scholarly journals An On-Demand Charging for Connected Target Coverage in WRSNs Using Fuzzy Logic and Q-Learning

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5520
Author(s):  
Phi Le Nguyen ◽  
Van Quan La ◽  
Anh Duy Nguyen ◽  
Thanh Hung Nguyen ◽  
Kien Nguyen
Keyword(s):  
Fuzzy Logic ◽  
Network Lifetime ◽  
Sensor Nodes ◽  
Target Number ◽  
Q Learning ◽  
On Demand ◽  
Charging Time ◽  
Sufficient Power ◽  
Wireless Rechargeable Sensor Networks ◽  
Wireless Medium

In wireless rechargeable sensor networks (WRSNs), a mobile charger (MC) moves around to compensate for sensor nodes’ energy via a wireless medium. In such a context, designing a charging strategy that optimally prolongs the network lifetime is challenging. This work aims to solve the challenges by introducing a novel, on-demand charging algorithm for MC that attempts to maximize the network lifetime, where the term “network lifetime” is defined by the interval from when the network starts till the first target is not monitored by any sensor. The algorithm, named Fuzzy Q-charging, optimizes both the time and location in which the MC performs its charging tasks. Fuzzy Q-charging uses Fuzzy logic to determine the optimal charging-energy amounts for sensors. From that, we propose a method to find the optimal charging time at each charging location. Fuzzy Q-charging leverages Q-learning to determine the next charging location for maximizing the network lifetime. To this end, Q-charging prioritizes the sensor nodes following their roles and selects a suitable charging location where MC provides sufficient power for the prioritized sensors. We have extensively evaluated the effectiveness of Fuzzy Q-charging in comparison to the related works. The evaluation results show that Fuzzy Q-charging outperforms the others. First, Fuzzy Q-charging can guarantee an infinite lifetime in the WSRNs, which have a sufficient large sensor number or a commensurate target number. Second, in other cases, Fuzzy Q-charging can extend the time until the first target is not monitored by 6.8 times on average and 33.9 times in the best case, compared to existing algorithms.

scholarly journals Collaborative Wireless Power Transfer in Wireless Rechargeable Sensor Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Azka Amin ◽  
Xi-Hua Liu ◽  
Muhammad Asim Saleem ◽  
Shagufta Henna ◽  
Taseer-ul Islam ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Network Lifetime ◽  
Wireless Power Transfer ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Power Transfer ◽  
Wireless Power ◽  
Transfer Energy ◽  
On Demand ◽  
Wireless Rechargeable Sensor Networks

Wireless power transfer techniques to transfer energy have been widely adopted by wireless rechargeable sensor networks (WRSNs). These techniques are aimed at increasing network lifetime by transferring power to end devices. Under these wireless techniques, the incurred charging latency to replenish the sensor nodes is considered as one of the major issues in wireless sensor networks (WSNs). Existing recharging schemes rely on rigid recharging schedules to recharge a WSN deployment using a single global charger. Although these schemes charge devices, they are not on-demand and incur higher charging latency affecting the lifetime of a WSN. This paper proposes a collaborative recharging technique to offload recharging workload to local chargers. Experiment results reveal that the proposed scheme maximizes average network lifetime and has better average charging throughput and charging latency compared to a global charger-based recharging.

scholarly journals A Fuzzy Logic-Based On-Demand Charging Algorithm for Wireless Rechargeable Sensor Networks With Multiple Chargers

2020 ◽  
Author(s):  
Abhinav Tomar
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Performance Metrics ◽  
Network Partitioning ◽  
On Demand ◽  
Network Parameters ◽  
Comparison Results ◽  
Wireless Rechargeable Sensor Networks ◽  
Joint Consideration

