Design Of Sensor Networks Research Articles

MODEL OF A CHAOTIC ULTRA-WIDEBAND INFORMATION TRANSMISSION SYSTEM FOR WIRELESS SENSOR NETWORKS

Distributer measuring systems, consisting of interconnected wireless sensor nodes with autonomous supply, have plenty of applications in different areas of engineering end technology. Therefore, wireless sensor network design and optimization is a vital problem in informational technologies and computer engineering. Regardless of particular application and architecture, a wireless sensor network consists of several small sensor nodes somehow distributed in an area of inspection. Such a design ensures a distributed parameter measuring, according to which, each node performs preprocessing procedures for the data, which are to be aggregated by a so-called sink node and sent via other networks, e.g., wired or wireless local area network. For healthcare solutions, it is important to ensure a stable connection with the network and server back-end to provide a high-level analysis based on predicting methods of machine learning algorithms. This paper describes an application of an ultra-wideband communication model based on chaos synchronization as a low-power and efficient alternative solution for building wireless sensor networks. Thus, using chaotic signals as information carriers in wireless communication has several advantages, including a wide smooth spectrum, high information capability, cybersecurity, and low power consumption. The synchronization problem is one of the most vital tasks to be solved to design a chaos application for ultra-wide-band communication. Being well-studied for periodic signals, modern synchronization theory contains plenty of solutions for classical telecommunication and radio engineering systems, however, it is not developed enough for chaotic systems. Hence, a one-directional dissipative synchronization between two Chen systems is studied in the first section. The second section is devoted to the computer simulation of the model, described in the previous section. All the models built and the simulations performed have been done using MATLAB/Simulink software. The negative impact of channel noise and inequality of system parameters is considered. The possible way how to improve technical characteristics is also provided. Proposed models are to be used to design and develop low-cost wireless sensor networks for multi-channel healthcare solutions.

On transmission range of sensors in sparse wireless sensor networks

One of the main challenges in the design of wireless ad-hoc and sensor networks is to reduce energy consumption and radio interference and collision, which are strictly and strongly correlated to the transmission range of the nodes. In this article, the transmission ranges of sensors in sparse Wireless Sensor Networks (WSN) is theoretically analyzed and is shown that the transmission ranges of sensors can be significantly reduced in this type of WSNs without losing any connection. Next, a method is presented to calculate reduced transmission ranges. In this method, an iterative transmission range adjustment mechanism is used to select an almost transmission range while no information is needed on the location of nodes or the distance between nodes. This method is very efficient and its communication overhead is low. It can be used as a pre-step of any routing algorithm in order to decrease energy consumption and radio interference in targeted scenarios. The simulation results show the efficiency of the proposed method. We got at least 50% improvement in energy consumption in comparison with full power network, in sparse WSNs.

Sensor Network Design based on the Observability and Precision degree

The Unscented Kalman Filter is a state estimation method used in nonlinear dynamic systems to estimate the mean and covariance of a random variable undergoing a nonlinear transformation, knowing the process model and the measurements. Therefore, an adequate choice of the measured variables improves the performance of the filter technique. In this context, the sensor network design problem allows selecting a set of variables that minimizes the global estimation error when the instrumentation budget is limited. This is solved using a level traversal tree search algorithm, whose computation time is reduced by evaluating the design criteria sequentially. In this work, it is proposed to address the effect of the circumstantial loss of measurements on the system observability and the estimates precision. The success of the sensor network design methodology is demonstrated for the copolymerization process of Methyl Methacrylate and Vinyl Acetate, widely studied in the literature.

Making technological innovations accessible to agricultural water management: Design of a low-cost wireless sensor network for drip irrigation monitoring in Tunisia

Unsustainable use of water resources and climate change will exacerbate the existing tensions surrounding resources, especially in the Mediterranean context. Despite investments in costly modern equipment, the performance of irrigated agriculture remains below expectations, notably because of the lack of available water data and the limited use of decision support tools. Although a variety of soil moisture sensors are available on the market, they are not widely used by the agricultural community because of their high cost and complexity. Access to information at an unprecedented level, via easily accessible low-cost and low-tech sensors, may be a major lever for improved identification of achievable gains in performance, and to guide actors toward efficient water management. To explore this hypothesis, an open source wireless soil moisture sensor, low-energy and economically and technically accessible, was developed. The tool was designed according to water users’ requirements and applied to a Tunisian irrigation scheme subject to major water use efficiency issues. The functioning of the wireless sensor network was tested on pilot plots over a growing season and compared with commercial sensors. A single parameter calibration can be performed in either the laboratory or the field. This low-cost sensor can be used for real-time irrigation monitoring and as a decision-making tool for water management.

