Low-power Wireless Sensor Networks Research Articles

Intelligent Unmanned Robot Using IOT for Military Applications

Most military organization now takes the help of robots to carry out many risky jobs that cannot be done by the soldier. These robots used in the military are usually employed with an integrated system, including video screens, sensors, grippers, and cameras. The military robots also have different shapes according to the purposes of each robot. Here the new system is proposed with the help of low low-power IOT wireless sensor network to trace out the intruders (unknown persons) and the robot will take the necessary action automatically. Thus the proposed system, an Intelligent Unmanned Robot (IUR) using IOT saves human lives and reduces manual error on the defense side. This is a specially designed robotic system to save human life and protect the country from enemies. Robots are specially designed for humans to make our lives easier. Robots are designed for various purposes like military purposes, industry, for home home-based applications. At the border, different tanks, missiles, guns, etc. are used by the enemy. This causes problems and harms our forces or soldiers. For this, a robot is designed and developed for military purposes application to protect our army. robots are used to detect obstacles that are found in their path. If it finds any obstacle in its path, then using a gun mechanism it will able to shoot that obstacle. To make it a multifunctional robot all the actions performed by the user same actions performed by a robot using the stretch sensor. All these mechanisms are embedded in the propeller.

Low-Wind-Speed Galloping Wind Energy Harvester Based on a W-Shaped Bluff Body

Galloping-based piezoelectric energy harvesting systems are being used to supply renewable electricity for low-power wireless sensor network nodes. In this paper, a W-shaped bluff body is proposed as the core component of a piezoelectric wind energy harvester. Experiments and simulations have shown that the W-shaped bluff body can improve harvesting efficiency at low wind speeds. For the W-shaped structure, the finite element simulation results indicate that the structure can help improve the aerodynamic performance to obtain high aerodynamic force. The experimental results demonstrate that compared with the traditional bluff bodies, the piezoelectric wind energy harvester with the W-shaped bluff body (WEHW) can generate higher output voltages and has a lower cut-in speed. When the length L is 30 mm and the rear groove angle β is 30°, the W-shaped structure can induce the best harvesting performance. When an external load resistance of 820 KΩ is connected and the wind speed is 5 m/s, the WEHW generates an average output power of 0.28 mW.

Minimal-Overlap Centrality for Multi-Gateway Designation in Real-Time TSCH Networks

This article presents a novel centrality-driven gateway designation framework for the improved real-time performance of low-power wireless sensor networks (WSNs) at system design time. We target time-synchronized channel hopping (TSCH) WSNs with centralized network management and multiple gateways with the objective of enhancing traffic schedulability by design . To this aim, we propose a novel network centrality metric termed minimal-overlap centrality that characterizes the overall number of path overlaps between all the active flows in the network when a given node is selected as gateway. The metric is used as a gateway designation criterion to elect as a gateway the node leading to the minimal number of overlaps. The method is then extended to multiple gateways with the aid of the unsupervised learning method of spectral clustering . Concretely, after a given number of clusters are identified, we use the new metric at each cluster to designate as cluster gateway the node with the least overall number of overlaps. Extensive simulations with random topologies under centralized earliest-deadline-first (EDF) scheduling and shortest-path routing suggest our approach is dominant over traditional centrality metrics from social network analysis, namely, eigenvector , closeness , betweenness , and degree . Notably, our approach reduces by up to 40% the worst-case end-to-end deadline misses achieved by classical centrality-driven gateway designation methods.

Multi-Network Latency Prediction for IoT and WSNs

The domain of Multi-Network Latency Prediction for IoT and Wireless Sensor Networks (WSNs) confronts significant challenges. However, continuous research efforts and progress in areas such as machine learning, edge computing, security technologies, and hybrid modelling are actively influencing the closure of identified gaps. Effectively addressing the inherent complexities in this field will play a crucial role in unlocking the full potential of latency prediction systems within the dynamic and diverse landscape of the Internet of Things (IoT). Using linear interpolation and extrapolation algorithms, the study explores the use of multi-network real-time end-to-end latency data for precise prediction. This approach has significantly improved network performance through throughput and response time optimization. The findings indicate prediction accuracy, with the majority of experimental connection pairs achieving over 95% accuracy, and within a 70% to 95% accuracy range. This research provides tangible evidence that data packet and end-to-end latency time predictions for heterogeneous low-rate and low-power WSNs, facilitated by a localized database, can substantially enhance network performance, and minimize latency. Our proposed JosNet model simplifies and streamlines WSN prediction by employing linear interpolation and extrapolation techniques. The research findings also underscore the potential of this approach to revolutionize the management and control of data packets in WSNs, paving the way for more efficient and responsive wireless sensor networks.

