Solar-powered Wireless Sensor Networks Research Articles

Plus-profile energy harvested prediction and adaptive energy management for solar-powered wireless sensor networks

Plus-profile energy harvested prediction and adaptive energy management for solar-powered wireless sensor networks

Performance Analysis of Solar Powered Wireless Sensor Network

Performance Analysis of Solar Powered Wireless Sensor Network

Solar Powered Wireless Sensor Network for Water Quality Monitoring and Classification

Water is essential for human being, also for animals and plants. In Indonesia, there are a lot of residential living in the riverbank which have poor water conditions. People frequenty use water from the river for daily activities. To determine the quality of water, samples are usually taken and tested in the laboratory. This method is less efficient in time and also cost. In order to determine and monitor the quality of water, this paper discuss the Wireless Sensor Network (WSN) to monitor the quality of water from a distance with the self powered sensor node. One of the issue in developing the WSN is the energy. Since this is implemented in outdoor, therefore it is possible to use solar panel to produce the energy. In this study three indicators; pH, TDS, and turbidity; were used to determine water quality based on the Indonesian Minister of Health Regulation. The results examine the WSN performance, and also the analysys of the solar energy supply for each sensor node. The WSN successfully works in detect and clasify tha water quality category and display it in the monitoring center or user. The sensors are calibrated and works with tolerable error of sensor reading of 5,1%. The WSN node is embedded with solar panel to supply the energy for node component. Therefore it able to extend the lifetime of the networks devices with renewable energy to implement the Green WSN.

Harvested Energy Prediction Technique for Solar-Powered Wireless Sensor Networks

Solar energy harvesting (EH) is one of the best promising approaches toward perpetual network operation, and it is implemented in various regions of interest (RoIs). However, saving external energy is the essential prime factor in any embedded sensor with finite storage capacity. Generally, the energy conversion rate of the solar system is too fast due to various environmental conditions. Besides, ambient resource energy is noncontrollable, and rechargeable battery only operates in outdoor ecological systems. Frequent environmental fluctuation in their prediction is imperative for initial energy control. Considering the challenges mentioned above, in this article, we propose a modified PROfile energy (Pro-energy) prediction technique to control unnecessary errors in solar-based harvesting systems related to the sensing devices, which estimates the most similar profile-based energy observation in previous time slots. Our proposed method uses prior energy measurements to show future energy status in the respective time slots. Experimental observations on various performance matrices validate that the modified Pro-energy prediction technique exhibits more promising and superior performance than existing EMWA, weather-conditioned moving average (WCMA), and Pro-energy methods.

Integration of Agricultural Wireless Sensor Networks to Web-of-Things Through an Edge-Computing-Enriched WSNs/WoT Gateway

To connect agricultural wireless sensor networks (WSNs) to web services and applications, the agricultural WSNs/WoT gateway is empowered with local data management and network maintenance functions for downstream WSNs in addition to traditional upstream data collection. This work demonstrates a low-cost, highly scalable, rapidly deployable web of things (WoT) gateway with edge computing capabilities. First, an agricultural WSNs/WoT topology is architected, connecting a ZigBee WSNs to the Web for remote monitoring the local environmental and agronomical information, and simultaneously for managing the solar-powered WSNs for a prolonged lifespan according to the instant data scrawled from the cloud. Second, a WSNs/WoT gateway is designed with the hardware platform Raspberry Pi 3, which serves multiple needs for bidirectional information exchange and local WSNs management. Finally, experimentation demonstrates the proposed hardware platform and architecture can perform edge computing, and efficiently realize the up and down transmission and distribution of data stream.

