Search Results (53)

Although wireless sensor networks (WSNs) have a wide range of applications, their efficient utilization is still limited by the sensor node battery life. To overcome this issue, wireless power transfer technology (WPT) has recently been used to wirelessly charge sensor nodes and extend their lifespan. This technique paved the way to develop a wireless rechargeable sensor network (WRSN) in which a mobile charger (MC) is employed to recharge the sensor nodes. Several wireless charging technologies have been proposed in this field, but they are all tied up in two classes: periodic and on-demand strategies. This paper proposes a proactive charging method as a new charging strategy that anticipates the node’s need for energy in advance based on factors such as the node’s remaining energy, energy consumption rate, and the distance to the MC. The goal is to prevent sensor nodes from depleting their energy before the arrival of the MC. Unlike conventional methods where nodes have to request energy, the proactive charging strategy identifies the nodes that need energy before they reach a critical state. Simulation results have demonstrated that the proactive charging approach using a single MC can significantly improve the network lifespan by 500% and outperform the Nearest Job Next with Preemption (NJNP) and First Come First Serve (FCFS) techniques in terms of the number of survival nodes by 300% and 650%, respectively. Full article

Outdoor wireless sensor networks (WSNs) operate autonomously in dynamic and unattended real-world environments, where sensor nodes are typically powered by their batteries. In hash outdoor settings, such as mountainous regions or underwater locations, recharging or replacing sensor node batteries is particularly challenging. For these WSN deployments, ensuring quality of service (QoS) control while conserving energy is crucial. This paper presents a novel QoS control algorithm for WSNs, built on extensions to the Gur game framework. The proposed approach not only enhances QoS performance compared to existing Gur game-based WSN control algorithms but also addresses their fundamental energy consumption challenges, enabling sustainable communication and extended network lifetimes. We evaluate the approach through comprehensive TinyOS-based WSN simulations and comparisons with existing algorithms. The results demonstrate that our approach, referred to as the robust Gur game, significantly enhances QoS control and achieves a 27.33% improvement in energy efficiency over the original Gur game and shuffle algorithms, showcasing the significant benefits of the proposed method. Full article

Unmanned aerial vehicles (UAVs) play a crucial role in various applications, including environmental monitoring, disaster management, and surveillance, where timely data collection is vital. However, their effectiveness is often hindered by the limitations of wireless sensor networks (WSNs), which can restrict communications due to bandwidth constraints and limited energy resources. Thus, the operational context of the UAV is intertwined with the constraints on WSNs, influencing how they are deployed and the strategies used to optimize their performance in these environments. Considering the issues, this paper addresses the challenge of efficient UAV navigation in constrained environments while reliably collecting data from WSN nodes, recharging the sensor nodes’ power supplies, and ensuring the UAV detours around obstacles in the flight path. First, an integer linear programming (ILP) optimization problem named deadline and obstacle-constrained energy minimization (DOCEM) is defined and formulated to minimize the total energy consumption of the UAV. Then, a deep reinforcement learning-based algorithm, named the DQN-based UAV detouring algorithm, is proposed to enable the UAV to make intelligent detour decisions in the constrained environment. The UAV must finish its tour (data collection and recharging sensors) without exceeding its battery capacity, ensuring each sensor has the minimum residual energy and consuming energy for transmitting and generating data, after being recharged by the UAV at the end of the tour. Finally, simulation results demonstrate the effectiveness of the proposed DQN-based UAV detouring algorithm in data collection and recharging the sensors while minimizing the total energy consumption of the UAV. Compared to other baseline algorithm variants, the proposed algorithm outperforms all of them. Full article

The emerging wireless energy transfer technology enables sensor nodes to maintain perpetual operation. However, maximizing the network performance while preserving short charging delay is a great challenge. In this work, a Wireless Mobile Charger (MC) and a directional charger (DC) were deployed to transmit wireless energy to the sensor node to improve the network’s throughput. To the best of our knowledge, this is the first work to optimize the data sensing rate and charging delay by the joint scheduling of an MC and a DC. We proved we could transmit maximum energy to each sensor node to obtain our optimization objective. In our proposed work, a DC selected a total horizon of 360° and then selected the horizon of each specific ${90}^{\circ }$ area based on its antenna orientation. The DC’s orientation was scheduled for each time slot. Furthermore, multiple MCs were used to transmit energy for sensor nodes that could not be covered by the DC. We divided the rechargeable wireless sensor network into several zones via a Voronoi diagram. We deployed a static DC and one MC charging location in each zone to provide wireless charging service jointly. We obtained the optimal charging locations of the MCs in each zone by solving Mix Integral Programming for energy transmission. The optimization objective of our proposed research was to sense maximum data from each sensor node with the help of maximum energy. The lifetime of each sensor network could increase, and the end delay could be maximized, with joint energy transmission. Extensive simulation results demonstrated that our RWSNs were designed to significantly improve network lifetime over the baseline method. Full article

