Wireless Sensor Network Topology Research Articles

Bridge Damage Detection with Support Vector Machine in Accelerometer-Based Wireless Sensor Network

Abstract Purpose This paper proposes an in-network vibration data processing using Wireless Sensor Network (WSN) leveraging Machine Learning (ML) for damage detection and localization. The study also presents the ML algorithms comparison that is suitable to be deployed in WSN and implemented the proposed cluster-based WSN topology on the bridge simulation test. Methods The bridge vibration data was acquired using accelerometer-based wireless sensor nodes. The data collected are transformed using Fast Fourier Transform (FFT) to obtain fundamental frequencies and their corresponding amplitudes. The machine learning method i.e., Support Vector Machine (SVM) with linear and Radial Basis Function (RBF) kernel was used to analyze the vibration data collected from the WSN. In-network data processing and cluster-based WSN topology is implemented and the programmable wireless sensor nodes is utilized in this study. Results The experiments were conducted using real programmable wireless sensor nodes and developed our test bed bridge which makes this work different from the previous studies. The classification and predicting results shows 97%, 96%, 97%, and 96% for accuracy, precision, recall rate, and f1-score, respectively. Conclusion Machine learning methods can potentially be combined with the vibration WSN for bridge damage detection and localization.

Optimizing Wireless Sensor Networks through Ant Colony-Based Localized Mesh Topology

The growing need for faster and more effective data handling and data transfer capabilities of a network require energy-efficient and more powerful smart sensor devices. These devices have become increasingly necessary in wireless sensor networks (WSN). The architecture or topology of WSN can greatly impact the organizational efficacy and connectivity among the sensor nodes employed within the given area. It is foremost important to implement a robust network topology which must be capable of ensuring continuous and reliable communication within the whole network. This research paper presents an effective approach based on Ant colony scheme to optimize mesh network topology ant colony optimization (ACO) is used to place the sensor nodes optimally in the given area of the whole network. The proposed research carries out comprehensive performance evaluation of the network under various QoS parameters such as bandwidth, throughput, delay, residual energy and routing load. These parameters shows the effectiveness or adaptability of the network with different and dynamically changing communication requirements suggested by this topology scheme. The network based on the proposed method has been simulated several times and the achieved simulation patterns have been analyzed under the mentioned QoS constraints.

Energy Efficient ECC Authenticated Key Exchange Protocol for Star Topology Wireless Sensor Networks

The article proposes a new energy-efficient protocol designed for star topology wireless sensor networks. The protocol has been implemented using ECC, although it can be easily adapted to any algebraic structure, where the discrete logarithm problem is computationally challenging. In addition to the formal description, the authors provide the results of an investigation concerned with the protocol's security properties, conducted by verifying the model using Scyther software. The publication also includes an analysis of the protocol's energy consumption, performed with the use of hardware platforms with ARM microcontrollers.

The Effect of Symmetry on the De-Anonymizability of Social Networks

This paper studies time-sensitive multicast in wireless sensor networks (WSNs) with link uncertainty, where information from source needs to be delivered to multiple receivers within an imposed delay constraint. Prior art on static WSNs minimizes the multicast delay via the construction of a multicast tree that approximates Steiner tree in length, which, however, may be invalidated by the time-varying network topology of WSNs with uncertain link states. Moreover, for multicast in WSNs with link uncertainty, the possible link failure necessitates a suitable measurement of the uncertain communication distance and calls for the performance guarantee in both delay and delivery ratio. In this work, by modeling a WSN as a random graph with each link associated with a transmission probability, we propose FlowerCast, an efficient multicast scheme, to jointly minimize the expected multicast delay and to maximize the expected delivery ratio of multicast under delay constraint. The core of FlowerCast is to quantify the uncertain communication distance by the expected transmission delay of a time-varying path, based on which a delay-optimal multicast tree is constructed in accordance with the directionality of delay. Candidate paths with high expected delivery ratio and low expected delay are then selected in a distributed manner to conditionally connect adjacent multicast members and thus transform the multicast tree into a multicast flower. Despite the NP-hardness of optimal candidate paths addition, the transformation with the highest expected delivery ratio of multicast under delay constraint can be guaranteed through a pseudo-polynomial time derandomization-based greedy approach. We further demonstrate the time and energy efficiency of FlowerCast through asymptotic analysis. To make full use of the possible overlapping links in a multicast flower, a hybrid routing strategy is presented to wisely switch between sequential routing and synchronous routing for extra enhancement of the multicast performance. Extensive experiments on various datasets verify the superiority of FlowerCast and hybrid routing over baselines and indicate their wide applicability to practical scenarios.

