Energy Harvesting Wireless Sensor Networks Research Articles

Joint optimal placement, routing, and energy allocation in wireless sensor networks with a shared energy harvesting module

Today, there is significant attention being paid to wireless sensor networks equipped with energy harvesting modules (energy harvesting wireless sensor networks). They have been experimentally applied to some practical scenarios, such as structural health monitoring. However, the constraints (e.g. energy-neutral operation, node placement, and routing protocols) of energy harvesting wireless sensor networks still need further consideration before their actual deployment on structures. In this article, we consider a novel energy harvesting wireless sensor networks system for structural health monitoring in which a common energy harvesting module (including its storage battery) provides shared access to all energy harvesting nodes. The individual nodes do not own the independent energy harvesting modules and can only withdraw energy from the shared battery, which has an optionally changing energy level. Our aim is to find optimal solutions for three problems: (1) the minimum number of nodes needed for deployment, (2) the most efficient path from each node to the sink, and (3) the maximum network utility under energy harvesting constraints. Therefore, a joint optimization algorithm including sensor placement, routing, and energy allocation is introduced and solved; a sensor location optimization scheme is proposed according to the Fisher information matrix; and the condition of the communication channels and the nodes’ energy harvesting status are considered. We then propose an algorithm to jointly minimize the number of nodes and maximize the data sampling quality under energy-neutral working settings. Simulation results show that the proposed algorithm always attains high-efficiency network energy distribution and achieves higher network utility than existing approaches.

Low-Cost Standard Signatures for Energy-Harvesting Wireless Sensor Networks

This work is motivated by a general question: can micro-scale energy-harvesting techniques be exploited to support low-cost standard security solutions on resource-constrained devices? We focus on guaranteeing integrity and authentication in Internet of Things (IoT) and Wireless Sensor Network (WSN) applications. In this article, we propose techniques to make ECDSA signatures low cost and implementable on resource-constrained devices. By combining precomputation techniques and energy-harvesting capabilities of modern sensor nodes, we achieve significant improvement over prior works. In addition, we show that the cost of ECDSA signatures can be reduced by up to a factor 10 by using harvesting-aware optimizations.

Twin Policy Management Model for Energy Harvesting Wireless Sensor Networks

Twin Policy Management Model for Energy Harvesting Wireless Sensor Networks

Energy Harvesting Wireless Sensor Node With Temporal Death: Novel Models and Analyses

Energy harvesting wireless sensor net-work (EH-WSN) is promising in applications, however the frequent occurrence of temporal death of nodes, due to the limited harvesting capability, presents a difficulty in meeting the quality-of-service requirements of the network. For a node with temporal death in an EH-WSN, this paper presents a new model, which consists of, a Markov model to trace the energy harvesting process, a queuing analytical model to model the working mechanism of the sensor node and a continuous fluid process to capture the evolution of the residual energy in the EH-WSN node. Using the Markov fluid queue (MFQ) theory, we discuss various performance aspects of the EH-WSN node with temporal death, including the temporal death occurrence probability, the probability density of the residual energy, the stationary energy consumption, the queue length distribution in the data buffer, the packet blocking probability, and so on. In order to obtain the dropping probability of a given packet, based on the structure of the MFQ, we develop an auxiliary MFQ and derive the formulations of two types of the packet dropping probabilities, i.e., the packet dropping probability due to energy depletion and that due to channel error. Numerical examples are provided to illustrate the theoretical findings, and new insights into understanding the impacts of the parameters on the performance metrics are presented.

Energy-aware data compression and transmission range control for energy-harvesting wireless sensor networks

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

Preserving Source Location Privacy for Energy Harvesting WSNs.

Fog (From cOre to edGe) computing employs a huge number of wireless embedded devices to enable end users with anywhere-anytime-to-anything connectivity. Due to their operating nature, wireless sensor nodes often work unattended, and hence are exposed to a variety of attacks. Preserving source-location privacy plays a key role in some wireless sensor network (WSN) applications. In this paper, a redundancy branch convergence-based preserved source location privacy scheme (RBCPSLP) is proposed for energy harvesting sensor networks, with the following advantages: numerous routing branches are created in non-hotspot areas with abundant energy, and those routing branches can merge into a few routing paths before they reach the hotspot areas. The generation time, the duration of routing, and the number of routing branches are then decided independently based on the amount of energy obtained, so as to maximize network energy utilization, greatly enhance privacy protection, and provide long network lifetimes. Theoretical analysis and experimental results show that the RBCPSLP scheme allows a several-fold improvement of the network energy utilization as well as the source location privacy preservation, while maximizing network lifetimes.

