Power Wireless Sensor Networks Research Articles

Operating Wireless Sensor Nodes without Energy Storage: Experimental Results with Transient Computing

Energy harvesting is increasingly used for powering wireless sensor network nodes. Recently, it has been suggested to combine it with the concept of transient computing whereby the wireless sensor nodes operate without energy storage capabilities. This new combined approach brings benefits, for instance ultra-low power nodes and reduced maintenance, but also raises new challenges, foremost dealing with nodes that may be left without power for various time periods. Although transient computing has been demonstrated on microcontrollers, reports on experiments with wireless sensor nodes are still scarce in the literature. In this paper, we describe our experiments with solar, thermal, and RF energy harvesting sources that are used to power sensor nodes (including wireless ones) without energy storage, but with transient computing capabilities. The results show that the selected solar and thermal energy sources can operate both the wired and wireless nodes without energy storage, whereas in our specific implementation, the developed RF energy source can only be used for the selected nodes without wireless connectivity.

Safety of Underground Coal Mine Using Artificial Intelligence and Wireless Sensor Network

Mining process plays the vital role in the modern era for the development of nation as well as the world wide. By using the mining only we can now a day’s having so many essential goods what we are using everywhere[2]. If we take the case of coal, then it is the most important product through which all the byproduct we are getting like fuel, wax, plastic, peat etc. In this paper purpose was to provide an implementable design scenario for underground coal mines using Artificial intelligence technique along with wireless sensor networks (WSNs)[1]. The main reason being that given the intricacies in the physical structure of a coal mine, only low power WSN nodes can produce accurate surveillance and accident detection data[4,6]. Because of the rapid growth of accident in mines area like roof fall, explosion, etc, the development of intelligent sensors, microcontrollers, and network technology, became essential; so as to make a reliable condition for our automatic real-time monitoring of coal mine[2]. The underground system collects temperature, humidity and methane values of coal mine through these intelligent sensor nodes in the mine, and provide the necessary safety prerequisite to avoid accidents. KeywordsCoal mine, Artificial Intelligence, Wireless Sensor Network, Embedded Board, safety.

Maximum power point tracking for optimizing energy harvesting process

There has been a growing interest in using energy harvesting techniques for powering wireless sensor networks. The reason for utilizing this technology can be explained by the sensors limited amount of operation time which results from the finite capacity of batteries and the need for having a stable power supply in some applications. Energy can be harvested from the sun, wind, vibration, heat, etc. It is reasonable to develop multisource energy harvesting platforms for increasing the amount of harvesting energy and to mitigate the issue concerning the intermittent nature of ambient sources. In the context of solar energy harvesting, it is possible to develop algorithms for finding the optimal operation point of solar panels at which maximum power is generated. These algorithms are known as maximum power point tracking techniques. In this article, we review the concept of maximum power point tracking and provide an overview of the research conducted in this area for wireless sensor networks applications.

Online Energy Harvesting Prediction in Environmentally Powered Wireless Sensor Networks

The increasing popularity of micro-scale power-scavenging techniques for wireless sensor networks (WSNs) is paving the way to energy-autonomous sensing systems. To sustain perpetual operations, however, environmentally powered devices must adapt their workload to the stochastic nature of ambient sources. Energy prediction models, which estimate the future expected energy intake, are effective tools to support the development of proactive power management strategies. In this paper, we present profile energy prediction model (Pro-Energy), an energy prediction model for multi-source energy-harvesting WSNs that leverages past energy observations to forecast future energy availability. We then propose Pro-Energy with variable-length timeslots (Pro-Energy-VLT), an extension of Pro-Energy that combines our energy predictor with timeslots of variable lengths to adapt to the dynamics of the power source. To assess the performance of our proposed solutions, we use real-life solar and wind traces, as well as publicly available traces of solar irradiance and wind speed. A comparative performance evaluation shows that Pro-Energy significantly outperforms the state-of-the-art energy predictors, by improving the prediction accuracy of up to 67%. Moreover, by adapting the granularity of the prediction timeslots to the dynamics of the energy source, Pro-Energy-VLT further improves the prediction accuracy, while reducing the memory footprint and the energy overhead of energy forecasting.