FlowerCast: Efficient Time-sensitive Multicast in Wireless Sensor Networks with Link Uncertainty

This article studies time-sensitive multicast in wireless sensor networks (WSNs) with link uncertainty, where information from the source needs to be delivered to multiple receivers within an imposed delay constraint. Prior art on static WSNs minimizes the multicast delay via the construction of a multicast tree that approximates the Steiner tree in length, which, however, may be invalidated by the time-varying network topology of WSNs with uncertain link states. Moreover, for multicast in WSNs with link uncertainty, the possible link failure necessitates a suitable measurement of the uncertain communication distance and calls for the performance guarantee in both delay and delivery ratio. In this work, by modeling a WSN as a random graph with each link associated with a transmission probability, we propose FlowerCast, an efficient multicast scheme, to jointly minimize the expected multicast delay and to maximize the expected delivery ratio of multicast under delay constraint. The core of FlowerCast is to quantify the uncertain communication distance by the expected transmission delay of a time-varying path, based on which a delay-optimal multicast tree is constructed in accordance with the directionality of delay. Candidate paths with a high expected delivery ratio and low expected delay are then selected in a distributed manner to conditionally connect adjacent multicast members and thus transform the multicast tree into a multicast flower. Despite the NP-hardness of optimal candidate paths’ addition, the transformation with the highest expected delivery ratio of multicast under delay constraint can be guaranteed through a pseudo-polynomial time derandomization-based greedy approach. We further demonstrate the time and energy efficiency of FlowerCast through asymptotic analysis. To make full use of the possible overlapping links in a multicast flower, a hybrid routing strategy is presented to wisely switch between sequential routing and synchronous routing for extra enhancement of the multicast performance. Extensive experiments on various datasets verify the superiority of FlowerCast and hybrid routing over baselines and indicate their wide applicability to practical scenarios.

Research on Optimization of Node Deployment in Wireless Sensor Networks Driven by Artificial Intelligence

The security deployment of WSN (Wireless Sensor Network) nodes also needs to ensure the performance of node coverage, which affects the accuracy and integrity of information detected by the network and the quality of service of the network. It is not enough to improve the network coverage and network performance of nodes only by adjusting the network topology of WSN, so there will be a problem of excessive node density in the network deployment area, which will lead to node redundancy and coverage hole, which will eventually affect the accuracy and integrity of monitoring information. In this paper, the deployment of WSN nodes driven by AI(Artificial Intelligence) is optimized. The data fusion theory is introduced, and the data fusion model is used to integrate node synchronization task scheduling, dynamic node deployment and dynamic network topology recovery.

Retracted: Wireless Sensor Network Topology Theory for Data Collection and Analysis of Sports Training Human Body

Retracted: Wireless Sensor Network Topology Theory for Data Collection and Analysis of Sports Training Human Body

Accurate Range Free Location based Partial Derivative and Stochastic Feedforward Neural Network Hyperbolic-based Agriculture Sensor Network Formation

