Energy Harvesting Sensor Nodes Research Articles

Self-triggered Control with Energy Harvesting Sensor Nodes

Distributed embedded systems are pervasive components jointly operating in a wide range of applications. Moving toward energy harvesting powered systems enables their long-term, sustainable, scalable, and maintenance-free operation. When these systems are used as components of an automatic control system to sense a control plant, energy availability limits when and how often sensed data are obtainable and therefore when and how often control updates can be performed. The time-varying and non-deterministic availability of harvested energy and the necessity to plan the energy usage of the energy harvesting sensor nodes ahead of time, on the one hand, have to be balanced with the dynamically changing and complex demand for control updates from the automatic control plant and thus energy usage, on the other hand. We propose a hierarchical approach with which the resources of the energy harvesting sensor nodes are managed on a long time horizon and on a faster timescale, self-triggered model predictive control controls the plant. The controller of the harvesting-based nodes’ resources schedules the future energy usage ahead of time and the self-triggered model predictive control incorporates these time-varying energy constraints. For this novel combination of energy harvesting and automatic control systems, we derive provable properties in terms of correctness, feasibility, and performance. We evaluate the approach on a double integrator and demonstrate its usability and performance in a room temperature and air quality control case study.

On Complete Targets Coverage in Rechargeable IoT Networks: A Message-Passing Approach

This article studies targets coverage in an energy harvesting (EH) Internet of Things (IoT) network. Specifically, it addresses the problem of activating subsets of EH sensor nodes to monitor targets, such as valuable assets or the ingresses and egresses of a building. A key requirement, so called complete targets coverage, is that all targets must be monitored by at least one sensor node at all times. To meet this requirement, we need to derive set covers, where each set cover is comprised of one or more sensor nodes that are activated simultaneously in each time slot. To this end, we show for the first time how the belief propagation message-passing framework can be used to derive these set covers. Advantageously, our approach does not require future energy arrivals information at devices. The results show that our message-passing approach is within 90% of the optimal coverage lifetime.

EICO: Energy-Harvesting Long-Range Environmental Sensor Nodes With Energy-Information Dynamic Co-Optimization

Intensive research on energy-harvested sensor nodes has been driven by the challenges in achieving stringent design goals of battery lifetime, information accuracy, transmission distance, and cost. This challenge is further amplified by the inherent power-intensive nature of long-range communication when sensor networks are required to span vast areas, such as agricultural fields and remote terrain. Solar power is a common energy source in wireless sensor nodes, however, it is not reliable due to fluctuations in available power stemming from the changing seasons and weather conditions. This article tackles these issues by presenting a perpetually powered, energy-harvesting sensor node which utilizes a minimally sized solar cell and is capable of long-range communication by dynamically co-optimizing energy consumption and information transfer, termed as energy-information dynamic co-optimization (EICO). This energy-information intelligence is achieved by adaptive dutycycling of information transfer based on the total amount of energy available from the harvester and charge storage element to optimize the energy consumption of the sensor node, while employing in-sensor analytics (ISA) to minimize loss of information. This is the first reported sensor node < 35 cm2 in dimension, which is capable of long-range communication over >1 km at continuous information transfer rates of up to 1 packet/second which is enabled by EICO and ISA.

Improving multimedia data transmission quality in wireless multimedia sensor networks though priority-based data collection

&lt;span&gt;Wireless multimedia sensor networks (WMSNs) are special kinds of wireless sensor networks (WSN) that can send multimedia data such as audio and video streams. Sensors used in WMSNs are smart, tiny, and resource constraint sensor nodes (SNs) distributed in a large area. Typically, multimedia data are large in comparison to other data types. As a result, WMSNs have to deal with high volumes of packet transmission, leading to a high rate of packet loss and network congestion. Network congestion can significantly affect the quality of service and usually lead to high energy consumption. Thus, to improve the quality of service (QoS) and transmission performance, it is necessary to deal with network congestion. In the past, different packet prioritizing methods were proposed to deal with this issue. However, improving QoS usually requires high energy to function correctly. Consequently, using rechargeable sensor nodes to reduce energy consumption is an acute solution. In this research, priority-based data collection is considered to cut down on data distortion and improve the QoS of the multimedia sensor network. Additionally, energy harvesting sensor nodes were used to reduce energy consumption due to the high transmission rate. The simulation result shows a noticeable improvement in the performance of our proposed method in comparison to previous methods.&lt;/span&gt;

