In the last decade, energy harvesting wireless sensor network (EHWSN) has been well developed. By harvesting energy from the surrounding environment, sensors in EHWSN remove the energy constraint and have an unlimited lifetime in theory. The long-lasting character makes EHWSN suitable for Industry 4.0 applications that usually need sensors to monitor the machine state and detect errors continuously. Most wireless sensor network protocols have become inefficient in EHWSN due to neglecting the energy harvesting property. In this paper, we propose CPEH, which is a clustering protocol specially designed for the EHWSN. CPEH considers the diversity of the energy harvesting ability among sensors in both cluster formation and intercluster communication. It takes the node’s information such as local energy state, local density, and remote degree into account and uses fuzzy logic to conduct the cluster head selection and cluster size allocation. Meanwhile, the Ant Colony Optimization (ACO) as a reinforcement learning strategy is utilized by CPEH to discover a highly efficient intercluster routing between cluster heads and the base station. Furthermore, to avoid cluster dormancy, CPEH introduces the Cluster Head Relay (CHR) strategy to allow the proper cluster member to undertake the cluster head that is energy depletion. We make a detailed simulation of CPEH with some famous clustering protocols under different network scenarios. The result shows that CPEH can effectively improve the network throughput and delivery ratio than others as well as successfully solve the cluster dormancy problem.
The automatic and unmanned operation is one of the most prominent characters in the Industry 4.0 era [
EHWSN typically consists of a base station (BS) and a larger number of sensors. Sensors generate their sensing data shortly and periodically to guarantee the monitoring accuracy and quickly respond to the error. EHWSN shall handle the heavy generated data timely and transmit all the information to the BS, which is high energy costing. However, in the majority of scenarios, the energy harvesting rates of sensors are limited and individual [
Clustering protocols work in rounds. Every round, the cluster head (CH) will collect the data from its cluster members (CMs) and upload the handled data to the BS. Since CMs are usually close to each other, the data they generate may have high correlations. The CH will take local data fusion to remove the redundancy, thus reducing the data quantity. The protocol can balance the workload over different sensors and utilize the network harvested energy properly by reconstructing the clusters, whereas despite the advantages, at the present stage, most clustering protocols are designed for traditional WSN. There will be some challenges for them to work in the EHWSN condition. First, the primary design purpose of current clustering protocols is to extend the lifetime of WSN. Nevertheless, the leading aspiration of EHWSN has turned to maximize the network performance under the energy harvesting restrictions. Second, both the cluster formation and the intercluster routing of current clustering protocols do not consider the different energy harvesting rates between sensors, resulting in a performance decline. Last, in EHWSN, the sensor may deplete the energy and return to the sleep state when serving as a CH. Current clustering protocols cannot respond to this condition, causing the cluster dormancy phenomenon.
In this paper, we propose CPEH, which is a clustering protocol designed explicitly for EHWSN. CPEH cares about the diversity of the energy harvesting ability among sensors in both cluster formation and intercluster communication. In the cluster formation process, CPEH considers the sensor’s local energy state, local node density, and remote degree. It then uses fuzzy logic to conduct the cluster head competition distributedly. In the intercluster routing discovery, the Ant Colony Optimization (ACO) is utilized to help the BS discover the proper paths for different CHs. To avoid cluster dormancy, CPEH introduces the Cluster Head Relay (CHR) strategy, which will choose the appropriate CM to undertake the sleep CH. Compared with other clustering protocols, the simulation result proves that CPEH will effectively improve the network delivery ratio and elevate the network throughput. Meanwhile, the CHR strategy can successfully maintain the cluster working normally when the initial CH runs out of its energy.