Dual-line data collection scheme for efficient mobile sink operation in solar-powered wireless sensor networks

Solar energy harvesting has been presented as a key solution to alleviate the lifespan problem of conventional battery-based wireless sensor networks, but it does not address the problem of uneven energy consumption across the network. In conventional sensor networks with a stationary sink node, the nodes around the sink node consume more energy than other nodes, which is known as the hotspot problem. In recent studies, mobile sink technology has been proved to be an effective solution to the problem, where a mobile sink visits sensor nodes and collects data. To reduce the energy consumption of the mobile sink, a subset of sensor nodes are designated as anchor nodes to collect data for a certain period, and the mobile sink visits only such anchor nodes. However, even with these approaches, small hotspots can still be formed near those anchor nodes, hence it is crucial to optimize the selection and management of anchor nodes. This study aims to propose a dual line-based anchor node selection scheme tailored to efficiently solve the hotspot problem in mobile sink-based solar-powered wireless sensor networks. The proposed scheme designates the areas, in which some nodes can be chosen as anchor nodes, in the form of two lines. This dual line-based anchor selection algorithm effectively operates for the best use of the solar energy harvested by each node in a balanced manner. The experimental results confirm that the proposed scheme minimizes hotspots in the network, reduces the blackout time of nodes, and improves the data collection rate.

Sponge Supercapacitor rule-based energy management strategy for wireless sensor nodes optimized by using dynamic programing algorithm

Hybrid energy storage systems composed of batteries and supercapacitors (SCs) can provide a stable and sustainable power source for wireless sensor network (WSN) nodes, where the energy management strategy (EMS) plays a significant role. However, the design of a traditional EMS is based on empirical rules and cannot fully utilize the SC. In this study, we propose the concept of a sponge SC and design a strategy based on it to prolong the lifetime of solar-powered WSN nodes. This sponge SC EMS is realized by using dynamic programming (DP), which is a global optimization algorithm. Thus, the proposed EMS is significantly improved over other traditional empirical-based strategies. The difference between the lifetime simulated by the proposed strategy and the optimal upper bound given by DP is within 40%, and the novel strategy extends the battery lifetime by 32%–77% compared with the best existing strategies investigated. The robustness of the proposed method is also validated. It can be confirmed that the strategy performs well under different workloads and sunlight conditions. The sponge SC rule-based hybrid EMS provides a theoretical near-optimal solution for the extension of the life of the WSN node, contributing to wider application prospects of the WSN node.

Maximizing network throughput by cooperative reinforcement learning in clustered solar-powered wireless sensor networks

Power management in wireless sensor networks is very important due to the limited energy of batteries. Sensor nodes with harvesters can extract energy from environmental sources as supplemental energy to break this limitation. In a clustered solar-powered sensor network where nodes in the network are grouped into clusters, data collected by cluster members are sent to their cluster head and finally transmitted to the base station. The goal of the whole network is to maintain an energy neutrality state and to maximize the effective data throughput of the network. This article proposes an adaptive power manager based on cooperative reinforcement learning methods for the solar-powered wireless sensor networks to keep harvested energy more balanced among the whole clustered network. The cooperative strategy of Q-learning and SARSA( λ) is applied in this multi-agent environment based on the node residual energy, the predicted harvested energy for the next time slot, and cluster head energy information. The node takes action accordingly to adjust its operating duty cycle. Experiments show that cooperative reinforcement learning methods can achieve the overall goal of maximizing network throughput and cooperative approaches outperform tuned static and non-cooperative approaches in clustered wireless sensor network applications. Experiments also show that the approach is effective in response to changes in the environment, changes in its parameters, and application-level quality of service requirements.

Comprehensive optimized hybrid energy storage system for long-life solar-powered wireless sensor network nodes

