Power Wireless Sensor Networks Research Articles

Massive MIMO Empowered Wireless Powered Sensor Networks: An Optimal Design With Statistical CSI

Wireless energy transfer (WET) has been anticipated as a viable solution to combat the challenges of scarce energy supply in Internet-of-Things (IoT) applications. To reap the benefit brought by large scale antenna arrays, massive multiple-input-multiple-out (MIMO) empowered wireless powered sensor networks (WPSNs) is investigated in this letter to meet the urgent demand for reliable and self-sustainable IoT applications. Considering a large-scale antenna array deployed at the access point for energy transfer to and information collection from the multiple-antenna wireless powered sensors, the hardening property in energy transfer is generalized and theoretically proved based on random matrix theory. Based on the hardening properties, the harvesting energy and the uplink sum-rate for sensor data collection are found to be asymptotically deterministic and depend on the statistical channel state information (CSI) only. The hardening properties in both energy and information transfers enable a novel optimal design on the WPSNs based on statistical CSI only. Particularly, the designs of the downlink energy beamforming and multiple uplink information beamforming can be decoupled, and global optimal solutions for the beamforming and time allocation to maximize the uplink sum-rate given downlink power constraint are found in semi-closed forms. Different from prior WPSNs designs, statistical CSI but not instantaneous CSI is exploited and frequent channel estimation and heavy communication overhead can be avoided with a much lower computation complexity, thus improving the practicability and sustainability of the WPSNs. The effectiveness of the proposed design is finally demonstrated by numerical simulations.

Optimal Transmission Strategy and Time Allocation for RIS-Enhanced Partially WPSNs

Wireless powered sensor networks (WPSNs) have evolved as a promising paradigm for energy-efficient communications. Recently, the proliferation of reconfigurable intelligent surface (RIS) has further been envisioned as a cost-effective solution for improving wireless power transfer (WPT) efficiency. In this paper, from the practical perspective of balancing the network sustainability and reliability, we consider a RIS-enhanced partially WPSN that composed of wireless-powered energy receivers (ERs) and battery-powered information receivers (IRs). Assuming the partially WPSN operates in time division multiple access (TDMA) mode, the joint optimization of covariance matrices, downlink/uplink (DL/UL) time allocation and RIS reflecting coefficients are investigated under the minimum DL rate constraint among all IRs for maximizing the achievable UL sum rate. Specifically, the single-IR single-ER (SISE) case is first studied based on the assumption of separate DL/UL RIS reflecting coefficients, in which an alternating optimization algorithm is proposed with semi-closed-form optimal solutions. In order to reduce the hardware overhead and signal processing complexity, we also investigate the case of identical DL/UL RIS reflecting coefficients, in which an iterative optimization algorithm is developed to tackle the coupled DL/UL transmissions. Then, we extend our work to the multiple-IRs multiple-ERs (MIME) case, where both the optimization problems corresponding to separate and identical DL/UL RIS reflecting schemes become more challenging to solve. To circumvent this intractability, we propose a successive convex relaxation (SCA) based alternating optimization algorithm and a low-complexity two-step algorithm. Finally, numerical results demonstrate the superior UL sum rate performance of our proposed algorithms over the adopted benchmarks.

Adaptive Capacitor Charging Circuit With Simplified Configuration for Efficient Piezoelectric Energy Harvesting

In this study, we present an adaptive capacitor charging circuit for efficient piezoelectric (PE) energy harvesting (EH), which simplifies the configuration for powering wireless sensor network (WSN) nodes. In the circuit, the PE harvester directly charges the storage capacitor through the rectifier, eliminating the energy loss in the dc–dc converter of conventional EH circuits. An adaptive control circuit is designed to optimize the average charging power. Theoretical analysis shows that the optimal average power of the novel circuit varies from 81.4% to 86.5% of the maximum power of the conventional EH circuit, without considering the power dissipation in the dc–dc converter. Moreover, the power losses of both the proposed and conventional circuits are estimated. When the excitation level is low, the net harvested power of the proposed circuit can be enhanced to as high as 180% of that of the conventional circuit. The experimental results validate that the power transferred to the load is increased by adopting the proposed interface. It is also proved that the circuit is adaptive to different harvesters and excitation conditions. In addition, the area of the proposed circuit is reduced, allowing it to be easily integrated into the WSN nodes.