Power allocation and relay selection for energy efficient cooperation in wireless sensor networks with energy harvesting

In this paper, we investigate a joint power allocation and relay selection scheme for energy efficient cooperation in energy harvesting-wireless sensor networks (EH-WSN). We mainly propose a simple heuristic algorithm to improve the energy efficiency of each node in a clustering based EH-WSN. First, the effect of cooperative communication is evaluated, and then, the energy sustainability of every node is taken into account, finally, we formulate an online optimization problem which can be solved near optimally with low computational complexity. Extensive simulation results are presented to show the outstanding performance of our proposed scheme in nodes’ transmitting power allocation and average working utility. Therefore, this joint optimization algorithm has a promising future in real applications.

On Nodes Placement in Energy Harvesting Wireless Sensor Networks for Coverage And Connectivity

Wireless sensor networks can be used to monitor targets continuously. This assumes sensor nodes have energy neutral operation, whereby the energy consumed to monitor targets is less than their harvested energy. In this paper, we consider a new problem: minimum energy harvesting node placement for energy neutral coverage and connectivity (MEHNP-ENCC). We aim to determine the locations to place the minimal number of nodes used for sensing and relaying such that deployed nodes 1) cover all targets, 2) have a path to the sink, and 3) have energy neutral operation. We first model MEHNP-ENCC as a mixed integer linear program (MILP). After that we propose an MILP-based approach called greedy MILP (GMILP), whereby a greedy heuristic is used to generate a collection of locations. We also propose two heuristics: 1) DirectSearch considers locations that cover one or more lines connecting targets to the sink, whilst 2) GreedySearch also considers locations farther afield from the said lines that have a high recharging rate. Simulation results show that DirectSearch requires 20% more sensor nodes than the optimal solution whilst this value is 10% for GreedySearch and GMILP.

Secrecy Performance Analysis of Energy Harvesting Wireless Sensor Networks With a Friendly Jammer

The broadcast nature of energy harvesting wireless sensor networks (EH-WSNs) allows sensor nodes (SNs) within the coverage range of a transmitter to capture its signals. However, an EH-WSN is vulnerable to eavesdropping and signal interception; therefore, security in the EH-WSNs is of significant interest, and this issue has been addressed over many years. However, no work has studied the existence of a friendly jammer to mitigate the security impact. Thus, this paper proposes a model and optimization scheme that uses a wirelessly powered friendly jammer to improve secrecy in EH-WSNs. The considered EH-WSN model includes multiple power stations, multiple SNs (sources) and their base station, a friendly jammer, and multiple passive eavesdroppers. We divide the model into two phases: 1) the power stations transfer RF energy to the source SNs and 2) the source SNs transmit information to their base station, while a friendly jammer generates jamming signals against multiple eavesdroppers. Using statistical characteristics of the signal-to-noise ratio, the closed-form expressions of the existence probability of the secrecy capacity and secrecy outage probability are derived. We also propose an optimal sensor scheduling scheme to enhance physical layer secrecy (i.e., best-node scheduling), and we demonstrate our method’s superior performance compared with a conventional round-robin scheduling scheme. The analysis of the simulation results supports our hypothesis, which is in line with Monte Carlo simulations.

Performance optimization of RF energy harvesting wireless sensor networks

We consider a single-input-multiple-output (SIMO) system where power radiated from the transmitter is received and harvested at the receiver. In the proposed algorithm, we study the influence of Channel State Information (CSI) on the maximum harvestable power at the receiver along with the optimization of receiver architecture, adaptive transmit beam forming, and receiver operation policy. On keen observation of the output response, our simulations results show that adaptive transmit beam forming with optimal time allocation for channel estimation will be favourable for maximization of harvested energy.

Fuzzy Power Allocation for Opportunistic Relay in Energy Harvesting Wireless Sensor Networks

Compared with line powered wireless sensor nodes, power allocation, and rate adaption in energy harvesting (EH) nodes are challenging problems, due to the intermittent nature of the energy arrival process and data causality constraints. To address this problem, in this paper we propose an online fuzzy logic-based power allocation (FPA) scheme for a cooperative and delay constraint EH wireless sensor network (EHWSN) using opportunistic relaying. An EHWSN model is developed with finite energy and data buffer constraints at the relay node. The performance of FPA scheme is evaluated in terms of throughput, fairness, delay, transmission time, and energy consumption. The performance of FPA is also compared with one offline policy and two online policies. It is observed that the delay and the energy consumption of FPA scheme for a given throughput are very close to the offline power allocation policy. The results show that for a given EH profile tradeoff exists between data buffer capacity and throughput for a given payload in all the schemes. In addition, the opportunistic behavior of relay can be controlled by increasing buffer size, however, that may compromise the throughput beyond a certain buffer size. Furthermore, the performance of FPA scheme is also evaluated under Uniform, Rayleigh and Beta distributed energy arrivals.

