizing TWPCO algorithm with randomization-based algorithm, which will be discussed in the next subsection.

TWPCO algorithm

The PCO technique introduced in the literature^{8,18,20,26,27} requires the performance of oscillators to be coordinated with the fact that an oscillator only fires when its timer reaches 1. According to the conventional method of PCO, there are several synchronous firefly behaviors, namely, in-phase, anti-phase, and phase-locking.²¹ For in-phase behavior, the oscillators are totally integrated, while for the anti-phase behavior, the oscillators must be integrated with an equal interval. The phase-locking behavior can only be integrated based on PCO synchronization, for example, in traveling waves, the synchronization is only possible with the presence of a constant offset. Overall, it can be concluded that up to this moment, this is the only study that has chosen to apply both biologically inspired network systems based on phase-locking of PCO model and non-biologically inspired network systems based on the anti-phase of PCO model. Biologically inspired network systems neglects high power utilization in the network as a result of synchronized transmission scheduling between sensor nodes, therefore high energy consumption ratio and fewer data gathering ratio was recorded. Hence, non-biologically inspired network systems are used in order to counteract high power utilization in the network to get better data gathering, as well as to minimize the energy needed for the sensor nodes during transmission.⁸

The details of the process are described as follows: Given a set of N oscillator $ϕ_{i}$ ; $where 1 \leq i \leq N$ and each oscillator $ϕ_{i}$ is associated with a phase $ϕ_{i}$ (such that $ϕ_{i} \in [0, T]$ ). However, the passing of time has caused the $ϕ_{i}$ to shift toward T (which is the maximum). At T, the oscillator $ϕ_{i}$ fires before $ϕ_{i}$ gets back to zero. Similarly, the oscillator $ϕ_{j}$ which is doubled with the firing oscillator $ϕ_{i}$ is prompted, thus causing the corresponding phase $ϕ_{j}$ to be moved by an infinitesimal amount $Δ (ϕ_{j})$ , where $ϕ_{j} = Δ (ϕ_{j}) + ϕ_{j}$ .

The total $Δ (ϕ_{j})$ is given by

ϕ_{j} = Δ (ϕ_{j}) + ϕ_{j}

(1)

where $Δ (ϕ_{j})$ expresses the phase response curve (PRC) that is commonly applied by researchers in evaluating the performance of a system without the knowledge of the mechanisms responsible for the behavior.^27,28 The PCO firing is performed by utilizing the traveling wave equation based on phase-locking of the PCO model. In particular, the proof of equation (1) is developed $(Δ (ϕ_{j}))$ in accordance with several other models such as the quadratic integrate and fire (QIF) model,²⁷ the radial isochron clock (RIC) model,²⁷ and the PRC function and satisfies:¹⁶

QIF model

Δ_{QIF} (ϕ) = PR C_{a} (1 - \cos (2 π ϕ))

(2)

where $PR C_{a} = 0.5, 1.0, - 0.5$ . In this model, an oscillator disregards all stimuli at the time of firing. In addition, it identifies multiple stimuli received simultaneously as one stimulus. For a given value of $PR C_{a}$ , the PRC can both delay and advance the firing. As $PR C_{a}$ approaches the value of one, the PRC becomes singular. It should be noted that this occurs regardless of the value of at which the PRC vanishes: t = 0, T. For the smallest values of $PR C_{a}$ , the QIF of PRC has a particularly simple form.

RIC model

Δ_{RIC} (ϕ) = - PR C_{a} * \sin (2 π ϕ)

(3)

where $PR C_{a} = 0.5, 1.0, - 0.5$ . In this model, an oscillator disregards all stimuli at the time of firing, and it also identifies multiple stimuli received simultaneously as one stimulus.

The PRC function

To create a desired TWPCO, regardless of the initial phase, an oscillator must advance its phase toward 1 − τ when it is catalyzed throughout $(0 \leq ϕ < 1 - τ)$ . In contrast, its phase must move away from 1 − τ when it is catalyzed throughout $(1 - τ < ϕ < 1)$ . Therefore, we have the following conditions on the PRC to create a TWPCO

{\begin{matrix} 0 < Δ (ϕ) \leq 1 - τ - ϕ & (0 \leq ϕ < 1 - τ) \\ Δ (ϕ) = 0 & (ϕ = 1 - τ) \\ 1 - τ - ϕ \leq Δ (ϕ) < 0 & (1 - τ < ϕ < 1) \end{matrix}

