Low-cost Wireless Sensor Research Articles

Online learning algorithm for time series forecasting suitable for low cost wireless sensor networks nodes.

Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.

Cross-layer cluster-based energy-efficient protocol for wireless sensor networks.

Recent developments in electronics and wireless communications have enabled the improvement of low-power and low-cost wireless sensors networks (WSNs). One of the most important challenges in WSNs is to increase the network lifetime due to the limited energy capacity of the network nodes. Another major challenge in WSNs is the hot spots that emerge as locations under heavy traffic load. Nodes in such areas quickly drain energy resources, leading to disconnection in network services. In such an environment, cross-layer cluster-based energy-efficient algorithms (CCBE) can prolong the network lifetime and energy efficiency. CCBE is based on clustering the nodes to different hexagonal structures. A hexagonal cluster consists of cluster members (CMs) and a cluster head (CH). The CHs are selected from the CMs based on nodes near the optimal CH distance and the residual energy of the nodes. Additionally, the optimal CH distance that links to optimal energy consumption is derived. To balance the energy consumption and the traffic load in the network, the CHs are rotated among all CMs. In WSNs, energy is mostly consumed during transmission and reception. Transmission collisions can further decrease the energy efficiency. These collisions can be avoided by using a contention-free protocol during the transmission period. Additionally, the CH allocates slots to the CMs based on their residual energy to increase sleep time. Furthermore, the energy consumption of CH can be further reduced by data aggregation. In this paper, we propose a data aggregation level based on the residual energy of CH and a cost-aware decision scheme for the fusion of data. Performance results show that the CCBE scheme performs better in terms of network lifetime, energy consumption and throughput compared to low-energy adaptive clustering hierarchy (LEACH) and hybrid energy-efficient distributed clustering (HEED).

Frequency-Adaptive Approach In IEEE 802.15.4 Wireless Body Sensor Networks: Continuous-Assessment or Periodic-Assessment?

IEEE 802.15.4 is arguably considered as a welldesigned standard protocol to address the need for low-rate, low-power and low-cost Wireless Body Sensor Networks (WBSNs).Within a WBSN, Node‘s communication occurs during theirconfined active periods. The increase in number of active WBSNswould consequently result in the elevation of the overlapping ratiobetween the active periods of neighbouring WBSNs. In this paper,we address the interference caused by neighbouring WBSNsand its devastating consequences. Additionally, two frequencyadaptive approaches are proposed as the solutions for decreasingthe negative impacts of internal intereference. Frequency hoppingcould be considered as a possible approach to alleviate theWBSNs performance degradation due to such interference. Inthat regard, two frequency-adaptive approaches are proposednamely: “Continuous-Assessment” and “Periodic-Assessment”schemes and are compared with previously introduced “Initial-Choice” and “Blind-Choice” schemes in terms of channel util-isation, success rate, Satisfaction rate and energy consumption(both sensors and coordinator). Eventually, it is concluded thatContinuous-Assessment scheme outperforms the other introducedschemes in terms of above-mentioned performance measures.

Low-Cost Alternatives for Urban Noise Nuisance Monitoring Using Wireless Sensor Networks

Noise pollution caused by vehicular traffic is a common problem in urban environments that has been shown to affect people's health and children's cognition. In the last decade, several studies have been conducted to assess this noise, by measuring the equivalent noise pressure level (called L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eq</sub> ) to acquire an accurate sound map using wireless networks with acoustic sensors. However, even with similar values of L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eq</sub> , people can feel the noise differently according to its frequency characteristics. Thus, indexes, which can express people's feelings by subjective measures, are required. In this paper, we analyze the suitability of using the psychoacoustic metrics given by the Zwicker's model, instead of just only considering L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eq</sub> . The goal is to evaluate the hardware limitations of a low-cost wireless acoustic sensor network that is used to measure the annoyance, using two types of commercial and off-the-shelf sensor nodes, Tmote-Invent nodes and Raspberry Pi platforms. Moreover, to calculate the parameters using these platforms, different simplifications to the Zwicker's model based on the specific features of road traffic noise are proposed. To validate the different alternatives, the aforementioned nodes are tested in a traffic congested area of Valencia City in a vertical and horizontal network deployment. Based on the results, it is observed that the Raspberry Pi platforms are a feasible low-cost alternative to increase the spatial-temporal resolution, whereas Tmote-Invent nodes do not confirm their suitability due to their limited memory and calibration issues.