Event Distortion-Based Clustering Algorithm for Energy Harvesting Wireless Sensor Networks

Wireless sensor networks (WSNs) consist of compact deployed sensor nodes which collectively report their sensed readings about an event to the Base Station (BS). In WSNs, due to the dense deployment, sensor readings can be spatially correlated and it is nonessential to transmit all their readings to the BS. Therefore, for more energy efficient, it is vital to choose which sensor node should report their sensed readings to the BS. In this paper, the event distortion-based clustering (EDC) algorithm is proposed for the spatially correlated sensor nodes. Here, the sensor nodes are assumed to harvest energy from ambient electromagnetic radiation source. The EDC algorithm allows the energy-harvesting sensor nodes to select and eliminate nonessential nodes while maintain an acceptable level of distortion at the BS. To measure the reliability, a theoretical framework of the distortion function is first derived for both single-hop and two-hop communication scenarios. Then, based on the derived theoretical framework, the EDC algorithm is introduced. Through extensive simulations, the performance of the EDC algorithm is evaluated in terms of achievable distortion level, number of alive nodes and harvested energy levels. As a result, EDC algorithm can successfully exploit both the spatial correlation and energy harvesting to improve the energy efficiency while preserving an acceptable level of distortion. Furthermore, the performance comparisons reveal that the two-hop communication model outperforms the single-hop model in terms of the distortion and energy-efficiency.

How Can Wake-up Radio Reduce LoRa Downlink Latency for Energy Harvesting Sensor Nodes?

LoRa is popular for internet of things applications as this communication technology offers both a long range and a low power consumption. However, LoRaWAN, the standard MAC protocol that uses LoRa as physical layer, has the bottleneck of a high downlink latency to achieve energy efficiency. To overcome this drawback we explore the use of wake-up radio combined with LoRa, and propose an adequate MAC protocol that takes profit of both these heterogeneous and complementary technologies. This protocol allows an opportunistic selection of a cluster head that forwards commands from the gateway to the nodes in the same cluster. Furthermore, to achieve self-sustainability, sensor nodes might include an energy harvesting sub-system, for instance to scavenge energy from the light, and their quality of service can be tuned, according to their available energy. To have an effective self-sustaining LoRa system, we propose a new energy manager that allows less fluctuations of the quality of service between days and nights. Latency and energy are modeled in a hybrid manner, i.e., leveraging microbenchmarks on real hardware platforms, to explore the influence of the energy harvesting conditions on the quality of service of this heterogeneous network. It is clearly demonstrated that the cooperation of nodes within a cluster drastically reduces the latency of LoRa base station commands, e.g., by almost 90% compared to traditional LoRa scheme for a 10 nodes cluster.

Dynamic Power Splitting Simultaneous Wireless Information and Power Transfer Split Receiver for Wireless Sensor Networks

Simultaneous wireless information and power transfer (SWIPT) presents a genuine opportunity toward achieving sustainable low-energy wireless devices. This paper introduces a receiver design for an energy harvesting sensor node (SN). The receiver is equipped with multiple radio frequency (RF) inputs. Furthermore, the receiver contains separate circuitries for information decoding (ID) and energy harvesting (EH) with dynamic power splitting (PS). The ID and EH circuits are connected to a power splitter that first combines all the RF inputs and then splits the power between the circuitries. Moreover, dynamic PS allows regulating the power split ratio between EH and ID circuitries. Consequently, the output of the EH circuit can be monitored and increased by adjusting the PS ratio. The proposed receiver design increases the harvestable energy by moving the ID into a separate circuitry. A voltage multiplier arranged in a Dickson scheme is used to level up the received voltage. At the transmitter side, the energy signal (ES) is sent through high power unmodulated continuous wave centered at the carrier frequency, while the information signal (IS) is sent via low power subcarriers around the carrier frequency. Such power allocation increases the harvestable power while reducing interference to external wireless networks. Moreover, different system measurements, including harvested energy from the received signal and data rates, are presented, where the split receiver scheme showed a significant improvement compared to the combined scheme in terms of harvestable power.

