Battery-powered Wireless Sensor Networks Research Articles

Towards perpetual sensor networks via Overlapped Mobile Charging

Wireless power transfer has given a promising alternative to replenish battery-powered wireless sensor networks (WSNs). We can employ mobile vehicles, robots, or unmanned aerial vehicle as mobile chargers to wirelessly charge sensor nodes. Most of previous works either assume that a mobile charger can charge only one sensor node at a time, or optimize for charging delay, radiation safety, etc. In this paper, we consider a fundamental problem: given one mobile charger that can charge multiple sensor nodes simultaneously, how we can schedule it to charge a given WSN to maximize the energy usage effectiveness (EUE)? We propose a novel charging paradigm, Overlapped Mobile Charging (OMC), the first of its kind to the best of our knowledge. Firstly, OMC clusters sensor nodes into multiple non-overlapped sets using k-means evaluated by the Davies–Bouldin Index, such that the sensor nodes in each set have similar recharging cycles. Secondly, for each set of sensor nodes, OMC further divides them into multiple overlapped groups, and charges each group at different locations for different time durations to make sure that each overlapped sensor node just receives its required energy from multiple charging locations. Simulations confirm the advantages of OMC in terms of EUE.

DCC-IACJS: A novel bio-inspired duty cycle-based clustering approach for energy-efficient wireless sensor networks

Clustering routing is one of the most significant mechanisms applied to extend the lifetime of battery-powered wireless sensor networks (WSNs). However, most existing clustering approaches fail to exploit the feature of node redundancy in WSNs, which as a result leads to unnecessary energy waste. In this regard, a new clustering model named DCCM, based on the duty cycle method, was proposed to reduce the number of working nodes to save energy. This model enables nodes to work alternately to slow energy depletion by offering a novel designed coverage relationship matrix (CRM) and cover sets (CSs). Furthermore, an improved adaptive clone jellyfish search (DCC-IACJS) algorithm was designed to optimize the proposed model to obtain the most desirable clustering scheme. To increase its superiority over the clustering schemes, a new adaptive parameter strategy, as well as a new clone scheme, was devised in the DCC-IACJS. Subsequently, to verify the effectiveness of the proposed clustering approach, comprehensive simulation experiments were conducted to compare it with other state-of-the-art counterparts, namely, GA-CSO-LBCM, FGF, and O-LEACH. Simulation results showed that DCC-IACJS can provide 10.24%, 7.04%, and 8.54% longer network lifetime than O-LEACH, FGF, and GA-CSO-LBCM, respectively.

Peukert's Law-Based State-of-Charge Estimation for Primary Battery Powered Sensor Nodes.

Accurate state-of-charge (SOC) estimation is essential for maximizing the lifetime of battery-powered wireless sensor networks (WSNs). Lightweight estimation methods are widely used in WSNs due to their low measurement and computation requirements. However, accuracy of existing lightweight methods is not high, and their adaptability to different batteries and working conditions is relatively poor. This paper proposes a lightweight SOC estimation method, which applies Peukert's Law to estimate the effective capacity of the battery and then calculates the SOC by subtracting the cumulative current consumption from the estimated capacity. In order to evaluate the proposed method comprehensively, different primary batteries and working conditions (constant current, constant resistance, and emulated duty-cycle loads) are employed. Experimental results show that the proposed method is superior to existing methods for different batteries and working conditions, which mainly benefits from the ability of Peukert's Law to better model the rate-capacity effect of the batteries.