An integrated thermal and RF energy harvesting system with rectifying combination and storage controller for IoT devices

This work presents an integrated thermal/RF energy harvester. The harvesting system can combine energy from two sources simultaneously (one DC and one AC) and deliver a regulated combined voltage of 1.75 V to a load and a storage controller. The output power of the harvester is in the range of hundreds of μWatts which is suitable for low-power wireless sensor networks. Maximum power point tracking (MPPT) is achieved by reconfiguring the number of stages of the DC path, the frequency driving the charge pump, and the capacitors of the matching network in the RF-path. The storage controller charges a supercapacitor to 3.5 V to power up the system during power shortage periods. The system is implemented in a 180 nm CMOS technology and utilizes a total on-chip capacitance of 2.4 nF. The peak end-to-end efficiency of the system reaches 60.3 % at output load power of 1.275 mW.

GPS-based fall detection system for old and specially-abled people

Falls are a serious public health concern for older people across the world. Modern telemedicine now depends heavily on remote monitoring of older patients and the ability to spot threats to human health. If a fall is not assisted in time, it can significantly reduce an older person's mobility, independence, and his/her quality of life. Older people who experience post-traumatic problems or mortality frequently do so because of falls. As a result, preventing falls consequences or providing essential help on time may depend on the early identification of falls. In this article, we propose an internet of things (IoT) based system that makes use of low-power wireless sensor networks, smart devices and cloud computing to detect falls and track positions for older and specially-abled people. The tracking is done by sending links of positions from the proposed system every 15 seconds to a specified google drive. On the other hand, an alert message will be delivered to the caregiver whenever a fall is happened. Thus, a MPU-6050 sensor and NEO-6M global positioning system (GPS) module are used with ESP32 microcontroller for the aforementioned purposes. A pilot study with several protocols was carried out to validate the cost-effective proposed system and achieved good results.

Performance analysis on the low-power energy harvesting wireless sensor networks with a novel relay selection scheme

Simultaneous wireless information and power transfer (SWIPT) has been utilized widely in wireless sensor networks (WSNs) to design systems that can sustain themselves by harvesting energy from the surrounding areas. In this study, we investigated the performance of the so-called low-power energy harvesting (LPEH) WSN. Being different from other studies, we equipped each relay with a battery whose characteristics were described by an on/off (1/0) decision scheme as per the Markov property. In this context, an optimal loop interference relay selection (OPLIRS) was proposed and investigated. Moreover, the crucial role of the log-normal distribution method in characterizing the LPEH WSN’s constraints was proven and emphasized. The system performance was evaluated in terms of the overall ergodic outage probability (OP) both analytically and numerically with Monte Carlo simulation. Readers can refer to this paper for guidelines on defining the networks’ constraints, analytically derivating the problems, or use the presented results for possible comparison studies.

Joint Clustering and Routing Optimisation for Low-power Wireless Sensor Networks

Wireless sensor networks (WSNs) have been one of the fields that have attracted a lot of attentions from many scientific researchers in recent years. The sensor nodes of the network are fixed or moved to detect the environment and impart the data accumulated from the remote monitored regions via wireless connections. It is indicated in complex environments such as forests, deep seas, urban areas, etc., the sensor nodes in WSNs are usually tiny and battery-driven devices. Thus, energy-effective data accumulation methods required to improve the network’s lifetime are very necessary. In this paper, we propose a joint technique of fuzzy clustering and heuristic ant routing (FCHAR) to save the energy for low-power WSNs. Simulation results are shown to demonstrate the benefits of the proposed FCHAR compared to other conventional ones.

Networking of Sensors With Fixed Transmission Ranges: Distributed Data Processing Over Graphs

In this letter, we address the data gathering problem in low-power wireless sensor networks (WSNs) with sensors of fixed transmission ranges. We model the NP-hard problem as an integer linear program that can be applied to networks of small sizes. Further, we propose a novel graphical model that apprehends the features of such networks, and design a polynomial-time approximation algorithm using the proposed graphical model for networks of large sizes. The performance is evaluated under data compaction strategies based on compressive sensing. Simulation results demonstrate the superior performance of the proposed method over its peer protocols.