Wireless sensor networks (WSN) authorize the control of different source environmental aspects and crop states as far as precision agriculture is concerned. Nevertheless, the complicated agricultural environment brings about the WSN topology to change often and hence link association likelihood is laborious to predict. Information pertaining to agriculture is an essential distress for localization-based service in the domain of WSNs. Smooth planning and control as the most typical range-free localization method localization performance is said to be good in even distributed networks. Nevertheless, it demonstrated extremely poor accuracy under that in an urgent issue that required to be addressed. In this work a novel topology construction method called, Partial Derivative Laurent Approximation and Stochastic Feedforward Hyperbolic (PDLA-SFH) based Agriculture Sensor Network Formation is proposed. The proposed method is split into three steps. They are agriculture sensor topology construction, average hop size distance validation and position estimation. First topology construction is performed by employing Game Theory Partial Derivative Regression Coefficient-based Topology model. Second, Laurent Approximation-based Hop Size Distance validation model is designed for optimal topology formation. Finally the Stochastic Feedforward Hyperbolic-based Position Estimation is modeled. The simulation results performed in NS3 showed that the proposed localization algorithms can attain better localization performance in terms of accuracy, time and error rate in comparison with other existing methods such as basic Digital Twins and Autonomous Groups Particles Swarm Optimization (AGPSO) under distinct arbitrary network topologies.

Probabilistic Detection of Indoor Events Using a Wireless Sensor Network-Based Mechanism.

Wireless sensor networks (WSNs) have been commonly utilized in event detection and environmental observation applications. The main aim of event detection is to define the presence or absence of an event. Various existing studies in the field of event detection depend on static or threshold values to reveal the occurrence of an event, which can result in imprecise sensor readings. Recently, many studies have utilized fuzzy logic to treat fluctuating sensor readings; as a result, they have decreased the number of false alarms created. However, there is some attention required when utilizing fuzzy logic. One aspect is that the efficiency and accuracy of the fuzzy membership function can be impacted by the utilization of heterogeneous sensors, which may increase the complexity of the fuzzy logic operation as the number of inputs rises. To address these issues, this paper proposes an approach named Probabilistic Collaborative Event Detection (PCED), which is a hybrid event detection technique that is based on a cluster WSN topology. The PCED approach utilizes a validated probabilistic technique for heterogeneous sensor nodes to transform sensing values into probability formulas and introduces a Cluster Head Decision Mechanism to make decisions based on the aggregated data from the sensors. The proposed approach employs fuzzy logic at the fusion center level to enhance the precision of event detection. The effectiveness of this method is thoroughly evaluated using MATLAB software, demonstrating an improvement in the probability of detection and a decrease in the probability of false alarms. PCED is compared to well-established event detection mechanisms such as the REFD mechanism. The results show that PCED reduces the occurrence of false alarms from 37 to 3 in certain scenarios, while improving detection accuracy by up to 19.4% over REDF and decreasing detection latency by up to 17.5%.

An optimal design method for communication topology of wireless sensor networks to implement fully distributed optimal control in IoT-enabled smart buildings

In smart buildings enabled by IoT technologies, wireless sensor networks (WSNs) are promising platforms to implement novel fully distributed optimal control approaches according to the edge computing paradigm. This requires knowledge from wireless communication and distributed computation fields where communication topologies are both critical. Communication topologies are designed considering network energy consumption and stability in wireless communication field, while considering optimization convergence speed in distributed computation field. But there is no inter-disciplinary design method considering these issues simultaneously. This study therefore proposes an optimal design method for communication topology of WSNs to implement fully distributed optimal control approaches. System control performance, network energy consumption and network stability are integrated into the objective function for the design. For a WSN consisting of n sensors, an integer programming problem with n(n − 1)/2 design variables, i.e., elements in Laplacian matrix representing the existence of communication links, is formulated and solved by the genetic algorithm (GA). The optimal topology of a WSN, on which a fully distributed optimal control approach is implemented for optimally controlling a multi-zone dedicated outdoor air system (DOAS), is designed by the proposed method. A co-simulation testbed is constructed to test and validate the proposed method by comparing the optimal topology with different topologies. The optimal topology provides satisfactory system control performance (CO2Ave = 784 ppm, CO2Max = 916 ppm, CO2 unmet hour = 1.82 h and EDOAS = 122.50 kWh), low network energy consumption (2564.12 J/Day) and high network stability (53.90 days). The proposed method facilitates the development and applications of IoT technologies in smart buildings.