Novel Data Compression Algorithm for Transmission Line Condition Monitoring

For the problem of data accumulation caused by massive sensor data in transmission line condition monitoring system, this paper analyzes the type and amount of data in the transmission line sensor network, compares the compression algorithms of wireless sensor network data at home and abroad, and proposes an efficient lossless compression algorithm suitable for sensor data in transmission line linear heterogeneous networks. The algorithm combines the wavelet compression algorithm and the neighborhood index sequence algorithm. It displays a fast operation speed and requires a small amount of calculation. It is suitable for battery powered wireless sensor network nodes. By combining wavelet correlation analysis and neighborhood index sequence coding, the compression algorithm proposed in this paper can achieve a high compression rate, has strong robustness to packet loss, has high compression performance, and can help to reduce network load and the packet loss rate. Simulation results show that the proposed method achieves a high compression rate in the compression of the transmission line parameter dataset, is superior to the existing data compression algorithms, and is suitable for the compression and transmission of transmission line condition monitoring data.

Energy efficient embedded systems and their application in wireless sensor networks

Embedded systems are specialised electronic systems that perform limited number of fixed operations and are used in many application areas. Such systems are based on using microprocessors for their implementation. Embedded systems are usually part of other systems where they are embedded into some embedding systems. They have to be efficient in electrical energy consumption, size of program code, time of operation, weight and cost. Embedded systems are inexpensive and are used in almost every electronic product or other electronic systems. Many embedded systems are mobile systems supplied by batteries and the available electrical energy must be used efficiently as much as possible. Application areas where embedded systems are used and where minimal consumption of energy is required are battery powered wireless sensor networks. The methods for reduction of energy consumption and for power management in embedded systems are considered and described in this paper. The accent is given on design and application of energy efficient embedded systems in wireless sensor networks and on possibilities to reduce energy consumption in such systems. The methods for energy harvesting, that are very attractive and very useful in wireless sensor networks applications, are also considered and described. One practically implemented battery supplied wireless sensor network for application for environmental data acquisition and monitoring in agriculture is described in the paper.

An event-driven multi-agent based distributed optimal control strategy for HVAC systems in IoT-enabled smart buildings

Smart buildings generally adopt a centralized optimal control for heating, ventilation and air-conditioning (HVAC) systems to improve the building system performance. As a crucial technology adopted in smart buildings, Internet of Things (IoT) based wireless sensor networks (WSNs) are promising platforms for implementing novel distributed optimal control to achieve distributed intelligence effectively. Such a future mist computing paradigm can be hindered by the limited energy resource of WSNs. The event-driven optimization method activates the optimization only when events occur. It can save the energy resource of WSNs, and thus pave the way for implementing the distributed optimal control architecture in IoT-based WSNs. This study therefore proposes an event-driven multi-agent based distributed optimal control strategy for HVAC systems for implementation in IoT-based battery-powered WSNs. The strategy consists of two novel schemes. First, an event determination scheme determines the event threshold by comprehensively considering the system performance and the total energy consumption of individual sensors implementing the distributed optimal control architecture. Second, an event-driven distributed optimization scheme solves the optimization problems with distributed optimization algorithms in IoT sensors of limited data processing capacity when an event occurs. Comparison and case studies are conducted to compare different strategies and validate the proposed strategy. Results show that different strategies require very different sensor energy consumption. The proposed strategy can provide satisfactory system performance while reducing the energy consumption of IoT sensors.

Comparison of Q-Coverage P-Connectivity Sensor Node Scheduling Heuristic Between Battery Powered WSN & Energy Harvesting WSN

A serious problem in Wireless Sensor Networks (WSNs) is to attain high-energy efficiency as battery powers a node, which has limited stored energy. They can not be suitably replaced or recharged. The appearance of renewable energy harvesting techniques and their combination with sensors give Energy Harvesting Wireless Sensor Networks (EH-WSNs). Therefore, the area shifts from energy preservation to the reliability of the network. For reliability, Coverage and Connectivity are important Quality of Service (QoS) parameters. Many sensor node scheduling heuristics have been developed in the past. Some of them focus on more than a single order of Coverage and Connectivity. If reliability is the main concern for a WSN, then there definitely is a need to incorporate Q-Coverage and P-Connectivity in WSN. The lifetime of a WSN decreases while considering Q-Coverage and P-Connectivity. After some time network dies and it is of no use. Therefore, there is a trade-off between the reliability and lifetime of a WSN. If EH-WSN is used, the lifetime of a WSN increases, as well as Q-Coverage and P-Connectivity QoS parameters can be used for achieving reliability. This paper proposes a sensor node scheduling heuristic for EH-WSN, considering Q-Coverage and P-Connectivity. A comparison of a Q-Coverage and PConnectivity sensor node scheduling heuristic, when used between battery powered WSN and EH-WSN, is done. The comparison shows that lifetime using EH-WSN is much greater as compared to battery powered WSN. This research is beneficial for real-time WSN applications where EH-WSN provides power regularly without any intervention.