A low-power Bluetooth-based wireless sensor network and its global confliction-solving impact localization method

During the whole service lifetime of aircraft structures with composite materials, impacts are inevitable and can usually cause severe but barely visible damages. Since the occurrences of impact are random and unpredictable, it is a hotspot direction to develop an online impact monitoring system that can meet strict limitations of aerospace applications including small size, light weight, and low power consumption. Piezoelectric (PZT) sensor, being able to generate impact response signals with no external power and cover a large-scale structure with only a small amount of them, is a promising choice. Meanwhile, for real systems, networks with multiple nodes are normally required to monitor large-scale structures in a global way to identify any impact localization confliction, yet the existing studies are mostly evaluated with single nodes instead of networks. Therefore, in this paper, based on a new low-power node designed, a Bluetooth-based digital impact monitoring PZT sensor network is proposed for the first time with its global confliction-solving impact localization method. Evaluations of the system as a network are researched and analyzed on a complex real aircraft wing box for a global confliction-solving impact localization, showing a satisfying high accuracy.

Modulation Configurations of Phase Locked Loops for High-Speed and High-Precision Wired and Wireless Applications

This paper summarizes the modulation configurations of phase locked loops (PLLs) and their integration in semiconductor circuits, e.g., the input modulation for cellular phones, direct-modulation for low power wireless sensor networks, feedback-loop modulation for high-speed transmission, and two-point modulation for short-range radio transceivers. In this survey, basic configuration examples of integrated circuits for wired and wireless applications which are using the PLL modulation configurations are explained. It is important to select the method for simply and effectively determining the characteristics corresponding to the specific application. The paper also surveys technologies for future PLL design for digitizing of an entire PLL to reduce the phase noise due to a modulation by using a feedback loop with a precise digital phase comparison and a numerically controlled oscillator with high linearity.

3D-printed dual-band energy harvester for WSNs in green IoT applications

This paper describes the 3D-printed open Double Square Loop (DSL) Frequency Selective Surfaces (FSSs) based Radio Frequency energy harvester specifically designed for the purpose of capturing Wi-Fi ambient RF energy. The proposed dual-band energy harvesting circuit is a suitable applicant for low-power green IoT Wireless Sensor Networks. The inclusion of FSS in the receiver improved the antenna’s gain by 42% and 74% at 2.4 GHz and 5.2 GHz, respectively, and consequently, enhanced the conversion efficiency. Due to FSS-backed dual-band antenna, the proposed configuration gathers a significant amount of RF ambient energy from the surroundings. The collected RF energy is then converted into DC voltage using a three-stage Villard RF rectifier cum multiplier. The Fused Deposition Modeling (FDM) of 3D printing is employed to create the designed prototype on a biodegradable Polylactic Acid substrate. RF measurements are taken with horn antenna and signal generator to evaluate the proposed design. At 2.4 GHz, the constructed prototype has a conversion efficiency of 67.90% and a corresponding output voltage of 2.36 V, whereas, at 5.2 GHz, it achieves an efficiency of 34.01% with output voltage of 1.67 V, at 0 dBm input power. The proposed configuration has a good correlation between simulated and measured outcomes.

TDD LoRa and Delta Encoding in Low-Power Networks of Environmental Sensor Arrays for Temperature and Deformation Monitoring

Densely distributed sensor networks can revolutionize environmental observations by providing real-time data with an unprecedented spatiotemporal resolution. However, field deployments often pose unique challenges in terms of power provisions and wireless connectivity. We present a framework for wirelessly connected distributed sensor arrays for near-surface temperature and/or deformation monitoring. Our research focuses on a novel time division duplex implementation of the LoRa protocol, enabling battery powered base stations and avoiding collisions within the network. In order to minimize transmissions and improve battery life throughout the network, we propose a dedicated delta encoding algorithm that utilizes the spatial and temporal similarity in the acquired data sets. We implemented the developed technologies in a AA battery powered hardware platform that can be used as a wireless data logger or base station, and we conducted an assessment of the power consumption. Without data compression, the projected battery life for a data logger is 4.74 years, and a wireless base stations can last several weeks or months depending on the amount of network traffic. The delta encoding algorithm can further improve this battery life with a factor of up to 3.50. Our results demonstrate the viability of the proposed methods for low-power environmental wireless sensor networks.

