Low-cost Wireless Sensor Nodes Research Articles

Transfer Learning Approaches for Gait-Based Wireless Person Identification

The human gait is a biometric feature that is unique to any individual. Since the human body affects the propagation of wireless signals, wireless networks can be used for identification of persons walking nearby. This biometric identification approach has been object of research in several studies described in the literature. In this work, we present a gait-based wireless user identification system that outperforms these existing solutions in terms of cost, effort and accuracy. Firstly, using a mesh network of low-cost wireless sensor nodes, the identification error rate could be reduced by more than 80in comparison to the best of prior studies. For a set of six persons to be identified, the identification accuracy could thus be improved to more than 99. Secondly, using the technique of Transfer Learning, the capabilities of the proposed system can be easily transferred to identify unknown persons and locations with only a few minutes of training effort. Training the system on the identification of six unknown persons, it has been found that the training effort can be reduced by about 96without a significant loss of accuracy.

Validation of Parallel Distributed Adaptive Signal Processing (PDASP) Framework through Processing-Inefficient Low-Cost Platforms

The computational complexity of the multiple-input and multiple-output (MIMO) based least square algorithm is very high and it cannot be run on processing-inefficient low-cost platforms. To overcome complexity-related problems, a parallel distributed adaptive signal processing (PDASP) architecture is proposed, which is a distributed framework used to efficiently run the adaptive filtering algorithms having high computational cost. In this paper, a communication load-balancing procedure is introduced to validate the PDASP architecture using low-cost wireless sensor nodes. The PDASP architecture with the implementation of a multiple-input multiple-output (MIMO) based Recursive Least Square (RLS) algorithm is deployed on the processing-inefficient low-cost wireless sensor nodes to validate the performance of the PDASP architecture in terms of computational cost, processing time, and memory utilization. Furthermore, the processing time and memory utilization provided by the PDASP architecture are compared with sequentially operated RLS-based MIMO channel estimator on 2×2, 3×3, and 4×4 MIMO communication systems. The measurement results show that the sequentially operated MIMO RLS algorithm based on 3×3 and 4×4 MIMO communication systems is unable to work on a single unit; however, these MIMO systems can efficiently be run on the PDASP architecture with reduced memory utilization and processing time.

Structural Health Monitoring System With Narrowband IoT and MEMS Sensors

Monitoring of civil infrastructures is critically needed to track aging, damages and ultimately to prevent severe failures which can endanger many lives. The ability to monitor in a continuous and fine-grained fashion the integrity of a wide variety of buildings, referred to as structural health monitoring, with low-cost, long-term and continuous measurements is essential from both an economic and a life-safety standpoint. To address these needs, we propose a low-cost wireless sensor node specifically designed to support modal analysis over extended periods of time with long-range connectivity at low power consumption. Our design uses very cost-effective MEMS accelerometers and exploits the Narrowband IoT protocol (NB-IoT) to establish long-distance connection with 4G infrastructure networks. Long-range wireless connectivity, cabling-free installation and multi-year lifetime are a unique combination of features, not available, to the best of our knowledge, in any commercial or research device. We discuss in detail the hardware architecture and power management of the node. Experimental tests demonstrate a lifetime of more than ten years with a 17000 mAh battery or completely energy-neutral operation with a small solar panel (60 mm x 120 mm). Further, we validate measurement accuracy and confirm the feasibility of modal analysis with the MEMS sensors: compared with a high-precision instrument based on a piezoelectric transducer, our sensor node achieves a maximum difference of 0.08% at a small fraction of the cost and power consumption.