Analysis of the Interdelivery Time in IoT Energy Harvesting Wireless Sensor Networks

In this article, we investigate an energy harvesting (EH) wireless sensor network for the Internet of Things (IoT) where monitoring applications require a continuous update of sensing information. The considered system consists of independent EH sensor nodes equipped with capacitors and providing, through unreliable channels, status updates to a non EH sink. The distribution of the interdelivery time, i.e., the time elapsed between two successive and successful status update deliveries, is derived in the closed-form expression considering a random EH arrival process. Moreover, the interdelivery violation probability metric, defined as the probability to exceed a predetermined interdelivery threshold, is analyzed. Our analysis reveals that the violation probability is highly dependent on the size of the capacitor. Both analytical and simulation results demonstrate the existence of an optimal capacitor size that achieves the minimum violation probability. Moreover, our findings reveal an interesting tradeoff in the system design. On one hand, a small capacitor charges quickly and thus status updates are sent more frequently but with lower transmit power and thus a high error rate. On the other hand, a large capacitor increases the transmit power and boosts the successful data transmission probability, at the expense of a higher waiting time before filling the capacitor and transmitting sensed data.

Online Transmission Policy for Energy Harvesting Sensor Node With Energy Loss

This letter considers a point-to-point data transmission scheme over a fixed number of slots where the energy harvesting transmitter operates in harvest-use-store (HUS) mode and an imperfect supercapacitor with storage loss and energy leakage loss is used as an energy storage device. For these settings, the dynamic programming (DP) method is known to achieve the optimal throughput, but the ` curse of dimensionality' necessitates the search of schemes that allow for an improved performance-complexity trade-off. The work focuses on deriving a computationally efficient online transmission policy that achieves the optimal or near-optimal throughput. This letter presents a novel computationally efficient uniform thresolding based online transmission policy that achieves near-optimal throughput. The work also presents numerical results which demonstrate that the proposed policy with supercapacitor-based energy storage achieves better or the same performance as compared to the battery-based energy storage, thus allowing for a longer lifetime of sensor networks.

Judicious data management for sustaining an energy harvesting sensor node

SummaryPerpetual lifetimes and low maintenance are few of the attractive aspects of sensor nodes that harvest ambient energy. However, their operation depends heavily on the energy profiles of their harvesting source(s). In this work, we study the suitability of energy harvesting sensor (EHS) Nodes, powered using indoor lighting and vibrations, for a simple temperature monitoring application. To help these nodes sustain, we have proposed schemes that allow the nodes to sample and transmit data judiciously. Along with an adaptive sampling based on autoregressive (AR) model, we have proposed a regulating function based transmission scheme that regulates the amount of transmitted data based on the energy available at the node and the characteristics of the data so that even with limited transmissions the fidelity of the data is not lost. With the help of thorough evaluations, we can conclude that an EHS node fares quite well. Results show that adaptive sampling and transmissions based on regulating function not only save energy at the sensor nodes, but they also reduce the amount of data generated and accumulated in a network.