(4)

From equations (3) and (4), we can generate the following new equation

Δ_{s} (ϕ) = - PR C_{a} * \sin \frac{π}{1 - τ} * ϕ + PR C_{b} (1 - τ - ϕ)

(5)

Moreover, from equations (2) and (4), equation (6) is formed as

Δ_{s} (ϕ) = PR C_{a} * \cos \frac{π}{2 (1 - τ)} * ϕ + PR C_{b} (1 - τ - ϕ)

(6)

where $P R C_{a} (- (P R C_{b} (1 - τ)) / (π) < P R C_{a} \leq ((1 - P R C_{b}) * (1 - τ)) / (π))$ and $0 < PR C_{b} \leq 1$ are the factors that determine the characteristics of PRC.

Figure 2 presents the evaluation of PRC $(Δ_{s} (ϕ))$ that satisfies the RIC model and the QIF model where $PR C_{a} = 0.1$ and $PR C_{b} = 0.5$ must be located in the two lines when $τ = 0.2$ .

Figure 2.

Evaluation of PRC $(Δ_{s} (ϕ))$ with RIC model and QIF model.

Figure 3 presents the assessment of PRC $(Δ_{s} (ϕ))$ , which also satisfies the RIC model and the QIF model where $PR C_{a} = 0.5$ and $PR C_{b} = 0.3$ need to be located in the two lines when $τ = 0.2$ .

Figure 3.

Evaluation of PRC $(Δ_{s} (ϕ))$ with RIC model and QIF model.

By comparing the above evaluations as shown in Figures 2 and 3, the PRC that satisfies the QIF model in equation (6) obtained superior results than the PRC model that satisfies the RIC model in equation (5). Our new generated equation (6) indicates that the PRC satisfies the QIF model as assisted in the firefly and pacemaker. The pacemaker may be related to the TWPCO mechanism broadcast to oscillator number of sensor nodes N forward the pacemaker information together with a constant offset phase-variation $1 - τ$ .

As a result, the TWPCO equation proved from equation (6) and applied in equation (1) catalyzes the sensor node and modifies its phase as follows

ϕ_{j} = ϕ_{j} + PR C_{a} * \cos \frac{π}{2 * T} * ϕ_{j} + PR C_{b} * (T - ϕ_{j})

(7)

On the whole, the traveling wave phenomenon is possible to be arranged in data gathering and diffusion according to the biologically inspired network systems of phase-locking in the PCO model.^2,8,9,11,20 In the TWPCO model, the collected data are being broadcasted by individual sensor in the range of its timer. Therefore, this allows the network to amend the phase of its own timer whenever a transmission from another node is spotted. It is important for a sensor node to cooperate with its surroundings to allow itself to get into the phase-locking state in which the sensor data are discharged. In this scenario, the timing of a message being discharged is considered as a traveling wave phenomenon, in which the center takes place at the sensor node and attempts to either gather or disperse information from or to all sensor nodes.

In this case, the phase timer $ϕ_{i}$ and level $l_{i}$ including offset $τ$ are present in each sensor node $n_{i}$ (where $1 \leq i \leq N$ ). According to the formula, the level field is able to signify the sensors hop count from the base station. At the beginning of the process, every level of the sensor is appointed to a high value. The offset $τ$ refers to the interval between the nodes of level l and l − 1 which takes place amid the communication of data. Therefore, a transmission which comprises administered information and sensor data is delivered by the sensor node $n_{i}$ when the phase $ϕ_{i}$ reaches T. The phase will come back to 0 following the previous step. Therefore, when the message by node $n_{j}$ from $n_{i}$ at level $l_{i} < l_{j}$ is collected, the node $l_{j}$ will be altered to its level as $l_{j} = l_{i} + 1$ .

TWPCO mechanism suggests that the phase will be activated and adjusted by the node as presented in equation (7), where considering that $PR C_{a}$ and $PR C_{b}$ are the determinants that influence the pace of assemblage. Without considering the introductory phase of nodes, the traveling wave phenomenon which is in accordance with phase-locking in the PCO is executed amid the regular transmission among sensor nodes by applying equation (7) in the PRC function.