Multi-Boundary Empirical Path Loss Model for 433 MHz WSN in Agriculture Areas Using Fuzzy Linear Regression.

Path loss models are essential tools for estimating expected large-scale signal fading in a specific propagation environment during wireless sensor network (WSN) design and optimization. However, variations in the environment may result in prediction errors due to uncertainty caused by vegetation growth, random obstruction or climate change. This study explores the capability of multi-boundary fuzzy linear regression (MBFLR) to establish uncertainty relationships between related variables for path loss predictions of WSN in agricultural farming. Measurement campaigns along various routes in an agricultural area are conducted to obtain terrain profile data and path losses of radio signals transmitted at 433 MHz. Proposed models are fitted using measured data with "initial membership level" (μAI). The boundaries are extended to cover the uncertainty of the received signal strength indicator (RSSI) and distance relationship. The uncertainty not captured in normal measurement datasets between transmitter and receiving nodes (e.g., tall grass, weed, and moving humans and/or animals) may cause low-quality signal or disconnectivity. The results show the possibility of RSSI data in MBFLR supported at an μAI of 0.4 with root mean square error (RMSE) of 0.8, 1.2, and 2.6 for short grass, tall grass, and people motion, respectively. Breakpoint optimization helps provide prediction accuracy when uncertainty occurs. The proposed model determines the suitable coverage for acceptable signal quality in all environmental situations.

Optimization of PZT Wafers’ placement with enhanced level of robustness using multiple interacting networks

Continuous monitoring and in particular structural health monitoring (SHM) of structures is used, nowadays, by asset managers to ensure a high level of safety and extend the lifetime of their assets. One of the main bottlenecks faced in the development of an SHM system is the design and implementation of sensor networks. This includes selecting the sensor type and optimizing the number and positions of sensors. This study proposes a novel approach to account for the potential loss in sensing elements within the network, by having multiple independent networks but at the same time interacting for enhanced performance. The developed model considers the PZT locations as continuous variables where any point on the surface can be a sensor location. The model can handle complex structures and customize to different wave characteristics and coverage levels.The objective function in the developed model aims at maximizing a weighted sum of two dependent terms: the coverage which is the percentage of covered control points within the network, and the robustness which is the average number of networks covering each covered control point. The proposed model was solved using a genetic algorithm and was tested on several geometries. To mimic a faulty sensor within the network, a complete network was removed in each case, and it was found that the coverage was not impacted. The effect of the partitioning of large sections on the performance of the optimizer was explored.Experimental validations were carried out to evaluate the model's accuracy in damage localization within the optimized sensor networks and to emphasize the importance of each of the coverage and robustness. The results demonstrated the proficiency of the model developed in distributing the piezoelectric ceramics (PZTs) on the tested specimens, and its ability to detect damages more precisely in more robust configurations where multiple networks participate in covering wide regions.

AI-based model driven approach for adaptive wireless sensor networks design

AI-based model driven approach for adaptive wireless sensor networks design

Design and analysis of energy aware interior gateway routing algorithm with particle swarm optimization

SummaryIn wireless sensor networks (WSNs), the data must reach a processing node with less energy consumption and delay rate. However, with the specific consideration of the unique properties of sensor networks such as limited power, stringent bandwidth, dynamic topology due to node failures, adding/removing nodes, or even physical mobility, high network density and large‐scale deployments have posed many challenges in the design and management of sensor networks. These challenges have demanded energy awareness and robust protocol design. Efficient utilization of the sensor's energy resources and maximizing the network lifetime were the primary design considerations for the proposed work. As a result, the new interior gateway routing with particle swarm optimization (IGRPSO) is presented with minimal latency and a higher packet delivery ratio in WSN; optimum link weights are calculated in a fraction of the original data to the new protocol. Network entropy maximization is a conceptual framework that guides the development of the protocol and the computational techniques required to determine which traffic distributions are not only best but also realizable through link‐state routing. Therefore, a routing algorithm provides the optimal and economically optimal route. We used the MATLAB simulation environment to compare and contrast the performance of the dynamic source routing (DSR) protocol and enhanced interior gateway routing protocol, DSR protocol with particle swarm optimization with that of the IGRPSO protocol, which has the maximum packet delivery ratio of 75.74% and less packet drop of 25.37%, less energy consumption of 0.3674 J.