A security-based observability method for optimal PMU-sensor placement in WAMS

Optimal PMU placement (OPP) problem is one of the most important issues in wide area measurement systems (WAMS). Delivering the measured data to the control center is possible through communication infrastructure (CI) that is a part of the WAMS. However, to well estimate the state of the system, observability of the measurement system is necessary. In this paper, a security-based observability (SBO) method for the PMUs and related CI is proposed. A hybrid wireless sensor-based structure including energy harvesting sensor nodes (EHSNs) and plug-in powered sensor nodes (PPSNs) is utilized as the communication system. The contingency-constraint optimal PMU placement and related sensor-based CI is proposed and formulated. To increase the reliability of the CI, a reliability index for the wireless sensors is added as a constraint to the objective function which includes the total cost minimization subject to power system observability and communication constraints. The effect of zero-injection buses (ZIBs) and radial buses in the power system are also included in the model to get more practical results. A mixed-integer linear programming (MILP) is used for contingency constraint PMU placement and a binary-coded genetic algorithm (BCGA) is accordingly used for solving the examined communication optimization. The IEEE 13-bus and IEEE 37-bus test feeder systems are utilized to evaluate the effectiveness of the proposed method.

RETRACTED ARTICLE: Soft computing approach based energy and correlation aware cooperative data collection for wireless sensor network

Energy plays the predominant constraint in wireless sensor network (WSN) hence, an energy and correlation aware cooperative data collection (ECCDC) method of soft computing approach has been discussed in this work. This work is an attempt to maximize the life span of the network by identifying and rectifying the major energy exploiting activities of WSN and it also defines a hierarchical network structure with balanced clusters and energy harvesting sensor nodes. These sensor nodes select the Cluster Heads (CHs) among themselves using a low overhead passive clustering approach. The cluster balancing has been performed by acknowledging only the most deserved sensor nodes as their members. The members co-operatively collect the data from the region of interest. During every time slot, the cluster members are chosen for reporting, based on the mutual correlation, link quality of the member with the CH and the residual energy of the member nodes. The fraction of data collected at the CH is then sent through the optimized inter-cluster paths to the sink, wherein the received data are used for estimation, based on the node’s position and correlation pattern. This proposed system of optimize method reduces the energy consumed, due to the transmission of redundant data and the data loss by preferring optimized data paths.

Precoding Design for Energy Efficiency Maximization in MIMO Half-Duplex Wireless Sensor Networks with SWIPT.

This paper explores the energy efficiency (EE) maximization problem in single-hop multiple-input multiple-output (MIMO) half-duplex wireless sensor networks (WSNs) with simultaneous wireless information and power transfer (SWIPT). Such an energy efficiency maximization problem is considered in two different scenarios, in which the number of energy-harvesting (EH) sensor nodes are different. In the scenario where the single energy-harvesting sensor node is applied, the modeled network consists of two multiple-antenna transceivers, of which the energy-constrained energy-harvesting sensor node harvests energy from the signals transmitted from the source by a power splitting (PS) scheme. In the scenario of multiple EH sensor nodes, K energy-constrained sensor nodes are applied and the same quantity of antennas are equiped on each of them. The optimization problem is formulated to maximize the energy efficiency by jointly designing the transceivers’ precoding matrices and the PS factor of the energy-harvesting sensor node. The considered constraints are the required harvested energy, the transmission power limit and the requirement on the data rate. The joint design of the precoding matrices and the PS factor can be formulated as an optimization problem, which can be transformed into two sub-problems. An alternating algorithm based on Dinkelbach is proposed to solve the two sub-problems. The convergence of the proposed alternating algorithm, the solution optimality and the computational complexity are analyzed in the paper. Simulation results demonstrate the convergence and effectiveness of our proposed algorithm for realizing the maximum energy efficiency.

Optimal Design of a Wide Area Measurement System Using Hybrid Wireless Sensors and Phasor Measurement Units