The rest of this paper is organized as follows. Section
Clustering protocols have been well studied in the last decade. The Low Energy Adaptive Clustering Hierarchy (LEACH) [
It has been proved that the CH selection is typically an NP hard problem. Recently, with the fast development in computational intelligence, many researchers try to use the metaheuristic approach or fuzzy logic technology to resolve the CH selection. In [
All the clustering protocols introduced above are designed for the traditional WSN. Currently, there are only several clustering protocols for EHWSN. In [
As shown in Figure The BS is energy unlimited and always has sufficient knowledge of the entire network. Sensors are randomly placed throughout the entire network. All the sensors have the same initial energy and maximum energy capacity. However, the energy harvesting rates among sensors are different. Both the BS and the sensor are motionless once deployed All the sensors can communicate with the BS directly and have the ability to adjust their transmitting power according to the distance All the network links are symmetric. Sensors can estimate the communication distance based on the received signal power The network is homogeneous. The data generated by sensors in the same cluster have a relatively high correlation. The cluster head can make the local data aggregation to reduce the redundancy. The aggregation ratio is the same in different clusters
The primary network structure of the CPEH. CPEH is a multihop intercluster communication-based clustering protocol.
For energy harvesting, we assume that sensors can continuously harvest energy from their surrounding environments at a fixed speed. However, due to the diversity of location and hardware, the energy harvesting rates differ between sensors.
As shown in Figure
The radio energy dissipation model used in this paper.
Notice that
Figure
The basic communication timeline of CPEH. CPEH works in rounds. Every round consists of a setup phase and a working phase.
Owing to the exchanging of control messages, the setup phase is relatively energy costing. To reduce the communication overhead, CPEH combines several frames into one working phase. In each frame, based on the TDMA schedule, the awake CM will transmit the sensing data to the CH in its associated slot and keep silent in others to save energy and avoid the collision. Once the CH detects the current energy lower than the sleep threshold, it will execute the CHR strategy to find the replacer and broadcast the result to all the awake CMs in the particular slot (Slot CHR). The chosen CM will become the successive CH in the following frames. Notice that if the network burden is heavy, the CHR strategy may be executed several times to maintain the cluster usually. After receiving the data from all the awake CMs, the CH will first aggregate the data and then upload the fusion data to the BS through the multihop routing.
The direct-sequence spread spectrum (DSSS) is utilized to avoid intercluster collision. CPEH assigns a universal spreading code and a unique spreading code to every sensor. The universal spreading code is used to exchange control messages in the setup phase and route packets between CHs in the working phase. On the contrary, once a sensor becomes a CH, it will confirm all the members using its unique spreading code for the intracluster communication. Hence, the adjacent clusters will have different spreading codes, eliminating the collisions among clusters.
Since the harvested energy may not support the continuous work, sensors under CPEH will operate in a sleep-wake mode. Define
The cluster construction of CPEH is based on fuzzy logic. At the beginning of the setup phase, each awake sensor will broadcast a
The fuzzy logic system of CPEH is shown in Figure
The structure of the fuzzy logic system. Sensor inputs the local energy state, remote degree, and the local node density. The system outputs the chance and the size.
Membership function for local energy state.
Membership function for local node degree.
Membership function for remote degree.
The fuzzy logic system has two outputs: chance and size. We give the chance nine fuzzy linguistic variables: Very Low, Low, Rather Low, Low Medium, Medium, High Medium, Rather High, High, and Very High. The corresponding membership functions are shown in Figure
Membership function for remote degree.
Membership function for size.
Once the sensor inputs the crisp values into the fuzzy logic system, the fuzzifier will transform those values to the associated linguistic variables based on the membership functions. Then, the Mamdani [
The detailed fuzzy rules. The fuzzy rule base has 27 different rules.