An integrated system for the acoustic monitoring of goat farms

Effective precision livestock farming requires the availability of reliable and up-to-date data characterizing the animals and environment of interest. This work presents the design, architecture, and implementation of a wireless acoustic sensor network for monitoring goat farms on a 24/7 basis. In addition, we define a hierarchical organization of the involved sound classes exhaustively covering every aspect of the encountered goat vocalizations. Moreover, we developed an annotation tool tailored to the specifics of the problem at hand, i.e. a big, real-world data environment, able to meaningfully assist the annotation of goat vocalizations via a suitable sound classification module. On top of that, a mobile phone application was developed facilitating authorized users to remotely access information describing the situation on the farm site. Importantly, such a non-invasive monitoring framework was installed in 4 different sites located in Northern Italy while taking into account their diverse characteristics.

Design of a sensor network for the quantitative analysis of sport climbing.

We describe the design of a modular sensorized climbing wall for motion analysis in a naturalistic environment. The wall is equipped with force sensors to measure interaction forces between the athlete and the wall, which can be used by experienced instructors, athletes, or therapists, to gain insights into the quality of motion. A specifically designed triaxial load cell is integrated into each hold placement, invisible to the climber, and compatible with standard climbing holds. Data collected through the sensors is sent to an app running on a portable device. The wall can be adapted to different uses. To validate our design, we recorded a repeated climbing activity of eleven climbers with varying degrees of expertise. Analysis of the interaction forces during the exercise demonstrates that the sensor network design can provide valuable information to track and analyze exercise performance changes over time. Here we report the design process as well as the validation and testing of the sensorized climbing wall.

Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations

Abstract. The atmospheric boundary layer (ABL) defines the volume of air adjacent to the Earth's surface for the dilution of heat, moisture, and trace substances. Quantitative knowledge on the temporal and spatial variations in the heights of the ABL and its sub-layers is still scarce, despite their importance for a series of applications (including, for example, air quality, numerical weather prediction, greenhouse gas assessment, and renewable energy production). Thanks to recent advances in ground-based remote-sensing measurement technology and algorithm development, continuous profiling of the entire ABL vertical extent at high temporal and vertical resolution is increasingly possible. Dense measurement networks of autonomous ground-based remote-sensing instruments, such as microwave radiometers, radar wind profilers, Doppler wind lidars or automatic lidars and ceilometers are hence emerging across Europe and other parts of the world. This review summarises the capabilities and limitations of various instrument types for ABL monitoring and provides an overview on the vast number of retrieval methods developed for the detection of ABL sub-layer heights from different atmospheric quantities (temperature, humidity, wind, turbulence, aerosol). It is outlined how the diurnal evolution of the ABL can be monitored effectively with a combination of methods, pointing out where instrumental or methodological synergy are considered particularly promising. The review highlights the fact that harmonised data acquisition across carefully designed sensor networks as well as tailored data processing are key to obtaining high-quality products that are again essential to capture the spatial and temporal complexity of the lowest part of the atmosphere in which we live and breathe.

Traffic Sensors Selection for Complete Link Flow Observability through Simulated Annealing

Sensor selection is a key issue in sensor network design. Due to limited equipment and maintenance costs, and to minimize processing and communication overhead, only a limited number of sensors can reasonably be used, and they must also be reasonably placed. Indeed, the selection of an appropriate number of physical sensors is critical to ensure reliable estimates when monitoring large-scale systems such as traffic flows, water or gas transport infrastructures, or underwater wireless sensor networks. Regarding traffic systems, the full link flow observability problem consists of selecting the minimum number of traffic sensors to be installed from a given larger set (Salari, 2019). In particular, it involves selecting a subset of p, possibly redundant, sensors from a larger set of n ≫ p potential sensors in order to preserve the structural observability property of the entire traffic network. To solve this problem, the classical concept of system observability is exploited as a criterion for sensor selection. In this paper, we refer to the design of a simulated annealing heuristic to approximately solve the problem. The resulting subset of sensors is then used to design a Luenberger observer to properly estimate the system state. Some numerical simulations demonstrate the effectiveness of the proposed approach.

AoI Minimization for WSN Data Collection With Periodic Updating Scheme

In this paper, we consider the design of a wireless sensor network (WSN) that aims at monitoring the environment and collecting data periodically. In view of the limited energy and computational capability of the sensor nodes, a mobile edge computing (MEC) server is deployed in the WSN as a data processing unit. The goal of the design is to maintain the freshness of the data, which is characterized by the criterion of the age of information (AoI). Therefore, we analyze the long-term average AoI of the considered network. Then, the energy and time constraints for the WSN are modeled with consideration of transmission and computation. Next, a non-convex average AoI minimization problem is formulated subject to the energy and time constraints by jointly optimizing the sampling rate, computing scheduling, and transmit power. To tackle the challenging problem, the geometric programming and successive convex approximation (SCA) technique are applied to develop an algorithm with convergence guarantee. Moreover, to exhibit the benefits of the MEC server, a joint design is investigated for the WSN without the MEC server. Finally, the numerical results demonstrate the efficiency of our proposed SCA-based algorithm and show the impact of the sampling rate on the AoI performance.

