Implementation Of Sensor Network Research Articles

Literature Review: Energy Efficiency Mechanisms using Data Reduction in Wireless Sensor Networks (WSNs) Applications

It has been stated that the implementation of Wireless Sensor Networks (WSN) has majorproblems that can affect its performance. One of the problems he faced was the limited energy source (battery-powered). Therefore, in an attempt to use energy as best as possible, several mechanisms have been proposed. Energy efficiency in WSN is a very interesting issue to discuss. This problem is a challenge for researchers. This paper focuses on the discussion of how research has developed in energy efficiency efforts in the WSNs over the past 10 years. One of the proposed mechanisms is data reduction. This paper discusses data reduction divided into 4 Parts; 1) aggregation, 2) adaptive sampling, 3) compression, and 4) network coding. Data reduction is intended to reduce the amount of data sent to the sink. Data reduction approachescan affect the accuracy of the information collected. Data reduction is used to improve latency, QoS (Quality of Service), good scalability, and reduced waiting times. This paper discusses more adaptive sampling techniques and network coding. It was concluded that using data reduction mechanisms in target detection applications proved efficient compared to without using data reduction mechanisms. To save energy, data reduction (especially with adaptive sampling algorithms) can save up to 79.33% energy.

Early faults diagnosis and severity assessment of rolling element bearings on wireless signal transfer

Machine condition monitoring in remote locations and harsh environment where network infrastructure is not feasible, or hardwired network connectivity is not possible, wireless communications provides an alternative which also offer installation cost savings, improve reliability and quicker deployment. This paper describes the implementation of wireless sensor network (WSN) for early fault diagnosis of rolling element bearings based on signal autocorrelation technique. A low-power 2.4 GHz wireless HART transceiver, a low-cost wireless vibration transmitter, 26.76 mv/g accelerometers and a 1420 wireless gateway with AMS software was implemented. The research describes the methodology of acquiring peak values data in high frequency region. The noise was averaged out by applying four-time averaging and natural frequencies or fault frequencies of bearing elements was captured. The experimental results show that the signal autocorrelation algorithm can successfully diagnose the roller bearing faults at early stage on wireless signal transfer. As the raw data was processed before wireless transmission on analyzing unit and spectrum was transferred in JPG format on display unit, minimum power consumption has been noted. The technique provided a better alternative of wired system for real time condition monitoring of roller bearings in rotating equipment installed in remote area.

Time synchronization enhancements in wireless networks with ultra wide band communications

The emergence of low cost Ultra Wide Band (UWB) transceivers has enabled the implementation of Wireless Sensor Networks (WSN) based on this communication technology. These networks are composed of distributed autonomous low cost nodes (also known as motes) with their own processing unit, memory and communications. Usually these nodes are power-limited and due to the poor performance and quality of their clocks, time synchronization is in the order of milliseconds and in some specific scenarios till microseconds. The integration of commercial UWB transceivers in these nodes can improve the synchronization accuracy. In particular, we focus on WSN nodes based on off-the-shelf commercial products and commodity hardware. In this paper we analyze step by step, from a practical and experimental point of view, the different elements involved in the time synchronization using UWB technology on WSN with static nodes. From our experimental results, with timestamps captured during the packet exchanges, we analyze and discuss the application of different communication schemes and simple statistical methods (in order to be run in WSN nodes). The results obtained with timestamps captured at the UWB transceivers and by using linear regression show that the lowest time synchronization error achieved between two nodes is 0.14 ns. Employing the same setup and performing the synchronization with the timestamps captured internally at the microcontrollers of the nodes, the error rises to 31 ns, due to the higher time period of the microcontrollers’ timers and the inaccuracies that affect the acquisition of the timestamps. Nevertheless, the synchronization of the microcontrollers’ clocks allows the setting up of a common time reference at the network nodes, enabling the implementation of applications with tight synchronization requirements, such as collaborative beamforming and ranging.

