Wireless Sensor Network Applications Research Articles

A Cloud-IoT Architecture for Latency-Aware Localization in Earthquake Early Warning.

An effective earthquake early warning system requires rapid and reliable earthquake source detection. Despite the numerous proposed epicenter localization solutions in recent years, their utilization within the Internet of Things (IoT) framework and integration with IoT-oriented cloud platforms remain underexplored. This paper proposes a complete IoT architecture for earthquake detection, localization, and event notification. The architecture, which has been designed, deployed, and tested on a standard cloud platform, introduces an innovative approach by implementing P-wave "picking" directly on IoT devices, deviating from traditional regional earthquake early warning (EEW) approaches. Pick association, source localization, event declaration, and user notification functionalities are also deployed on the cloud. The cloud integration simplifies the integration of other services in the architecture, such as data storage and device management. Moreover, a localization algorithm based on the hyperbola method is proposed, but here, the time difference of arrival multilateration is applied that is often used in wireless sensor network applications. The results show that the proposed end-to-end architecture is able to provide a quick estimate of the earthquake epicenter location with acceptable errors for an EEW system scenario. Rigorous testing against the standard of reference in Italy for regional EEW showed an overall 3.39 s gain in the system localization speed, thus offering a tangible metric of the efficiency and potential proposed system as an EEW solution.

Cooperative optimization techniques in distributed MAC protocols – a survey

Purpose Cross-layer approach in media access control (MAC) layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in wireless sensor network (WSN) and Internet of Things (IoT) applications. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Game theory optimization for distributed may increase the network performance. The purpose of this study is to survey the various operations that can be carried out using distributive and adaptive MAC protocol. Hill climbing distributed MAC does not need a central coordination system and location-based transmission with neighbor awareness reduces transmission power. Design/methodology/approach Distributed MAC in wireless networks is used to address the challenges like network lifetime, reduced energy consumption and for improving delay performance. In this paper, a survey is made on various cooperative communications in MAC protocols, optimization techniques used to improve MAC performance in various applications and mathematical approaches involved in game theory optimization for MAC protocol. Findings Spatial reuse of channel improved by 3%–29%, and multichannel improves throughput by 8% using distributed MAC protocol. Nash equilibrium is found to perform well, which focuses on energy utility in the network by individual players. Fuzzy logic improves channel selection by 17% and secondary users’ involvement by 8%. Cross-layer approach in MAC layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in WSN and IoT applications. Cross-layer and cooperative communication give energy savings of 27% and reduces hop distance by 4.7%. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Research limitations/implications Other optimization techniques can be applied for WSN to analyze the performance. Practical implications Game theory optimization for distributed may increase the network performance. Optimal cuckoo search improves throughput by 90% and reduces delay by 91%. Stochastic approaches detect 80% attacks even in 90% malicious nodes. Social implications Channel allocations in centralized or static manner must be based on traffic demands whether dynamic traffic or fluctuated traffic. Usage of multimedia devices also increased which in turn increased the demand for high throughput. Cochannel interference keep on changing or mitigations occur which can be handled by proper resource allocations. Network survival is by efficient usage of valid patis in the network by avoiding transmission failures and time slots’ effective usage. Originality/value Literature survey is carried out to find the methods which give better performance.

Hybrid Energy Regulated Constant Gain Kalman-Filter for Optimized Target Detection and Tracking in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are one of the most researched areas worldwide as the wide-scale networks possess low cost, are small in size, consume low power, and can be deployed in various environments. Among various applications of WSNs, target tracking is a highly demanding and broadly investigated application of wireless sensor networks. The parameter of accurate tracking is restricted because of the limited resources present in the wireless sensor networks, noise of the network, environmental factors, and faulty sensor nodes. Our work aims to enhance the accuracy of the tracking process as well as energy utilization by combing the mechanism of clustering with the prediction. Here, we present a hybrid energy-regulated constant gain Kalman filter-based target detection and tracking method, which is an algorithm to make the best use of energy and enhance precision in tracking. Our proposed algorithm is compared with the existing approaches where it is observed that the proposed technique possesses efficient energy utilization by decreasing the transference of unimportant data within the sensor network, achieving accurate results.