AoI Minimization Data Collection Scheduling for Battery-Free Wireless Sensor Networks

Age of Information (AoI) is a new metric for measuring the freshness of sensory data in wireless sensor networks. The Battery-Free Wireless Sensor Network (BF-WSN) is proposed to break through the lifetime limitation of battery-powered wireless sensor networks. However, the emerging BF-WSN also brings challenges to the minimization of AoI, on account of its energy characteristics. In this paper, we investigate the AoI minimization data collection scheduling problem for BF-WSNs. The off-the-shelf works for the AoI minimization data collection scheduling problem either focus on simple networks with no more than three nodes or assume that battery-free sensor nodes have specific energy harvesting process, such as Bernoulli process and Poisson process. Different from these works, we first consider the AoI minimization data collection scheduling for one-hop BF-WSNs with multiple battery-free sensor nodes transmitting their sensory data to the sink node, where the energy harvesting processes of battery-free sensor nodes are non-specific. We propose the optimal offline algorithm and the online algorithm for the problem, respectively. The optimality of the offline algorithm and the competitive ratio of the online algorithm are theoretical proved and analyzed. Numerical results are provided to verify the performances of the proposed algorithms.

Data Aggregation Scheduling in Battery-Free Wireless Sensor Networks

To break through the limitation of battery-powered wireless sensor networks, a novel kind of network, named battery-free wireless sensor network (BF-WSN), is proposed. Battery-free sensor nodes in BF-WSNs harvest energy from power sources in their ambient environment, such as solar power, wind power and radio frequency (RF) signal power, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc.</i> , instead of batteries. Therefore, the energy consumption of battery-free sensor nodes are not limited by the battery capacity anymore. However, they still have limited energy harvesting rates and energy capacities. Data aggregation is a fundamental operation in sensor networks where the sensory data gathered by the relay nodes can be merged by in-network computation, such as taking the maximum, average, or sum, etc., of them. Due to the energy features of BF-WSNs, the data aggregation scheduling problem in BF-WSNs is more complicated and the previous aggregation scheduling algorithms designed for battery-powered WSNs are no longer applicable. This paper investigates the Minimum-Latency Aggregation Scheduling problem in BF-WSNs, which is proved to be NP-hard. Then, we propose the Data Aggregation Scheduling algorithm to solve the problem. Finally, the theoretical analysis and extensive simulation results are provided to verify the performance of the proposed algorithm.

RDCPF: A Redundancy-Based Duty-Cycling Pipelined-Forwarding MAC for Linear Sensor Networks.

Existing duty-cycling and pipelined-forwarding (DCPF) protocols applied in battery-powered wireless sensor networks can significantly alleviate the sleep latency issue and save the energy of networks. However, when a DCPF protocol applies to a linear sensor network (LSN), it lacks the ability to handle the bottleneck issue called the energy-hole problem, which is mainly manifested due to the excessive energy consumption of nodes near the sink node. Without overcoming this issue, the lifespan of the network could be greatly reduced. To that end, this paper proposes a method of deploying redundant nodes in LSN, and a corresponding enhanced DCPF protocol called redundancy-based DCPF (RDCPF) to support the new topology of LSN. In RDCPF, the distribution of energy consumption of the whole network becomes much more even. RDCPF also brings improvements to the network in terms of network survival time, packet delivery latency, and energy efficiency, which have been shown through the extensive simulations in comparison with existing DCPF protocols.