An immune chaotic adaptive evolutionary algorithm for energy-efficient clustering management in LPWSN

Recently, low power wireless sensor networks (LPWSNs) have been widely used in the military and education. However, the lifetime of LPWSN is affected by the energy of the sensor, so the clustering design has received a lot of attention. In this study, the problem of designing optimal clustering for LPWSN is formulated as a cluster head selection problem, considering energy, which is an NP-hard problem. Therefore, a new structural model of the clustering design problem is constructed. This model can represent the process of sensor node clustering and information transfer. In this paper, a new immune chaotic adaptive evolutionary algorithm (ICAEA) is proposed and used in the clustering design of LPWSNs to obtain a better cluster head selection scheme. Then we design new advanced operators, such as immune operator and chaotic operator, which improve the convergence speed of the algorithm on the basis of evolutionary algorithm (EA). Moreover, ICAEA avoids local optima by introducing an adaptive operator, improves the convergence accuracy of the algorithm, and improves the performance of the optimization. Simulations are conducted to determine the performance improvements of ICAEA in terms of network lifetime and energy consumption compared to the latest clustering methods of R-LEACH, Q-LEACH, and ICCHR. The experimental results show that the proposed ICAEA algorithm outperforms RLEACH, Q-LEACH and ICCHR in terms of network lifetime under multiple identical experimental conditions. Moreover, ICAEA consumes less energy than R-LEACH, Q-LEACH and ICCHR in terms of system energy consumption.

Energy-Efficient Deployment in Static and Mobile Heterogeneous Multi-Hop Wireless Sensor Networks

We study a heterogeneous wireless sensor network (WSN) where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> heterogeneous access points (APs) gather data from densely deployed sensors and transmit their sensed information to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> heterogeneous fusion centers (FCs) via multi-hop wireless communication. The heterogeneous optimal deployment of APs and FCs is modeled as an optimization problem with total wireless communication power consumption of the network as its objective function. We consider both static WSNs, where APs and FCs retain their deployed position, and mobile WSNs where APs and FCs can move from their initial deployment to their optimal locations. Based on the derived necessary conditions for the optimal deployment in static WSNs, we propose an iterative algorithm to deploy APs and FCs. In addition, we study the necessary conditions of the optimal movement-efficient deployment in mobile WSNs with constrained movement energy and present iterative algorithms to find such deployments, accordingly. Simulation results show that our proposed deployment algorithms outperform the existing methods in the literature, and achieve a lower total wireless communication power in both static and mobile WSNs.

Unlock Self-Sustainability of Reconfigurable Intelligent Surface in Wireless Powered IoT Networks

This article discusses the self-sustainability of reconfigurable intelligent surface (RIS) in wireless powered Internet of Things (IoT) networks. Our vision is that RIS helps improve energy harvesting and data transmission capabilities simultaneously, without the extra utilization of RF spectrum and energy consumption. The inherent properties of RIS are first discussed to unveil its distinctive features, followed by a broader range of use cases motivated by RIS as their enabling technology. The focus is on the application of RIS in the wireless powered IoT networks, and its potential to interconnect and support these practical use cases. Such an application is then thoroughly evaluated in a case study of an RIS-assisted wireless powered sensor network, with system throughput, energy transmission time consumption, and energy harvesting as the key performance metrics. The comprehensive performance evaluation showcases the self-sustainable property of RIS being unlocked in the considered scenario, identifying a clear pathway toward the future wireless powered IoT networks. We further pave the way by exploring research challenges and open issues related to emerging technological development.

Battery lifespan enhancement strategies for edge computing-enabled wireless Bluetooth mesh sensor network for structural health monitoring