Applying an Adaptive Neuro-Fuzzy Inference System to Path Loss Prediction in a Ruby Mango Plantation

The application of wireless sensor networks (WSNs) in smart agriculture requires accurate path loss prediction to determine the coverage area and system capacity. However, fast fading from environment changes, such as leaf movement, unsymmetrical tree structures and near-ground effects, makes the path loss prediction inaccurate. Artificial intelligence (AI) technologies can be used to facilitate this task for training the real environments. In this study, we performed path loss measurements in a Ruby mango plantation at a frequency of 433 MHz. Then, an adaptive neuro-fuzzy inference system (ANFIS) was applied to path loss prediction. The ANFIS required two inputs for the path loss prediction: the distance and antenna height corresponding to the tree level (i.e., trunk and bottom, middle, and top canopies). We evaluated the performance of the ANFIS by comparing it with empirical path loss models widely used in the literature. The ANFIS demonstrated a superior prediction accuracy with high sensitivity compared to the empirical models, although the performance was affected by the tree level.

Teaching Reform of WSN Laboratory Class based on a Virtual Simulation Platform

Wireless Sensor Networks (WSNs) are considered as one of the most rapidly developing technologies in recent years. The WSN course, as a core curriculum of the Internet of Things (IoT) major, also plays an important role in higher education teachings. However, the inadequate facilities, equipment and spaces have been main constraints of universities’ WSN laboratory classes. In this paper, we designed a specific situation and scene for smart home, one of the commonly used applications of WSN, and developed a Virtual Simulation Experimental Platform (VSEP) based on Unity 3D engine, aiming to enhance students’ learning interest and improve their practical skills. In particular, we firstly analyzed the current limitations in WSN experimental teachings, with design concepts and objectives and experiment teaching content of VSEP introduced thereafter. In addition, we discussed systems architecture and functions design, and experimental methods and steps. Finally, an anonymous survey was utilized to examine the teaching reform result. The study showed that most students have found the virtual platform helped them understand the theoretical concepts and experimental processes better, and is an effective way to learn the course.

New Online DV-Hop Algorithm via Mobile Anchor for Wireless Sensor Network Localization

In many applications of Wireless Sensor Networks (WSNs), event detection is the main purpose of users. Moreover, determining where and when that event occurs is crucial; thus, the positions of nodes must be identified. Subsequently, in a range-free case, the Distance Vector-Hop (DV-Hop) heuristic is the commonly used localization algorithm because of its simplicity and low cost. The DV-Hop algorithm consists of a set of reference nodes, namely, anchors, to periodically broadcast their current positions and assist nearby unknown nodes during localization. Another potential solution includes the use of only one mobile anchor instead of these sets of anchors. This solution presents a new challenge in the localization of rang-free WSNs because of its favorable results and reduced cost. In this paper, we propose an analytical probabilistic model for multi-hop distance estimation between mobile anchor nodes and unknown nodes. We derive a non-linear analytic function that provides the relation between the hop counts and distance estimation. Moreover, based on the recursive least square algorithm, we present a new formulation of the original DV-Hop localization algorithm, namely, online DV-Hop localization, in WSNs. Finally, different scenarios of path planning and simulation results are conducted.

Application of wireless sensor network based on OBE in english cultural teaching resources

Application of wireless sensor network based on OBE in english cultural teaching resources

An Energy-Efficient Routing Algorithm for WSNs Using Fuzzy Logic.