On Reliable Data Delivery in Stochastic Energy Harvesting Wireless Sensor Networks

In wireless sensor networks (WSNs), sensor nodes with energy harvesting components have motivation to expend additional energy in conditions of excess, since the surplus energy would be wasted due to energy buffer overflow. In this paper, we focus on using such surplus energy to adjust the redundancy level of erasure codes, so that the data delivery reliability can be improved while the network lifetime is still well conserved. For a single flow, we formulate the problem as the maximisation of end-to-end packet delivery probability under energy constraints. Considering the energy profile as a stochastic process, we propose a Lyapunov optimisation based algorithm called the erasure coding scheduling algorithm (ECSA) to solve the problem. Through a combination of both theoretical analysis and simulation, we show the effectiveness of ESCA in yielding a near-optimal data delivery reliability.

The Scheme of Power Allocation for Decode-and-Forward Relay Channel in Energy Harvesting WSNs

The Scheme of Power Allocation for Decode-and-Forward Relay Channel in Energy Harvesting WSNs

Fully Autonomous Wireless Sensor Network for Freight Wagon Monitoring

This paper presents a wireless sensor network (WSN) for onboard monitoring of railway freight wagons, in which all the network nodes are energetically autonomous. Unlike the usual energy-harvesting WSNs, here it is proposed to supply not only the sensor nodes but also the sink node by means of an energy harvester, in order to realize independent networks for each wagon. This choice allows the overcoming of several issues related to a single network for the whole train, but it requires a significant reduction of the energy consumption of the power-hungry sink node. In order to reduce the energy consumption to values sustainable by energy harvesters without penalizing the quality of service, this paper proposes a bi-periodic communication scheme for the local wireless transmission, a dynamic management of the GPS receiver, and a consumption model of the GPRS transceiver, which allows an optimized management of the sleep modes. The proposed solutions are compared with the existing ones, and the theoretical predictions are validated by measurements on a system prototype under different operating conditions.

DEARER: A Distance-and-Energy-Aware Routing With Energy Reservation for Energy Harvesting Wireless Sensor Networks

We consider cluster-based routing protocols for energy harvesting wireless sensor networks. Since the energy harvesting process does not match the real energy demand, sensor nodes suffer from occasional energy shortages, especially when they serve as cluster head (CH) nodes. To address this problem, we propose a cluster-based routing protocol referred to as distance-and-energy-aware routing with energy reservation (DEARER). The DEARER protocol encourages nodes with high energy-arrival rate or being close to the sink to serve as CH nodes. Also, DEARER allows non-CH nodes to reserve a portion of the harvested energy for future use. In doing so, the DEARER selects “enabler” nodes as CH nodes and provides them with more energy, thereby mitigating the energy shortage events at CH nodes. By theoretical analysis and numerical experiments, we demonstrate that the DEARER protocol outperforms direct transmission and also approaches the genie-aided routing, where CH nodes are selected based on the real-time energy information of each node.

Sensor Selection and Power Allocation Strategies for Energy Harvesting Wireless Sensor Networks

In this paper, we investigate the problem of jointly selecting a predefined number of energy-harvesting (EH) sensors and computing the optimal power allocation. The ultimate goal is to minimize the reconstruction distortion at the fusion center. This optimization problem is, unfortunately, non-convex. To circumvent that, we propose two suboptimal strategies: 1) a joint sensor selection and power allocation (JSS-EH) scheme that, we prove, is capable of iteratively finding a stationary solution of the original problem from a sequence of surrogate convex problems and 2) a separate sensor selection and power allocation (SS-EH) scheme, on which basis we can identify a sensible sensor selection and analytically find a power allocation policy by solving a convex problem. We also discuss the interplay between the two strategies. Performance in terms of reconstruction distortion, impact of initialization, actual subsets of selected sensors, and computed power allocation policies is assessed by means of computer simulations. To that aim, an EH-agnostic sensor selection strategy, a lower bound on distortion, and an online version of the SS-EH and JSS-EH schemes are derived and used for benchmarking.