A Smart Agricultural System Based on PLC and a Cloud Computing Web Application Using LoRa and LoRaWan

The increasing challenges of agricultural processes and the growing demand for food globally are driving the industrial agriculture sector to adopt the concept of 'smart farming'. Smart farming systems, with their real-time management and high level of automation, can greatly improve productivity, food safety, and efficiency in the agri-food supply chain. This paper presents a customized smart farming system that uses a low-cost, low-power, and wide-range wireless sensor network based on Internet of Things (IoT) and Long Range (LoRa) technologies. In this system, LoRa connectivity is integrated with existing Programmable Logic Controllers (PLCs), which are commonly used in industry and farming to control multiple processes, devices, and machinery through the Simatic IOT2040. The system also includes a newly developed web-based monitoring application hosted on a cloud server, which processes data collected from the farm environment and allows for remote visualization and control of all connected devices. A Telegram bot is included for automated communication with users through this mobile messaging app. The proposed network structure has been tested, and the path loss in the wireless LoRa is evaluated.

Efficient Strategies for Signal Aggregation in Low-Power Wireless Sensor Networks With Discrete Transmission Ranges

Sensors with different transmission ranges are preferred for future-ready low-power wireless sensor networks because of their functional advantages. However, different transmission ranges introduce connectivity constraints that affect the signal aggregation process. In this letter, we address the sensor signal aggregation problem in such networks and propose solutions. We introduce a mathematical framework that captures the connectivity restrictions and propose an integer linear program for small-scale networks and an approximation technique for large-scale networks. The performance of the proposed method is evaluated under compression techniques based on compressed sensing.

Securing Age-of-Information (AoI)-Enabled 5G Smart Warehouse Using Access Control Scheme

Low-power wireless sensor networks (WSNs) and Internet of Things (IoT) have great impact for the real-time applications in future 5th generation (5G) mobile networks due to the wireless-powered communication technologies. The Age of Information (AoI) plays a crucial performance metric in an IoT-enabled real-time smart warehouse application, where the freshness of the aggregated data is very important. However, wireless medium communication among the beacon nodes and the user equipments (tracking nodes) gives an opportunity to an adversary not only to eavesdrop the data but also to corrupt the data by means of deleting, modifying or inserting malicious information during communication among the entities involved in the smart warehouse environment. To mitigate these issues, we design a security scheme for AoI-enabled 5G smart warehouse through an access control mechanism, where the secure communication among the beacon nodes and the tracking nodes will take place by mutual device authentication and key agreement process. The fresh data collected at the enterprise cloud is then used for big data analytics for better predictions and analysis, such as optimal device scheduling so that the data becomes very fresh. The rigorous security analysis and comparative study show that the proposed mechanism has significantly better security and comparable communication and computational costs as compared to the relevant schemes. In addition, through the real-time testbed experiments, we show that the proposed scheme is practical in 5G smart warehouse context.

Energy-efficient Data Aggregation in Low-power Wireless Networks with Sensors of Discrete Transmission Ranges: A Mathematical Framework for Network Design

The advancements in sensor technology and the evolution of new generation technologies such as the Internet of Things have led to extensive deployment of low-power wireless sensor networks for various surveillance and monitoring applications. The volume and velocity of the data generated by a large number of sensors and monitors used in such applications are huge. The harnessing of such big data is critical to the real-time control of underlying real-world processes. Future generation networks favor the deployment of sensors with different discrete transmission ranges. Such multiple transmission ranges introduce different connectivity constraints in the network. Advanced strategies are under investigation for designing energy-efficient data aggregation schemes in such connectivity-constrained networks. To aid such works, we introduce a mathematical framework that captures the salient features of low-power networks with sensors of multiple transmission ranges. The proposed framework can serve as a baseline platform to investigate different networking problems in such systems. Further, we consider a cluster-based multihop network with fixed intra-cluster and inter-cluster transmission ranges and address the problem of designing an optimal network configuration for minimizing the data transmission in the network. We model the problem as an integer linear program, which can be applied only to networks of small sizes because of the hardness of the problem. To address the problem in large-scale networks, we design a polynomial-time approximation method using the proposed mathematical framework. The performance of the proposed methods is evaluated under compression schemes based on compressive sensing.