Randomization-based algorithm

In the randomization-based formula, offset $τ_{i}$ is inconstantly picked between zero and $τ^{\max}$ as presented below

τ_{i}^{prev} = τ ε T_{i, t}^{\max} < τ_{i}^{tran s^{t}}

(8)

τ_{i}^{next} = τ ε T_{i, t}^{\min} < τ_{i}^{tran s^{t}}

(9)

where $T_{i}$ refers to the set of time estimated for transmission of messages in $ε_{i}$ . However, they will be considered as zero if $τ_{i}^{Prev}$ or $τ_{i}^{next}$ is not achieved. Following it, the relation is adopted as a guide for the node $n_{i}$ to alter the offset $τ_{i}$

T_{i} = (1 - α) * T_{i} + α * T_{i}^{mid}

(10)

where

τ_{i}^{mid} = {\begin{matrix} \begin{matrix} \frac{T_{i}^{prev} + T_{i}^{next}}{2} if τ_{i}^{next} > τ_{i}^{prev} > 0 \\ \frac{T_{i}^{prev}}{2} if τ_{i}^{next} = 0, τ_{i}^{prev} > 0 \\ T^{\max} otherwise \end{matrix} & \begin{matrix} T_{i}^{prev} = τ_{i}^{stim} - τ_{i}^{prev} \\ T_{i}^{next} = τ_{i}^{stim} - τ_{i}^{next} \end{matrix} \end{matrix}

It is important to understand that in the randomization-based formula, the sensor node $n_{i}$ neither stores message communication nor comprises any information of message transmission timing $F_{i}$ in the messages. However, this formula is not really helpful in solving the hidden node issue despite it is considered as uncomplicated, and what it only needs is a minimum control overhead compared to other mechanisms. From Figure 4, it can be seen that both the message transmission timing and offset $τ_{i}$ with sensor nodes are stable when the randomization-based formula is applied in the process. Referring to the same figure, it can be observed that sensor nodes A, B, and C share the similar hop counts from the sink. In this case, the communication broadcast timings are circulated along with the offset $τ$ . It was observed that both sensor nodes A and C acted as two-hop neighbor sensor nodes, but no prompt transmission occurred between them. Regarding the RTWPCO mechanism, information must be achieved from a single-hop upstream neighbor sensor node, specifically sensor node D as shown in Figure 4. This aims to prevent massive utilization between the two-hop neighbor sensor nodes. The organized information of sensor node $n_{i}$ has a timer $t_{i}$ and keeps phase $ϕ_{i}$ (such that $ϕ_{i} \in [0, T]$ ), level $l_{i}$ , offset $τ_{i} (0 < τ_{i} \leq τ^{\max})$ , communication broadcast timing table $ε_{i}$ , and sensor data $D_{i}$ . T refers to data gathering period, while level $l_{i}$ signifies the number of hops from the sink that was initially set to a high value by keeping in mind that a sink always contain the level of 0. Offset $τ_{i}$ sets the communication broadcast period among sensor node $n_{i}$ and a sensor node, thus triggering sensor node $n_{i}$ that was originally fixed to $τ^{\max}$ . It is important to acknowledge that sensor node density highly influences the process of setting the greatest offset $τ^{\max}$ , communication broadcast timing table $ε_{i}$ , and sensor data $D_{i}$ . Moreover, $ε_{i}$ is utilized to keep information on communication broadcast timing of sensor node $n_{j}$ within two hops from sensor node $n_{i}$ .

Figure 4.

Message broadcast timing and offset in the randomization-based algorithm: (a) level and (b) time.

Figure 5 depicts the pseudocode of the randomization-based algorithm and offset $τ_{i}$ with stable sensor nodes when applying the randomization-based algorithm, thus providing the value of $τ$ as illustrated in Figure 4.

Figure 5.

Pseudocode of the randomization-based algorithm.

Additionally, even if the receipt of the message satisfies $l_{j} = l_{i} - 1$ , node $n_{i}$ develops its communication broadcast timing table $ε_{i}$ as follows. Initially, it utilizes the received communication broadcast timing information $F_{j}$ to compute the estimated communication broadcast time $t_{(j; k)}^{trans} = t - f_{j; k}$ of node $n_{k}$ which refers to a single or two-hop neighbor nodes achieved by $n_{i}$ . Without any access to node $n_{k}$ in its communication broadcast timing table $ε_{i}$ , node $n_{i}$ will still insert a recent access $e_{i; k} = (k, l_{i}, t_{(j; k)}^{trans})$ to the table. Moreover, assuming that an access for node $n_{k}$ is present in its communication broadcast timing table $ε_{i}$ and $t_{(i; k)}^{trans} > t_{(j; k)}^{trans}$ is satisfied, node $n_{i}$ writes the access as $e_{i; k} = (k, l_{i}, t_{(j; k)}^{trans)}$ .