<italic>ESync</italic>: Energy Synchronized Mobile Charging in Rechargeable Wireless Sensor Networks

Recent years have witnessed many new promising technologies to power wireless sensor networks, which motivate some fundamental topics to be revisited. Different from energy harvesting, which generates dynamic energy supply, the mobile charger is able to provide a stable and reliable energy supply for sensor nodes and, thus, enables sustainable system operations. While previous mobile charging protocols focus on either the charger travel distance or the charging delay of sensor nodes, in this work, we propose a novel energy synchronized mobile charging (ESync) protocol, which simultaneously reduces both of them. Observing the limitation of traveling salesman problem (TSP)-based solutions, when the nodes' energy consumption is diverse, we construct a set of nested TSP tours based on their energy consumption, and only nodes with low remaining energy are involved in each charging round. Furthermore, we propose the concept of energy synchronization to synchronize the charging request sequence of nodes with their sequence on the TSP tours. Experimentation and simulation demonstrate that ESync can reduce charger travel distance and nodes' charging delay by about 30% and 40%, respectively.

Enerji verimli ve düşük maliyetli sensör ağlarının ISM bandı FSK alıcı vericiyle tasarlanması

Safe wireless link and long battery life are indispensable feature for a portable and outdoor used device with build-in sensors. For this purpose, duty cycling, ultra-low power electronics design, dynamic power management, wireless network architecture and medium access control concepts in the design are explained in detailed. In this work, an ultra-low power wireless sensor network is designed with 4cmx4cm size and light weight nodes sending once every three minutes 115.2 kbps 5 byte package that have up to 131.25 year battery life time in theory with 3V (2x1.5V) 1000mAh battery. 868 and 915 MHz programmable 115.2 kbps ISM band FSK RF transceiver with a low power wake-up scheme is utilized for low cost unlicensed-used wireless sensor networks designed with Back Inverted F Antennas with 100m communication range for outdoor applications and 25m for indoor applications. Sensor node requires 0.5 uA, 2.5 mA, 0.8 uA, 14.6 mA and 25mA during sleep, measurement, sniff, receive and transmit mode respectively @ 868 MHz with 3V battery. The sensors has temperature compensation feature with -40-85 °C working range.

Wireless Powered Sensor Networks: Collaborative Energy Beamforming Considering Sensing and Circuit Power Consumption

This letter studies a wireless powered sensor network, in which a number of sensor nodes send common information to a far apart information access point (AP) via distributed beamforming, by using the wireless energy transferred from a set of nearby multi-antenna energy transmitters (ETs). We consider practical sensing and circuit power consumption at sensor nodes, in addition to their transmission power. In this case, each sensor node is activated in information transmission only when its harvested power is larger than the sensing and circuit power. Under this setup, we aim to maximize the received signal-to-noise ratio at the information AP, by jointly optimizing the collaborative energy beamforming at ETs, and the distributed information beamforming and the inactive/active status of sensor nodes, subject to individual power constraints at ETs and sensor nodes. We propose both optimal and suboptimal solutions to this problem based on the exhaustive search and the greedy algorithm for selecting active sensor nodes, respectively.

Design and test of a real-time shelf out-of-stock detector system

The shelf out-of-stock (SOOS) problem is relevant for retailers and producers since the absence of products on the shelf can lead to a significant reduction of shoppers and a consequent drop on sales. For this purpose, it is necessary to study and introduce approaches able to measure and prevent the lack of products on the shelves and thereby promptly ensuring their refill. In this context, the paper describes the design of an embedded wireless sensor network able to detect in real time a shelf out-of-stock event. In particular, we present a shelf detector sensor based on a novel, low cost and low power wireless sensor network design that can automatically discover out-of-stock in a shelf for all the stores of a retail chain. The use of an automatic method for detecting products not available on the shelf is the first being installed for a long time in a high number of stores. This paper aims to present the hardware infrastructure of the embedded sensor network devoted to real time shelf out-of-stock management and to demonstrate the feasibility and the scalability of the system discussing interesting results about scalability, long life battery based installations and data analysis.