Energy-Aware Multilevel Clustering Scheme for Underwater Wireless Sensor Networks

The expansion of wireless sensor networks in the underwater environment resulted in underwater wireless sensor networks. It has dramatically impacted the research arena because of its widespread and real-time applications. But successful implementation of underwater wireless sensor networks faces many issues. The primary concern in the underwater sensor network is sensor nodes’ energy depletion problem. In this paper, to improve the lifetime of the underwater wireless sensor network, an Energy-Aware Multi-level Clustering Scheme is proposed. The underwater network region is considered 3D concentric cylinders with multiple levels. Further, each level is divided into various blocks, representing one cluster. The proposed algorithm follows vertical communication mode from the sea bed to the surface area in a bottom-up fashion. Multiple levels with varying heights overcome the communication issues due to high water pressure towards the sea bed. Simulations are carried out to show the efficiency of the proposed algorithm, which performs better in terms of a prolonged network lifetime and average residual energy. The simulation result shows significant improvement in the network lifetime compared with current algorithms.

Single Probe Imitation of Multi-Depth Capacitive Soil Moisture Sensor Using Bidirectional Recurrent Neural Network

This work proposes a single probe imitation of multidepth capacitive soil moisture sensor for low-cost and energy-efficient implementation of IoT-assisted wireless sensor network (IoWSN) farm monitoring infrastructure. A conditioning circuit (CC) is devised to capture the behavior of soil water movement and its impact on soil moisture around the probe at different depths. The captured correlation is used to train the proposed neural network (NN) models to estimate the soil water content (SWC) at different soil depths based on a single measurement taken at the reference depth. To adjust the weight of the neurons, training and test dataset are collected through a measurement campaign. The data are collected during the cropping period of paddy vegetation by deploying 150 sensor nodes at different depths in bare land before sowing. Two NN models—artificial neural network (ANN) and bidirectional long short term memory network (BLSTM)—are proposed and compared based on the accuracy of SWC estimation. To demonstrate the efficacy of the concept, the proposed sensor design is compared with relevant soil moisture sensors reported over the past five years. The root mean square error (RMSE), <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R^{2}}$ </tex-math></inline-formula> and mean absolute percentage error (MAPE)-based analysis validate the significance of the proposed NN models.

Reliability Analysis of Wireless Sensor Network for Smart Farming Applications.

Wireless Sensor Networks are subjected to some design constraints (e.g., processing capability, storage memory, energy consumption, fixed deployment, etc.) and to outdoor harsh conditions that deeply affect the network reliability. The aim of this work is to provide a deeper understanding about the way redundancy and node deployment affect the network reliability. In more detail, the paper analyzes the design and implementation of a wireless sensor network for low-power and low-cost applications and calculates its reliability considering the real environmental conditions and the real arrangement of the nodes deployed in the field. The reliability of the system has been evaluated by looking for both hardware failures and communication errors. A reliability prediction based on different handbooks has been carried out to estimate the failure rate of the nodes self-designed and self-developed to be used under harsh environments. Then, using the Fault Tree Analysis the real deployment of the nodes is taken into account considering the Wi-Fi coverage area and the possible communication link between nearby nodes. The findings show how different node arrangements provide significantly different reliability. The positioning is therefore essential in order to obtain maximum performance from a Wireless sensor network.

Energy‐efficient routing protocol and optimized passive clustering in WSN for SMART grid applications

SummaryDue to low cost, ease of implementation and flexibility of wireless sensor networks (WSNs), WSNs are considered to be an essential technology to support the smart grid (SG) application. The prime concern is to increase the lifetime in order to find the active sensor node and thereby to find once the sensor node (SN) dies in any region. For this reason, an energy‐efficient Dynamic Source Routing (DSR) protocol needs to provide the right stability region with a prolonged network lifetime. This work is an effort to extend the network's existence by finding and correcting the considerable energy leveraging behaviors of WSN. We build a comprehensive model based on real measures of SG path loss for different conditions by using the characteristics of WSN nodes and channel characteristics. This method also establishes a hierarchical network structure of balanced clusters and an energy‐harvesting SN. The cluster heads (CHs) are chosen by these SN using a low overhead passive clustering strategy. The cluster formation method is focused on the use of passive clustering of the particle swarm optimization (PSO). For the sake of eliminating delayed output in the WSN, energy competent dynamic source routing protocol (EC‐DSR) is used. Chicken swarm optimization (CSO) in which optimum cluster path calculation shall be done where distance and residual energy should be regarded as limitation. Finally, the results are carried out with regard to the packet distribution ratio, throughput, overhead management, and average end‐to‐end delay to demonstrate the efficiency of the proposed system.

