Heterogeneous Sensor Networks Research Articles

Application of Q-learning algorithm based on heterogeneous sensor networks in higher education teaching management

With the rapid advancement of information technology and online technologies, it has become imperative for all colleges and universities to address the challenge of leveraging modern management methods to enhance management efficiency, level, and practices. In the realm of educational institution management, the learning management system stands as the cornerstone of contemporary campus management and the entire campus computer network. In this article, the Q-learning algorithm, specifically applied to a heterogeneous cellular network, is employed to investigate the issues pertaining to teaching management in colleges and universities.The design and implementation of the university teaching management system mainly uses heterogeneous cellular networks to establish a physical model of the Q-learning algorithm, and then in the Rayleigh channel, the coverage factor and the system rate expression are derived in a closed form, and in a closed form Derive the energy efficiency expression, and then optimize the density of the basic micro-station algorithm to maximize performance. In the process of system design, this paper uses an algorithm to analyze the current research status of university teaching management, then proposes the research purpose and subject content, and analyzes the university's teaching management function and potential future functional requirements. When designing the university learning management system, it describes in detail the overall design concept, key technologies, system structure, and operating system structure and system topology. This research mainly focuses on the research of campus information management and Q learning algorithm. Based on the cellular network, a large number of campus management cases were analyzed and data fitting at the same time, and a set of Q-learning algorithm teaching management system based on heterogeneous cellular network was designed.

Distributed Backlog-Aware Protocol for Heterogeneous D2D Communication-Assisted Wireless Sensor Networks

Distributed Backlog-Aware Protocol for Heterogeneous D2D Communication-Assisted Wireless Sensor Networks

Urban Delay-Tolerant Multicast Using Uncontrolled Mobile Relay

The development of network functionalities in the urban environment is accompanied by the emergence of new publicly available data sources. They are the basis of the introduced research architecture and environment which are used to investigate the new multicast algorithms proposed in this paper. These message-oriented algorithms are primarily intended to meet the needs of opportunistic routing in heterogeneous urban sensor networks. Although, due to their generalized and protocol-agnostic design, they can be of use in other network applications and research areas. Uncontrolled mobile relay devices are the key elements of the presented delay-tolerant multicast framework. Multicast structures are modeled in four Polish cities based on open data on the location of public transportation vehicles and elements of urban infrastructure. Over 16,000 graphs were built and analyzed. It has been shown that the use of uncontrolled mobile relay enables the construction of time-spanning time-changing multicast structures. Their features are determined by the topology of a given city area, the distribution of destination nodes, as well as the number and the routes of mobile relay nodes. The efficacy and efficiency of the algorithms depend on the radio range of the nodes, maximum time span of forwarded messages, and network structure knowledge availability.

Retracted: SCR-CC: A Novel Sensing Clustering Routing Algorithm Based on Collaborative Computing in Heterogeneous Sensor Networks

Retracted: SCR-CC: A Novel Sensing Clustering Routing Algorithm Based on Collaborative Computing in Heterogeneous Sensor Networks

Iterative Vector-based Localization in a Large Heterogeneous Sensor Network

Iterative Vector-based Localization in a Large Heterogeneous Sensor Network

Modeling and Analyzing Urban Sensor Network Connectivity Based on Open Data

The optimization of network topology is crucial to achieve efficient data transmission in wireless sensor networks. Recently it has been proven that emerging open data sources can be used for modeling the structures of heterogeneous urban sensor networks. With this, leveraging real location data of various networked and sensing devices became feasible and essential. This approach enables the construction and analysis of more accurate representations based on frequently updated actual network infrastructure topology data, as opposed to using synthetic models or test environments. The presented modeling method serves as the basis for the designed architecture and implemented research environment. This paper introduces a set of algorithms which transform devices’ location data into graph-based wireless network connectivity models. Each algorithm is thoroughly discussed and evaluated. Moreover, static (momentary) and dynamic (time-spanning) network topologies are constructed in four large Polish cities based on publicly available data. Multidimensional simulation-based analysis is conducted to investigate the characteristics of the modeled structures. Directions for further research are suggested as well.

A systematic review of data fusion techniques for optimized structural health monitoring

Advancements in structural health monitoring (SHM) techniques have spiked in the past few decades due to the rapid evolution of novel sensing and data transfer technologies. This development has facilitated the simultaneous recording of a wide range of data, which could contain abundant damage-related features. Concurrently, the age of omnipresent data started with massive amounts of SHM data collected from large-size heterogeneous sensor networks. The abundance of information from diverse sources needs to be aggregated to enable robust decision-making strategies. Data fusion is the process of integrating various data from heterogeneous sources to produce more useful, accurate, and reliable information about system behavior. This paper reviews recent developments in data fusion techniques applied to SHM systems. The theoretical concepts, applications, benefits, and limitations of current methods and challenges in SHM are presented, and future trends in data fusion methods are discussed. Furthermore, a set of criteria is proposed to evaluate contents and information from original and review papers in this field, and a road map is provided discussing possible future work.