Real-time monitoring of the power system by phasor measurement units (PMUs) leads to the development of such devices in a wide area measurement system (WAMS). However, the power system observability cannot be obtained by employing only PMUs. The communication infrastructure (CI) is a significant part of the WAMS that has to be optimally designed and implemented to collect data from PMUs and deliver them to control centers. In this paper, a novel hybrid wireless sensor network is proposed for the connection of PMUs throughout the system to enable convenient and low-cost communication media. The problem of observability in the communication system is checked along with the optimal placement of PMUs in the power system to reach full observability. A hybrid wireless sensor network including plug-in powered sensor nodes (PPSNs) and energy harvesting sensor nodes (EHSNs) is utilized for increasing the reliability of the communication system. In the proposed co-optimal PMU-sensor placement problem, the main objective is to minimize the total cost of PMU placement and the related communication system, considering full observability of the power system and CI. To achieve better results, the zero-injection bus (ZIB) effect and system observability redundancy index (SORI) are considered as a constraint in the objective function. A binary-coded genetic algorithm is used for solving the proposed mixed-objective optimization problem subject to different technical operating constraints. The proposed method is examined on IEEE 13-bus and IEEE 37-bus test feeder systems. The results show the applicability and effectiveness of the proposed method compared with the conventional methods in this subject area.

Age-Minimal Transmission for Energy Harvesting Sensors With Finite Batteries: Online Policies

An energy-harvesting sensor node that is sending status updates to a destination is considered. The sensor is equipped with a battery of finite size to save its incoming energy, and consumes one unit of energy per status update transmission, which is delivered to the destination instantly over an error-free channel. The setting is online in which the harvested energy is revealed to the sensor causally over time after it arrives, and the goal is to design status update transmission times (policy) such that the long term average age of information (AoI) is minimized. The AoI is defined as the time elapsed since the latest update has reached at the destination. Two energy arrival models are considered: a random battery recharge (RBR) model, and an incremental battery recharge (IBR) model. In both models, energy arrives according to a Poisson process with unit rate, with values that completely fill up the battery in the RBR model, and with values that fill up the battery incrementally in a unit-by-unit fashion in the IBR model. The key approach to characterizing the optimal status update policy for both models is showing the optimality of renewal policies , in which the inter-update times follow a renewal process in a certain manner that depends on the energy arrival model and the battery size. It is then shown that the optimal renewal policy has an energy-dependent threshold structure, in which the sensor sends a status update only if the AoI grows above a certain threshold that depends on the energy available in its battery. For both the random and the incremental battery recharge models, the optimal energy-dependent thresholds are characterized explicitly , i.e., in closed-form, in terms of the optimal long term average AoI. It is also shown that the optimal thresholds are monotonically decreasing in the energy available in the battery, and that the smallest threshold, which comes in effect when the battery is full, is equal to the optimal long term average AoI.

Co-optimal PMU and communication system placement using hybrid wireless sensors

Abstract In smart distribution networks, the problem of measuring and estimating the state of the system is vital. Communication infrastructure, as an inseparable part of the wide area measurement system (WAMS), has to be optimally designed and placed to collect information from phasor measurement units (PMUs) and deliver them to control centers. In this paper, a novel hybrid wireless sensor network (WSN) is proposed for connecting of PMUs throughout the system to enable a convenient and low cost communication media. Having found the optimal placement of PMUs in power system to reach a full observability, the problem of observability in communication system will be handled to provide proper transmission of measured data to the control centers. In the proposed co-optimal PMU-sensor placement problem, the main objective is to minimize the total cost of PMU placement in power system and related communication system considering full observability and reliability constraints. For increasing the reliability of communication system, a hybrid WSN including plug-in powered sensor nodes (PPSNs) and energy harvesting sensor nodes (EHSNs) is utilized. A binary-coded genetic algorithm (BCGA) is used for solving the proposed mixed-objective optimization problem subject to different technical operating constraints. The proposed method in examined on a sample 11-bus test system and IEEE 37-bus test feeder systems. The results show the applicability and effectiveness of the proposed algorithm compared to the conventional methods in this subject area.