Increasing throughput in energy-based opportunistic spectrum access energy harvesting cognitive radio networks

The performance of large-scale cognitive radio (CR) networks with secondary users sustained by opportunistically harvesting radio-frequency (RF) energy from nearby primary transmissions is investigated. Using an advanced RF energy harvester, a secondary user is assumed to be able to collect ambient primary RF energy as long as it lies inside the harvesting zone of an active primary transmitter (PT). A variable power (VP) transmission mode is proposed, and an energy-based opportunistic spectrum access (OSA) strategy is considered, under which a secondary transmitter (ST) is allowed to transmit only if its harvested energy is larger than a predefined transmission threshold and it is outside the guard zones of all active PTs. The transmission probability of the STs is derived. The outage probabilities and the throughputs of the primary and the secondary networks, respectively, are characterized. Compared with prior work, the throughput can be increased by as much as 29%. The energy-based OSA strategy can be generally applied to a non-CR setup, where distributed power beacons (PBs) are deployed to power coexisting wireless signal transmitters (WSTs) in a wireless powered sensor network.

A Terrestrial Microbial Fuel Cell for Powering a Single-Hop Wireless Sensor Network.

Microbial fuel cells (MFCs) are envisioned as one of the most promising alternative renewable energy sources because they can generate electric current continuously while treating waste. Terrestrial Microbial Fuel Cells (TMFCs) can be inoculated and work on the use of soil, which further extends the application areas of MFCs. Energy supply, as a primary influential factor determining the lifetime of Wireless Sensor Network (WSN) nodes, remains an open challenge in sensor networks. In theory, sensor nodes powered by MFCs have an eternal life. However, low power density and high internal resistance of MFCs are two pronounced problems in their operation. A single-hop WSN powered by a TMFC experimental setup was designed and experimented with. Power generation performance of the proposed TMFC, the relationships between the performance of the power generation and the environment temperature, the water content of the soil by weight were measured by experiments. Results show that the TMFC can achieve good power generation performance under special environmental conditions. Furthermore, the experiments with sensor data acquisition and wireless transmission of the TMFC powering WSN were carried out. We demonstrate that the obtained experimental results validate the feasibility of TMFCs powering WSNs.

Energy-efficient and low-delay scheduling scheme for low power wireless sensor network with real-time data flows

In this paper, we propose an energy-efficient and low-delay scheduling EELDS scheme for interruptible preamble sampling-based medium access control MAC protocols e.g., BoX-MAC-2 and X-MAC in IEEE 802.15.4 wireless sensor network WSN, which considers energy efficiency while reducing delay and contention in the context of real-time data gathering and remote monitoring applications. It is built on MAC layer and exploits local information to coordinate the MAC schedules of nodes in a slowly-changing topology, just with light overhead. The scheme also considers the adaptation to data aggregation with real-time data flows. Therefore, our scheme allows for much longer network lifetime while satisfying the delay constraints. The implementation and simulation results show the performance of the proposed scheme.

Optimal Charging in Wireless Rechargeable Sensor Networks

Recent years have witnessed several new promising technologies to power wireless sensor networks, which motivate some key topics to be revisited. By integrating sensing and computation capabilities to the traditional radio-frequency identification (RFID) tags, the Wireless Identification and Sensing Platform (WISP) is an open-source platform acting as a pioneering experimental platform of wireless rechargeable sensor networks. Different from traditional tags, an RFID-based wireless rechargeable sensor node needs to charge its onboard energy storage above a threshold to power its sensing, computation, and communication components. Consequently, such charging delay imposes a unique design challenge for deploying wireless rechargeable sensor networks. In this paper, we tackle this problem by planning the optimal movement strategy of the mobile RFID reader, such that the time to charge all nodes in the network above their energy threshold is minimized. We first propose an optimal solution using the linear programming (LP) method. To further reduce the computational complexity, we then introduce a heuristic solution with a provable approximation ratio of (1 + θ)/(1 - ε) by discretizing the charging power on a 2-D space. Through extensive evaluations, we demonstrate that our design outperforms the set-cover-based design by an average of 24.7%, whereas the computational complexity is O((N/ε) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). Finally, we consider two practical issues in system implementation and provide guidelines for parameter setting.