Wireless sensor networks (WSN) offer an automated, convenient, and low-cost option for building IoT networks for monitoring the structural health of complex infrastructures. The limited bandwidth of a typical WSN is not favorable for intensive data transfer. Therefore, utilizing edge computing that allows raw data to be processed and compressed at IoT devices enables efficient data transfer to the central terminal for data storage and further analytics. However, edge computing requires an additional microcontroller unit (MCU), which significantly increases the edge device power consumption and thus reduces the device's battery lifespan. In this paper, three strategies are developed for saving the battery power of sensor devices with edge computing: (1) managing the connectivity settings of the wireless protocol settings, (2) managing the operations of the edge computing chip, and (3) adding battery capacity with parallel connections. The first strategy focuses on fine-tuning the trade-off between the WSN data throughput and power consumption. The second strategy develops two modes for power saving in certain computationally relaxing periods: Shutdown mode and Low-power mode. The Shutdown mode suspends the operations of the edge computing chip, allowing the edge computing chip to run for a limited time per day. The Low-power mode allows continuous operation of the edge computing chop at low clock speed, which consumes less current. The third strategy doubles the device lifespan by doubling the battery charge capacity using parallel connections. Combining all three strategies significantly enhances the sensor battery lifespan from three weeks to at least six months, notably reducing the frequency of on-site hardware maintenance while providing smooth data transfer due to edge computing.

Performance analysis of wireless powered sensor network with opportunistic scheduling over generalized [formula omitted] shadowed fading channels

This paper investigates the performance of a multi-node wireless powered sensor network (WPSN) with an opportunistic scheduling scheme over κ−μ shadowed fading channels. The system assumes that all the sensor nodes (SNs) are energy constrained and harvest energy from a hybrid access point (HAP) in the downlink. In contrast, the node with the best end-to-end instantaneous signal-to-noise ratio (SNR) is scheduled for information transmission to HAP in the uplink. For the underlying system model, approximate closed-form analytical expressions is developed with the help of the moment matching method, which is then used to evaluate the system performances such as outage probability, effective throughput, and average bit error rate (BER). In addition, we also perform an asymptotic analysis by assuming that the system operates at a high SNR region, which gives us valuable insights about the diversity order and coding gain. The accuracy of the analysis is further confirmed with Monte-Carlo simulations, which validate the correctness of the theoretical analysis.

Age of information in energy harvesting dual-sensor status update systems with HARQ-MRC

We consider an energy harvesting powered wireless sensor network consisting of two sensors and one destination, where the sensors with finite-sized batteries harvest energy to transmit the sensed status update to the destination. A two-stage communication model, containing both control data and status update transmissions, is considered. Specifically, the destination first transmits the control data to the sensors using hybrid automatic repeat request with selection combining (HARQ-SC) for allocating wireless resources. Then, trigged by a common controller, the sensors synchronously sense and transmit the same status update to the destination using truncated HARQ with maximal ratio combining (HARQ-MRC). The system is interested in not only the fresh sensed data (e.g., humidity, temperature, moisture content) as the newest data usually carries the most valuable information, but also the steady harvested energy as the battery-powered sensors are energy-limited. We first present the energy state transition diagram by a Markov chain to characterize the energy harvesting model, and then provide the corresponding energy transition matrix as well as the steady-state distribution. Considering both the preemptive and non-preemptive transmissions, we derive the average age of information (AoI) expression as a function of energy arrival probability, update generation probability, and maximum allowable number of transmissions. Simulation results validate the theoretical results and demonstrate that the preemptive and non-preemptive schemes can outperform each other for minimizing the average AoI in different system configurations.

Deep reinforcement learning based scheduling for minimizing age of information in wireless powered sensor networks

For real-time monitoring system, the age of information (AoI) is usually used to quantify the freshness of information at a monitor about some stochastic processes observed by the source node. In this paper, we consider the wireless powered sensor networks (WPSNs) where multiple sensor nodes send update packets to the base station. Time is divided into slots of equal duration and at each slot either wireless energy transfer or packet transmission is conducted. We aim to minimize the long-term average weighted sum-AoI of different processes at the base station. Specifically, we first formulate the average weighted sum-AoI minimization problem as a multi-stage stochastic non-linear integer programming (NLP) subject to the energy causality constraints. Second, we design an algorithm which first applies Lyapunov optimization to decouple the multi-stage stochastic NLP into per-frame deterministic NLP problems. Then in each frame, our algorithm utilizes the model-free DRL to solve the per-frame NLP problem with very low computational complexity where one exploration policy is designed to obtain multiple one-hot candidate actions based on single real-number output of neural network. We demonstrate through simulations that, our proposed algorithm can achieve greatly smaller average weighted sum-AoI than the available DQN-based algorithm and also alleviate the problem that some source nodes may have large instantaneous AoIs.