Multimodal Asynchronous Kalman Filter for monitoring unstable rock slopes

Unstable rock slopes pose a hazard to inhabitants and infrastructure in their vicinity, necessitating advanced monitoring methodologies for timely risk assessment and mitigation. Recent geotechnical monitoring techniques often rely on sensor data fusion to enhance forecasting for imminent failures. Our investigation extends beyond a single sensor type to data fusion for heterogeneous sensor networks using a Multimodal Asynchronous Kalman Filter. We illustrate the application of the proposed method on a case study data set consisting of data from an on-site sensor network enriched by remote sensing data. Employing a Multimodal Asynchronous Kalman Filter, we capitalise on the distinct resolutions inherent in each sensor input. The outcome was a combined dataset with a high spatiotemporal resolution. Our approach facilitates the estimation of essential physical attributes for monitored objects, encompassing translation, rotation, velocities and accelerations. The case study site was an unstable rock section of ca. 50.000 m 3 in Aurland, Norway, which collapsed as a multi-stage failure in July 2023. Our method can be transposed to various sites with distinct sensor networks, enhancing state estimations for objects on unstable rock slopes. These estimations can significantly improve applications such as risk assessment and robust early-warning systems, enhancing predictions of critical failure points.

Distributed homology-based algorithm for solving Set k-Cover problem in heterogeneous directional sensor networks

Distributed homology-based algorithm for solving Set k-Cover problem in heterogeneous directional sensor networks

Enhancement of Performance Metrics of Heterogeneous Wireless Camera Sensor Network with Functionalization of Extensive Zonal Stable Election Protocol using Threshold Amplification and Residual Energy

We report a new improved energy competent and optimized data packet flow protocol with Hierarchical Clustering utilized in Wireless camera Sensor Network.The existing Extensive Zonal Stable Election Protocol has been modified along with the threshold parameters amplification and residual energy. It incorporates dynamic hybrid method with finite number of Member Sensor nodes (MSN) in proximity with the base station share their data directly , while the rest of the farther nodes form a cluster for data transference using Cluster Head. The performance metrics accompanied by heterogeneity, longer network survival and better throughput have been improved. The network field was divided into 4 zones with a gateway for defined region 2, 3, and 4. The criterion for zone division remained on the energy status (residual) of the MSNs and distance from the BS and the formulated field characteristics in the simulation were kept unknown. The obtained results demonstrate that our proposed modified version of EZSE protocol considerably performs better than existing EZ-SEP, Z-SEP, SEP, LEACH, Mod-Leach protocols during entire stability timeframe. The notable achievement is also reported in throughput as the same is enhanced more than by ~ 39%, 43%, 49% ,56%, 53% while total packets communicated with base station has been increased more than by ~ 127%, 131%, 147%, 151%, 148% stability of the network is also improved more than by ~ 37%, 42%, 45%, 49%, 51% with the corresponding increase in the heterogeneity of networks.

A Design and Challenges in Energy Optimizing CR-Wireless Sensor Networks

Background: The progress of the Cognitive Radio-Wireless Sensor Network is being influenced by advancements in wireless sensor networks (WSNs), which significantly have unique features of cognitive radio technology (CR-WSN). Enhancing the network lifespan of any network requires better utilization of the available spectrum as well as the selection of a good routing mechanism for transmitting informational data to the base station from the sensor node without data conflict. Aims: Cognitive radio methods play a significant part in achieving this, and when paired with WSNs, the above-mentioned objectives can be met to a large extent. Methods:: A unique energy-saving Distance- Based Multi-hop Clustering and Routing (DBMCR) methodology in association with spectrum allocation is proposed as a heterogeneous CR-WSN model. The supplied heterogeneous CR-wireless sensor networks are separated into areas and assigned a different spectrum depending on the distance. Information is sent over a multi-hop connection after dynamic clustering using distance computation. Results: The findings show that the suggested method achieves higher stability and ensures the energy-optimizing CR-WSN. The enhanced scalability can be seen in the First Node Death (FND). Additionally, the improved throughput helps to preserve the residual energy of the network which helps to address the issue of load balancing across nodes. Conclusions: Thus, the result acquired from the above findings shows that the proposed heterogeneous model achieves the enhanced network lifetime and ensures the energy optimizing CR-WSN.

An Energy-Saving and Efficient Deployment Strategy for Heterogeneous Wireless Sensor Networks Based on Improved Seagull Optimization Algorithm.