A Two-Stage Range-Free Localization Method for Wireless Sensor Networks

Range-free localization plays an important role in low-cost and large scale wireless sensor networks. Many existing range-free localization methods encounter high localization error, especially for the network with a coverage hole. One reason for high localization error is unreasonable distance estimation method. Another reason is that unknown nodes use the shortest distance which has large cumulative distance error to estimate their positions. In this paper, a two-stage centralized range-free localization algorithm (TCRL) is proposed. In the first stage, we design a novel rational distance estimation method to alleviate the distance estimation error between neighbor nodes based on the connectivity information and geometric features. In the second stage, a novel neighborhood function is derived from the estimated distances between neighbor nodes. Then a new localization strategy is proposed based on greedy idea. Finally, the proposed algorithm is compared with the same type algorithms in two network scenarios, namely, random deployment and random deployment with a coverage hole. The simulation results show that TCRL achieves more accurate and reliable results than most of existing range-free methods in the two network scenarios.

Citizen sensors for SHM: use of accelerometer data from smartphones.

Ubiquitous smartphones have created a significant opportunity to form a low-cost wireless Citizen Sensor network and produce big data for monitoring structural integrity and safety under operational and extreme loads. Such data are particularly useful for rapid assessment of structural damage in a large urban setting after a major event such as an earthquake. This study explores the utilization of smartphone accelerometers for measuring structural vibration, from which structural health and post-event damage can be diagnosed. Widely available smartphones are tested under sinusoidal wave excitations with frequencies in the range relevant to civil engineering structures. Large-scale seismic shaking table tests, observing input ground motion and response of a structural model, are carried out to evaluate the accuracy of smartphone accelerometers under operational, white-noise and earthquake excitations of different intensity. Finally, the smartphone accelerometers are tested on a dynamically loaded bridge. The extensive experiments show satisfactory agreements between the reference and smartphone sensor measurements in both time and frequency domains, demonstrating the capability of the smartphone sensors to measure structural responses ranging from low-amplitude ambient vibration to high-amplitude seismic response. Encouraged by the results of this study, the authors are developing a citizen-engaging and data-analytics crowdsourcing platform towards a smartphone-based Citizen Sensor network for structural health monitoring and post-event damage assessment applications.

Low Cost Wireless Sensor Network Based Intelligent Retina Controlled Computer

A very serious problem has been identified for past many years that paralyzed human beings became isolated from the society for not having a proper existence and due to their reluctances of doing normal life works. Therefore, we have researched on the Human Machine Interaction using Physical Human Body Gestures and came up to proposed solution of the said issue as Low Cost Retina Controlled Computer Using Head mount and Wrist Tilt for Persons who are Paralyzed and cannot be able to move their hands and even they can’t able to walk. Conventionally, we named it as simple as Retina Controlled Computer, is based on Google Glass vision using motion recognition, Zigbee Wireless Technology, Retina Controlled Device that allows human beings with paralysis resulting from Paralysis to do everything on computer even controlling the personal mobile phone using only their Eye and Head movements. This whole project will be based mainly on the new technology of Low Cost Wireless Zigbee Sensors, Arduino Microcontrollers, and motion and retina recognition using Accelerometer. Arduino Microcontroller consists of an open-source hardware board designed around an 8-bit Atmel AVR microcontroller, or a 32-bit Atmel ARM. Therefore, making an Intelligent Low Cost Eye Controlled Computer, based on new technologies will play a vital role in revolutionizing the field of Biomedical engineering and opening the new horizons of research.

Energy Efficient Data Mining Scheme for High Dimensional Data

In this paper, we propose energy efficient big data mining scheme for forest cover type and gas drift classification. Efficient machine learning and data mining techniques provide unprecedented opportunity to monitor and characterize physical environments, such as forest cover type, using low cost wireless sensor networks. The experimental validation on two different sensor network datasets, forest cover type and gas sensor array drift dataset from publicly available UCI machine learning repository. Coupled with an appropriate feature selection, the complete scheme leads towards an energy efficient protocol for intelligent monitoring of large physical environments instrumented with wireless sensor networks.

Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Sensing

The recent development of low cost wireless sensors enables novel internet-of-things (IoT) applications, such as the monitoring of water distribution networks. In such scenarios, the lifetime of the wireless sensor network (WSN) is a major concern, given that sensor node replacement is generally inconvenient and costly. In this paper, a compressive sensing-based scheduling scheme is proposed that conserves energy by activating only a small subset of sensor nodes in each timeslot to sense and transmit. Compressive sensing introduces a cardinality constraint that makes the scheduling optimization problem particularly challenging. Taking advantage of the network topology imposed by the IoT water monitoring scenario, the scheduling problem is decomposed into simpler subproblems, and a dynamic-programming-based solution method is proposed. Based on the proposed method, a solution algorithm is derived, whose complexity and energy-wise performance are investigated. The complexity of the proposed algorithm is characterized and its performance is evaluated numerically via an IoT emulator of water distribution networks. The analytical and numerical results show that the proposed algorithm outperforms state-of-the-art approaches in terms of energy consumption, network lifetime, and robustness to sensor node failures. It is argued that the derived solution approach is general and it can be potentially applied to more IoT scenarios such as WSN scheduling in smart cities and intelligent transport systems.

Pervasive Radio Mapping of Industrial Environments Using a Virtual Reality Approach.

Wireless communications in industrial environments are seriously affected by reliability and performance issues, due to the multipath nature of obstacles within such environments. Special attention needs to be given to planning a wireless industrial network, so as to find the optimum spatial position for each of the nodes within the network, and especially for key nodes such as gateways or cluster heads. The aim of this paper is to present a pervasive radio mapping system which captures (senses) data regarding the radio spectrum, using low-cost wireless sensor nodes. This data is the input of radio mapping algorithms that generate electromagnetic propagation profiles. Such profiles are used for identifying obstacles within the environment and optimum propagation pathways. With the purpose of further optimizing the radio planning process, the authors propose a novel human-network interaction (HNI) paradigm that uses 3D virtual environments in order to display the radio maps in a natural, easy-to-perceive manner. The results of this approach illustrate its added value to the field of radio resource planning of industrial communication systems.

A Comprehensive Performance Study of the Contention Access Period of the Slotted IEEE 802.15.4

The application based on Wireless Sensor Networks (WSN) needs the real-time transaction and the Quality of Service (QoS). Actuation, the IEEE 802.15.4 can access to the Media Access Control MAC and Physical PHY for low bit-rate, low-cost wireless sensor networks with QoS support. In this paper, we implement the allocation of the mechanism of the Guaranteed Time Slot GTS based on the analytical model of the beacon enabled IEEE 802.15.4 and analyse the performances of the network. We analyze the impact of the Beacon Order BO and the Superframe Order SO on the performance of the relation between the two parameters BO and SO based on commonly known metrics particularly delay, throughput and frame drop rate. In addition to that, the study investigates how protocol simulations are conducted on various simulation settings.

Cumulative-Sum-Based Localization of Sound Events in Low-Cost Wireless Acoustic Sensor Networks

Wireless acoustic sensor networks (WASNs) are known for their potential applications in multiple areas, such as audio-based surveillance, binaural hearing aids or advanced acoustic monitoring. The knowledge of the spatial position of a source of interest is usually a requirement for many of these applications. Therefore, source localization is an important problem to be addressed in WASNs. Unfortunately, most localization algorithms need costly signal processing stages that prevent them from being implemented in low-cost sensor networks, requiring additional modules for signal acquisition and processing. This paper presents a low-complexity method for acoustic event detection and localization considering a change detection statistical framework. Two possible implementation approaches based on the efficient cumulative sum (CUSUM) algorithm are presented and discussed. Results from simulations and a real deployment show that the proposed techniques can be easily implemented in low-cost sensor networks, providing good localization accuracy and making good use of the available node resources.