On-Demand Energy Transfer and Energy-Aware Polling-Based MAC for Wireless Powered Sensor Networks.

To improve the performance of the wireless powered sensor network (WPSN), this paper proposes a frequency division duplex (FDD)-based on-demand energy transfer protocol and an energy-aware polling-based medium access control (MAC) protocol, called composite energy and data first (CEDF), by using the numbers of data packets and energy packets to determine polling priorities. The performance of the proposed MAC protocol, i.e., CEDF, along with the on-demand energy transfer protocol was evaluated through simulations, with comparison to the closely related protocols such as the round robin (RR) and data first (DF) polling protocols. Compared with RR and DF, our proposed CEDF performs much better in terms of throughput, data packet loss rate, and delay. Additionally, the doubly near–far problem in WPSNs under our proposed on-demand energy transfer protocol and CEDF was investigated to come up with good solutions to alleviate such a problem.

Investigation of temperature gradient between ambient air and soil to power up wireless sensor network device using a thermoelectric generator

This paper proposes a study of an energy harvesting system for powering wireless sensor network (WSN) devices. The thermal energy harvesting system used is based on the thermal energy source between ambient air at the soil surface with five depth levels. Measurement was taken for 46 days in a garden area located in Melaka, Malaysia. A feasibility study of soil temperature measurement to obtain a temperature gradient can be used for harvesting by using thermoelectric generators (TEG) modules. Then, the efficiency of TEG with several different configurations based on temperature gradient data has been tested in the laboratory. The results revealed that the depth of soil 6 cm between sensors 1 and 3 will gave the best representation of level average temperature different around 1 ℃. Based on the temperature gradient data, the combination of three TEG SP1848 in a series connection with DC-DC step-up circuit DC1664 will produce an optimum voltage output of about 3 V. This output voltage is enough to operate low power IoT device derived from thermal energy.

Ultra-Low Power Wireless Sensor Networks Based on Time Slotted Channel Hopping with Probabilistic Blacklisting

Devices in wireless sensor networks are typically powered by batteries, which must last as long as possible to reduce both the total cost of ownership and potentially pollutant wastes when disposed of. By lowering the duty cycle to the bare minimum, time slotted channel hopping manages to achieve very low power consumption, which makes it a very interesting option for saving energy, e.g., at the perception layer of the Internet of Things. In this paper, a mechanism based on probabilistic blacklisting is proposed for such networks, which permits to lower power consumption further. In particular, channels suffering from non-negligible disturbance may be skipped based on the perceived quality of communication so as to increase reliability and decrease the likelihood that retransmissions have to be performed. The only downside of this approach is that the transmission latency may grow, but this is mostly irrelevant for systems where the sampling rates are low enough.

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.

Residual Energy Estimation-Based MAC Protocol for Wireless Powered Sensor Networks.

This paper presents a residual energy estimation-based medium access control (REE-MAC) protocol for wireless powered sensor networks (WPSNs) composed of a central coordinator and multiple sensor devices. REE-MAC aims to reduce overhead due to control messages for scheduling the energy harvesting operation of sensor devices and provide fairness for data transmission opportunities to sensor devices. REE-MAC uses two types of superframes that operate simultaneously in different frequency bands: the wireless energy transfer (WET) superframe and wireless information transfer (WIT) superframe. At the beginning of each superframe, the coordinator estimates the change in the residual energy of individual sensor devices caused by their energy consumption and energy harvesting during the previous superframe. It then determines the devices’ charging priorities, based on which it allocates dedicated power slots (DPSs) within the WET superframe. The simulation results demonstrated that REE-MAC exhibits superior performance for the harvested energy, average freezing time, and fairness to existing representative WPSN MAC protocols.

Design of a perfect and multi-resonant metamaterial absorber for electromagnetic energy harvesting applications

In this work, a perfect absorber based on a split ring resonator structure is proposed and numerically analyzed. The software CST STUDIO was employed to carry out the numerical analysis and the optimization of the proposed structure. The electromagnetic properties of the proposed metamaterial cell were analyzed in the first phase of this study demonstrating that such structure resonates at 2.4 GHz and 4.2 GHz simultaneously. In fact, this structure has negative permittivity and permeability in these two bands. The optimization process has led us to obtain a compact resonator, which has a total size of 15 mm × 15 mm. Subsequently, the capacity of this structure as an absorber of electromagnetic energy is analyzed. The obtained results reveal that this structure has absorption efficiencies of 98.2% and 99.7% for the first and second bands respectively. Also, other characteristic parameters were evaluated. This shows that the proposed structure has a high electrical performance and can be used for the collection of electromagnetic energy, which can be used to power wireless sensor networks.