RTWPCO mechanism design

In this article, RTWPCO model was classified into two parts. First part involves the use of phase-locking TWPCO that is related to sensor nodes, which was discovered in the flashing synchronization behavior of fireflies, which requires the sensor node to transmit the data packet to the base station or sink during transmission. The second part is using random method on anti-phase of the PCO model that uses the TDMA protocol in order to minimize the high power utilization in the network to get better the data gathering of the sensor nodes during transmitting.

Multiple factors were classified into several varied categories that are the sensor node, the method, the energy model, the CSMA/CA model, and the equation. The sensor node factors are the packet phase size, the packet header size, the data packet size, and the maximum number of nodes. The methods used in the developed simulator are built using Java programming language running on Microsoft Windows 7 Home Premium 64-bit edition. The energy model factors for MICAz²⁹ are the initial energy, the transmit power, the receive power, the idle power, the sleep power, and the transmit data rate. The CSMA/CA factors³⁰ are presented in Table 2. Finally, the notations factors are presented in Table 1.

Table 1.

Specification of notations.

Notations	Specification
$PR C_{a}$ and $PR C_{b}$	$PR C_{a}$ and $PR C_{b}$ are parameters that determine the speed of convergence.
$Δ (ϕ_{i})$	$Δ (ϕ_{i})$ is called phase response curve (PRC) which commonly used by researchers to evaluating the performance of a system without knowing fundamental scheme of behavior.
i	i is the number of oscillator where $1 \leq i \leq N$
CSMA/CA	The CSMA/CA technique is used for the process of message transmission.
l	The level indicates the number of hops from the base station, and is initialized with a large value.
n	The number of sensor nodes.
$τ$	The offset defines the interval of message transmission between a sensor node of level l and one of l − 1.
T	T is a time which is the maximum.
$ϕ_{i}$	$ϕ_{i}$ is associated with a phase $ϕ_{i}$ (such that $ϕ_{i} \in [0, T]$ )

CSMA/CA: carrier-sense multiple access with collision avoidance.

A fundamental sensor node for the base station or the root node and surrounding sensor nodes of the recommended methods in the sender were generated and deployed. These nodes were deployed with the intention to the width (Y) and the height (X) of deployment area based on uniform random distribution between 0.0 and 3.0, which is offered by the Java programming language. The generated sensor nodes were then represented by X, Y values (i.e. two-dimensional (2D) location within the deployment area), the transmission range value, and the initial energy value. For estimation, the proposed mechanism utilizes a WSN. After generating the nodes, it was crucial to figure out the location of the radio range by utilizing the following equation function of distance^7,21

d = \sqrt{{(X_{2} - X_{1})}^{2} + {(Y_{2} - Y_{1})}^{2}}

(11)

It is important to validate that all sensor node packets in the same radio range with the value of 50 in order to allow for the sensor nodes packet to be recorded. All vital parameters for the sensor node packet must be programmed, particularly the parameters of CSMA/CA model parameters of ZigBee 802.15.4 as presented in Table 2.²⁹

Table 2.

Parameters of CSMA/CA.

Parameters	CSMA parameters
Parameters	Value	Specification
CCA_DURATION	0.000128	Clear Channel Assessment ccaDetectionTime (s) (0.000128 = 128 µs)
BACKOFF_UNIT	0.001000	Base unit for all backoff calculations// (802.15.4 default: 320 µs)
macMinBE	1	Minimum backoff exponent (802.15.4 default: 3 (0–3))
aMaxBE	3	Maximum backoff exponent (802.15.4 default: 5)
macMaxCSMABackoff	4	Maximum number of backoffs (802.15.4 default: 4 (0–5))

CSMA/CA: carrier-sense multiple access with collision avoidance; CSMA: carrier-sense multiple access.

Figure 6 semantically illustrates that the sensors are deployed randomly with the restriction of height, X = 100 m and width Y = 100 m of the deployment field while the base station is placed at the center X = 50 m*Y = 50 m. In addition, sensor nodes are deployed randomly with no prior assumptions regarding the connectivity and the position.

Figure 6.

Generate and deploy the sensor node and base station.

