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The paper introduces FlashPage, a high-speed SSD caching system designed for ultra-fast media, with the goal of enhancing web page delivery in proxy servers. Traditional SSD caching schemes, designed primarily for slow HDD-based primary storage, encounter difficulties when applied to capacity-class SSDs as primary storage. This limits the high-performance capabilities of caching media. To address this issue, FlashPage operates within the Linux virtual filesystem layer, shortening the hit-handling path and minimizing lookup overhead. It incorporates a compact radix tree to fast locate cached data. These approaches reduce the software overhead for a 4kB read hit by over 5 times. FlashPage also employs novel admission and eviction policies to minimize flash wear while maintaining a high hit rate. As a second-level storage cache, FlashPage predicts the hotness of potential demotion candidates in the first-level storage cache (i.e., page cache), achieving a 10.1% higher hit rate and reducing write traffic by 10.4% compared to LRU. Evaluations using Varnish and Squid HTTP caches show its effectiveness, with up to 29.6% and 38.2% faster web request processing compared to Bcache and DM-Cache, state-of-the-art caching schemes in mainline Linux kernels.

In the majority of Internet of Things (IoT) applications, persistent and stable operation is a crucial requirement. While environmental energy-harvesting technologies can enhance IoT’s persistence, they do not guarantee stability. Therefore, we aim to address the stability challenges in solar-powered IoT (SP-IoT) by employing wireless power transmission (WPT) through unmanned aerial vehicles (UAVs). This study focuses on determining the optimal charging mobility of drones for WPT to enhance the stability of nodes operating in a wide area network (WAN)-based SP-IoT environment. The proposed scheme identifies nodes with insufficient solar energy harvesting and defines the optimal charging mobility parameters (hovering position, hovering time, and moving path) to efficiently transmit the drone’s energy to these nodes in a balanced manner. The experimental results confirm that the proposed scheme significantly improves the stability of solar-powered IoT nodes by optimally utilizing the limited energy of the drone.

Wireless sensor networks (WSNs) are used to collect large amounts of data over a wide area. However, the sensor nodes used in WSNs have a short lifespan because they are generally battery-operated. Research is actively underway to increase the lifespan of WSNs via techniques such as energy harvesting and wireless power transfer (WPT). This study proposes a scheme for clustering and adjusting the WSN data collection rate to alleviate the hotspot problem and improve network connectivity. The proposed scheme is targeted at WSNs in which the sensor nodes harvest solar energy. In addition, an unmanned aerial vehicle (UAV) traverses along a given path and delivers energy to sensor nodes using WPT, while collecting data as a sink node. In this scheme, the number of cluster heads is determined by considering the number of nodes at each hop distance and the maximum amount of data that can be transferred with the available energy. Further, sensor nodes limit the amount of data they collect to reduce the relay load on intermediate nodes. To achieve this, nodes consider the amount of data transferred by parent nodes, especially nodes in the hotspot, and the number of child nodes. The sink node traverses along a given path, and collects the data accumulated in the cluster head while supplying its remaining energy to the cluster head. The amount of energy it transfers is determined by considering the number of cluster heads. Consequently, nodes in the hotspot are prevented from becoming blacked out from a lack of energy, and the energy harvesting efficiency is increased. Simulation results indicate that the proposed scheme reduces the number of blackout nodes in the hotspot areas near cluster heads. Furthermore, the data collection and monitoring performance increases with the increase in network connectivity.

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.

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.

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.

In wireless sensor networks, a node that increases its sensing rate to gather more data consumes more energy. Consequently, it will be exhausted earlier than other nodes. In this article, we propose a sensing rate control scheme to increase data resolution without increasing the number of blackout nodes in energy-harvesting wireless sensor networks. In the proposed scheme, each node selects a compression algorithm and adjusts the sensing rate to gather more data while the node operates properly. When a node is estimated to have extra energy, it gathers additional data. In contrast, when it is estimated to be exhausted, it gathers less data to save energy. It also selects an appropriate compression algorithm according to the compression ratio of the selected algorithm to reduce the workload of intermediate nodes. An experimental result verifies that the proposed scheme gathers more data with a lower number of blackout nodes than other schemes.

Energy-harvesting nodes are now being employed in wireless sensor networks to extend the lifetime of the network by harvesting energy from the surrounding environments. However, unpremeditated energy consumption can incur energy problems, such as the blackout of nodes (due to their exceeding energy consumption over the amount of harvested energy) or inevitable disposal of harvested energy (in excess of the battery capacity). In this article, we propose an adaptive data compression and transmission range extension scheme that minimizes the blackout of sensor nodes and increases the amount of data collected at the sink node using the harvested energy efficiently. In this scheme, each node estimates the amount of harvested and consumed energy. When it determines that its remaining energy will exceed its storage capacity, it exploits the energy to compress the data or increase the transmission range. At this point, of the two methods, the method that can more effectively increase the network performance can be selected. The results of experiments conducted indicate that the proposed scheme significantly reduces the extent of node blackouts and increases the data collection rate of the sink node.