Energy-efficient and low-delay scheduling scheme for low power wireless sensor network with real-time data flows

In this paper, we propose an energy-efficient and low-delay scheduling (EELDS) scheme for interruptible preamble sampling-based medium access control (MAC) protocols (e.g., BoX-MAC-2 and X-MAC) in IEEE 802.15.4 wireless sensor network (WSN), which considers energy efficiency while reducing delay and contention in the context of real-time data gathering and remote monitoring applications. It is built on MAC layer and exploits local information to coordinate the MAC schedules of nodes in a slowly-changing topology, just with light overhead. The scheme also considers the adaptation to data aggregation with real-time data flows. Therefore, our scheme allows for much longer network lifetime while satisfying the delay constraints. The implementation and simulation results show the performance of the proposed scheme.

Improving the limits of detection in potentiometric sensors

Potentiometric sensors will generally suffer from unwanted responses as a result to changing temperatures by generating an electromotive force. Typically, this voltage drift has a non-linear character and therefore it is difficult to compensate using linear algorithms implemented in the analogue domain. A solution is proposed to improve the sensor characteristics by combining the digitized output of two CO2 rubidium silver iodide sensors with a specially designed digital algorithm to improve the limits of detection (LOD). Experiments show that this method has the capability to improve the LOD of the sensor with a factor 4.5x during temperature variations of 22 °C over a measurement period of 22 h. It enables potentiometric sensors to be used in low power wireless sensor networks for long term air quality control. Furthermore, the influence of depletion of the rubidium silver iodide electrolyte layer can be effectively compensated by determining the decay of the active layer according to the Nernst equation. Knowing the function of depletion over time helps to correct the sensor output and thereby improves the accuracy of the sensor.

Harvested Power Wireless Sensor Network Solution for Disaggregated Current Estimation in Large Buildings

In modern economies, large service buildings are responsible for an important part of the global electrical energy consumption. The implementation of energy saving strategies can benefit from disaggregated consumption monitoring. To tackle this problem, a number of technological solutions exist, being however, expensive in equipment, installation and maintenance. Because of its decentralized operation principle, wireless sensor networks (WSNs) are an important tool to implement disaggregated electrical energy monitoring. The development of a self-powered, battery-free current sensor node for large WSNs may contribute to the implementation of monitoring solutions for large buildings. In this paper, a solution to monitor disaggregated consumption is presented, based on a contact-less power source for Zigbee nodes using a split-core toroidal coil current transformer (SCCT). The proposed device is able to power a battery-free wireless node estimating also the current drawn by the electrical load with a single SCCT. The SCCT is successfully applied to power a battery-free wireless device running a complex communication software stack. The proposed system is described through simulation as well as the experimental results.

Joint Connectivity-Coverage Temperature-Aware Algorithms for Wireless Sensor Networks

Temperature variations have a significant effect on low power wireless sensor networks as wireless communication links drastically deteriorate when temperature increases. A reliable deployment should take temperature into account to avoid network connectivity problems resulting from poor wireless links when temperature increases. A good deployment needs also to adapt its operation and save resources when temperature decreases and wireless links improve. Taking into account the probabilistic nature of the wireless communication channel, we develop a mathematical model that provides the most energy efficient deployment in function of temperature without compromising the correct operation of the network by preserving both connectivity and coverage. We use our model to design three temperature-aware algorithms that seek to save energy (i) by putting some nodes in hibernate mode as in the Stop-Operate (SO) algorithm, or (ii) by using transmission power control as in Power-Control (PC), or (iii) by doing both techniques as in Stop-Operate Power-Control (SOPC). All proposed algorithms are fully distributed and solely rely on temperature readings without any information exchange between neighbors, which makes them low overhead and robust. Our results identify the optimal operation of each algorithm and show that a significant amount of energy can be saved by taking temperature into account.