This article mainly focuses on the aspect of naming the mechanism of energy consumption operation, transmitting operation, and receiving operation:^7,21

Energy consumption operation.

Energy consumption event in WSN^9,10,31 has sleep, idle, transmit, and receive modes, which are mainly attributed to the radio system in active mode. The energy consumed in transmit and receive modes are large compared to sleep and idle modes.

(a) Sleep mode

ConsumEnergy = E_{sleep} * advphase

(12)

where $E_{sleep}$ is the sleep power that measures $1 μ A \times 10^{- 6} \times 3 V = 0.000003 (W)$ by MICAz energy model, and advphase is calculated by equation (7).

(b) Idle mode

ConsumEnergy = E_{idle} * advphase

(13)

where $E_{idle}$ is the idle power that measures (W) in MICAz energy model, and advphase is calculated by equation (7).

ConsumEnergy = E_{TX} * \frac{Packe t_{size}}{Dat a_{rate}}

(14)

where $E_{TX}$ transmits power that measures $(W)$ in MICAz energy model, $Packe t_{size}$ is data packet size that measures $(bit)$ , and $Dat a_{rate}$ transmits data packet rate that measures $(bps)$ in MICAz which is equal $250, 000 bps$ the WSN communication bandwidth.

(d) Receive mode:

The receive power can be classified into listening and receiving.

Listening

ConsumEnergy = E_{RX} - E_{idle} * CC A_{Duration}

(15)

where $E_{RX}$ is the receive power that measures (W) in MICAz energy model, $E_{idle}$ is the idle power that measures (W) in MICAz energy model, and $CC A_{Duration}$ is the clear channel assessment $CC A_{Detection}$ Time (s) (0.000128 = 128 µs) in carrier-sense multiple access (CSMA) model.

Receiving

Consum Energy = E_{RX} - E_{idle} * \frac{Packe t_{size}}{Dat a_{rate}}

(16)

where $E_{RX}$ is the received power that measures (W) in MICAz energy model, $E_{idle}$ is the idle power that measures (W) in MICAz energy model, $Packe t_{size}$ is the data packet size that measures (bit), and $Dat a_{rate}$ is transmitted packet data rate that measures (bps) in MICAz, which is equal to 250,000 bps, the WSN communication bandwidth.

2. Transmitting operation.

Sensor node behavior refers to the performance of the sent message after a series of cycle and when the nodes become stable. Previously, we paragraph mentioned that the performance of a sensor node $n_{i}$ is dependent on its current phase $ϕ_{i}$ , in which the sensor node obtains control messages from the surrounding nodes:

(a) Wake-up.

The sensor node $n_{i}$ is considered to be in the wakeup state when the phase $ϕ_{i} = T - τ^{\max}$ . In this phase, the message transmission timing table $ε_{i}$ , and sensor data $D_{i}$ are discharged by the node while waiting for the messages to be sent by the neighbors.

(b) Message reception from downstream nodes.

When the message is obtained from its downstream node $n_{j}$ at time $τ$ , where $l_{j} = l_{i} + 1$ , it is then included to the overall data $D_{i}$ . Similarly, the node $n_{j}$ expresses a new item being registered $e_{(i, j)} = (j, l, τ)$ into its data transmission timing table $ε_{i}$ .

When a message is obtained from another node $n_{j}$ at time $τ$ where $l_{j} = l_{i}$ , the new item will be registered by the node $e_{(i, j)} = (j, l, τ)$ into its data transmission timing table $ε_{i}$ .

(d) Message transmission.

Following the phase $ϕ_{i} = T$ , a broadcast packet will be transported by the node $n_{i}$ to the surrounding nodes in the wakeup state including those within the transmission range. The phase $ϕ_{i}$ returns to zero and time $τ$ is recorded as the transmission time $τ_{i}^{trans}$ when T is achieved. The CSMA/CA technique is utilized amid the process of message transmission.

(e) Message reception from upstream nodes.

The node $n_{i}$ stays in the wakeup state for $T^{\max}$ in the process of returning to zero. In the process of obtaining the message from its upstream node $n_{i}$ at time t where $l_{j} < l_{i}$ , the node $n_{i}$ will adapt to its level as $l_{i} = l_{j} + 1$ , thus causing its phase to change along with equation (7).