Fault-Tolerant Routing for Wireless Sensor Networks using a Gaussian Network

Abstract: Wireless Sensor Networks (WSNs) are utilised for sensing various physical and environmental variables like temperature, pressure, motion, and pollution. A component of a network to continue operating even when some sensor nodes fail is regarded as fault tolerance in wireless sensor networks. Due to battery life deadlines and the challenges associated with charging or replacing failing nodes, the deployment of sensor nodes in the target area is done in a dense manner to maximise coverage and connection. This paper implements a hybrid fault-tolerant routing methodology to address the problem of fault tolerance in hierarchical topology for wireless sensor networks (WSNs). The network location is partitioned into small square grids, with a Gaussian integer serving as each grid's cluster head. A Gaussian network is created through communicating these cluster heads together. Discovering the Gaussian network's shortest path and multi-path routing, the project implements a hybrid Fault-tolerant Clustering routing protocol based on Gaussian network for Wireless Sensor Network (FCGW). The purpose of FCGW is to improve fault tolerance and reduce energy consumption for Wireless Sensor Networks. Experimental results using MATLAB shows that FCGW protocol has high data reliability. In addition, the FCGW protocol consumes lesser energy in the network compared to other protocols namely FT-LEACH, HEED and PSO-UFC

Cascading Robustness Analysis of Wireless Sensor Networks with Varying Multisink Placement.

In practical wireless sensor networks (WSNs), cascading failures are closely related to network load distribution, which in turn strongly relies on the locations of multiple sink nodes. For such a network, understanding how the multisink placement affects its cascading robustness is essential but still largely missing in the field of complex networks. To this end, this paper puts forward an actual cascading model for WSNs based on the multisink-oriented load distribution characteristics, in which two load redistribution mechanisms (i.e., global routing and local routing) are designed to imitate the most commonly used routing schemes. On this basis, a number of topological parameters are considered to quantify the sinks' locations, and then, the relationship between these quantities with network robustness is investigated on two typical WSN topologies. Moreover, by employing the simulated annealing approach, we find the optimal multisink placement for maximizing network robustness and compare the topological quantities before and after the optimization to validate our findings. The results indicate that for the sake of enhancing the cascading robustness of a WSN, it is better to place its sinks as hubs and decentralize these sinks, which is independent of network structure and routing scheme.

A Multi Model Approach: A Data Engineering Driven Pipeline Model for Detecting Anomaly in Sensor Data using Stacked LSTM

Abstract: This study uses a self-supervised learning technique based on auto encoders to find anomalous nodes. Only temporal variables have been taken into account and researched so far for use in identifying anomalies in wireless sensor networks (WSNs). This method fully utilises the geographic and temporal information of the WSN for anomaly identification by incorporating the extraction of geographic location features, intermodal WSN correlation features, and temporal WSN data flow characteristics into the design of the autoencoder. First, by focusing on a single mode from a local spatial perspective, a fully connected network is used to temporal nodes. Second, the spatial and temporal characteristics of the data flows of the nodes and their neighbours are retrieved by concentrating on a specific mode and seeing the WSN topology from a global spatial perspective for anomaly identification. The adaptive fusion method's weighted summation step is then used to extract the relevant features from the various models. An LSTM is used in this study to solve the problem of long-term dependence in the temporal dimension. The decoder's reconstructed output and the hidden layer representation of the are used to calculate the anomaly probability of the current system utilising a fully linked network.