No. | Input variables | Output variables | |||
---|---|---|---|---|---|
Local energy state | Remote degree | Local node density | Chance | Size | |
1 | Bad | Near | Sparse | Very Low | Very Small |
2 | Bad | Near | Normal | Low | Very Small |
3 | Bad | Near | Dense | Rather Low | Small |
13 | Moderate | Medium | Sparse | Low Medium | Low Medium |
14 | Moderate | Medium | Normal | Medium | Medium |
15 | Moderate | Medium | Dense | High Medium | High Medium |
25 | Good | Far | Sparse | Rather High | Rather Large |
26 | Good | Far | Normal | High | Large |
27 | Good | Far | Dense | Very High | Very Large |
After the fuzzy inference, the awake sensor will then broadcast a
The non-CH sensors may receive more than one
After the cluster construction, the CH will broadcast a TDMA schedule that assigns the slots for its members. In the working phase, sensors will transmit their data in their associated slot and keep silent in others to save energy and avoid the collision. CPEH combines several frames into one round. Hence, awake sensors may deplete their energy and turn to sleep in the middle of the current round. If the depleted sensor is the CM, it will just give up its slot, whereas if the depleted sensor is the CH, the CHR strategy will be executed, which we will discuss later. Notice the sleep sensors at the setup phase may wake up halfway in the working round. However, those sensors will keep silent until the round ending since there is no slot designed.
Cluster Construction Process for sensor 1: At the beginning of the Round 2: 3: Broadcast a 4: 5: On receiving the 6: add 7: calculate its local energy state and local node density 8: infer its chance and size base on the fuzzy logic system 9: Broadcast a 10: 11: 12: broadcast a 13: 14: on receiving a 15: 16: broadcast a 17: 18: on receiving a 19: 20: remove sensor 21: 22: 23: 24: On receiving the 25: 26: Accept the sensor 27: 28: 29: 30: Sending the 31: On receiving the 32: On receiving the 33: 34: 35: 36:
Since the cluster construction is conducted fully distributedly by sensors, we analyze the control message complexity, reflecting the overhead caused by the message exchanging. Assuming there are
An effective intercluster routing protocol can significantly decrease network energy consumption and improve network performance. However, most clustering protocols only pay attention to the cluster construction but neglect the impact of intercluster routing on the final system performance. The CPEH considers both the energy cost and the energy state in the routing discovery and decides the final result in the view of the entire network. As the basic principle of ACO is the positive feedback mechanism [
The routing discovery is based on iterations, which is both energy costing and complex. We assign this work to the BS, which can reduce the overhead of CHs and get the result quickly. After the cluster construction, each CH will transmit a control packet to the BS, containing its current residual energy and energy harvesting rate. The BS will calculate the energy states of each CH
The BS place
When the ant reaches the BS, the BS will get the route information that can be represented by
Based on the objective function, the BS can find the best route in one iteration. CPEH then uses the max–min ant system model [
The pheromone is updated according to (
The BS will repeat those steps until the iteration time is reached. The best route information will be broadcasted to all the CHs at the end of the setup phase.
The fuzzy logic system of CPEH is aimed at selecting the sensor with a good local energy state to become the CH. However, the heavy workload may still exhaust the CH’s energy halfway in the working round, especially when the frame number is large. Most clustering protocols cannot handle this condition, causing the whole cluster to be silent until the end of the working round and wreaking the sensing accuracy. To solve this, CPEH utilizes the CHR strategy, a simple greedy approach, to help the depleted CH find the proper CM to undertake its job. Once the current CH finds that its energy has fallen below
We believe that the CHR strategy can maintain the cluster’s stability and keep the intercluster routing effectively since only the CMs near the current CH will be the candidates. On the other hand, by choosing the CM with the best energy state among the candidates, the CHR strategy can also better utilize the network energy. The CHR strategy may be executed several times in one working round. It can visibly improve the network performance.
This section compares the performance of CPEH with three popular clustering protocols: LEACH, EAUCF, and MOFCA. We focus on a
To have an intuitive insight into the advantages of CPEH, we consider two different scenarios. Precisely, Scenario 1: the BS is located in the middle of the network, and the harvest rates of sensors range from 25
The sensors and BS deployment case in different scenarios.