Data Aggregation in Low-Power Wireless Sensor Networks With Discrete Transmission Ranges: Sensor Signal Aggregation Over Graph

Conventional wireless sensor networks (WSNs) consist of sensors with continuous transmission range, which depends on the relative positions of the transmitter and the receiver. However, sensors with different discrete transmission ranges are preferred for future generation low-power sensor networks because of certain functional advantages. The discrete transmission ranges introduce connectivity constraints in transmitting the sensor data. The proliferation of low-power WSNs has led to the explosion of the volume of the data to be processed. As the transmission of the data is the major cause of energy depletion of sensors that critically affect the network lifetime, energy-efficient aggregation of the sensor data is an important networking problem. In this work, we address the data aggregation problem in networks with sensors of discrete transmission ranges. We model the problem as a solvable integer linear program. However, this method applies only to networks of small sizes because of the hardness of the program. To solve the problem in networks of large sizes, we introduce a graphical framework that captures the characteristics of the networks with sensors of discrete transmission ranges and design a polynomial-time approximation technique to find a solution. Furthermore, we embed compression techniques based on compressed sensing (CS), which are established to yield high data compaction for temporally and spatially correlated distributed sensor data streams, and evaluate the performance of the proposed methods.

An immune chaotic adaptive evolutionary algorithm for energy-efficient clustering management in LPWSN

Recently, low power wireless sensor networks (LPWSNs) have been widely used in the military and education. However, the lifetime of LPWSN is affected by the energy of the sensor, so the clustering design has received a lot of attention. In this study, the problem of designing optimal clustering for LPWSN is formulated as a cluster head selection problem, considering energy, which is an NP-hard problem. Therefore, a new structural model of the clustering design problem is constructed. This model can represent the process of sensor node clustering and information transfer. In this paper, a new immune chaotic adaptive evolutionary algorithm (ICAEA) is proposed and used in the clustering design of LPWSNs to obtain a better cluster head selection scheme. Then we design new advanced operators, such as immune operator and chaotic operator, which improve the convergence speed of the algorithm on the basis of evolutionary algorithm (EA). Moreover, ICAEA avoids local optima by introducing an adaptive operator, improves the convergence accuracy of the algorithm, and improves the performance of the optimization. Simulations are conducted to determine the performance improvements of ICAEA in terms of network lifetime and energy consumption compared to the latest clustering methods of R-LEACH, Q-LEACH, and ICCHR. The experimental results show that the proposed ICAEA algorithm outperforms RLEACH, Q-LEACH and ICCHR in terms of network lifetime under multiple identical experimental conditions. Moreover, ICAEA consumes less energy than R-LEACH, Q-LEACH and ICCHR in terms of system energy consumption.

Long-Range Low-Power Multi-Hop Wireless Sensor Network for Monitoring the Vibration Response of Long-Span Bridges.

Recently, vibration-based monitoring technologies have become extremely popular, providing effective tools to assess the health condition and evaluate the structural integrity of civil structures and infrastructures in real-time. In this context, battery-operated wireless sensors allow us to stop using wired sensor networks, providing easy installation processes and low maintenance costs. Nevertheless, wireless transmission of high-rate data such as structural vibration consumes considerable power. Consequently, these wireless networks demand frequent battery replacement, which is problematic for large structures with poor accessibility, such as long-span bridges. This work proposes a low-power multi-hop wireless sensor network suitable for monitoring large-sized civil infrastructures to handle this problem. The proposed network employs low-power wireless devices that act in the sub-GHz band, permitting long-distance data transmission and communication surpassing 1 km. Data collection over vast areas is accomplished via multi-hop communication, in which the sensor data are acquired and re-transmitted by neighboring sensors. The communication and transmission times are synchronized, and time-division communication is executed, which depends on the wireless devices to sleep when the connection is not necessary to consume less power. An experimental field test is performed to evaluate the reliability and accuracy of the designed wireless sensor network to collect and capture the acceleration response of the long-span Manhattan Bridge. Thanks to the high-quality monitoring data collected with the developed low-power wireless sensor network, the natural frequencies and mode shapes were robustly recognized. The monitoring tests also showed the benefits of the presented wireless sensor system concerning the installation and measuring operations.