Battery replacement or recharging is essential for sensor nodes because they are typically powered by batteries in wireless sensor network (WSN) applications. Therefore, creating an energy-efficient data transfer technique is required. The base station (BS) receives data from one sensor node and routes the data to another sensor node. As a result, an energy-efficient routing algorithm using fuzzy logic (EERF) represents a novel approach that is suggested in this study. One of the reasoning techniques utilized in scenarios where there is a lot of ambiguity is fuzzy logic. The remaining energy, the distance between the sensor node and the base station, and the total number of connected sensor nodes are all inputs given to the fuzzy system of the proposed EERF algorithm. The proposed EERF is contrasted with the current systems, like the energy-aware unequal clustering using fuzzy logic (EAUCF) and distributed unequal clustering using fuzzy logic (DUCF) algorithms, in terms of evaluation criteria, including energy consumption, the number of active sensor nodes for each round in the network, and network stability. EAUCF and DUCF were outperformed by EERF.

Providing an effective key management scheme to increase transaction security of homogeneous mobile wireless sensor networks

Network security is one of the key concerns with wireless sensor networks. Because sensor nodes employ radio channel frequencies, which are riskier than traditional networks, the attacker may be able to force the node to compromise, disrupt data integrity, eavesdrop, or insert false data into the network, wasting network resources. Creating dependable sensor networks that offer the highest level of security while using the fewest resources is thus one of the problems. The designers of wireless sensor networks can take into account providing an efficient key management system that can enhance any efficiency features such as communication overhead, calculation rate, memory demand, and energy consumption rate. The energy level of nodes is determined in this article using a novel approach based on fuzzy systems and simple to implement in both hardware and software. In this study, the memory needed to carry out the plan was decreased, and the search performance was raised by integrating elliptic curve cryptography with an AVL search tree and a LEACH model. Also, the frequency range of radio channels in this study is 2.4 GHz. Based on the theoretical analysis as well as the outcomes of the experiments, the suggested key management strategy for wireless sensor networks improves security while also reducing computational overhead by 23%, energy consumption by 14%, and memory consumption parameters by 14% . 18% of people have used the network. Additionally, it was demonstrated that the suggested approach is scalable and extendable. Because of this, the suggested technique has a wide range of applications in massive wireless sensor networks.

Advanced 3D-architected carbon-based photothermoelectric energy harvesters for self-powered and responsive magnetic and UV sensing

Towards solving the technical challenges of self-powering wireless sensor networks (WSNs), feasibility studies of high-efficiency energy harvesters are expected to be break-through technologies. Herein, we propose a closed-loop self-powering system, for which we design a novel 3D-architecture carbon-based photothermoelectric organic composite phase-changing material (PCM) energy harvester for self-powered and responsive magnetic and UV sensing by infusing with Fe3O4 nanoparticles and tetradecanol as PCM. The closed-loop system possesses excellent energy storage, conversion efficiency up to 89.83 % as well as photothermoelectric conversion capacity with maximum output voltage and current of 196.6 mV and 8.64 mA, respectively. More importantly, the voltage provided by the phase-change-based photothermoelectricity enables it to be used as a microcapacitor with a specific capacitance of 31.05 F/g at 0.01 A/g. Moreover, it is responds to magnetic field and UV radiation with high sensitivity, the average response time being 1 s. This potential breakthrough composite paves way for use in a variety of high-energy self-powered agile sensing WSNs applications.

AutoBar: Automatic Barrier Coverage Formation for Danger Keep Out Applications in Smart City.