Design implementation of wireless multimedia sensor networks for dryland agriculture

This study discusses the application of Wireless Sensor Network (WSN) technology to specific dry land agriculture because it was founded in the Province of East Nusa Tenggara (NTT) and a small part of Maluku. Its uniqueness has become a comparative advantage for the regions, especially for the academics of the University of Nusa Cendana (Undana). In general, the use of WSN in agriculture is still basic, relying on analog and manual sensors to obtain information on land conditions and vegetation in the land. Also generally still using scalar data and not optimizing the use of multimedia data, especially data based on videos and images. The contribution and novelty of this research are: (a) The application of multi-sensor technology with the Wireless Multimedia Sensor Network (WMSN) will optimize various data from land conditions so that it is hoped that more analysis of monitoring data results is expected, precise and efficient; (b) The application of WMSN with Internet-of-Thing (IoT) will be applied to the dry land agriculture. This research produces a design for implementing WMSN technology on dry agricultural land which is expected to increase agricultural yields in the area.

IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare—A Review

Smart city is a collective term for technologies and concepts that are directed toward making cities efficient, technologically more advanced, greener and more socially inclusive. These concepts include technical, economic and social innovations. This term has been tossed around by various actors in politics, business, administration and urban planning since the 2000s to establish tech-based changes and innovations in urban areas. The idea of the smart city is used in conjunction with the utilization of digital technologies and at the same time represents a reaction to the economic, social and political challenges that post-industrial societies are confronted with at the start of the new millennium. The key focus is on dealing with challenges faced by urban society, such as environmental pollution, demographic change, population growth, healthcare, the financial crisis or scarcity of resources. In a broader sense, the term also includes non-technical innovations that make urban life more sustainable. So far, the idea of using IoT-based sensor networks for healthcare applications is a promising one with the potential of minimizing inefficiencies in the existing infrastructure. A machine learning approach is key to successful implementation of the IoT-powered wireless sensor networks for this purpose since there is large amount of data to be handled intelligently. Throughout this paper, it will be discussed in detail how AI-powered IoT and WSNs are applied in the healthcare sector. This research will be a baseline study for understanding the role of the IoT in smart cities, in particular in the healthcare sector, for future research works.

Sensor Networks for Structures Health Monitoring: Placement, Implementations, and Challenges—A Review

The development of structural health monitoring (SHM) systems and their integration in actual structures has become a necessity as it can provide a robust and low-cost solution for monitoring the structural integrity of and the ability to predict the remaining life of structures. In this review, we aim at focusing on one of the important issues of SHM, the design, and implementation of sensor networks. Location and number of sensors, in any SHM system, are of high importance as they impact the system integration, system performance, and accuracy of assessment, as well as the total cost. Hence we are interested in shedding the light on the sensor networks as an essential component of SHM systems. The review discusses several important parameters including design and optimization of sensor networks, development of academic and commercial solutions, powering of sensors, data communication, data transmission, and analytics. Finally, we presented some successful case studies including the challenges and limitations associated with the sensor networks.

Energy-efficient distributed heterogeneous clustered spectrum-aware cognitive radio sensor network for guaranteed quality of service in smart grid

The development of a modern electric power grid has triggered the need for large-scale monitoring and communication in smart grids for efficient grid automation. This has led to the development of smart grids, which utilize cognitive radio sensor networks, which are combinations of cognitive radios and wireless sensor networks. Cognitive radio sensor networks can overcome spectrum limitations and interference challenges. The implementation of dense cognitive radio sensor networks, based on the specific topology of smart grids, is one of the critical issues for guaranteed quality of service through a communication network. In this article, various topologies of ZigBee cognitive radio sensor networks are investigated. Suitable topologies with energy-efficient spectrum-aware algorithms of ZigBee cognitive radio sensor networks in smart grids are proposed. The performance of the proposed ZigBee cognitive radio sensor network model with its control algorithms is analyzed and compared with existing ZigBee sensor network topologies within the smart grid environment. The quality of service metrics used for evaluating the performance are the end-to-end delay, bit error rate, and energy consumption. The simulation results confirm that the proposed topology model is preferable for sensor network deployment in smart grids based on reduced bit error rate, end-to-end delay (latency), and energy consumption. Smart grid applications require prompt, reliable, and efficient communication with low latency. Hence, the proposed topology model supports heterogeneous cognitive radio sensor networks and guarantees network connectivity with spectrum-awareness. Hence, it is suitable for efficient grid automation in cognitive radio sensor network–based smart grids. The traditional model lacks these capability features.