Exact Performance Analysis of Ambient RF Energy Harvesting Wireless Sensor Networks With Ginibre Point Process

Ambient radio frequency (RF) energy harvesting methods have drawn significant interests due to their ability to provide energy to wireless devices from ambient RF sources. This paper considers ambient RF energy harvesting wireless sensor networks where a sensor node transmits data to a data sink using the energy harvested from the signals transmitted by the ambient RF sources. We analyze the performance of the network, i.e., the mean of the harvested energy, the power outage probability, and the transmission outage probability. In many practical networks, the locations of the ambient RF sources are spatially correlated and the ambient sources exhibit repulsive behaviors. Therefore, we model the spatial distribution of the ambient sources as an $\alpha $ -Ginibre point process ( $\alpha $ -GPP), which reflects the repulsion among the RF sources and includes the Poisson point process as a special case. We also assume that the fading channel is Nakagami- $m$ distributed, which also includes Rayleigh fading as a particular case. In this paper, by exploiting the Laplace transform of the $\alpha $ -GPP, we introduce semi-closed-form expressions for the considered performance metrics and provide an upper bound of the power outage probability. The derived expressions are expressed in terms of the Fredholm determinant, which can be computed numerically. In order to reduce the complexity in computing the Fredholm determinant, we provide a simple closed-form expression for the Fredholm determinant, which allows us to evaluate the Fredholm determinant much more efficiently. The accuracy of our analytical results is validated through simulation results.

An Optimization Framework for Mobile Data Collection in Energy-Harvesting Wireless Sensor Networks

Recent advances in environmental energy harvesting technologies have provided great potentials for traditional battery-powered sensor networks to achieve perpetual operations. Due to dynamics from the temporal profiles of ambient energy sources, most of the studies so far have focused on designing and optimizing energy management schemes on single sensor node, but overlooked the impact of spatial variations of energy distribution when sensors work together at different locations. To design a robust sensor network, in this paper, we use mobility to circumvent communication bottlenecks caused by spatial energy variations. We employ a mobile collector, called SenCar , to collect data from designated sensors and balance energy consumptions in the network. To show spatial-temporal energy variations, we first conduct a case study in a solar-powered network and analyze possible impact on network performance. Next, we present a two-step approach for mobile data collection. First, we adaptively select a subset of sensor locations where the SenCar stops to collect data packets in a multi-hop fashion. We develop an adaptive algorithm to search for nodes based on their energy and guarantee data collection tour length is bounded. Second, we focus on designing distributed algorithms to achieve maximum network utility by adjusting data rates, link scheduling, and flow routing that adapts to the spatial-temporal environmental energy fluctuations. Finally, our numerical results indicate the distributed algorithms can converge to optimality very fast and validate its convergence in case of node failure. We also show advantages of our framework such as it can adapt to spatial-temporal energy variations and demonstrate its superiority compared to the network with static data sink.

A Performance Evaluation of Solar Energy Prediction Approaches for Energy-Harvesting Wireless Sensor Networks

Energy harvesting from the surrounding environment has been a superior way of eliminating the burden of having to replace depleted batteries in wireless sensor networks (WSNs), thereby achieving a perpetual lifetime. However, the ambient energy is highly time-variable and depends on the environmental conditions, which raises the need to design new approaches for predicting future energy availability. This paper presents a performance evaluation and comparison of three recently-proposed solar energy prediction algorithms for WSNs. In order to provide an accurate performance of the algorithms, real-world measurements obtained from a solar panel were considered. Also, the performance characteristics of the algorithms in four seasons –winter, spring, summer and autumn – were demonstrated. To do this, a month in each season was selected for performance comparison, discussing the performance of the algorithms in each season.

Barrier coverage in energy harvesting sensor networks

Wireless Sensor Networks (WSNs) can be used effectively to form barriers that monitor strips of land for applications such as homeland security and critical infrastructure protection. When WSNs are used as barriers, they are typically deployed for extended period of time, so it is of great importance that the network has a long lifetime. Sensors are highly constrained in their energy usage, even when deploying the current energy-harvesting technology. It is necessary that the harvested energy be used effectively. The lifetime problem for WSNs with no energy harvesting has been studied extensively. In this paper, we study lifetime issues of the k-barrier coverage problem for energy harvesting WSNs. First we develop an algorithm that finds a repeating sleep/wakeup schedule that can provide the perpetual operation of the maximum number of barriers in an energy harvesting WSN. Then in the case that perpetual operation can not be achieved, we give an exact solution to the problem of achieving maximum lifetime for the application of k-barrier coverage in energy harvesting WSNs.