Barrier coverage is a fundamental application in wireless sensor networks, which are widely used for smart cities. In applications, the sensors form a barrier for the intruders and protect an area through intrusion detection. In this paper, we study a new branch of barrier coverage, namely warning barrier coverage (WBC). Different from the classic barrier coverage, WBC has the inverse protect direction, which moves the sensors surrounding a dangerous region and protects any unexpected visitors by warning them away from the dangers. WBC holds a promising prospect in many danger keep out applications for smart cities. For example, a WBC can enclose the debris area in the sea and alarm any approaching ships in order to avoid their damaging propellers. One special feature of WBC is that the target region is usually dangerous and its boundary is previously unknown. Hence, the scattered mobile nodes need to detect the boundary and form the barrier coverage themselves. It is challenging to form these distributed sensor nodes into a barrier because a node can sense only the local information and there is no global information of the unknown region or other nodes. To this end, in response to the newly proposed issue of the formation of barrier cover, we propose a novel solution AutoBar for mobile sensor nodes to automatically form a WBC for smart cities. Notably, this is the first work to trigger the coverage problem of the alarm barrier, wherein the regional information is not pre-known. To pursue the high coverage quality, we theoretically derive the optimal distribution pattern of sensor nodes using convex theory. Based on the analysis, we design a fully distributed algorithm that enables nodes to collaboratively move toward the optimal distribution pattern. In addition, AutoBar is able to reorganize the barrier even if any node is broken. To validate the feasibility of AutoBar, we develop the prototype of the specialized mobile node, which consists of two kinds of sensors: one for boundary detection and another for visitor detection. Based on the prototype, we conduct extensive real trace-driven simulations in various smart city scenarios. Performance results demonstrate that AutoBar outperforms the existing barrier coverage strategies in terms of coverage quality, formation duration, and communication overhead.

A DV-Hop optimization localization algorithm based on topological structure similarity in three-dimensional wireless sensor networks

Localization technology performs an essential part in wireless sensor networks (WSNs). The Distance vector hop (DV-Hop) localization algorithm has been frequently used in the WSNs with its simplicity, feasibility and minimal hardware requirements. However, the low localization accuracy of the DV-Hop algorithm has limited its further application in WSNs. To overcome the accuracy problem of the DV-Hop algorithm, we propose an improved DV-Hop algorithm based on topological structure similarity, called TDSDV-Hop. Different from the traditional DV-Hop algorithm, the topological structure similarity is applied to correct the hop count, reducing the inaccuracy caused by the minimum hop count. The hop counts will be converted from discrete to precise continuous values by the topological structure similarity. In this paper, firstly, the topological structure similarity is defined, followed by the proof of the theory that the distance between two nodes is inversely proportional to the topological structure similarity. Secondly, a hop-correction equation that can correct the hop count between neighbor nodes is set up. Then the TDSDV-Hop algorithm incorporates hop-correction formula and simulates the optimal hop-correction parameters in order to acquire more accurate values of the hop count. And the coordinates of unknown nodes are calculated by the least square method. Finally, the levy adaptive improved bird swarm algorithm (LSABSA) is utilized to optimize coordinates of unknown nodes by constructing a new objective function. The simulation results show that the localization performance of the TDSDV-Hop algorithm is better than that of DV-Hop, GWODV-Hop, CRWDV-Hop and CVLR algorithm in terms of localization accuracy, localization time and communication cost.

Current Security Threats in Applications of Wireless Sensor Network

Wireless sensor networks have a broader application range in almost every field of human endeavours, which exposes them to a variety of security threats on a daily basis from cyber criminals. It is a remote monitoring system for events or phenomena in areas such as smart grids, intelligent healthcare, circular economies in smart cities, and underwater surveillance. Cybersecurity threats have long been a source of concern in the field of wireless sensor networks. The goal of cyber security in this era is to certify the authenticity of networks confidentiality, data integrity and availability of network assets. Various security mechanisms, particularly key management cryptographic, authentication mechanisms, and intrusion detection systems have been developed from several machine learning algorithms, and so on, which have been used to ensure network security. In this paper, we focused on outlining diverse application areas of wireless sensor networks with their security threats, major challenges and given some common mechanism to counter security threats for in-depth research insight on security in applications of wireless sensor networks. In addition, an analysis of the common attacks on wireless sensor networks has been provided.