A Moving Vehicle Height Monitoring Sensor System for Overheight Impact Avoidance

Bridges, overpasses, and road construction sites with reduced vertical clearance lead to collision threats from crossing vehicles that exceed their clearance due to their inherent height or improper loading. These accidents can pose slight or severe physical damage to property and, primarily, damage to involved individuals in these vehicles or their affected components (i.e., collateral damage around and above an overpass or bridge). Furthermore, the resulting consequences may also incur fatalities, injuries, structural damages, and monetary damages. It has severely impacted the repair and/or replacement costs of the affected structures. Such accidents and consequences have been observed at a national level and could be reduced with a proper implementation of an overheight sensing system to prevent them from happening as often. This paper introduces the design, prototype, and implementation of a low power sensor network to monitor and characterize vehicle height and other characteristics in real time, thus alerting overheight vehicles well in advance of a possible collision.

An Implementation of a Multi-Hop Underwater Wireless Sensor Network using Bowtie Antenna

Water quality is a growing area of research, with more and more focus in the UK and globally on environmental issues and water quality. Current methods of monitoring environmental data such as air quality have continued to develop, spurred on by the growth of the Internet of Things. However, water quality monitoring mainly still depends on manual sample collection. This research presents the first implementation of a multi-hop underwater radio frequency sensor network using bowtie antennas combined with the 433 MHz frequency and a controlled flooding routing approach. The experimental work was conducted in the water reservoir and demonstrates the potential of multi-hop routing in underwater sensor networks to extend range to 19 meters as well as improvements on communication distances from 7 meters previously to 17 meters using radio frequency communications in an underwater environment. Simulated results show that the experimental platform used could enable the long-term deployment of an underwater wireless sensor network that used RF for periods of over a year with support for a 100 sensor node network broadcasting twice daily remaining active for 418 days or a 100 sensor node network broadcasting hourly remaining active for 406 days before any node deaths.

Optimization of placement of piezoelectric wafers based on a hybrid model using pitch-catch and pulse-echo configurations

The development of Structural Health Monitoring (SHM) systems and integration in our structures is a necessity. It has proven to provide a robust and low-cost solution for monitoring structural integrity and can predict the remaining life of our structures. One of the most important aspects of SHM systems is the design and implementation of sensor networks. This study proposes a new hybrid model for optimizing sensor placement on convex and non-convex structures. We propose a novel framework in which two detection mechanisms are considered: pitch-catch and pulse-echo to provide coverage for a given surface. These two mechanisms will complement each other to minimize the number of sensors used while maintaining a high coverage. This combination also allows for better coverage of the corners and regions in the proximity of geometrical discontinuity (such as holes and openings). The monitored area is discretized into a set of control points. For a control point to be covered, it should satisfy the user-defined coverage level which is the number of sensing paths crossing that point. These sensing paths are provided by two modes of communications (pitch-catch and pulse-echo) between the actuator-sensor pairs. The model, which is solved using a genetic algorithm (GA), provides flexibility by allowing the user to input different parameters such as the attenuation distance of the propagating waves and the sensing path limits of both coverage configurations that can be determined through experimentation. The efficiency of the proposed model is then demonstrated by simulating different geometrical shapes. Significant improvement in the coverage of the monitored area, reaching 34.6%, was achieved when compared to the coverage provided by some preliminary solutions such as uniformly placing the sensors on the plate under study. Also, the advantage of combining both configurations (pitch-catch and pulse-echo) in the same model was investigated. It was shown that the latter highly impacted the coverage in the blind zones (corners and edges) where a single configuration is not effective. Afterward, experimental validation was carried out to evaluate the model’s accuracy in damage localization within the optimized sensor networks. The results demonstrated the proficiency of the model developed in distributing the sensors on the tested specimens.

Design and Implementation of LoRa-Based Wireless Sensor Network with Embedded System for Smart Agricultural Recycling Rapid Processing Factory

Design and Implementation of LoRa-Based Wireless Sensor Network with Embedded System for Smart Agricultural Recycling Rapid Processing Factory

Wireless sensor networks for agriculture systems

This article describes the implementation of a wireless sensor network for monitoring the state of the environment in greenhouses. A distinctive feature of the work is low power consumption, low cost and the ability to monitor the entire history of measurements in real-time.