On the Use of an IoT Integrated System for Water Quality Monitoring and Management in Wastewater Treatment Plants

The deteriorating water environment demands new approaches and technologies to achieve sustainable and smart management of urban water systems. Wireless sensor networks represent a promising technology for water quality monitoring and management. The use of wireless sensor networks facilitates the improvement of current centralized systems and traditional manual methods, leading to decentralized smart water quality monitoring systems adaptable to the dynamic and heterogeneous water distribution infrastructure of cities. However, there is a need for a low-cost wireless sensor node solution on the market that enables a cost-effective deployment of this new generation of systems. This paper presents the integration to a wireless sensor network and a preliminary validation in a wastewater treatment plant scenario of a low-cost water quality monitoring device in the close-to-market stage. This device consists of a nitrate and nitrite analyzer based on a novel ion chromatography detection method. The analytical device is integrated using an Internet of Things software platform and tested under real conditions. By doing so, a decentralized smart water quality monitoring system that is conceived and developed for water quality monitoring and management is accomplished. In the presented scenario, such a system allows online near-real-time communication with several devices deployed in multiple water treatment plants and provides preventive and data analytics mechanisms to support decision making. The results obtained comparing laboratory and device measured data demonstrate the reliability of the system and the analytical method implemented in the device.

Development and power consumption analysis of low cost wireless sensor node for fire detection

Development and power consumption analysis of low cost wireless sensor node for fire detection

Development and power consumption analysis of low cost wireless sensor node for fire detection

Fire becomes a problem when it goes untamed and destroys in part or whole the ecological balance of the area. Thus arises a need to develop a system for early detection of fire with significant accuracy. In this work, a wireless sensor network has been developed for fire detection and its essential parameters, such as effective detection distance and power consumption have been monitored. The hardware developed for this system comprises of the components as a controller board and a transceiver. Without the inclusion of energy saving algorithm the system has shown a considerable lifetime and effective detection distance.

Design, Development and Realization of Flexible Programmable Low-cost Wireless Sensor Nodes for Industrial Application Using IOT

One of the promising application of IoT is Remote monitoring system, this kind of system is used in various fields of engineering and agriculture. In this type of systems number of wireless sensor nodes is deployed for remote monitoring and these nodes are capable of acquiring data for further processing. In this paper we have designed low-cost wireless sensor node (WSN) which uses At-tiny85 and Nordic nRF24L01, working at 2.4 GHz frequency in ISM band. We designed a wireless node which is flexible in case of interfacing any sensor and can send that sensor data to one master reader. The reader is itself a gateway which can send data to cloud. User can see that data on the designed dashboard online in any web enabled device he prefers. For programming all the feature on Attiny85, Arduino IDE Application has been used, PCB design has been made on Eagle PCB, and for data collection on IoT MQTT Protocol has been used with a software called MQTT FX.

Delay-Aware MAC Protocol for Wireless Sensor and Actor Networks in Smart Grid

The conventional electric grid is converting in to the new emerging smart grid by utilizing information and communication technologies. The smart grid allows both the utilities and consumers to monitor and manage energy consumption effectively with the help of wireless sensor and actor networks (WSANs). WSANs have large number of low-power, low-cost, and multi-functional wireless sensor and actor nodes. The wireless sensor nodes collect the data from the physical condition of environment, while actor nodes perform different tasks according to the application requirements. For the monitoring and controlling of smart grid assets, WSANs can be considered as potential tools having the capability of low cost, high latency, and flexibility. In smart grid environment, WSANs have the problem of delay in channel access due to packet collisions. In this research work, we compare the performance results of various medium access control protocols in order to analyze which one among them is the best with respect to better throughput and minimum delay for WSANs in the smart grid environment.

Slotted ALOHA on LoRaWAN-Design, Analysis, and Deployment.

LoRaWAN is one of the most promising standards for long-range sensing applications. However, the high number of end devices expected in at-scale deployment, combined with the absence of an effective synchronization scheme, challenge the scalability of this standard. In this paper, we present an approach to increase network throughput through a Slotted-ALOHA overlay on LoRaWAN networks. To increase the single channel capacity, we propose to regulate the communication of LoRaWAN networks using a Slotted-ALOHA variant on the top of the Pure-ALOHA approach used by the standard; thus, no modification in pre-existing libraries is necessary. Our method is based on an innovative synchronization service that is suitable for low-cost wireless sensor nodes. We modelled the LoRaWAN channel with extensive measurement on hardware platforms, and we quantified the impact of tuning parameters on physical and medium access control layers, as well as the packet collision rate. Results show that Slotted-ALOHA supported by our synchronization service significantly improves the performance of traditional LoRaWAN networks regarding packet loss rate and network throughput.