Energy Optimization With Higher Information Quality for SHM Application in Wireless Sensor Networks

Energy is an important resource in wireless sensor nodes which gets depleted soon, especially in harsh environments, where the sensor’s batteries once deployed cannot be reached easily for replacement or recharging activities. Therefore, designing energy-efficient routing protocol for wireless sensor networks is the objective for the applications like structural health monitoring (SHM), military surveillance, and so on. In this paper, energy optimization is performed by the clustering technique. In clustering, the energy harvesting sensor nodes, which are close to the sink and having a higher probability of energy arrival rate, are elected as cluster head nodes. The remaining nonelected node reserves a portion of their harvested energy to be used by them when they are elected as cluster heads. Thus, utilizing this reserved harvested energy, the cluster head nodes can survive longer. Routing the data from cluster head nodes to the sink is done with the shortest path available. Also, sensor node placement optimization is important in SHM applications, as if the desired information quality is achieved with less number of sensor nodes, then it is a cost-effective method. Thus, using the exhaustive search method, the sensor node location optimization is performed in the study presented in this paper.

Revisiting Effectiveness of Energy Conserving Opportunistic Transmission Schemes in Energy Harvesting Wireless Sensor Networks

Opportunistic transmission schemes improve the lifetime of conventional wireless sensor networks (WSNs) by reducing the number of transmissions. However, this comes at the expense of performance since fewer measurements are available. We show that in energy harvesting (EH) WSNs, in which the sensor nodes harvest energy from the environment, this trade-off is fundamentally different. For a general model in which the nodes experience independent and non-identical fading and the EH process at a node is stationary and ergodic, we present lower bounds on the mean squared error (MSE) for two important classes of channel-based opportunistic transmission schemes, namely, censoring and ordered transmissions. For the latter, we present two novel variants that arise depending on whether the energy in the battery of an EH sensor node is accounted for before ordering or not. For censoring, the lower bound leads to an insightful and explicit characterization of the optimum censoring threshold for each node. For ordered transmissions, it helps determine the optimal number of nodes that should be selected to transmit. We find that the ordered transmission schemes can outperform the censoring scheme. We also propose a hybrid scheme that combines the best features of the above schemes for EH WSNs.

Rate-Energy Outage Analysis of MISO SWIPT With Multiple Energy Harvesting Sensors

We study a multi-input-single-output (MISO) transmission system consisting of a multi-antenna basestation (BS) and a single-antenna user in the presence of multiple single-antenna radio frequency (RF) energy harvesting (EH) sensor nodes, where the user decodes information and multiple EH sensor nodes accumulate energy through the received signal from the BS. The sensor nodes coordinate their transmissions to perform virtual MISO beamforming, which requires sufficient individual and sum transmission power across multiple sensor nodes. For this reason, we consider simultaneously both the individual and sum EH constraints for SWIPT networks. Specifically, under both the individual and sum EH constraints, we propose beam selection of random unitary beamforming to maximize the transmission rate of the MISO system. Then we derive the exact rate-energy outage probability in closed-form for the system with identical average value of harvested energy across all sensor nodes and the exact probability in recursive form for the system with distinct average value of harvested energy at each sensor node. Numerical results confirm that the analyses are exact and the outage performance improves as the number of antennas at the BS increases and/or as the average received power at the user and sensor nodes increases.

Fair and Low Complexity Node Selection in Energy Harvesting Wireless Sensor Networks

The use of energy harvesting in wireless sensor networks is an emerging wireless communication technology with a wide range of applications. Maximizing the number of samples collected by the sensor nodes and transmitted to the sink is a key element in order to minimize uncertainties for those applications. This work considers energy harvesting sensor nodes that are transmitting to a nonenergy harvesting sink. Using a zero-forcing (ZF) receiver, the sink selects the largest possible set of transmitting sensor nodes to maximize the received quantity of information while the selected transmissions should satisfy a given quality of service defined by signal-to-noise ratio and certain fairness constraint. The maximization problem is formulated as an integer nonlinear program and it is proved to be NP-hard. Thus, two low complexity and efficient heuristic algorithms are proposed to solve this problem. Two other variants are also proposed in order to improve the system fairness. We demonstrate via simulations in a node selection context that the proposed algorithms which consider the energy state of the system better exploit the full system resources compared to state-of-the-art algorithms which only consider channel conditions. Interestingly, simulation results show that the performance of the proposed algorithms varies as a function of the energy availability. Hence, they are adapted to the energy harvesting context.