The Internet of Things technology provides convenience for data acquisition in environmental monitoring and environmental protection and can also avoid invasive damage caused by traditional data acquisition methods. An adaptive cooperative optimization seagull algorithm for optimal coverage of heterogeneous sensor networks is proposed in order to address the issue of coverage blind zone and coverage redundancy in the initial random deployment of heterogeneous sensor network nodes in the sensing layer of the Internet of Things. Calculate the individual fitness value according to the total number of nodes, coverage radius, and area edge length, select the initial population, and aim at the maximum coverage rate to determine the position of the current optimal solution. After continuous updating, when the number of iterations is maximum, the global output is output. The optimal solution is the node's mobile position. A scaling factor is introduced to dynamically adjust the relative displacement between the current seagull individual and the optimal individual, which improves the exploration and development ability of the algorithm. Finally, the optimal seagull individual position is fine-tuned by random opposite learning, leading the whole seagull to move to the correct position in the given search space, improving the ability to jump out of the local optimum, and further increasing the optimization accuracy. The experimental simulation results demonstrate that, compared with the coverage and network energy consumption of the PSO algorithm, the GWO algorithm, and the basic SOA algorithm, the coverage of the PSO-SOA algorithm proposed in this paper is 6.1%, 4.8%, and 1.2% higher than them, respectively, and the energy consumption of the network is reduced by 86.8%, 68.4%, and 52.6%, respectively. The optimal deployment method based on the adaptive cooperative optimization seagull algorithm can improve the network coverage and reduce the network cost, and effectively avoid the coverage blind zone and coverage redundancy in the network.

Optimum energy efficient multi‐hop protocol for homogenous and heterogeneous 3D underwater acoustic sensor networks

SummaryInformation transmission is extremely challenging in underwater acoustic sensor networks (UASNs) because, in the acoustic channel, the packet loss is high compared to other channels due to low bandwidth and a long propagation delay. So, designing an efficient energy algorithm is a big challenge in the acoustic channel. In this paper, first, a cluster head (CH) election fitness function based on a hybrid particle swarm optimization and gravitational search algorithm (PSO‐GSA) is proposed for a three‐dimensional (3D) underwater sensor network (UASN). The proposed algorithm includes CH election, CH load, and neighbor CH distance for multi‐hop transmission (MH‐PSOGSA). Then, based on the proposed algorithm, a relay‐based traffic‐aware energy‐efficient routing protocol (R‐TAEERP) with a weight function is proposed to offload the heavy consumption of the CHs. MATLAB simulations were accomplished to compare the performance of MH‐PSOGSA and R‐TAEERP with the existing representative protocols. The heterogeneity in energy and traffic has been considered. The simulation results show better performance of the proposed algorithm than other existing representative protocols in terms of energy consumption, the performance of the stable region, and the lifetime of the network.

Heterogeneous Cluster-Based Information-Centric Sensor Networks With User Security Satisfaction

In heterogeneous cluster-based Information-Centric Wireless Sensor Networks (ICWSNs), sensor nodes acquire different application-specific data. They are clustered based on proximity, where cluster heads act as cache nodes. Meanwhile, grouping sensor nodes based on the information type gathered can improve the ICWSN performance. Motivated by the heterogeneous nature of ICWSNs, application-specific communities can be formed within each cluster, where community leaders can be selected to cache application-specific data. In this case, cluster heads gather aggregated data from community leaders rather than basic sensing nodes. However, this creates an issue of the energy-latency and security trade-off and affects user security satisfaction. In this work, we propose solving this issue by studying cluster-based ICWSNs with heterogeneous communities and comparing them to conventional heterogeneous cluster-based ICWSNs. Based on the formulated analytical model, we then propose SLAC-H, a Security Level Aware cluster heads’ and community leaders’ selection algorithm for Cluster-based ICWSNs with Heterogeneous communities. SLAC-H addresses the energy-latency and security issue by optimizing energy and coverage supported by sensor nodes in a cluster-based ICWSN with heterogeneous communities subject to security constraints. Simulation results show that compared to existing works, SLAC-H achieves lower latency and energy consumption while fulfilling higher user security satisfaction.

Anomaly detection for high‐dimensional dynamic data stream using stacked habituation autoencoder and union kernel density estimator

AbstractWith the rapid development of communication technology, various complex heterogeneous sensor network applications produce a large number of high‐dimensional dynamic data streams, which results in more difficult to anomaly detection than ever before. So, anomaly detection for high‐dimensional dynamic data streams is of a more and more challenging problem. This paper proposes a novel method for detecting anomalies in high‐dimensional dynamic data streams by utilizing several components. Firstly, it uses a stacked habituation autoencoder with habituation physiological mechanism to detect similarity anomalies more easily and capture feature relationships. Secondly, a union kernel density estimator with micro‐cluster is designed to improve online anomaly detection accuracy by estimating the data density. Lastly, candidate anomaly sets and a delayed processing approach are utilized to cope with conceptual drift and evolution in the data stream, allowing the system to adapt to changes in the data over time. Extensive experiments on four high‐dimensional dynamic data streams of the Internet of Things show that the proposed method is very effective.