Dual‐band sensor–antenna design for low energy consumption/cost wireless sensor nodes

Wireless sensor networks (WSNs) consist of nodes with a limited power source. Reducing the energy consumption is an effective way to extend the lifespan of the sensor node. In this contribution, a new approach of designing of a new class radio frequency sensor-antenna for low energy consumption and low-cost wireless sensor nodes is proposed. Unlike the architecture of a conventional wireless sensor node, the proposed approach does not require any processor device that consumes power. It consists of integrating a capacitive sensor in a narrowband antenna that can send raw information, through its operating frequency which can be tuned depending on the value of the physical parameter to measure. As a result, a corresponding table between the resonant frequency of the sensor-antenna and the measured physical parameter is obtained. Furthermore, by using a dual-band antenna, the proposed configuration is able to measure, simultaneously, two physical parameters. A prototype of the sensor-antenna has been simulated and its behaviour has been validated with measurements. From the obtained results, it can be noted that the use of the proposed sensor-antenna can be a good alternative to reduce considerably the complexity and hence the cost of wireless sensor nodes for WSN applications.

Accurate Indoor Sound Level Measurement on a Low-Power and Low-Cost Wireless Sensor Node.

Wireless sensor networks can provide a cheap and flexible infrastructure to support the measurement of noise pollution. However, the processing of the gathered data is challenging to implement on resource-constrained nodes, because each node has its own limited power supply, low-performance and low-power micro-controller unit and other limited processing resources, as well as limited amount of memory. We propose a sensor node for monitoring of indoor ambient noise. The sensor node is based on a hardware platform with limited computational resources and utilizes several simplifications to approximate more complex and costly signal processing stage. Furthermore, to reduce the communication between the sensor node and a sink node, as well as the power consumed by the IEEE 802.15.4 (ZigBee) transceiver, we perform digital A-weighting filtering and non-calibrated calculation of the sound pressure level on the node. According to experimental results, the proposed sound level meter can accurately measure the noise levels of up to 100 dB, with the mean difference of less than 2 dB compared to Class 1 sound level meter. The proposed device can continuously monitor indoor noise for several days. Despite the limitations of the used hardware platform, the presented node is a promising low-cost and low-power solution for indoor ambient noise monitoring.

Wireless sensor network node deployment based on multi-objective immune algorithm

Wireless sensor network (WSN) is made up of a large number of low-cost wireless sensor nodes, which can collect all types of data in its lifetime. Node deployment of WSN is a NP complete problem, and it can significantly influence the network coverage and energy consumption in WSN. In this paper, we try to exploit multi-objective immune algorithm to solve the node deployment problem in WSN. The proposed node deployment algorithm aims to maximise the degree of network coverage and minimise the energy consumption. In the proposed multi-objective immune algorithm, each antibody refers to a candidate solution in node deployment process, and the antibodies are randomly initialised with a specific range. In addition, we design a fitness function by two ranking modes. Finally, experiments are conducted to test the performance of the proposed algorithm. Particularly, we use some typical performance metric in this experiment, such as coverage degree, energy consumption, and maximum moved distance. Experimental results demonstrate that the proposed can both enhance the network coverage degree and reduce the energy consumption by minimising the moved distance.

Implementation of a Low-Cost Energy and Environment Monitoring System Based on a Hybrid Wireless Sensor Network

A low-cost hybrid wireless sensor network (WSN) that utilizes the 917 MHz band Wireless Smart Utility Network (Wi-SUN) and a 447 MHz band narrow bandwidth communication network is implemented for electric metering and room temperature, humidity, and CO2 gas measurements. A mesh network connection that is commonly utilized for the Internet of Things (IoT) is used for the Wi-SUN under the Contiki OS, and a star connection is used for the narrow bandwidth network. Both a duty-cycling receiver algorithm and a digitally controlled temperature-compensated crystal oscillator algorithm for frequency reference are implemented at the physical layer of the receiver to accomplish low-power and low-cost wireless sensor node design. A two-level temperature-compensation approach, in which first a fixed third-order curve and then a sample-based first-order curve are applied, is proposed using a conventional AT-cut quartz crystal resonator. The developed WSN is installed in a home and provides reliable data collection with low construction complexity and power consumption.