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.

Reducing Power Consumption in Hexagonal Wireless Sensor Networks Using Efficient Routing Protocols

Power consumption and network lifetime are vital issues in wireless sensor network (WSN) design. This motivated us to find innovative mechanisms that help in reducing energy consumption and prolonging the lifetime of such networks. In this paper, we propose a hexagonal model for WSNs to reduce power consumption when sending data from sensor nodes to cluster heads or the sink. Four models are proposed for cluster head positioning and the results were compared with well-known models such as Power Efficient Gathering In Sensor Information Systems (PEGASIS) and Low-Energy Adaptive Clustering Hierarchy (LEACH). The results showed that the proposed models reduced WSN power consumption and network lifetime.

Efficient Link Scheduling Based on Estimated Number of Packets in Queue on Industrial Wireless Sensor Networks

The links of low power wireless sensor networks are error prone and the transmission on a wireless link is determined probabilistically by the packet reception rate (PRR) of the link. On the other hand, there is a very strict requirement in the end-to-end reliability and delay of sensor data in industrial wireless sensor networks (IWSNs). The existing approaches to provide the end-to-end reliability in IWSNs is retransmitting the packet when failure occurs. These approaches transmit a packet multiple times in successive time slots to provide the required reliability. These approaches, however, can increase the average delay of packets and the number of packets buffered in a queue. This paper proposes a new scheme to estimate the probabilistic amount of packets, called queue level (QL), in the buffer of each node based on the PRRs of the wireless links. This paper also proposes a QL-based centralized scheduling algorithm to assign time slots efficiently in TDMA-based IWSNs. The proposed scheduling algorithm gives higher priority to the nodes with higher QL. By assigning time slots first to the node with the highest QL, we can reduce the average end-to-end delay of packets and reduce the amount of buffered packets in the queue while satisfying the required end-to-end reliability. The performance of the proposed scheduling algorithm have been evaluated through a simulation using the Cooja simulator and compared with the existing approach. In the simulation on an sample network with the target end-to-end reliability of 99%, all of the flows were shown to guarantee the target reliability in both algorithms: on average, 99.76% in the proposed algorithm and 99.85% in the existing approach. On the other hand, the proposed algorithm showed much better performance than the existing approach in terms of the average end-to-end delay of packets (about 47% less) and the number of maximally buffered packets in the queue of each node (maximally, more than 90% less).

A Novel Transmission Policy for Intelligent Reflecting Surface Assisted Wireless Powered Sensor Networks

This paper proposes a novel transmission policy for a wireless powered sensor network (WPSN) assisted by an intelligent reflecting surface (IRS) to enhance the performance of wireless energy transfer (WET) and wireless information transfer (WIT) with certain phase shifts. To achieve its self-sustainability, IRS collects energy from the energy station to support its own control circuit operation. Our proposed policy for the considered WPSN is called IRS assisted harvest-then-transmit time switching (IRS-HTT-TS), which is capable to schedule the transmission time slots by switching between energy collection and energy reflection modes. We provide a joint design of the transmission time slots, the power allocation as well as the discrete phase shifts of the WET and WIT, aiming to maximize the system sum throughput. This formulates a problem as a mixed-integer non-linear program (MINLP), which is NP-hard and non-convex. To effectively solve this problem, we first relax it to the one with continuous phase shifts, and then propose a two-step approach and decompose the original problem into two sub-problems. We address the first sub-problem in closed form with respect to the phase shifts of the WIT. For the second sub-problem, we apply the Lagrange dual method and Karush-Kuhn-Tucker conditions to derive the optimal closed-form transmission time slots, power allocation, and phase shift of the WET, respectively. The optimal discrete phase shifts can be obtained by quantizing the continuous values. Numerical results demonstrate the effectiveness of the proposed policy and validate the beneficial role of the IRS in comparison to the benchmark schemes.