Unifying energy harvesting, sensing, and communication for ultra-low power structure health monitoring

A new ultra-low power (ULP) wireless sensor network (WSN) is proposed to monitor the vibration properties of critical structures such as buildings, bridges, and the wings and bodies of aircrafts. The new scheme integrates energy harvesting, data sensing, and wireless communication into a unified process, and it is fundamentally different from all the existing WSNs. In the new WSN, self-powered sensors are employed to harvest vibration energy and measure vibration intensity simultaneously, by utilizing the fact that the harvested energy accumulated through time is proportional to the vibration amplitude and frequency. Once the harvested energy reaches a threshold, it is released as an impulse with a wireless transmitter. An estimate of the structure vibration intensity can then be obtained by measuring the number of binary impulses in a unit time. Such an approach does not require complicated analog-to-digital conversion or signal processing, and it can achieve an ULP performance unrivaled by existing technologies. Optimum and sub-optimum impulse density estimation algorithms are proposed to take advantage of the spatial correlation among signals from the sensors. Analytical and simulation results demonstrate that the proposed scheme can efficiently operate at a low signal-to-noise ratio (SNR).

Collaborative Mobile Charging

The limited battery capacity of sensor nodes has become one of the most critical impediments that stunt the deployment of wireless sensor networks (WSNs). Recent breakthroughs in wireless energy transfer and rechargeable lithium batteries provide a promising alternative to power WSNs: mobile vehicles/robots carrying high volume batteries serve as mobile chargers to periodically deliver energy to sensor nodes. In this paper, we consider how to schedule multiple mobile chargers to optimize energy usage effectiveness, such that every sensor will not run out of energy. We introduce a novel charging paradigm, collaborative mobile charging, where mobile chargers are allowed to intentionally transfer energy between themselves. To provide some intuitive insights into the problem structure, we first consider a scenario that satisfies three conditions, and propose a scheduling algorithm, PushWait, which is proven to be optimal and can cover a one-dimensional WSN of infinite length. Then, we remove the conditions one by one, investigating chargers’ scheduling in a series of scenarios ranging from the most restricted one to a general 2D WSN. Through theoretical analysis and simulations, we demonstrate the advantages of the proposed algorithms in energy usage effectiveness and charging coverage.

Lessons learned on solar powered wireless sensor network deployments in urban, desert environments

The successful deployment of a large scale solar powered wireless sensor network in an urban, desert environment is a very complex task. Specific cities of such environments cause a variety of operational problems, ranging from hardware faults to operational challenges, for instance due to the high variability of solar energy availability. Even a seemingly functional sensor network created in the lab does not guarantee reliable long term operation, which is absolutely necessary given the cost and difficulty of accessing sensor nodes in urban environments. As part of a larger traffic flow wireless sensor network project, we conducted several deployments in the last two years to evaluate the long-term performance of solar-powered urban wireless sensor networks in a desert area. In this article, we share our experiences in all domains of sensor network operations, from the conception of hardware to post-deployment analysis, including operational constraints that directly impact the software that can be run. We illustrate these experiences using numerous experimental results, and present multiple unexpected operational problems as well as some possible solutions to address them. We also show that current technology is far from meeting all operational constraints for these demanding applications, in which sensor networks are to operate for years to become economically appealing.

On the multi‐hop performance of receiver based MAC protocol in routing protocol for low‐power and lossy networks‐based low power and lossy wireless sensor networks

In this study, the authors focus on utilising receiver-based MAC protocol (RB-MAC) and adaptive RB-MAC in routing protocol for low power and lossy wireless sensor networks routing protocol in low-power and lossy wireless channel. This results in a cross-layer approach in which routing decisions can be made based on MAC-layer functionalities. The authors study the effects of multiple receiver nodes, size of preamble and variable duty-cycle in number of retransmissions. Retransmissions increase energy consumptions and delay. Analytical and numerical results confirm that the RB-MAC and adaptive RB-MAC outperform the existing state-of-the art sender-based MAC protocols in terms of end-to-end energy-efficiency, reliability and delay. Simulation result demonstrates that using receiver-based MAC protocols in RPL-based lossy channel imply less retransmissions than sender-based MAC.