A Centralized Energy Efficient Distance Based Routing Protocol for Wireless Sensor Networks

Wireless sensor network (WSN) typically consists of a large number of low cost wireless sensor nodes which collect and send various messages to a base station (BS). WSN nodes are small battery powered devices having limited energy resources. Replacement of such energy resources is not easy for thousands of nodes as they are inaccessible to users after their deployment. This generates a requirement of energy efficient routing protocol for increasing network lifetime while minimizing energy consumption. Low Energy Adaptive Clustering Hierarchy (LEACH) is a widely used classic clustering algorithm in WSNs. In this paper, we propose a Centralized Energy Efficient Distance (CEED) based routing protocol to evenly distribute energy dissipation among all sensor nodes. We calculate optimum number of cluster heads based on LEACH’s energy dissipation model. We propose a distributed cluster head selection algorithm based on dissipated energy of a node and its distance to BS. Moreover, we extend our protocol by multihop routing scheme to reduce energy dissipated by nodes located far away from base station. The performance of CEED is compared with other protocols such as LEACH and LEACH with Distance Based Thresholds (LEACH-DT). Simulation results show that CEED is more energy efficient as compared to other protocols. Also it improves the network lifetime and stability period over the other protocols.

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.

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.

Static Three-Dimensional Fuzzy Routing Based on the Receiving Probability in Wireless Sensor Networks

A Wireless Sensor Network (WSN) is a collection of low-cost, low-power and large-scale wireless sensor nodes. Routing protocols are an important topic in WSN. Every sensor node should use a proper mechanism to transmit the generated packets to its destination, usually a base station. In previous works, routing protocols use the global information of the network that causes the redundant packets to be increased. Moreover, it leads to an increase in the network traffic, to a decrease in the delivery ratio of data packets, and to a reduction in network life. In this paper, we propose a new inferential routing protocol called SFRRP (Static Three-Dimensional Fuzzy Routing based on the Receiving Probability). The proposed protocol solves the above mentioned problems considerably. The data packets are transmitted by hop-to-hop delivery to the base station. It uses a fuzzy procedure to transmit the sensed data or the buffered data packets to one of the neighbors called selected node. In the proposed fuzzy system, the distance and number of neighbors are input variables, while the receiving probability is the output variable. SFRRP just uses the local neighborhood information to forward the packets and is not needed by any redundant packet for route discovery. The proposed protocol has some advantages such as a high delivery ratio, less delay time, high network life, and less network traffic. The performance of the proposed protocol surpasses the performance of the Flooding routing protocol in terms of delivery ratio, delay time and network lifetime.

Coverage Maximization and Energy Conservation for Mobile Wireless Sensor Networks

WSN is a group of low-cost, low-power, multifunctional and small size wireless sensor nodes that work together to sense the environment, perform simple data processing and communicate wirelessly over a short distance. Mobile wireless sensor networks (WSN) coverage can be enhanced by moving the sensors so that a better arrangement is achieved. However, movement is a high energy consumption task. To maximize coverage the sensors need to be placed not too close to each other so that the sensing capability of the network is fully utilised; however they must not be located too far from each other to avoid coverage holes. It is desired to achieve optimal coverage and at the same time not to relax the mobility energy consumption issue due to the fact that sensors have a limited energy supply. This research is interested in solving the coverage and energy conservation issues of mobile wireless sensors using PSO. In this paper the WSN coverage maximization problem is considered by taking into account the energy spent for sensor repositioning. Thus there are two objectives to be optimized, namely maximizing the coverage and conserving the energy. The two objectives are tackled one by one, starting with the coverage maximization followed by energy conservation. Hence a two-phase PSO approach is proposed. The results show that the proposed algorithm successfully achieves its objectives to reduce the energy usage while at the same time improve the coverage. The energy usage is reduced by cutting down the maximum distance moved.