We define each round as 0.1 h, and the network total simulation time is 72 h. To test the network performance under different network loads, we change the number of the frames in each round from 50 to 120. Ten different sensor deployments are randomly generated in each scenario to get the average result to eliminate the contingency. The relevant parameters are summarized in Table
The corresponding simulation parameters used in this paper.
Parameters | Values |
---|---|
Network area | [400 m, 400 m] |
Number of sensors | 400 |
Base station location | (200, 200), (200, 400) |
Sensor initial energy | 1 J |
Sensor energy capacity | 2 J |
Number of frames | 50-120 |
Sensor energy harvesting rate | (25 |
Data packet size | 500 byte |
Control packet size | 25 byte |
50 nJ/bit | |
10 pJ/bit/m2 | |
0.0013 pJ/bit/m4 | |
5 nJ/bit/signal | |
87 m | |
Competition radius | |
Impact factor in fuzzy logic | |
Number of ants | 10 |
Number of iterations in ACO | 20 |
Initial pheromone trail density | |
Impact factor in ACO | |
Constant factor | |
Pheromone decay coefficient | |
Number of CHR candidates |
Figure
Network packer delivery ratio in different scenarios.
We illustrate the average network throughput in one round under different clustering protocols in Figure
Average network throughput in one round in different scenarios.
Since the sense is a cooperative work in the homogeneous network, the number of awake nodes will affect the sensing accuracy. We record the average number of awake nodes at every round beginning and summarize the result in Figure
Average awake sensor number.
Figure
Average cluster dormancy ratio.
We summarize the average energy cost of transmitting a data packet under different protocols and show the result in Figure
Average cluster silence ratio.
For EAUCF and MOFCA, when the CH falls into sleep during the working round, the routing topology will be changed. The last-hop and next-hop CHs of the sleeping CH will have to communicate directly, causing the long-range transmission and destroying the energy efficiency. Conversely, the CHR strategy can keep the intercluster routing topology robust and stable, leading to continuous, efficient intercluster communication.
In this paper, we propose CPEH, which is a clustering protocol designed explicitly for EHWSN. CPEH mainly consists of two parts. The first part focuses on cluster construction. We adopt the fuzzy logic system to handle the uncertain nature of EHWSN and construct clusters more appropriately. The second part utilizes the ACO algorithm to optimize the intercluster routing, which can inherently achieve a better path than greedy algorithms used in most clustering protocols. We execute a comprehensive simulation of CPEH with some representative clustering protocols under different network conditions. The result proves that CPEH can always achieve the best performance in network delivery ratio and throughput. Furthermore, the CHR strategy of CPEH can effectively solve the cluster dormancy problem, ensuring the cluster works normally. The advantages of CPEH make it a suitable protocol for Industry 4.0 era applications. For future work, we will extend CPEH to handle the multi-BS condition and consider sensors’ movement.
The authors do not list the raw simulation data in this paper owing to the spatial confined. Readers can get the detailed data by connecting to the corresponding author.
The authors have declared that no competing interests exist concerning this study.
This work was supported in part by the National Natural Science Foundation of China under project contract nos. 61701082, 61701116, 61601093, 61971113, and 61901095, in part by National Key R&D Program under project contract nos. 2018YFB1802102 and 2018AAA0103203, in part by Guangdong Provincial Research and Development Plan in Key Areas under project contract nos. 2019B010141001 and 2019B010142001, in part by Sichuan Provincial Science and Technology Planning Program under project contract nos. 2018HH0034, 2019YFG0418, 2019YFG0120, 2020YFG0039, and 2018JY0246, in part by Ministry of Education China Mobile Fund Program under project contract no. MCM20180104, in part by Yibin Science and Technology Program—Key Projects under project contract nos. 2018ZSF001 and 2019GY001, and in part by Central University Business Fee Program under project contract no. A03019023801224 and Fundamental Research Funds for the Central Universities under Grant ZYGX2019Z022.