Solar energy harvesting is promising to provide long-term power autonomy for wireless sensor networks. Energy storage devices like lithium-ion batteries are usually integrated to solar-powered sensor nodes to overcome the intermittency of solar power. However, the cycle life of lithium-ion batteries is short, which limits the lifetime of the nodes. Therefore, supercapacitor-battery hybrid energy storage system has been used to extend the cycle life of battery. The design of hybrid energy storage systems significantly affects the performance of wireless sensor network nodes in many ways. However, hybrid energy storage system optimization is a comprehensive problem because the configuration and power-split strategy need to be considered concurrently, which has not been done in the existing studies of solar-powered sensor nodes. In this study, a two-port hybrid diode topology is proposed, accompanied by an adaptive supercapacitor buffer energy management strategy to achieve integrated optimization. The hybrid diode semi-active topology uses a unidirectional DC/DC converter accompanied by two diodes to replace the bidirectional DC/DC converter in the existing semi-active topology. Accordingly, the energy lose is decreased by 15.5% while the cost, size, and controlling complexity are also decreased. Moreover, an adaptive supercapacitor buffer energy management strategy is designed based on the new topology to decrease the battery degradation. Simulation and experimental results indicate that the proposed hybrid energy storage system increases the battery lifetime to at least 3 times that of existing hybrid energy storage systems. Finally, a long-life solar-powered wireless sensor network node with a relatively small size and cost is achievable.

Energy saving algorithm and simulation test in wireless sensor networks

In recent years, scientists have begun to introduce dynamic elements into wireless networks. With the introduction of mobile sink node, the phenomenon of “hot node” and “energy hole” can be effectively avoided, so as to realize more network connection and improve network flexibility. Therefore, it is imperative to design energy-saving algorithm with popular sink code. In this paper, a multi hop data forwarding algorithm is proposed for solar powered wireless sensor networks. The algorithm divides the monitoring area of the network and the communication area of the node. Through the sensor node, the next hop node is selected from the appropriate area, thus forming the path from the data source point to the base station. At the same time, in order to reduce the energy consumption and delay in the network, a multi-objective programming model of the next hop data forwarding node is established. The reasonable area of static and dynamic area is calculated by mathematical analysis. Finally, the paper calculates the network’s life cycle, energy consumption and transmission time, and compares the static sink with the network using only mobile sink.

Efficient FEC Scheme for Solar-Powered WSNs Considering Energy and Link-Quality

In solar-powered wireless sensor networks (SP-WSNs), the best use of harvested energy is more important than minimizing energy consumption since energy can be supplied periodically. Meanwhile, as is well known, the reliability of the communication between sensor nodes is very limited due to the resource constraints of sensor nodes. In this paper, we propose an efficient forward error correction (FEC) scheme which can give solar-powered wireless sensor networks more reliable communication. First, the proposed scheme provides energy-adaptive operation for the best use of solar energy. It calculates the amount of surplus energy which can be used for extra operations and then determines the number of additional parity bits for FEC according to this amount of surplus energy. At the same time, it also provides a link quality model that is used to calculate the appropriate number of parity bits for error recovery required for the current data communication environment. Finally, by considering these two parity sizes, it is possible to determine the number of parity bits that can maximize the data reliability without affecting the blacking out of nodes. The evaluation of the performance of the approach was performed by comparing the amount of data collected at the sink node and the number of blackout nodes with other schemes.

Energy-Efficient Cluster Management Using a Mobile Charger for Solar-Powered Wireless Sensor Networks.

In solar-powered wireless sensor networks (SP-WSNs), sensor nodes can continuously harvest energy to relieve the energy constraint problem in battery-powered WSNs. With the advent of wireless power transmission (WPT) technology, the nodes can be charged remotely if the energy harvested is insufficient. However, even in SP-WSNs with WPT, an energy imbalance problem is observed, in which the energy consumption of the nodes around a sink node increases abnormally if the sink node is stationary. To solve this problem, recent studies have been conducted using a mobile sink node instead of a stationary one. Generally, a clustering scheme is used for the efficient utilization of a mobile sink. However, even in the case of mobile sinks, it is still necessary to minimize the energy burden of the cluster heads and their surrounding nodes. In this study, we propose a scheme that mitigates the energy imbalance problem of SP-WSNs by using a WPT-capable mobile sink and an efficient clustering scheme. In the proposed scheme, the energy imbalance is minimized by electing the cluster heads effectively after considering the energy state of the nodes, and by enabling the sink node to charge the energy of the cluster heads while collecting data from them. Consequently, this scheme allows the sink node to collect more data with fewer blackouts of the sensor nodes.