Mobile chargers have greatly promoted the wireless rechargeable sensor networks (WRSNs). While most recent works have focused on recharging the WRSNs in an on-demand fashion, little attention has been paid on joint consideration of multiple mobile chargers (MCs) and multi-node energy transfer for determining the charging schedule of energy-hungry nodes. Moreover, most of the schemes leave out the contemplation of multiple network parameters while making scheduling decisions and even they overlook<br>the issue of ill-timed charging response to the nodes with uneven energy consumption rates. In this paper, we address the aforesaid issues together and propose a novel scheduling scheme for on-demand charging in WRSNs. We first present an efficient network partitioning method for distributing the MCs so as to fairly balance their workload. We next adopt the fuzzy logic which blends various network parameters for determining the charging schedule of the MCs. We also formulate an expression to determine the charging threshold for the nodes that vary depending on their energy consumption rate. Extensive simulations are conducted to demonstrate the effectiveness and competitiveness of our scheme. The comparison results reveal that the proposed scheme improves charging<br>performance compared to the state-of-the-art schemes with respect to various performance metrics.<br>

scholarly journals An Area Coverage Scheme Based on Fuzzy Logic and Shuffled Frog-Leaping Algorithm (SFLA) in Heterogeneous Wireless Sensor Networks

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2251
Author(s):  
Amir Masoud Rahmani ◽  
Saqib Ali ◽  
Mohammad Sadegh Yousefpoor ◽  
Efat Yousefpoor ◽  
Rizwan Ali Naqvi ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Fuzzy Logic ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Sensor Node ◽  
Area Coverage ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Shuffled Frog Leaping Algorithm ◽  
Shuffled Frog Leaping

Coverage is a fundamental issue in wireless sensor networks (WSNs). It plays a important role in network efficiency and performance. When sensor nodes are randomly scattered in the network environment, an ON/OFF scheduling mechanism can be designed for these nodes to ensure network coverage and increase the network lifetime. In this paper, we propose an appropriate and optimal area coverage method. The proposed area coverage scheme includes four phases: (1) Calculating the overlap between the sensing ranges of sensor nodes in the network. In this phase, we present a novel, distributed, and efficient method based on the digital matrix so that each sensor node can estimate the overlap between its sensing range and other neighboring nodes. (2) Designing a fuzzy scheduling mechanism. In this phase, an ON/OFF scheduling mechanism is designed using fuzzy logic. In this fuzzy system, if a sensor node has a high energy level, a low distance to the base station, and a low overlap between its sensing range and other neighboring nodes, then this node will be in the ON state for more time. (3) Predicting the node replacement time. In this phase, we seek to provide a suitable method to estimate the death time of sensor nodes and prevent possible holes in the network, and thus the data transmission process is not disturbed. (4) Reconstructing and covering the holes created in the network. In this phase, the goal is to find the best replacement strategy of mobile nodes to maximize the coverage rate and minimize the number of mobile sensor nodes used for covering the hole. For this purpose, we apply the shuffled frog-leaping algorithm (SFLA) and propose an appropriate multi-objective fitness function. To evaluate the performance of the proposed scheme, we simulate it using NS2 simulator and compare our scheme with three methods, including CCM-RL, CCA, and PCLA. The simulation results show that our proposed scheme outperformed the other methods in terms of the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime.

scholarly journals Distributed fuzzy logic based cluster head election scheme (DFLCHES) for prolonging the lifetime of the wireless sensor network

2017 ◽  
Vol 7 (1.5) ◽  
pp. 111
Author(s):  
S. Ramakrishnan ◽  
S. Prayla Shyry
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Significant Feature ◽  
Cluster Head Election ◽  
Wide Range

Wireless sensor networks (WSNs) is considered as the predominant technology due to their high suitability and adaptability that makes it possible to be deployed in wide range of applications like civil and military domain. But energy-constraint is the significant feature that needs to be addressed for sensor networks since energy drain of sensor nodes affects network lifetime, stability and co-operation of sensor nodes in the event of enforce reliable data dissemination. Cluster head election has to been performed periodically in order to handle energy balance for facilitating reliable packet delivery. Most of the cluster head election schemes of the literature elect a node as cluster head either randomly or by elucidating their stochastic probabilities. Hence a Distributed Fuzzy Logic based Cluster Head Election Scheme (DFLCHES) that discriminates and discards packets from the sensor nodes that has the least probability of being elected as cluster head is proposed. DFLCHES utilizes five significant parameters such as trust, energy, node density, hop count and centrality measure for quantifying the probability of cluster head election. This DFLCHES is run on each neighbor nodes of the cluster members to facilitate the action of discrimination. DFLCHES also balances the energy consumption of the cluster members during transmission as it discards packets from ineligible nodes. Further the action of cluster head election has to be optimized periodically for reducing and balancing energy consumption for prolonging the network lifetime. In DFLCHES, the process of optimizing cluster head depends on the incorporation of the concept of Genetic algorithms for enabling and ensuring reliable routing.