Wireless sensor networks (WSNs) play a crucial role in the Internet of Things (IoT) for ubiquitous data acquisition and tracking. However, the limited battery life of sensor nodes poses significant challenges to the long-term scalability and sustainability of these networks. Wireless power transfer technology offers a promising solution by enabling the recharging of energy-depleted nodes through a wireless portable charging device (WPCD). While this approach can extend node lifespan, it also introduces the challenge of bottleneck nodes—nodes whose remaining energy falls below a critical value of the threshold. The paper addresses this issue by formulating an optimization problem that aims to identify the optimal traveling path for the WPCD based on ant colony optimization (WPCD-ACO), with a focus on minimizing energy consumption and enhancing network stability. To achieve it, we propose an objective function by incorporating a time-varying z phase that is managed through linear programming to efficiently address the bottleneck nodes. Additionally, a gateway node continually updates the remaining energy levels of all nodes and relays this information to the IoT cloud. Our findings indicate that the outage-optimal distance achieved by WPCD-ACO is 6092 m, compared to 7225 m for the shortest path and 6142 m for Dijkstra’s algorithm. Furthermore, the WPCD-ACO minimizes energy consumption to 1.543 KJ, significantly outperforming other methods: single-hop at 4.8643 KJ, GR-Protocol at 3.165 KJ, grid clustering at 2.4839 KJ, and C-SARSA at 2.5869 KJ, respectively. Monte Carlo simulations validate that WPCD-ACO is outshining the existing methods in terms of the network lifetime, stability, survival rate of sensor nodes, and energy consumption. Full article

Greenhouses have taken on a fundamental role in agriculture. The Internet of Things (IoT) is a key concept used in greenhouse-based precision agriculture (PA) to enhance vegetable quality and quantity while improving resource efficiency. Integrating wireless sensor networks (WSNs) into greenhouses to monitor environmental parameters represents a critical first step in developing a complete IoT solution. For further optimization of the results, including actuator nodes to control the microclimate is necessary. The greenhouse must also be remotely monitored and controlled via an internet-based platform. This paper proposes an IoT-based architecture as a decision support system for farmers. A web platform has been developed to acquire data from custom-developed wireless sensor nodes and send commands to custom-developed wireless actuator nodes in a greenhouse environment. The wireless sensor and actuator nodes (WSANs) utilize LoRaWAN, one of the most prominent Low-Power Wide-Area Network (LPWAN) technologies, known for its long data transmission range. A real-time end-to-end deployment of a remotely managed WSAN was conducted. The power consumption of the wireless sensor nodes and the recharge efficiency of installed solar panels were analyzed under worst-case scenarios with continuously active nodes and minimal intervals between data transmissions. Datasets were acquired from multiple sensor nodes over a month, demonstrating the system’s functionality and feasibility. Full article

In this manuscript, we present in detail the design and implementation of the hardware and software to produce a standalone wireless sensor node, called SensorQ system, for the detection of a toxic chemical agent. The proposed wireless sensor node prototype is composed of a micro-controller unit (MCU), a radio frequency (RF) transceiver, a dual-band antenna, a rechargeable battery, a voltage regulator, and four integrated sensing devices, all of them integrated in a package with final dimensions and weight of 200 × 80 × 60 mm and 0.422 kg, respectively. The proposed SensorQ prototype operates using the Long-Range (LoRa) wireless communication protocol at 2.4 GHz, with a sensor head implemented on a hetero-core fiber optic structure supporting the surface plasmon resonance (SPR) phenomenon with a sensing section (L = 10 mm) coated with titanium/gold/titanium and a chemically sensitive material (zinc oxide) for the detection of Di-Methyl Methyl Phosphonate (DMMP) vapor in the air, a simulant of the toxic nerve agent Sarin. The transmitted spectra with respect to different concentrations of DMMP vapor in the air were recorded, and then the transmitted power for these concentrations was calculated at a wavelength of 750 nm. The experimental results indicate the feasibility of detecting DMMP vapor in air using the proposed optical sensor head, with DMMP concentrations in the air of 10, 150, and 150 ppm in this proof of concept. We expect that the sensor and wireless sensor node presented herein are promising candidates for integration into a wireless sensor network (WSN) for chemical warfare agent (CWA) detection and contaminated site monitoring without exposure of armed forces. Full article