Improving Energy Efficiency of Wireless Sensor Networks through Topology Optimization

- Relevance of the field of wireless sensor networks (WSNs) is increasing, and one of the most pressing challenges is in energy usage. This makes it a resource restraint type network for wireless sensor nodes that contain small unchangeable battery. Sensor network design has been influenced by and depends on the application by factors such as scalability, power consumption, environment etc. Most of the energy is used for communications among the three energy-saving activities: sensing, processing and communication. In this paper, an energy efficient (energy conserving) routing protocol called Wireless Sensor Network Energy Reduction Routing Coordinate Algorithm (WSNERRCA) is proposed. This provides a more efficient energy consumption pattern in WSN, by using eight straight line routing coordinate to sink. It transmits data within nodes transmission range (single-hop) and multi-hopping along routes (coordinates) thereby saving energy and optimizing delivery. The energy-model is simulated using NS-2 and the residual energy computed with the aid of AWK programming language coding. This model out-performed its counterpart (EEEWSNMIA) by 6%, as seen in recent research work published by Elsevier based on the criteria of conserving the highest energy of the sensor network with a hundred and twenty nodes while upholding optimally the QoS factors.

A Fuzzy Rule-Based System to Infer Subjective Noise Annoyance Using an Experimental Wireless Acoustic Sensor Network

Over the last few years, several works have been conducted on the design and development of wireless acoustic sensor networks (WASNs) to monitor acoustic noise levels and create noise maps. The information provided by these WASNs is based on the equivalent noise pressure level over time T (Leq,T), which is used to assess the objective noise level. According to some authors, noise annoyance is an inherently vague and uncertain concept, and Leq,T does not provide any information about subjective annoyance to humans. Some fuzzy models have been proposed to model subjective annoyance. However, the use of fuzzy rule-based systems (FRBS) that have been adapted to acoustic sensor node resource limitations in real WASN to provide the degree of subjective noise annoyance in real-time remains a largely unexplored region. In this paper, we present the design and implementation of an FRBS that enables the sensor nodes of a real WASN deployed in the city of Linares (Jaen), Spain to infer the degree of subjective noise annoyance in real-time. The hardware used for the sensor nodes is a commercial model, Arduino Due. The results demonstrate that the sensor nodes have sufficient processing capacity and memory to infer the subjective annoyance in real-time, and the system can correctly detect situations that can be considered more annoying by humans.

Analysis of the NTRU Post-Quantum Cryptographic Scheme in Constrained IoT Edge Devices

Research on post-quantum cryptography aims to solve the problematic of modern public-key cryptography being broken by attacks coming from quantum computers in the future and, moreover, by using classical electronics. This task is so critical that the National Institute of Standards and Technology (NIST) is in the final process of standardizing post-quantum schemes for the future protection of embedded applications. Though there are some research work done on embedded systems, it is important to study the impact of these proposals in realistic environments for the Internet of Things (IoT), where the limited computational resources and the strict requirements for power consumption can become incompatible with the usage of cryptographic schemes. In this work, the performance of one of the finalists of the standardization process called NTRU is studied and implemented in a custom wireless sensor node designed for applications in the extreme edge of the IoT. The cryptosystem is implemented and evaluated within the processes of the Contiki-NG operating system. Furthermore, additional experiments are performed to check if commonly integrated hardware peripherals for cryptography inside modern microcontrollers can be used to achieve better performance with NTRU, not only at the single node level but also at the network level, where the NTRU key encapsulation mechanism is tested in a real communication process. The results derived from these experiments show that NTRU is suitable for modern microcontrollers targeting wireless sensor networks design, while old devices present in popular platforms might not afford the cost of its implementation.

Experimental evaluation of multi-PHY 6TiSCH networks

The architectural design of Wireless Sensor Networks (WSNs) for the Industrial Internet of Things (IIoT) applications requires the careful planning and selection of an appropriate operational strategy. Harmonization of standards is crucial to ensure easier certification and commercialization of IIoT solutions. The ongoing research activities are directed toward designing agile, reliable, and secure transmission technologies and protocols. Recently, Time Slotted Channel Hopping (TSCH) standardization bodies have started to consider support for multiple physical layers thus accommodating a wide range of applications. This paper presents the results of the extensive experimental measurement campaign to study the performance of the 6TiSCH (IPv6 over the TSCH mode of IEEE 802.15.4e) network while supporting multiple physical layers (PHYs). For measurement purposes, all experiments were performed on OpenMote-B hardware. These devices are equipped with an Atmel AT86RF215 dual radio transceiver implementing IEEE 802.15.4g. The performance evaluation is provided for the following metrics: network formation time, Packet Delivery Ratio (PDR), latency, and duty cycle. Results are encouraging, particularly in terms of high PDR for all tested PHYs. Performance evaluation indicates the importance of proper node positioning, link quality estimation and careful selection of network parameters. Moreover, collected experimental results create a dataset that provides insights into the tested PHYs' performance and their potential for indoor 6TiSCH networking.