Firebug Optimized Modified Bee Colony Algorithm for Trusted WSN Routing

Wireless sensor networks (WSNs) have become a crucial technology because they are used in several contexts, such as public safety, environmental monitoring, healthcare, military operations, and surveillance systems. The applications of WSNs have increased because they can be deployed in areas where cable and power supply are difficult to use. However, a variety of security assaults can easily penetrate these networks. To solve this problem, a Firebug Optimized Modified Bee Colony algorithm (FOMBC) has been proposed, which achieves the maximum network security goal with lower energy consumption in WSN. The proposed method is carried out into two phases namely Secure node selection and Orthogonal Routing (OR). In the first phase, secure node among the WSN’s initial nodes is selected based on characteristics such as trust, delay, and Quality of Services. In the second phase, Firebug Optimized Modified Bee Colony (FOMBC) Optimization technique is used to find optimal route based on factors like trust, distance, latency, and quality of service. The effectiveness of the proposed FOMBC approach is evaluated against existing techniques like Trust-Aware Routing Framework (TARF), SOAR (Simple Opportunistic Adaptive Routing Protocol), Particle Swarm Optimization, and Whale Optimization Algorithm for Secure Routing (PSWOSR). According to the experimental findings, the proposed FOMBC technique achieves high detection, maximum throughput, least latency, and minimum range. The proposed method achieves network lifetime of 4.7%, 5.8%, and 9.87% than the existing TARF, SOAR, and PSWOSR, respectively.

Clock Rigidity and Joint Position-Clock Estimation in Ultrawideband Sensor Networks

Joint position and clock estimation is crucial in many wireless sensor network applications, especially in distance-based estimation with time-of-arrival (TOA) measurements. In this work, we study the graph properties under which an ultra-wideband (UWB) sensor network is localizable and clock synchronizable with one round TOA-based timestamp measurements. A novel clock rigidity theory is proposed and its topological condition is proved to have close relationship to bearing rigidity. Based on clock rigidity, a graphical approach for analyzing the joint position and clock problem is investigated. It is shown that a position-clock framework with certain graph properties can be uniquely determined up to some trivial variations corresponding to both position and clock parameters. Simulation results are presented to demonstrate how clock rigidity theory is exercised in clock estimation and joint position-clock estimation.

OPTIMIZATION OF ENERGY CONSUMPTION IN SENSOR NODE BASED ON RECEIVED SIGNAL STRENGTH INDICATOR (RSSI) AND SLEEP AWAKE METHOD

The transmission power management in Wireless Sensor Networks (WSN) is a critical problem. This research investigated optimizing power consumption based on transmission power (Tx Power) level according to RSSI and periodic transmission time. We investigated the RSSI value by varying Tx Power Level to get the optimum Tx Power Level. We found the optimum periodic transmission time by transmitting the data with various transmission times. By varying the Tx Power Level, we found the optimum Tx Power Level, resulting in the power consumption decreasing by about 42% and the power supply’s lifetime increasing by about 71% in the 280 m distance between the sensor node and gateway, with a 108 Wh power supply. By varying the periodic transmission time, we found that the optimum periodic transmission time is 8 seconds. Combining the optimum Tx Power Level and periodic transmission time, we found that the power supply’s lifetime is 40 times longer. This result is helpful for WSN applications in remote areas.

Enhanced DV-Hop using shuffled shepherd algorithm for localizing sensor nodes in 3-D space

For many wireless sensor networks applications, a powerful localization algorithm is required to determine the exact positions of sensor nodes. In this paper, a new localization algorithm is presented which combines the distance vector hop (DV-Hop) algorithm with the shuffled shepherd optimization algorithm to determine the position for each unknown node within 3-D space. Studying the localization problem in 3-D space is more realistic, but more challenging due to the enlarged search space. Comparison of our proposed algorithm with its alternatives in literature shows that it offers improved accuracy and more stable performance with respect to changes in nodes density and communication range.

Innovative DMHS Algorithm Application in Wireless Sensor Networks for Efficient Routing in High-Risk Environments.