Design and Analysis of A High Resuluation Navigation System for The Intillegent Transportation System

This paper suggests an information gathering and monitoring system designed for vehicular ad-hoc networks(VANETs) and that implemented in a large area.The suggested infrastructure consists of low cost wireless sensors covering certain areas and connected to “the monitoring and control center” through a master node, the study is divided into four phases: the first phase discusses the information gathering process in vehicles and sensors level, the second phase discusses how to send the brief road traffic information to the master node. This process is based on the concepts of the summarization and aggregation and study the effect of using one or two channels for that purpose, the third phase focuses on the information transportation between the master nodes until it reaches to “the monitoring and control center”, The mechanism of the information transportation in this phase is proposed to be in an ad-hoc manner using four ad-hoc protocols Ad-hoc On Demand Distance Vector Routing Protocol AODV,Dynamic Source routingDSR, Optimized Link State Routing Protocol OLSR and Temporally-Ordered Routing Algorithm Protocol TORA. The best protocol willbe selectedaccording to its data delivery, latency and average throughput on the radio channel and the fourth phase deals with dissemination of the gathered information to the vehicles.The proposed system is designed and simulated using “OPNET” package. The results indicate the ability of applying real time and high resolution monitoring system using these techniques.

A Low-Cost Wireless Voltage Sensor for Monitoring MV/HV Utility Assets

Voltage sensing of the utility network provide critical information for asset management, prioritizing asset replacements, increasing situational awareness and providing increased visibility of the grid. For ease of use, and to increase commercial appeal among utilities, these sensors should be designed to have a low cost, long life ( $> {10}~{\hbox {years}}$ ), be self-powered, and require no maintenance. This paper deals with the development of a novel low-cost wireless voltage sensor for medium- and high-voltage (MV/HV) utility assets such as cables, transformers, switchgear, capacitor banks, and conductors. A review of existing techniques along with their drawbacks is outlined in this paper. Further, the challenges pertaining to the development a low-cost floating voltage sensor such as variability of voltage, self-calibration requirements, and distance-to-earth variations are presented. These challenges are circumvented by deriving a detailed mathematical model of the sensor. Further, using a set of valid assumptions, a new moving average voltage sensing (MAVS) algorithm is proposed, tested using simulations and validated using a high-voltage prototype. The wireless voltage sensor prototype is tested at up to 35 kV and is built to accommodate electric field energy harvesting in addition to voltage sensing. The prototype has a low-volume production cost of $150 and shows promising results by providing self-calibrated measurements capable of tracking the voltage variation with less than 2.5% error.

A Framework for Obesity Control Using a Wireless Body Sensor Network

Low-cost low-power consumption small wireless sensor devices have empowered the development of wireless body area networks (WBANs). In WBANs many sensors are attached to human body for sensing particular health related information to improve healthcare and quality of life. Obesity is one of the most common problems all over the world, which is amongst main causes of cardiovascular diseases. In this research, we explore hardware and software architecture of WBAN for obesity monitoring. The proposed framework consists of few sensor nodes that monitor body motion, calories calculator, and a personal server running on a personal smart phone or a personal computer. The focus of this research is to make obesity patients easier to get rid of this disease.

The Effect of Sheet Resistivity and Storage Conditions on Sensitivity of RuO&lt;sub&gt;2&lt;/sub&gt; Based pH Sensors

The increasing fresh water deficiency due to environmental pollution demands accurate, reliable and highly sensitive sensors for online monitoring of water pollution. Solid state sensors are helpful for fabricating and implementing low cost wireless sensors for monitoring of pollution. In water pollution determination, the measurement of pH plays an important role. Among the semiconductor sensitive materials RuO2 shows good sensitivity to hydrogen ions, high accuracy and resistance to interferences caused by other dissolved ions. In this work, thick film RuO2 based pH sensitive electrodes are fabricated by screen printing. The sensors were characterized by electromotive force measurements, SEM, optical microscopy and EDS analysis. The effects of sheet resistivity of the material and storage conditions are discussed. The sensor exhibits a sensitivity of 60 mV/pH in wide pH range of 2 to 10. The obtained response was very close to the theoretical Nernstian behavior. The best performance was attained for a sensor fabricated from 10 kΩ/sq. thick film paste and stored at water for 25 days.