Wireless Sensor Networks: A Review on Sensor Deployment and Routing Protocols for Different Application

In the past years, many research works has been performed on the basics of the wireless sensor networks for different application namely precision agriculture, environment, medical care, security and surveillance. Whereas, WSN faces the different kinds of challenges that includes energy consumption, routing protocol algorithm and selection of the sensor node from the particular application environment. Therefore, the implementation of the wireless sensor networks has overcome a numerous amount of challenges in the practical life. In this paper, we mainly focused on the two issues which mainly affect the transmission of the data to the base station they are sensor node deployment which is used to improve the coverage area of the environment and the routing protocol which is used to select the node to maintain a particular route for a network. Atlast, we have discussed about design challenges that are faced during the designation of the wireless sensor networks.

Scalable Two-Dimensional Bloom Filter Membership Scheme on Massive Scale Wireless Sensor Networks

Wireless sensor nodes help people to monitor any operational mechanism. It is important to be implemented in industrial automation in detecting unauthorized activity or malicious sensor nodes by the outsider. However, a massive number of devices or things which are needed to be controlled become a challenge. A fast mechanism to recognize a member or a valid device is needed to minimize production costs and avoid future procedural errors. It becomes an open problem for the implementation of massive scale Wireless Sensor Networks (WSN). Hence, a scalable two-dimensional Bloom filter is proposed in this paper to deal with this issue. The low computation complexity and the storage efficiency requirements by the two-dimensional Bloom filter are proven by the experimental results. The insertion, deletion and query procedure of the proposed method only need O (1), while the storage usage of two-dimensional Bloom filters not only lower than counting Bloom filter in average 131 bits difference but also approaches accommodative Bloom filter in about 10 bits difference. These two parameters support a fast membership scheme. Furthermore, the analysis of value initialization has been performed. To the best of our knowledge, the investigation of this parameter has not been addressed by other studies. This process aims to get the best scenario to increase scalability. Several recommendations for selecting dimension number has been stated which is useful for efficient storage usage and reduce false positive.

Enhanced traffic-adaptive slotted MAC for IoT-based smart monitoring grid

The implementation of a wireless sensor network has been utilized in many applications of urban and rural areas. One such application is smart-grid Internet of Things, (IoT)-based monitoring systems that work on real-time communication. They require an intelligent scheduling mechanism to scrap delay in dynamic traffic flows without undergoing any efficiency loss. This study presents a data-driven TDMA-based paradigm, called traffic-adaptive slot-assignment MAC (TASA-MAC) that supports IoT-channel dynamics in time-critical large-scale monitoring systems while combating the packet delay. The proposed work showcases low run-time scheduler-complexity for low-cost end nodes independent of the network’s scalability owing to TASA-MAC’s optimal fitness-function parameters under intermittent IoT-traffic scenarios, i.e., energy cost and residual energy ratio. The proposed scheduler minimizes the delay of exigency packets while also prompts the system to deliver them before their deadline expiry. The analytical and simulation model of TASA-MAC demonstrates its superiority in terms of moderated delay, packet throughput, and energy demand as compared to the existing models.

Development and Implementation of a Hybrid Wireless Sensor Network of Low Power and Long Range for Urban Environments.

The urban population, worldwide, is growing exponentially and with it the demand for information on pollution levels, vehicle traffic, or available parking, giving rise to citizens connected to their environment. This article presents an experimental long range (LoRa) and low power consumption network, with a combination of static and mobile wireless sensors (hybrid architecture) to tune and validate concentrator placement, to obtain a large coverage in an urban environment. A mobile node has been used, carrying a gateway and various sensors. The Activation By Personalization (ABP) mode has been used, justified for urban applications requiring multicasting. This allows to compare the coverage of each static gateway, being able to make practical decisions about its location. With this methodology, it has been possible to provide service to the city of Malaga, through a single concentrator node. The information acquired is synchronized in an external database, to monitor the data in real time, being able to geolocate the dataframes through web mapping services. This work presents the development and implementation of a hybrid wireless sensor network of long range and low power, configured and tuned to achieve efficient performance in a mid-size city, and tested in experiments in a real urban environment.