Efficient routing is essential for the proper functioning of wireless sensor networks (WSNs). Recent research has focused on optimizing energy and delay for these networks. Nevertheless, there is a dearth of studies that have examined the effects of volatile settings, such as chemical plants, coal mines, nuclear power plants, and battlefields, where connectivity is inconsistent. In such contexts, sensor networks may face security incidents, and environmental factors such as node movement and death can result in dynamic changes to the network topology. A novel design algorithm grounded on Dynamic Minimum Hop Selection (DMHS) was introduced in this paper. The key principle behind DMHS is to use a probabilistic forwarding decision-making process through a distributed route discovery strategy that utilizes dynamically adjusted minimum hop counts of nodes. Simulation results indicate that the life cycle of the DMHS algorithm increases by more than 12% over 700 nodes when compared to the traditional energy-saving algorithm. Furthermore, our algorithm performs better in the average delivery rate of node, and has a 10% to 21% improvement compared to the other algorithms. Overall, the DMHS algorithm represents an important contribution to the development of WSNs that can function robustly in high-risk and unstable environments.

A reinforcement learning based routing protocol for software-defined networking enabled wireless sensor network forest fire detection

Critical event reporting Wireless Sensor Networks (WSNs) applications need vital requirements (extended network lifetime, reliability, real time responsiveness, and scalability) to be met to ensure outstanding efficiency. Previous frameworks only consider few individual requirements, thus ignoring the other equally important ones. Ensuring that an active path is available at all times is crucial for enabling the timely transmission of critical data and maintaining the quality of service required to efficiently support delay-sensitive applications. This paper proposes an application-specific Routing Protocol based on Reinforcement Learning (RL) for Software Defined Network (SDN)-enabled WSN forest fire detection (RPLS). First, we designed a clustering algorithm that delays re-clustering to save energy by keeping the same topology for several rounds. Unlike existing works, this algorithm decreases the cluster radius based not only on the energy parameters but also on the quality of the links. After the network clustering, the power of the SDN controller is used to intelligently define using RL the optimal paths for the sensor nodes and accordingly reduce the load on these constrained nodes. For routing strategy, we formulate an RL-based reward function considering not only the energy efficiency parameters but also the anticipated and post-failure reliability parameters to ensure real time responsiveness and optimize energy consumption. Finally, we conducted comparisons by means of simulations in forest fires detection scenario. Compared to RL-SDWSN, the results show an improvement of 14.064 % in network operational lifetime and 16.41 % in response time.

Radio Link Model for Node Deployment in Underground Mine Sensor Networks

This paper presents an experimental characterization of the proposed radio link model for an underground mine sensor network. Power efficiency and range are critical factors to consider when designing a wireless sensor network, particularly for low data rate applications where the goal is to have a long-lasting, low-maintenance network. A ‘deploy and forget’ strategy is desirable because it allows the network to operate autonomously without requiring frequent maintenance or intervention from network operators. DASH7 and IEEE 802.15.4f are both excellent choices for low-power, long-range wireless sensor networking applications. The proposed radio link model was developed and evaluated for 433 MHz DASH7 in underground mines, considering the practical electromagnetic properties of mine walls and the propagation medium, which helps in calculating accurate signal characteristics. Radio wave propagation is a critical factor that needs to be considered when designing a wireless sensor network for complex mine structures. The received signal strength indicator (RSSI) and packet error rate (PER) are two key parameters that are used to measure wave propagation and assess the quality of the radio link between sensor nodes. The radio link design has been optimized for complex mine structures by utilizing these parameters in a model, leading to improved performance and reliability. The measurements were carried out in the world’s second largest salt mine at Khewra, Pakistan, with representative irregular mine structures. The RSSI and PER were measured at different node positions and with variable separation between the nodes. The proposed model allows for the easy placement of nodes on either the rooftop or near the side walls of the mine corridors, with an average variation of 6% in RSSI and 1.9% in PER. The proposed model was validated using off-the-shelf wizzi sensor nodes received from Wizzi Lab, France, and was programmed to measure RSSI and PER while operating under the 433 MHz DASH7 protocol. An agreement between modeled and measured parameters has been noted, making the proposed model a decent method for efficient node deployment in underground mine sensor networks.