Applying TTIED-CMYK Algorithm in Wireless Sensor Networks Based on Raspberry pi and DHT-11

Recently, there has been a wide and continuous development in wireless sensor networks WSNs. These networks are directly employed in our daily lives, so it has become necessary to maintain the confidentiality of information in these networks. This paper provided a detailed explanation of the types of attackers according to their layer, in addition to the design of a wireless sensor network consisting of a temperature and humidity sensor called the DHT-11 and a Raspberry Pi to implement the Text-To-Image Encryption/Decryption algorithm based on CMYK mode (TTIED-CMYK) in these networks. An encrypted image with dimensions of 2×2 and a size not exceeding 70 bytes was obtained for a text consisting of eleven characters. In addition to that, the encryption time is very short, not exceeding 1 microsecond.

Edge-Computing Meshed Wireless Acoustic Sensor Network for Indoor Sound Monitoring.

This work presents the design of a wireless acoustic sensor network (WASN) that monitors indoor spaces. The proposed network would enable the acquisition of valuable information on the behavior of the inhabitants of the space. This WASN has been conceived to work in any type of indoor environment, including houses, hospitals, universities or even libraries, where the tracking of people can give relevant insight, with a focus on ambient assisted living environments. The proposed WASN has several priorities and differences compared to the literature: (i) presenting a low-cost flexible sensor able to monitor wide indoor areas; (ii) balance between acoustic quality and microphone cost; and (iii) good communication between nodes to increase the connectivity coverage. A potential application of the proposed network could be the generation of a sound map of a certain location (house, university, offices, etc.) or, in the future, the acoustic detection of events, giving information about the behavior of the inhabitants of the place under study. Each node of the network comprises an omnidirectional microphone and a computation unit, which processes acoustic information locally following the edge-computing paradigm to avoid sending raw data to a cloud server, mainly for privacy and connectivity purposes. Moreover, this work explores the placement of acoustic sensors in a real scenario, following acoustic coverage criteria. The proposed network aims to encourage the use of real-time non-invasive devices to obtain behavioral and environmental information, in order to take decisions in real-time with the minimum intrusiveness in the location under study.

The effect of chaotic mapping on naked mole-rat algorithm for energy efficient smart city wireless sensor network

Naked mole-rat algorithm (NMRA) is a recently introduced swarm intelligent algorithm based on the matting pattern of naked mole-rats. Though the algorithm is competitive but still shows poor exploration properties and slow convergence. In the present work, chaos theory has been introduced to enhance the exploration properties of NMRA. Apart from that, parametric adaptation has been added to analyse the effect of mating factor (λ) using various chaotic maps (such as chebyshev map, circle map, iterative map etc.) on NMRA. Based on these chaotic maps, ten variants of NMRA are proposed, and their performance is evaluated for CEC 2005 test suite subjected to different population and dimension sets. Further, the best variant among the proposed variants is evaluated for CEC 2014 benchmark functions, 100-digit challenge problems (CEC 2019) and three engineering design problems. In addition, to check the effectiveness of proposed best variant, it is further applied to real time problem of energy efficient and prolonged lifetime design of wireless sensor network for smart city framework. The results of best proposed variant are compared with other state-of-the-art algorithms namely differential evolution with adaptive properties (JADE), whale optimization algorithm with opposition and exponential properties (OEWOA), sine cosine crow search algorithm (SCCSA), self-adaptive differential evolution (SaDE), success-history based differential evolution along with parameter adaption (SHADE), equilibrium optimizer (EO), flower pollination algorithm based on fractional order calculus (FA-FPO), Gaussian-salp swarm algorithm (GSSA), variable neighbourhood bat algorithm (VNBA), blended biogeography-based optimization (B-BBO), improved elephant herding optimization (IMEHO), original NMRA and others. Experimental results validate the performance of the proposed NMRA with Chebyshev map (CNMRA) as the best well supported by Wilcoxon rank-sum test and Friedman rank (f-rank) test, concerning other algorithms used for comparison.