Development of Sensors-Based Agri-Food Traceability System Remotely Managed by A Software Platform for Optimized Farm Management.

The huge spreading of Internet of things (IoT)-oriented modern technologies is revolutionizing all fields of human activities, leading several benefits and allowing to strongly optimize classic productive processes. The agriculture field is also affected by these technological advances, resulting in better water and fertilizers’ usage and so huge improvements of both quality and yield of the crops. In this manuscript, the development of an IoT-based smart traceability and farm management system is described, which calibrates the irrigations and fertigation operations as a function of crop typology, growth phase, soil and environment parameters and weather information; a suitable software architecture was developed to support the system decision-making process, also based on data collected on-field by a properly designed solar-powered wireless sensor network (WSN). The WSN nodes were realized by using the ESP8266 NodeMCU module exploiting its microcontroller functionalities and Wi-Fi connectivity. Thanks to a properly sized solar power supply system and an optimized scheduling scheme, a long node autonomy was guaranteed, as experimentally verified by its power consumption measures, thus reducing WSN maintenance. In addition, a literature analysis on the most used wireless technologies for agri-food products’ traceability is reported, together with the design and testing of a Bluetooth low energy (BLE) low-cost sensor tag to be applied into the containers of agri-food products, just collected from the fields or already processed, to monitor the main parameters indicative of any failure or spoiling over time along the supply chain. A mobile application was developed for monitoring the tracking information and storing conditions of the agri-food products. Test results in real-operative scenarios demonstrate the proper operation of the BLE smart tag prototype and tracking system.

An Efficient Cluster Management Scheme Using Wireless Power Transfer for Mobile Sink Based Solar-Powered Wireless Sensor Networks

An Efficient Cluster Management Scheme Using Wireless Power Transfer for Mobile Sink Based Solar-Powered Wireless Sensor Networks

Solar-CTP: An Enhanced CTP for Solar-Powered Wireless Sensor Networks

Wireless sensor networks (WSNs) suffer not only from short lifetimes because of limited energy but also from an energy imbalance between the nodes close to the sink and the other nodes. To fundamentally resolve the issue of short lifetimes, recent studies have utilized environmental energy, such as solar power. Additionally, WSNs that employ energy-aware dynamic topology control are also being studied to address the energy imbalance. This paper proposes an improved collection tree protocol (CTP) scheme, called solar-CTP, that uses the two approaches of energy-harvesting and energy-aware topology control simultaneously. The proposed scheme is derived from the CTP scheme, which is a widely adopted data collection strategy designed for typical battery-based WSNs with a fixed sink. We tailor the CTP scheme for solar-powered WSNs operating with a mobile sink. Performance verification confirms that our scheme significantly reduces the number of blackout nodes compared to other CTP variants, thus increasing the amount of data collected by the sink.

Efficient Energy Supply Using Mobile Charger for Solar-Powered Wireless Sensor Networks.

An energy-harvesting wireless sensor network mitigates the energy shortage problems of existing battery-based wireless sensors; however, its hotspot area sensor nodes still experience 3 blackouts, thereby reducing network connectivity. Techniques that transfer energy directly to sensor nodes using wireless power transfer (WPT) have been studied in recent years to address this issue. In this paper, we propose a technique that uses a drone (quadcopter), which is a type of unmanned aerial vehicle (UAV), as a mobile sink. The drone selects and manages anchor nodes that aggregate data temporarily, collects data by visiting the anchor nodes to mitigate the hotspot issue, and then prevents blackouts by supplying energy to low-energy nodes, thereby improving network connectivity. The anchor nodes are carefully selected after considering the energy capacity of the drone, the size of the network, the amount of collected data, and the energy consumed by the nodes to increase the network’s energy efficiency. Furthermore, energy is transferred from the drone to the anchor nodes to support their energy consumption. In our study, this method reduced the blackouts of sensor nodes, including anchor nodes, in hotspot regions, and increased network connectivity, thereby improving the amount of data gathered by the mobile sink.