scholarly journals A Complete Feasible and Nodes-Grouped Scheduling Algorithm for Wireless Rechargeable Sensor Networks in Tunnels

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3410 ◽  
Author(s):  
Xiaoming Liu ◽  
Yu Guo ◽  
Wen Li ◽  
Min Hua ◽  
Enjie Ding
Keyword(s):  
Sensor Networks ◽  
State Of The Art ◽  
Scheduling Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Distribution Problem ◽  
Design Constraint ◽  
Charging Time ◽  
Wireless Rechargeable Sensor Networks ◽  
Charging Strategy

Limited energy in each node is the major design constraint in wireless sensor networks (WSNs), especially in mine tunnel scenario where the WSNs are required to work perpetually. To overcome this limit, wireless rechargeable sensor networks (WRSNs) have been proposed and studied extensively over the last few years. To keep the sensor nodes working perpetually, one fundamental question is how to design the charging scheme. Considering the special tunnel scenario, this paper proposes a Complete Feasible Charging Strategy (CFCS) to ensure the whole WRSNs is working perpetually. We divide the whole WRSN into several subnetworks and use several mobile chargers (MCs) to charge every subnetwork periodically and orderly. For a subnetwork, we formulate the main problem as a charging time distribution problem. A series of theorems are deduced to restrict the charging configurations, and a group nodes mechanism is proposed to expand the scale of the WRSNs. Finally, we conduct extensive simulations to evaluate the performance of the proposed algorithms. The results demonstrate which of the CFCS boundary theorems is correct and that our proposed CFCS can keep the WRSNs working perpetually. Furthermore, our Nodes-Grouped mechanism can support more nodes in WRSN compared to the state-of-the-art baseline methods.

scholarly journals Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
E. Golden Julie ◽  
S. Tamil Selvi
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Energy Consumption ◽  
Network Lifetime ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Aware ◽  
Cluster Heads ◽  
Clustering Protocol ◽  
Neuro Fuzzy

Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.

scholarly journals Mobile Sink in Fuzzy Logic and Meta-heuristic Firefly Algorithm based Routing Scheme to Extend Network Lifetime of WSN (MSFLMFLA)

2021 ◽  
Author(s):  
Pranali Rajendea Navghare ◽  
Sudhakar Pandey ◽  
Deepika Agrawal
Keyword(s):  
Fuzzy Logic ◽  
Network Lifetime ◽  
Firefly Algorithm ◽  
Mobile Sink ◽  
Residual Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Network Clustering ◽  
Routing Scheme

Abstract Nowadays, wireless sensor network (WSN) improves people's lives by assisting them with a variety of applications. The major challenge in WSN is consumption of power as well as lifetime of the network. Clustering is the important method for saving energy in WSN because the separation of the sender and the receiver is related to transmission energy. In this paper we used fuzzy rules for clustering. Wireless sensor networks are susceptible to energy holes, in which sensors nearer to a static sink rapidly lose energy. To solve this problem and extend network life we used a mobile sink (MS). In this paper, a customized mobile sink node called the mobile data sender (MDS) has been tried to introduce for collecting data from the sensors by going to visit every node and then going to send this to the base station. This paper proposes nature inspired heuristic discrete firefly algorithm to optimal way accumulate information from sensor nodes in order to decrease the path travelled by the MDS while doing the tour. So, in this paper, we consider Mobile Sink in Fuzzy Logic and Meta-heuristic Firefly Algorithm based routing scheme to extend network lifetime of WSN (MSFLMFLA). Here we compare throughput and network lifetime with the static sink in FLMFLA and EHR-DC and LEACH protocol. The results of simulation shows that the MSFLMFLA increases the throughput, residual energy and also increases the data received by the sink.

scholarly journals LBCSM: Load Balanced Constant Scheduling Model for Node Recharging in Wireless Rechargeable Sensor Networks using Multiple Mobile Charging Devices

2019 ◽  
Vol 8 (12) ◽  
pp. 3543-3550
Keyword(s):  
Sensor Networks ◽  
Network Lifetime ◽  
Large Scale ◽  
Sensor Nodes ◽  
Small Scale ◽  
Resource Limitations ◽  
Wireless Rechargeable Sensor Networks ◽  
Battery Capacity ◽  
Multiple Charging ◽  
Load Balanced