The recent advancement in wireless power transmission (WPT) has led to the development of wireless rechargeable sensor networks (WRSNs), since this technology provides a means to replenish sensor nodes wirelessly, offering a solution to the energy challenges faced by WSNs. Most of the recent previous work has focused on charging sensor nodes using wireless charging vehicles (WCVs) equipped with high-capacity batteries and WPT devices. In these schemes, a vehicle can move close to a sensor node and wirelessly charge it without physical contact. While these schemes can mitigate the energy problem to some extent, they overlook two primary challenges of applied WCVs: off-road navigation and vehicle speed limitations. To overcome these challenges, previous work proposed a new WRSN model equipped with one drone coupled with several pads deployed to charge the drone when it cannot reach the subsequent stop. This wireless charging pad deployment aims to deploy the minimum number of pads so that at least one feasible routing path from the base station can be established for the drone to reach every SN in a given WRSN. The major weakness of previous studies is that they only consider deploying a wireless charging pad at the locations of the wireless sensor nodes. Their schemes are limited and constrained because usually every point in the deployed area can be considered to deploy a pad. Moreover, the deployed pads suggested by these schemes may not be able to meet the connected requirements due to sparse environments. In this work, we introduce a new scheme that utilizes the Quad-Tree concept to address the wireless charging pad deployment problem and reduce the number of deployed pads at the same time. Extensive simulations were conducted to illustrate the merits of the proposed schemes by comparing them with different previous schemes on maps of varying sizes. In the case of large maps, the proposed schemes surpassed all previous works, indicating that our approach is more suitable for large-scale network environments. Full article

With the rapid development of the Internet of Things (IoT), improving the lifetime of nodes and networks has become increasingly important. Most existing medium access control protocols are based on scheduling the standby and active periods of nodes and do not consider the alarm state. This paper proposes a Q-learning and efficient low-quantity charge (QL-ELQC) method for the smoke alarm unit of a power system to reduce the average current and to improve the lifetime of the wireless sensor network (WSN) nodes. Quantity charge models were set up, and the QL-ELQC method is based on the duty cycle of the standby and active times for the nodes and considers the relationship between the sensor data condition and the RF module that can be activated and deactivated only at a certain time. The QL-ELQC method effectively overcomes the continuous state–action space limitation of Q-learning using the state classification method. The simulation results reveal that the proposed scheme significantly improves the latency and energy efficiency compared with the existing QL-Load scheme. Moreover, the experimental results are consistent with the theoretical results. The proposed QL-ELQC approach can be applied in various scenarios where batteries cannot be replaced or recharged under harsh environmental conditions. Full article

Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental monitoring, agriculture, manufacturing, smart health, home automation, wildlife monitoring, and surveillance. Population expansion, changes in the climate, and resource constraints all offer problems to modern IoT applications. To solve these issues, the integration of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has come forth as a game-changing solution. For example, in agricultural environment, IoT-based WSN has been utilized to monitor yield conditions and automate agriculture precision through different sensors. These sensors are used in agriculture environments to boost productivity through intelligent agricultural decisions and to collect data on crop health, soil moisture, temperature monitoring, and irrigation. However, sensors have finite and non-rechargeable batteries, and memory capabilities, which might have a negative impact on network performance. When a network is distributed over a vast area, the performance of WSN-assisted IoT suffers. As a result, building a stable and energy-efficient routing infrastructure is quite challenging in order to extend network lifetime. To address energy-related issues in scalable WSN-IoT environments for future IoT applications, this research proposes EEDC: An Energy Efficient Data Communication scheme by utilizing “Region based Hierarchical Clustering for Efficient Routing (RHCER)”—a multi-tier clustering framework for energy-aware routing decisions. The sensors deployed for IoT application data collection acquire important data and select cluster heads based on a multi-criteria decision function. Further, to ensure efficient long-distance communication along with even load distribution across all network nodes, a subdivision technique was employed in each tier of the proposed framework. The proposed routing protocol aims to provide network load balancing and convert communicating over long distances into shortened multi-hop distance communications, hence enhancing network lifetime.The performance of EEDC is compared to that of some existing energy-efficient protocols for various parameters. The simulation results show that the suggested methodology reduces energy usage by almost 31% in sensor nodes and provides almost 38% improved packet drop ratio. Full article