Latency-efficient Data Collection Scheduling in Battery-free Wireless Sensor Networks

The lifetime of battery-powered Wireless Sensor Networks (WSNs) are limited by the batteries equipped in sensors. The appearance of Battery-free Wireless Sensor Networks (BF-WSNs) breaks through this limitation, in which battery-free sensors harvest energy from sustainable but uncontrollable energy sources in ambient environment, such as solar power, wind power, radio frequency signal power, and so on. The energy characteristics of BF-WSNs make it more challenging for data collection scheduling in BF-WSNs. Latency of data collection is a crucial measurement to evaluate the performance of data collection schedules. In this article, we study the problem of generating data collection schedules with minimum latency for BF-WSNs and propose latency-efficient data collection scheduling algorithms for line BF-WSNs and general BF-WSNs, respectively. Theoretical analysis and extensive simulations are conducted to verify the efficiency and effectiveness of the proposed algorithms.

Traffic Models for Energy Harvesting Based Wireless Sensor Networks

Background: Energy consumption is an important parameter in wireless sensor networks since it affects the lifetime of sensor nodes. Methods: Battery powered wireless sensor networks cannot sustain for long hence impractical for real-time applications. With energy harvesting and relevant protocols, this issue of extending the lifetime of nodes has been solved largely. The performance can be enhanced further if proper traffic analysis and modeling are done as a proactive approach. Results: A proper understanding of the traffic dynamics provides a base for further network optimization and detection of anomalies within the network. Much of the reported work in energy harvesting based WSN till now, is to design the efficient protocols only, traffic analysis and modeling is the ignored parameter. In this paper traffic models appropriate for the energy harvesting based system have been analyzed and their performance is evaluated for a MAC protocol. Conclusion: Results show that Weibull distribution is the most useful model for traffic modeling in energy harvesting based wireless sensor networks.

EDMARA2: a hierarchical routing protocol for EH-WSNs

The broad deployment of battery-powered wireless sensor networks (WSNs) enables a great variety of remarkable applications including remote environmental monitoring, healthcare, air quality monitoring, and so on. On the other hand, energy harvesting (EH) technologies have attracted widespread attention for their perpetual energy supply for sensor nodes. The conjunction of former and latter constitutes the EH-WSNs which no longer rely on a finite battery. Although EH-WSNs could harvest energy from the environment, the harvested and demanded energy do not meet. Thus, such networks suffer from intermittent energy shortage. The primary goal of protocols in such networks is to mitigate the energy shortage at the EH-WSN nodes. To resolve this issue, we propose EDMARA2 (Euclidean Distance Matrix Reconstruction Aided Approach), a hierarchical routing protocol for EH-WSNs based on EDM Reconstruction, k-medoids, and ILP which improves the energy efficiency of EH-WSNs. Hence, it reduces the packet loss. Experiments indicate that EDMARA2 surpasses approaches such as Direct Communication and DEARER about $$2.2\times$$ in the energy efficiency and $$0.5\times$$ in the packet loss percentage. It also yields results almost as efficient as Genie-aided approach.

Enhanced feature extraction method for motor fault diagnosis using low-quality vibration data from wireless sensor networks

Wireless sensor networks (WSNs), which are usually powered by batteries, have been extensively used in condition monitoring and fault diagnosis of motors. To extend the battery service life, the length of the acquired and transmitted signal should be short and the sampling resolution should be reduced. In this case, the motor signal quality is low, which affects the fault diagnosis accuracy. To address this issue, this study proposes an enhanced feature extraction method for motor fault diagnosis using low-quality vibration signals acquired from a battery-powered WSN node. First, the vibration signal is converted to an image using a wavelet synchrosqueezed transform technique. Second, the constructed image is enhanced using a histogram equalization. Finally, the enhanced image is inputted into a convolutional neural network (CNN) model, and the motor fault type can be recognized from the CNN output. The effectiveness and efficiency of the proposed method are validated by comparing its performance in the brushless direct motor test rig with the performance of several traditional methods. The relationship between the fault diagnosis accuracy and WSN performances is investigated and discussed. The proposed method shows potential applications for remote motor fault diagnosis using the low-quality vibration signal acquired from a WSN node with limited battery capacity.