In large-scale Wireless Rechargeable Sensor Networks (WRSNs), limited battery capacity of nodes may reduce the network longevity. For enhancing the network lifetime, the nodes in the network can recharged periodically based on their operational executions. The rechargeable sensor nodes in the network are replenished using external sources. Using single charging device can be feasible only for small scale WSNs, whereas in managing large scale wireless sensor networks, multiple charging devices are to be modelled for efficiently recharging the sensor nodes, since single devices are having energy constraints to recharge more number of nodes. On focussing those issues, this paper contributes on developing a new model called Load Balanced Constant Scheduling (LBCS) for the replenishment of the sensor nodes. Moreover, multiple Mobile Charging Devices (MCDs) are used here for recharging the sensor nodes effectively, without facing resource limitations. In this model, constant and time based charge scheduling approach and charging route for MCD has been frame optimally. The scheduling mode focuses on a concrete classification procedure for avoiding needless visits of nodes having adequate energy. Providing further improvement in schedule based node replenishment, algorithm for Charging Route Definition (CRD) is also developed in this work. For evidencing the efficiency of the proposed model, the work is simulated and evaluated. The simulation results are compared with some existing models based on the network lifetime, time taken for recharge and efficiency.

Fuzzy Logic-Based Cluster Heads Percentage Calculation for Improving the Performance of the LEACH Protocol

2015 ◽  
Vol 4 (4) ◽  
pp. 100-118 ◽  
Author(s):  
Omar Banimelhem ◽  
Eyad Taqieddin ◽  
Moad Y. Mowafi ◽  
Fahed Awad ◽  
Feda' Al-Ma'aqbeh
Keyword(s):  
Fuzzy Logic ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Base Station ◽  
Sensor Nodes ◽  
Number Of Clusters ◽  
Network Characteristics ◽  
Clustering Protocol ◽  
Proper Number ◽  
Leach Protocol

In wireless sensor networks, cluster-based routing was proven to be the most energy-efficient strategy to deal with the scaling problem. In addition, selecting the proper number of clusters is a critical decision that can impose a significant impact on the energy consumption and the network lifetime. This paper presents FL-LEACH, a variant of the well-known LEACH clustering protocol, which attempts to relax the stringent strategy of determining the number of clusters used by LEACH via fuzzy logic decision-making scheme. This relates the number of clusters to a number of network characteristics such as the number of sensor nodes, the area of the sensing field, and the location of the base station. The performance of FL-LEACH was evaluated via simulation and was compared against LEACH using standard metrics such as network lifetime and remaining network energy. The results depicted that the proposed approach has the potential to substantially conserve the sensor node energy and extend lifetime of the network.

scholarly journals A Joint Energy Replenishment and Data Collection Strategy in Heterogeneous Wireless Rechargeable Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2930
Author(s):  
Mengqiu Tian ◽  
Wanguo Jiao ◽  
Yaqian Chen
Keyword(s):  
Sensor Networks ◽  
Data Collection ◽  
Network Lifetime ◽  
Selection Strategy ◽  
Data Loss ◽  
Remote Areas ◽  
Charging Time ◽  
Data Collection Strategy ◽  
Wireless Rechargeable Sensor Networks ◽  
Energy Replenishment

In wireless rechargeable sensor networks, mobile vehicles (MVs) combining energy replenishment and data collection are studied extensively. To reduce data overflow, most recent work has utilized more vehicles to assist the MV to collect buffered data. However, the practical network environment and the limitations of the vehicle in the data collection are not considered. UAV-enabled data collection is immune to complex road environments in remote areas and has higher speed and less traveling cost, which can overcome the lack of the vehicle in data collection. In this paper, a novel framework joining the MV and UAV is proposed to prolong the network lifetime and reduce data overflow. The network lifetime is correlated with the charging order; therefore, we first propose a charging algorithm to find the optimal charging order. During the charging period of the MV, the charging time may be longer than the collecting time. An optimal selection strategy of neighboring clusters, which could send data to the MV, was found to reduce data overflow. Then, to further reduce data overflow, an algorithm is also proposed to schedule the UAV to assist the MV to collect buffered data. Finally, simulation results verified that the proposed algorithms can maximize network lifetime and minimize the data loss simultaneously.

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