Battery replacement or recharging is essential for sensor nodes because they are typically powered by batteries in wireless sensor network (WSN) applications. Therefore, creating an energy-efficient data transfer technique is required. The base station (BS) receives data from one sensor node and routes the data to another sensor node. As a result, an energy-efficient routing algorithm using fuzzy logic (EERF) represents a novel approach that is suggested in this study. One of the reasoning techniques utilized in scenarios where there is a lot of ambiguity is fuzzy logic. The remaining energy, the distance between the sensor node and the base station, and the total number of connected sensor nodes are all inputs given to the fuzzy system of the proposed EERF algorithm. The proposed EERF is contrasted with the current systems, like the energy-aware unequal clustering using fuzzy logic (EAUCF) and distributed unequal clustering using fuzzy logic (DUCF) algorithms, in terms of evaluation criteria, including energy consumption, the number of active sensor nodes for each round in the network, and network stability. EAUCF and DUCF were outperformed by EERF. Full article

Due to the advancement of sensor gadgets and telecommunication technology, wireless sensor networks (WSNs) have drawn a lot of observations in the recent period. Inaccessible terrain, disaster zones, or polluted conditions are typically where they are deployed at random, making battery replacement or recharge challenging or even impossible. Network lifespan is therefore extremely important to a WSN. An abundance of power-effective strategies in a diverse wireless sensor network are surveyed in this paper. We first provide an overview of the fundamental network radio representation and how it may be utilized to analyze different trade-offs between network deployment costs and an energy-efficient clustering approach. We also highlight a few protocols that can be utilized in heterogeneous networks that are energy efficient. Full article

Wireless rechargeable sensor networks (WRSN) have been emerging as an effective solution to the energy constraint problem of wireless sensor networks (WSN). However, most of the existing charging schemes use Mobile Charging (MC) to charge nodes one-to-one and do not optimize MC scheduling from a more comprehensive perspective, leading to difficulties in meeting the huge energy demand of large-scale WSNs; therefore, one-to-multiple charging which can charge multiple nodes simultaneously may be a more reasonable choice. To achieve timely and efficient energy replenishment for large-scale WSN, we propose an online one-to-multiple charging scheme based on Deep Reinforcement Learning, which utilizes Double Dueling DQN (3DQN) to jointly optimize the scheduling of both the charging sequence of MC and the charging amount of nodes. The scheme cellularizes the whole network based on the effective charging distance of MC and uses 3DQN to determine the optimal charging cell sequence with the objective of minimizing dead nodes and adjusting the charging amount of each cell being recharged according to the nodes’ energy demand in the cell, the network survival time, and MC’s residual energy. To obtain better performance and timeliness to adapt to the varying environments, our scheme further utilizes Dueling DQN to improve the stability of training and uses Double DQN to reduce overestimation. Extensive simulation experiments show that our proposed scheme achieves better charging performance compared with several existing typical works, and it has significant advantages in terms of reducing node dead ratio and charging latency. Full article

Wireless sensor networks (WSNs) have wide applicability in services used in daily life. However, for such networks, limited energy is a critical issue. The efficiency of a deployed sensor network may be subject to energy supply. Wireless rechargeable sensor networks have recently been proposed and discussed. Most related studies have involved applying static rechargeable sensors to an entire rechargeable environment or having mobile chargers patrol the environment to charge sensors within it. For partially rechargeable environments, improving the recharge efficiency and extending the lifetime of WSNs are considerable challenges. Scientists have devoted attention to energy transmission technologies and mobile sensor network (MSN) applications. In this paper, we propose a flexible charging protocol in which energy can be transmitted from certain energy supply regions to other regions in an MSN. Mobile rechargeable sensors are deployed to monitor the environment. To share energy in a certain region, the sensors move to replenish their energy and transmit energy to sensors outside the energy supply region. The efficiency of the proposed protocol is also discussed in the context of various situations. The evaluation results suggest that the flexible protocol is more efficient than other charging protocols in several situations. Full article

To extend a network’s lifetime, wireless rechargeable sensor networks are promising solutions. Chargers can be deployed to replenish energy for the sensors. However, deployment cost will increase when the number of chargers increases. Many metrics may affect the final policy for charger deployment, such as distance, the power requirement of the sensors and transmission radius, which makes the charger deployment problem very complex and difficult to solve. In this paper, we propose an efficient method for determining the field of interest (FoI) in which to find suitable candidate positions of chargers with lower computational costs. In addition, we designed four metaheuristic algorithms to address the local optima problem. Since we know that metaheuristic algorithms always require more computational costs for escaping local optima, we designed a new framework to reduce the searching space effectively. The simulation results show that the proposed method can achieve the best price–performance ratio. Full article