A Novel Topology Optimization Protocol Based on an Improved Crow Search Algorithm for the Perception Layer of the Internet of Things

In wireless sensor networks, each sensor node has a finite amount of energy to expend. The clustering method is an efficient way to deal with the imbalance in node energy consumption. A topology optimization technique for wireless sensor networks based on the Cauchy variation optimization crow search algorithm (CM-CSA) is suggested to address the issues of rapid energy consumption, short life cycles, and unstable topology in wireless sensor networks. At the same time, a clustering approach for wireless sensor networks based on the enhanced Cauchy mutation crow search algorithm is developed to address the issue of the crow algorithm's sluggish convergence speed and ease of falling into the local optimum. It utilizes the Cauchy mutation to improve the population's variety and prevent settling for the local optimum, as well as to broaden the range of variation and the capacity to carry out global searches. When the leader realizes he is being followed, the discriminative probability is introduced to improve the current person's location update approach. According to the simulation findings, the suggested CM-CSA algorithm decreases the network's average energy consumption by 66.7%, 50%, and 33.3% and enhances its connectivity performance by 52.9%, 37.6%, and 23.5% when compared to the PSO algorithm, AFSA method, and basic CSA algorithm.

ZigBee-Based Wireless Sensor Network Topology Design and Comparison in Residential Areas

When designing a wireless sensor network based on ZigBee, it is very important to choose the right network topology, especially in networks with many nodes such as residential areas. Choosing the wrong topology will have an impact on the performance of the wireless sensor network as a whole because it will cause a large delay value. To overcome this, in this study a ZigBee-based wireless sensor network simulation was carried out using an environmental model consisting of several residential units, with one ZigBee device for each house. In addition, three ZigBee network topologies namely mesh, star, and tree are used in this simulation. This is done to determine which topology model will work best in a residential environment. The housing used for the simulation in this study is Citraland Waterfront City Housing which is located in Palu City, Central Sulawesi Province, Indonesia. By using simulations in the Opnet Modeler 14.5 application, it is known that the star topology on the ZigBee network is suitable for application in residential areas with a large number of nodes. This can be seen from the highest throughput values and the lowest media access delay, end-to-end delay, hop number, and packet dropped values compared to tree and mesh topologies.

Reliable Data Transmission in Underwater Wireless Sensor Networks Using a Cluster-Based Routing Protocol Endorsed by Member Nodes

Considering Underwater Wireless Sensor Networks (UWSNs) have limited power resources (low bandwidth, long propagation delays, and non-rechargeable batteries), it is critical that they develop solutions to reduce power usage. Clustering is one solution because it not only saves energy consumption but also improves scalability and data integrity. The design of UWSNs is vital to the development of clustering algorithms. The limited energy of sensor nodes, narrow transmission bandwidth, and unpredictable topology of mobile Underwater Acoustic Wireless Sensor Networks (UAWSNs) make it challenging to build an effective and dependable underwater communication network. Despite its success in data dependability, the acoustic underwater communication channel consumes the greatest energy at a node. Recharging and replacing a submerged node’s battery could be prohibitively expensive. We propose a network architecture called Member Nodes Supported Cluster-Based Routing Protocol (MNS-CBRP) to achieve consistent information transfer speeds by using the network’s member nodes. As a result, we use clusters, which are produced by dividing the network’s space into many minute circular sections. Following that, a Cluster Head (CH) node is chosen for each circle. Despite the fact that the source nodes are randomly spread, all of the cluster heads are linked to the circle’s focal point. It is the responsibility of the MNS-CBRP source nodes to communicate the discovered information to the CH. The discovered data will then be sent to the CH that follows it, and so on, until all data packets have been transferred to the surface sinks. We tested our techniques thoroughly using QualNet Simulator to determine their viability.

Environmental Sensing in High-Altitude Mountain Ecosystems Powered by Sedimentary Microbial Fuel Cells.