3. Receiving operation.

While waiting for the message to arrive and in fulfilling the condition of $l_{i} = l_{j} + 1$ , the transmission timing table $ε_{i}$ of node $n_{i}$ is revised. The first step involves the process of applying the transmission timing information of the received message $F_{j}$ in determining the message transmission time $τ_{(j, k)}^{trans} = τ - f_{(j, k)}$ of node $n_{k}$ , whereby $n_{k}$ expresses a one- or two-hop neighbor of node $n_{i}$ . When the new registry of node $n_{k}$ is absent in the message transmission timing table $ε_{i}$ , it will be automatically created by node $n_{i}$ $e_{(i, k)} = (k, l_{i}, τ_{(j, k)}^{trans})$ in the table. Contrastingly, when a new item is present for node $n_{k}$ in its message transmission timing table $ε_{i}$ and when the condition $τ_{(i, k)}^{trans} > τ_{(j, k)}^{trans}$ is fulfilled, node $n_{i}$ overwrites the entry as $e_{(i, k)} = (k, l_{i}, τ_{(j, k)}^{trans})$ . When phase $ϕ_{i}$ reaches $T^{\max}$ and node $n_{i}$ is accelerated, the node will revise its offset $T_{i}$ . If it is not conducted in the right manner, then the node has to wait for a stimulation, which can only be achieved after a message from an upstream node is obtained. Node $n_{i}$ is associated with a $τ_{i}^{Prev}$ and $τ_{i}^{next}$ as follows

τ_{i}^{prev} = τ ε T_{i, t}^{\max} < τ_{i}^{tran s^{t}}

(17)

τ_{i}^{next} = τ ε T_{i, t}^{\min} < τ_{i}^{tran s^{t}}

(18)

where $T_{i}$ expresses the set of time estimated for transmission of messages in $ε_{i}$ . Assuming that $τ_{i}^{Prev}$ or $τ_{i}^{next}$ are not achieved, it is considered to be zero. The offset $τ_{i}$ is then amended by the node $n_{i}$ based on the relation. Alternatively, use equation (19)

T_{i} = (1 - α) * T_{i} + α * T_{i}^{mid}

(19)

where

τ_{i}^{mid} = {\begin{matrix} \begin{matrix} \frac{T_{i}^{prev} + T_{i}^{next}}{2} & if τ_{i}^{next} > τ_{i}^{prev} > 0 \\ \frac{T_{i}^{prev}}{2} & if τ_{i}^{next} = 0, τ_{i}^{prev} > 0 \\ T^{\max} otherwise, \end{matrix} & \begin{matrix} T_{i}^{prev} = τ_{i}^{stim} - τ_{i}^{prev} \\ T_{i}^{next} = τ_{i}^{stim} - τ_{i}^{next} \end{matrix} \end{matrix}

Performance evaluation of RTWPCO

In this study, an observation and an evaluation of the simulation experimental results were conducted for the purpose of assessing the performance under numerous WSN environments. According to the experimental results obtained, our proposed mechanism plays a crucial role in improving energy efficiency as well as data gathering. These particular improvements achieved by the RTWPCO mechanism are resulted from the process of adapting to the quality evaluation of the functions, thus further assisting the process of improving the accuracy of the firefly based on the formula of the PCO.²¹ In verifying the RTWPCO mechanism, the results of the experiments were compared to those obtained through the TWPCO mechanism⁸ and PCO mechanism. In the following part, the experimental setup and the evaluation of the accuracy and results of the RTWPCO mechanism are further clarified.

Simulation environment

The suggested RTWPCO mechanism was replicated by setting up various sensor nodes. The reproduction of the experiments was carried out with a laptop (Intel CoreTM $i 5 - 2410 M$ , CPU at $2.30 GHz$ and $2.30 GHz$ , 4GB RAM, Windows 7) by utilizing a simulator implemented in Java. In this manner, whenever a node receives transmission messages from two different nodes at the same time, the recipient node would not be able to receive both messages. The data gathering ratio cycle, T was set to 1 s, while the highest substitute, $t^{\max}$ , was set to 0.1 s. According to equations (7) and (10), $PR C_{a}$ , $PR C_{b}$ , and $α$ were set to $0.01$ , $0.5$ , and $0.5$ , respectively. The introductory phase readings were unsystematically arranged. The sizes of the message header and the individual sensor data were set to 2B, while message transmission control information was fixed to 1B. The CSMA/CA parameters included a backoff time slot of 1 ms with a backoff of 4 ms. Following it, the results of the RTWPCO mechanism were compared to those of TWPCO⁸ and PCO. Moreover, in order to maintain the objectivity, the TWPCO and PCO were re-implemented. This involved using the Java programming language so that both methods could operate or run using similar simulators together with constant software and hardware platforms. As presented in Table 3, a concise analysis of other parameters used in the simulation is further explained.