Combining Solar Energy Harvesting with Wireless Charging for Hybrid Wireless Sensor Networks

The application of wireless charging technology in traditional battery-powered wireless sensor networks (WSNs) grows rapidly recently. Although previous studies indicate that the technology can deliver energy reliably, it still faces regulatory mandate to provide high power density without incurring health risks. In particular, in clustered WSNs there exists a mismatch between the high energy demands from cluster heads and the relatively low energy supplies from wireless chargers. Fortunately, solar energy harvesting can provide high power density without health risks. However, its reliability is subject to weather dynamics. In this paper, we propose a hybrid framework that combines the two technologies - cluster heads are equipped with solar panels to scavenge solar energy and the rest of nodes are powered by wireless charging. We divide the network into three hierarchical levels. On the first level, we study a discrete placement problem of how to deploy solar-powered cluster heads that can minimize overall cost and propose a distributed 1:61(1+ϵ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -approximation algorithm for the placement. Then, we extend the discrete problem into continuous space and develop an iterative algorithm based on the Weiszfeld algorithm. On the second level, we establish an energy balance in the network and explore how to maintain such balance for wireless-powered nodes when sunlight is unavailable. We also propose a distributed cluster head re-selection algorithm. On the third level, we first consider the tour planning problem by combining wireless charging with mobile data gathering in a joint tour. We then propose a polynomial-time scheduling algorithm to find appropriate hitting points on sensors' transmission boundaries for data gathering. For wireless charging, we give the mobile chargers more flexibility by allowing partial recharge when energy demands are high. The problem turns out to be a Linear Program. By exploiting its particular structure, we propose an efficient algorithm that can achieve near-optimal solutions. Our extensive simulation results demonstrate that the hybrid framework can reduce battery depletion by 20 percent and save vehicles' moving cost by 25 percent compared to previous works. By allowing partial recharge, battery depletion can be further reduced at a slightly increased cost. The results also suggest that we can reduce the number of high-cost mobile chargers by deploying more low-cost solar-powered sensors.

Data Drainage: A Novel Load Balancing Strategy for Wireless Sensor Networks

Load balance is a vital goal for battery-powered wireless sensor networks (WSNs). In this letter, we propose a novel load balancing strategy for data transmission of WSNs, namely, super links-based data drainage, which makes full use of the advantages of super nodes with more powerful hardware and greater communication capacity to realize data traffic redistribution. Being different from conventional passive late-remedy approaches, this is a positive and early-intervention strategy. Specifically, an evaluation function is designed to select appropriate start points and end points of super links, and the core idea is to transfer data from locations relatively far from the sink with a jump of data traffic to those near the sink with little data traffic. Extensive simulations are conducted to validate the effectiveness and advantages of the new strategy.

Statistical-QoS Guaranteed Energy Efficiency Optimization for Energy Harvesting Wireless Sensor Networks.