The increasing need for fresh water in a climate change scenario requires remote monitoring of water bodies in high-altitude mountain areas. This study aimed to explore the feasibility of SMFC operation in the presence of low dissolved oxygen concentrations for remote, on-site monitoring of physical environmental parameters in high-altitude mountainous areas. The implemented power management system (PMS) uses a reference SMFC (SMFCRef) to implement a quasi-maximum power point tracking (quasi-MPPT) algorithm to harvest energy stably. As a result, while transmitting in a point-to-point wireless sensor network topology, the system achieves an overall efficiency of 59.6%. Furthermore, the control mechanisms prevent energy waste and maintain a stable voltage despite the microbial fuel cell (MFC)'s high impedance, low time response, and low energy production. Moreover, our system enables a fundamental understanding of environmental systems and their resilience of adaptation strategies by being a low-cost, ecological, and environmentally friendly alternative to power-distributed and dynamic environmental sensing networks in high-altitude mountain ecosystems with anoxic environmental conditions.

A novel authentication and access scheduling scheme to improve the performance of WSN

Wireless sensor network (WSN) is a kind of network specifically suitable for place where infrastructure and resources are playing a vital role. Moreover, nodes in a WSN are autonomous in nature. WSNs can be able to solve various real-time problems and issues like smart healthcare, smart office, smart energy, smart home, etc. As energy becomes one of the scarce supplies for this kind of network, attacks against authentication help to validate the legitimacy of sensor nodes become foremost important. Such attacks exhaust the power of nodes that are currently connected to a WSN, thereby reducing their lifetime. In this article, a zonal node authentication technique as well as optimal data access scheduling that renders data deliverance with improved quality of service and network lifetime is proposed. The results obtained from simulation for diverse WSN topologies accentuate the claim of our method over the existing solutions and demonstrate to be efficient in discovering legitimate sensor nodes with the optimal workload. Besides improved network lifetime, efficiency, and throughput, the proposed method also reinforces the security measures of the WSN by integrating node authentication.

Energy‐efficient animal tracking with multi‐unmanned aerial vehicle path planning using reinforcement learning and wireless sensor networks

AbstractIn recent years, the integration of wireless sensor networks (WSNs) and unmanned aerial vehicles (UAVs) has been a popular research domain in animal tracking due to animal habitats in harsh environments. A well‐designed WSN network provides a robust mechanism to detect the animal appearances which can be then collected by UAVs. In this paper, we first design a WSN‐based network model that eliminates the deficiencies of traditional WSNs by intelligently building a WSN topology. Second, we propose a multi‐agent (UAVs) Q‐learning‐based trajectory planning algorithm, which enables UAVs to acquire the detection information of the wild animal on time. Q‐learning algorithm coordinates the UAVs to visit WSN nodes containing fresh animal detection data in a timely manner according to past experience. The experimental results denotes that the proposed animal tracking system delivers significantly higher timely animal detection messages of 45% than the existing algorithms and the single agent Q‐learning approaches. The results also show that since the Zebras' movement contains some randomness, relatively high epsilon value (0.2) in the epsilon‐greedy mechanism of the Q‐learning provides highest rewards (timely delivered messages) for agents (UAVs). In the light of the results, it is clearly seen that intelligently designed WSN and UAV swarm combination could enable tracking of the wild animal in near real time.

A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT

Conservation of energy has been a major concern for Wireless Sensor Networks (WSNs) and IoT applications. Several strategies were devised, aimed at optimizing energy consumption in these applications, based on: (a) use of low-powered hardware devices, (b) deploying mobile/relay agents for data collection, (c) clustering, and (d) data aggregation. Amongst these, data aggregation is widely acknowledged as an important tool to conserve energy in WSN and IoTs. The paper provides a comprehensive survey of various data aggregation strategies, discusses the efficacy of these strategies in handling issues that are typical to WSN and IoT applications. These issues severely impact the performance metrics such as: energy efficiency, latency, fault-tolerance, network throughput, and network lifetime. Therefore, to optimize the data aggregation approach, an application developer needs to arrive at optimal tradeoffs between these parameters. A major contribution of the paper is to present a holistic review of data aggregation approaches emphasizing the effect of topology, security, mobility, interference, and fault-tolerance in WSN and IoTs. Based on gap areas in literature, we throw open few challenges and present them as “posers”, and put-forth suggestions for further research.