Table 3.

Parameters setup.

Parameters	Values
Parameters	Scenario I	Scenario II
Channel frequency (GHz)	2.4	2.4
Number of nodes	10, 20, 30, 40, 50, 60, 70, 80, 90, 100	30
MIN_TIME_STEP	0.00001	0.00001
Packet data size (bits)	16	8, 16, 40, 80, 160, 400, 800
Energy model	MICAz	MICAz
Data rate (kbps)	250	250
Transmit power (µW)	52.2	52.2
Receive power (µW)	59.1	59.1
Idle power (µW)	60	60
Sleep power (µW)	3	3
INITIAL_ENERGY (Joules)	100	100

Evaluation of the performance metrics

This section is concerned with the calculation and benchmarking of the performance metrics of the proposed mechanism using the similar metrics for other mechanisms. Similar to the other mechanisms of energy efficient, the performance of our RTWPCO mechanism was measured in relation to two significant effects: the effect of the number of sensor nodes and the effect of data packet size. Our mechanism mainly aims at updating the system performance through the reduction of the consumed energy and extension of the network lifespan. The metrics of the performance discussed are the data gathering ratio and the energy efficiency ratio.

Data gathering ratio

The data gathering ratio is described as the ratio of the overall sensor node data collected at the sink per cycle. This ratio is calculated as follows

Data Gathering = \frac{\sum_{i = 1}^{n} Topc k_{sink}}{SNC}

(20)

where n is described as the number of sensor nodes in the network, while $Topc k_{sink}$ refers to the total number of packets which are sent to the sink node for each sensor node i. It is important to note that SNC is the sensor nodes for each cycle.

Energy efficiency ratio

The energy efficiency ratio refers to the ratio of the overall consumption of energy to the number of packets, which are accepted by the core node. This particular ratio is measured in the following manner

Energy Efficiency = \frac{\sum_{i = 1}^{n} ToEnc (i)}{Topc k_{sink}}

(21)

where n expresses the number of sensor nodes in the network, ToEnc(i) is the total energy consumption for each sensor node i, and $Topc k_{sink}$ is described as the total number of packets sent to the sink node.

Discussion of experimental results

The main preliminary outcomes of this study obtained by comparing the efficiency of the proposed RTWPCO mechanism to the TWPCO and PCO mechanisms are reviewed.⁸ The outcomes are centered on two main facets: increasing the number of nodes from 10 to 100 sensor nodes and increasing the data packet size from 8 to 800 bits in the transmission state due to the packet transmission based application requirements as shown in Figures 7 –10. In this case, the proposed formula presents better behaviors in terms of data collection and energy conservation compared to the TWPCO and PCO formulas. With the results shown, the application of mathematical and biological models of the RTWPCO mechanism to WSN provided evidence of the suitability and strength of this mechanism.

Figure 7.

Energy efficiency ratio based on number of nodes in the transmission state of RTWPCO mechanism.

Figure 8.

Data gathering ratio based on number of nodes in the transmission state of RTWPCO mechanism.

Figure 9.

Energy efficiency ratio based on data packet size in the transmission state of RTWPCO mechanism.

Figure 10.

Data gathering ratio based on data packet size in the transmission state of RTWPCO mechanism.

In Figure 7, the TWPCO model at nodes 90 and 100 consumed 4.13808 and 5.27089 (m Joule) of energy, respectively, whereas the recommended RTWPCO mechanism only dominated about 1.78087 and 2.30856 (m Joule). Hence, it is demonstrated that the suggested model reduced the energy efficiency up to 48% compared to the TWPCO and PCO models for every node. As a result, the ratio of consumed energy achieved through the RTWPCO mechanism is approximately less than the TWPCO and PCO mechanisms. This particular outcome acts as one of the inputs to the proposed mechanism reported in this study. Meanwhile, the improvement is the result of various categories of the levels of nodes.