Efficient Location Service for a Mobile Sink in Solar-Powered Wireless Sensor Networks.

By utilizing mobile sinks in wireless sensor networks (WSNs), WSNs can be deployed in more challenging environments that cannot connect with the Internet, such as those that are isolated or dangerous, and can also achieve a balanced energy consumption among sensors which leads to prolonging the network lifetime. However, an additional overhead is required to check the current location of the sink in order for a node to transmit data to the mobile sink, and the size of the overhead is proportional to that of the network. Meanwhile, WSNs composed of solar-powered nodes have recently been actively studied for the perpetual operation of a network. This study addresses both of these research topics simultaneously, and proposes a method to support an efficient location service for a mobile sink utilizing the surplus energy of a solar-powered WSN. In this scheme, nodes that have a sufficient energy budget can constitute rings, and the nodes belonging to these rings (which are called ring nodes) maintain up-to-date location information on the mobile sink node and serve this information to the other sensor nodes. Because each ring node only uses surplus energy to serve location information, this does not affect the performance of a node’s general operations (e.g., sensing, processing, and data delivery). Moreover, because multiple rings can exist simultaneously in the proposed scheme, the overhead for acquiring the position information of the sink can be significantly reduced, and also hardly increases even if the network size becomes larger.

Maximizing Surveillance Quality of Boundary Curve in Solar-Powered Wireless Sensor Networks

Barrier coverage is an important issue aiming at finding a set of sensors from a given wireless sensor network for detecting the illegal crossing of a boundary. A large number of algorithms have been proposed for prolonging the network lifetime. However, most of them assumed that each sensor is battery powered and the Boolean Sensing Model (BSM) is applied. Sensors powered by battery have limited lifetime while the BSM is difficult to reflect the physical features of sensing. This paper proposes a barrier coverage mechanism, called MSQ, which considers the solar-powered sensors and allows the battery to be recharged for maintaining the perpetual lifetime of sensor networks. However, two challenges should be overcome. First, the schedule should take into account the state switching due to limited energy. Second, the cooperation between neighboring sensors should be considered since the Probabilistic Sensing Model (PSM) is applied. The evaluation of surveillance quality supported by the cooperative sensing should be further considered. Performance evaluations depict that the proposed mechanism improves the performance of existing studies in terms of surveillance quality and stability.

An Efficient Cluster Management Scheme Using Wireless Power Transfer for Solar-powered Wireless Sensor Networks with a Mobile Sink

An Efficient Cluster Management Scheme Using Wireless Power Transfer for Solar-powered Wireless Sensor Networks with a Mobile Sink

An Adaptive Energy Management Strategy to Extend Battery Lifetime of Solar Powered Wireless Sensor Nodes

Traditional energy management strategies of wireless sensor network (WSN) nodes with hybrid energy storage do not adequately combine the merits of supercapacitors (SCs) and batteries, and thus, battery degradation remains a problem that restricts the lifetime of the WSN nodes. In this paper, an adaptive rule-based energy management strategy is proposed to extend the battery lifetime of a solar-powered wireless sensor node. The energy models are constructed with the consideration of the battery degradation, and Poisson distribution is utilized to simulate the power consumption variation of the node. A rule-based energy management strategy that is adaptive with the SC capacity and load conditions is designed. Traditional energy management strategies of the WSN nodes are compared with the proposed one. The simulation results indicate that the proposed strategy can improve the node lifetime from several tens of days to more than ten years. Meanwhile, the strategy ensures the continuous availability of the solar-powered WSN nodes even at night throughout the long lifetime. The robustness of the strategy is also tested. It is revealed that the strategy is adaptive with the SC capacity and workload conditions. The proposed strategy is broadly applicable and promising to resolve the problem of limited battery lifetime for the solar-powered WSN nodes.