A Low-Cost Wireless Temperature Sensor: Evaluation for Use in Environmental Monitoring Applications

Abstract A wide range of environmental applications would benefit from a dense network of air temperature observations. However, with limitations of costs, existing siting guidelines, and risk of damage, new methods are required to gain a high-resolution understanding of spatiotemporal patterns of temperature for agricultural and urban meteorological phenomena such as the urban heat island. With the launch of a new generation of low-cost sensors, it is possible to deploy a network to monitor air temperature at finer spatial resolutions. This study investigates the Aginova Sentinel Micro (ASM) sensor with a custom radiation shield (together less than USD$150) that can provide secure near-real-time air temperature data to a server utilizing existing (or user deployed) Wi-Fi networks. This makes it ideally suited for deployment where wireless communications readily exist, notably urban areas. Assessment of the performance of the ASM relative to traceable standards in a water bath and atmospheric chamber show it to have good measurement accuracy with mean errors &amp;lt;±0.22°C between −25° and 30°C, with a time constant in ambient air of 110 ±15 s. Subsequent field tests also showed the ASM (in the custom shield) had excellent performance (RMSE = 0.13°C) over a range of meteorological conditions relative to a traceable operational Met Office platinum resistance thermometer. These results indicate that the ASM and radiation shield are more than fit for purpose for dense network deployment in environmental monitoring applications at relatively low cost compared to existing observation techniques.

A Passive Wireless Humidity Threshold Monitoring Sensor Principle Based on Deliquescent Salts and a Diffusion Based Irreversible State Change

Passive RFID transponder applications that require integrated sensors can greatly benefit from unconventional sensing strategies especially in situations where there is a need to continuously monitor environmental properties without having access to an integrated source of electric energy or an omnipresent reader station within communication range. Since, in many cases, information about the violation of a threshold value is of interest, alternative sensing strategies exploiting irreversible phenomena not considered or even avoided before, have the potential to find successful use in low-cost (e.g., chip-based sensor RFIDs) or lowest cost (e.g., chipless sensor RFIDs-either with or without transistors) wireless sensor applications. In this paper, a stand-alone, simple, passive, wireless humidity threshold sensor concept, and its realization are presented. This exploits the deliquescence phenomenon of salts. Based on a double planar coil arrangement, for which an electric model is given and a transfer function has been deduced, an oscillating circuit is formed. Its resonance frequency changes irreversibly, if a threshold relative humidity is exceeded for a certain exposition time. The sensor principle is demonstrated by the example of sodium chloride. Various measurements demonstrate the feasibility of the presented sensor approach. Sensor solutions based on a threshold activated irreversible state change might be a promising approach in order to monitor environmental parameters without a permanent supply of electric energy.

Samba: a real-time motion capture system using wireless camera sensor networks.

There is a growing interest in 3D content following the recent developments in 3D movies, 3D TVs and 3D smartphones. However, 3D content creation is still dominated by professionals, due to the high cost of 3D motion capture instruments. The availability of a low-cost motion capture system will promote 3D content generation by general users and accelerate the growth of the 3D market. In this paper, we describe the design and implementation of a real-time motion capture system based on a portable low-cost wireless camera sensor network. The proposed system performs motion capture based on the data-driven 3D human pose reconstruction method to reduce the computation time and to improve the 3D reconstruction accuracy. The system can reconstruct accurate 3D full-body poses at 16 frames per second using only eight markers on the subject's body. The performance of the motion capture system is evaluated extensively in experiments.

IMM Filter Based Human Tracking Using a Distributed Wireless Sensor Network

This paper proposes a human tracking approach in a distributed wireless sensor network. Most of the efforts on human tracking focus on vision techniques. However, most vision-based approaches to moving object detection involve intensive real-time computations. In this paper, we present an algorithm for human tracking using low-cost range wireless sensor nodes which can contribute lower computational burden based on a distributed computing system, while the centralized computing system often makes some information from sensors delay. Because the human target often moves with high maneuvering, the proposed algorithm applies the interacting multiple model (IMM) filter techniques and a novel sensor node selection scheme developed considering both the tracking accuracy and the energy cost which is based on the tacking results of IMM filter at each time step. This paper also proposed a novel sensor management scheme which can manage the sensor node effectively during the sensor node selection and the tracking process. Simulations results show that the proposed approach can achieve superior tracking accuracy compared to the most recent human motion tracking scheme.