There have been many studies performed about increasing network lifetime in wireless sensor networks that involve reducing data size, since the data transmission process takes up a large part of energy consumption. However, reducing data size results in increased delay time due to not only the compression computation time but also the waiting time to gather a sufficient amount of data for compression. Meanwhile, in solar-powered wireless sensor networks, the harvested energy may be surplus to the basic operations of sensor nodes. In this study, such surplus energy is utilized to reduce the delay time between nodes. Nodes with residual energy less than a certain threshold transfer data with compression in order to reduce energy consumption, and nodes with residual energy over the threshold (which means there is surplus energy) transfer data without compression to reduce the delay time between nodes by using the surplus energy. Simulation-based performance verifications show that the technique proposed in this study exhibits optimal performance in terms of both energy and delay times compared with traditional methods.

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.

In a wireless sensor network (WSN) environment with frequent errors, forward error correction (FEC) is usually employed at the link layer to achieve reliable transmission. In the FEC scheme, the error correction rate varies depending on the length of parity used for the recovery of broken data. The longer the parity length, the higher the possible error correction rate. However, this also means that the energy consumption increases. Meanwhile, in a solar-powered WSN, the energy of each node can be periodically collected, but the amount of collected energy varies drastically depending on the harvesting environment, including factors such as the weather, season and time of day. Therefore, each node must control energy consumption according to the energy harvesting rate. The scheme proposed in this study executes this control by adaptively adjusting the parity length of FEC according to the given energy budget of a node for the next period. This means that the error recovery rate can be increased as much as possible without adversely affecting the blackout time. Simulation results show that the proposed scheme improves the amount of data collected from the entire network for each environment compared with previous schemes.

Wireless rechargeable sensor nodes can collect additional data, which leads to an increase in the precision of data analysis, when enough harvested energy is acquired. However, because such nodes increase the amount of sensory data, some nodes (especially near the sink) may blackout because more transmitted data can make relaying nodes expend more energy. In this paper, we propose an energy-aware control scheme of data compression and sensing rate to maximize the amount of data collected at the sink, while minimizing the blackout time. In this scheme, each dominant node determines the data quota that all its descendant nodes can transmit during the next period, which operates with an efficient energy allocation scheme. Then, the node receiving the quota selects an appropriate data compression algorithm and sensing rate according to both its quota and allocated energy during the next period, so as not to exhaust the energy of nodes near the sink. Experimental results verify that the proposed scheme collects more data than other schemes, while suppressing the blackout of nodes. We also found that it adapts better to changes in node density and harvesting environments.

A number of studies have been actively conducted to address limited energy resources in sensor systems over wireless sensor networks. Most of these studies are based on energy-aware schemes, which take advantage of the residual energy from the sensor system's own or neighboring nodes. However, existing sensor systems estimate residual energy based solely on voltage and current consumption, leading to inaccurate estimations because the residual energy in real batteries is affected by temperature and load. This misinformation makes a complete nonsense of existing energy-aware research, which is not allowed in reliable WSN applications. In this study, therefore, an efficient residual-energy estimation scheme is proposed in consideration of not only the voltage but also the temperature and load characteristics of batteries. The performance of the proposed scheme was verified through an experiment and simulations in the actual environment, and its effect gets more notable when the scale of the WSN goes larger.

The nodes in a wireless sensor network (WSN) are often clustered to improve the efficiency of data transmission, but this can cause an imbalance in the energy available at each node. This impairs the transmission capability of some nodes and hence reduces network connectivity. This imbalance worsens as nodes become defunct. We propose a multilayer topology for long-term hybrid WSNs which contain both battery-powered and energy-harvesting nodes. Each node periodically selects its own layer, depending on the energy it has available, in order to balance energy levels and maintain network connectivity. Simulations show that this scheme improves the delivery of data across a WSN.

Most existing simulators for WSNs (wireless sensor networks) model battery-powered sensors and provide MAC and routing protocols designed for battery-powered WSNs. Recently, however, increasingly extensive studies of energy harvesting sensor systems require the development of appropriate simulators, but there are few related studies on such simulators. Unlike existing simulators, simulators for energy harvesting WSNs require a new energy model that is integrated with the energy harvesting, rechargeable battery, and energy consuming models. Additionally, the new model must enable applications of the well-known MAC and routing protocols designed for energy harvesting WSNs and have a convenient user-friendly interface. In this work, we design and implement a user-friendly simulator for solar energy harvesting WSNs.

A node in a solar-powered wireless sensor network (WSN) collects energy when the sun shines and stores it in a battery or capacitor for use when no solar power is available, in particular at night. In our scheme, each tiny node in a WSN periodically determines its energy budget, which takes into account its residual energy, and its likely acquisition and consumption. If it expects to acquire more energy than it can store, the data which has it has sensed is aggregated with data from other nodes, compressed, and transmitted. Otherwise, the node continues to sense data, but turns off its wireless communication to reduce energy consumption. We compared several schemes by simulation. Our scheme reduced the number of nodes forced to black out due to lack of energy so that more data arrives at the sink node.