Similarly, Figure 8 shows that the collection of data reduced or declined, while the amount of sensor nodes became greater in both mechanisms. In the process of increasing the number of sensor nodes from 10 to 100 nodes, the ratio of data collection of the RTWPCO mechanism tended to reduce when the number of sensor nodes reached above 40, which is believed to be the effect of the simulation results. When the RTWPCO, TWPCO, and PCO mechanisms are evaluated, the ratio of data collection of the RTWPCO mechanism is 70% greater due to the beneficial methods provided by the proposed mechanism. Furthermore, the ratio of data collection of the RTWPCO mechanism is approximately 100% when the number of sensor nodes was below 40, which is considered as one of the contributions of the proposed mechanism resulted by the classifications of the levels of nodes. By contrast, the packet data size of the RTWPCO mechanism seemed to be greater compared to the TWPCO and PCO mechanisms due to the presence of broadcast timing information of the packet data.

The data collection ratio and energy efficiency ratio based on the number of sensor nodes in the transmission state in the RTWPCO mechanism obtained superior results to counteract high energy and lost data based on the application requirements of WSN. This implies that our proposed mechanism tolerates the increasing number of received nodes.

As presented in Figure 9, the results of the suggested RTWPCO mechanism are compared to the TWPCO and PCO mechanisms, particularly on the total energy usage of the nodes according to the data packet size when the number of sensor node is equal to 30. It is clear that the data packet size of sensor 100B seemed to consume a huge amount of energy compared to the rest such as 50B during the transmission, thus causing faster energy drain. Meanwhile, the suggested RTWPCO mechanism spread the large data packet size to the high energy level (EL) nodes instead of the critical nodes. It makes the amount of energy used decreased by the data packet size in sensors 50B and 100B up to 15% and 55%, respectively. Therefore, it can be concluded that the consumed energy ratio of the RTWPCO mechanism is approximately less than the TWPCO and PCO for all data packet sizes due to the presence of greater amount of received packets in the TWPCO and PCO mechanisms that makes it more effective.

As presented in Figure 10, the outcomes of the suggested RTWPCO mechanism are compared to the TWPCO and PCO mechanisms in terms of the overall data collection of the nodes based on the data packet size, particularly when the number of sensor node is equal to 30. Apart from that, it was also observed that the data gathering reduced in both mechanisms when the data packet size was bigger. With the increase in the data packet size of sensor nodes from 8 to 800 bits, the scale of data gathering of the RTWPCO mechanism was found to be decreasing when the data packet size was greater than 2B. When the RTWPCO, TWPCO, and PCO mechanisms were evaluated, the data gathering ratio of the RTWPCO mechanism counted for 68% more than the TWPCO and PCO mechanisms. Moreover, the data gathering ratio of the RTWPCO mechanism reached approximately 100% when the data packet size was below 2B. Based on the results, the RTWPCO mechanism in data gathering ratio was as equal as TWPCO mechanism when exceeded 100B. This equal ratio of data gathering achieved by the two mechanisms is attributed to the limited memory of sensor nodes.

Conclusion

This article presented an RTWPCO mechanism, which is a self-organizing technique for energy-efficient sensor networks adapted from firefly synchronization. The designed RTWPCO classified into two stages. First stage involved the use of phase-locking traveling wave pulse-coupled, which requires the sensor node to transmit the data packet to the base station during transmission state. The second stage is using random method on anti-phase of the PCO model that uses the TDMA protocol in order to minimize the high power utilization in the network to get better the data gathering of the sensor nodes during data transmitting. From the simulation results, the proposed RTWPCO showed a better performance than the TWPCO and PCO, which reduced the consumption of power inside the network resulting to expand the lifetime of the whole WSN. Moreover, this mechanism decreasing the number of the dropped data led to increasing the data gathering ratio, thus enabling the proposed model to show higher reliability and energy efficiency in WSN.

Footnotes

Handling Editor: Seokcheon Lee

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Fundamental Research Grant mechanism (FRGS) UPM-FRGS-08-02-13-1364FR.

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Random traveling wave pulse-coupled oscillator algorithm of energy-efficient wireless sensor networks

Abstract

Keywords

Introduction

Related works

Proposed algorithm

TWPCO algorithm

Randomization-based algorithm

RTWPCO mechanism design

Performance evaluation of RTWPCO

Simulation environment

Evaluation of the performance metrics

Data gathering ratio

Energy efficiency ratio

Discussion of experimental results

Conclusion

Footnotes

Declaration of conflicting interests

Funding

References