Energy harvesting, which offers a never-ending energy supply, has emerged as a prominent technology to prolong the lifetime and reduce costs for the battery-powered wireless sensor networks. However, how to improve the energy efficiency while guaranteeing the quality of service (QoS) for energy harvesting based wireless sensor networks is still an open problem. In this paper, we develop statistical delay-bounded QoS-driven power control policies to maximize the effective energy efficiency (EEE), which is defined as the spectrum efficiency under given specified QoS constraints per unit harvested energy, for energy harvesting based wireless sensor networks. For the battery-infinite wireless sensor networks, our developed QoS-driven power control policy converges to the Energy harvesting Water Filling (E-WF) scheme and the Energy harvesting Channel Inversion (E-CI) scheme under the very loose and stringent QoS constraints, respectively. For the battery-finite wireless sensor networks, our developed QoS-driven power control policy becomes the Truncated energy harvesting Water Filling (T-WF) scheme and the Truncated energy harvesting Channel Inversion (T-CI) scheme under the very loose and stringent QoS constraints, respectively. Furthermore, we evaluate the outage probabilities to theoretically analyze the performance of our developed QoS-driven power control policies. The obtained numerical results validate our analysis and show that our developed optimal power control policies can optimize the EEE over energy harvesting based wireless sensor networks.

Maximizing the lifetime of wireless sensor networks in trains for monitoring long-distance goods transportation

One key issue in designing battery-powered wireless sensor networks is to properly control the energy consumption of the sensor nodes in order to prolong their operation time (i.e. lifetime). In this article, we present a real-life application of wireless sensor networks in trains to monitor the goods conditions in a long-distance transportation. We study the wireless sensor network deployment problem in developing a monitoring system with the goal of maximizing the network lifetime under constraints derived from the real application scenario. The key technical problem to solve is to determine the sensor placement and the transmission level for each sensor node, as well as the appropriate number of sensor nodes. We first formulate the problem with a realistic discrete power model as a mixed integer linear programming problem. Then, a two-step efficient deployment heuristic is proposed to satisfy these constraints step by step. The evaluation results indicate that the proposed heuristic performs almost the...

Resource Allocation in Wireless Powered Sensor Networks With Circuit Energy Consumption Constraints

Wireless power transfer is able to provide sustainable and relatively stable energy supply for battery-powered wireless sensor networks. This paper investigates how to optimally design a wireless powered sensor network with minimal power requirements. To this end, we formulate an optimization problem to minimize the total energy consumption at two remote radio units (RRUs) by jointly optimizing energy beamforming and time assignment, where the circuit energy consumption including basic circuit and information processing energy consumption at sensors is taken into account in order to achieve a more practical and general system design. To solve this non-convex optimization problem, an efficient solution method is presented on the basis of variable substitutions and semidefinite relaxation technique. We analyze the optimality of our proposed solution method. When the number of sensors is not more than four, the rank-one constraint is always guaranteed. When it is larger than four, we show that with our proposed solution method via simulations, an approximate global optimal result can be achieved. Simulation results also show that by jointly optimizing the energy beamforming and time assignment, the system required power can be greatly reduced, while the energy beamforming has greater effect than time assignment on the proposed system. Moreover, it is shown that for fixed non-zero circuit energy consumption (including the static part and the dynamic part), the total energy consumption at the RRUs almost linearly increases with the increment of transmission rate requirement. Besides, the total energy consumption at two RRUs caused by different numbers of enjoyers and collaborators is also discussed.

Application of wireless power transfer technologies and intermittent energy harvesting for wireless sensors in rotating machines

Battery-powered wireless sensor networks have been extensively deployed in condition monitoring and structural health monitoring systems, but the performance of wireless sensors are limited by battery capacity and difficulty of application in rotating machines. In this paper, a variety of commercial wireless charging solutions and coil-shaft configurations for magnetic coupling are compared, having in mind of the application of continuously charging wireless sensors on rotating machines. For the co-axial configuration of the transmitter coil and the receiver coil, a Qi standard compliant wireless charging kit and a custom charging circuit are successfully applied to charge wireless sensors on small rotating test rigs. In order to harvest and store intermittent energy input from the wireless power source, a prototype receiver circuit using a supercapacitor and low-dropout regulator is designed and validated. Based on the prototype circuit, the radial configuration of single transmitter coil and multiple receiver coils is demonstrated for wireless power transfer to the sensor nodes on the drivetrain of a small wind turbine test rig.