Active Learning for Efficient Soil Monitoring in Large Terrain with Heterogeneous Sensor Network.

Soils are a complex ecosystem that provides critical services, such as growing food, supplying antibiotics, filtering wastes, and maintaining biodiversity; hence monitoring soil health and domestication is required for sustainable human development. Low-cost and high-resolution soil monitoring systems are challenging to design and build. Compounded by the sheer size of the monitoring area of interest and the variety of biological, chemical, and physical parameters to monitor, naive approaches to adding or scheduling more sensors will suffer from cost and scalability problems. We investigate a multi-robot sensing system integrated with an active learning-based predictive modeling technique. Taking advantage of advances in machine learning, the predictive model allows us to interpolate and predict soil attributes of interest from the data collected by sensors and soil surveys. The system provides high-resolution prediction when the modeling output is calibrated with static land-based sensors. The active learning modeling technique allows our system to be adaptive in data collection strategy for time-varying data fields, utilizing aerial and land robots for new sensor data. We evaluated our approach using numerical experiments with a soil dataset focusing on heavy metal concentration in a flooded area. The experimental results demonstrate that our algorithms can reduce sensor deployment costs via optimized sensing locations and paths while providing high-fidelity data prediction and interpolation. More importantly, the results verify the adapting behavior of the system to the spatial and temporal variations of soil conditions.

Sampling Rate Offset Estimation and Compensation for Distributed Adaptive Node-Specific Signal Estimation in Wireless Acoustic Sensor Networks

Sampling rate offsets (SROs) between devices in a heterogeneous wireless acoustic sensor network (WASN) can hinder the ability of distributed adaptive algorithms to perform as intended when they rely on coherent signal processing. In this paper, we present an SRO estimation and compensation method to allow the deployment of the distributed adaptive node-specific signal estimation (DANSE) algorithm in WASNs composed of asynchronous devices. The signals available at each node are first utilised in a coherence-drift-based method to blindly estimate SROs which are then compensated for via phase shifts in the frequency domain. A modification of the weighted overlap-add (WOLA) implementation of DANSE is introduced to account for SRO-induced full-sample drifts, permitting per-sample signal transmission via an approximation of the WOLA process as a time-domain convolution. The performance of the proposed algorithm is evaluated in the context of distributed noise reduction for the estimation of a target speech signal in an asynchronous WASN.

EECHEP: Energy efficient cluster head election protocol for extending lifetime of heterogeneous sensor networks

EECHEP: Energy efficient cluster head election protocol for extending lifetime of heterogeneous sensor networks

An efficient fault-tolerant distributed Bayesian filter based on conservative fusion

This paper proposes a fault-tolerant distributed Bayesian filter for multi-sensor state estimation using a peer-to-peer sensor network with incoherent local estimates problems. The proposed approach uses a Gaussian mixture rather than a single Gaussian distribution to represent the fusion result, which can effectively reduce the negative impact of corrupted local estimates on the fusion results. The resulting filter performs Bayesian recursion via Gaussian mixture. To accommodate a heterogeneous sensor network, we develop a novel arithmetic average fusion employing a set of covariance-dependent weighting coefficients, where the fusion error covariance is effectively reduced in the case of fusing information with different qualities. For intersensor communication, a partial flooding scheme is investigated, in which only valid-likely Gaussian components are disseminated and fused between neighbor sensors. Theoretically, it is shown that under reasonable assumptions, the presented fault-tolerant distributed estimator can guarantee local stability with the exponentially bounded estimation error in the mean square. The effectiveness and superiority of our approach are validated through both simulation and experiment scenarios.

Routing Protocol for a Heterogeneous MSN With an Intermittent Mobile Sink

Choosing the first broadcaster to be the parent node as proposed by the leader-based routing (LBR) enables its Polling packet to be received earliest in a heterogeneous mobile sensor network (MSN) with short and intermittent availability of the mobile sink but may lead to an unreachable parent node. Furthermore, employing the three-way-handshaking as proposed by the link quality indicator (LQI)-based beaconless routing (LQI-BLR) may lead to a longer packet delivery delay. To avoid the aforementioned drawbacks simultaneously, the transmission power of the candidate parent node is included in the Polling packet in our proposed protocol. Along with the LQI, a mobile sensor can estimate its distance to the candidate parent node and its packet-receive ratio (PRR). If the PRR is higher than a preset threshold, the candidate can be chosen accordingly. Via simulations, we demonstrate that our proposed protocol can outperform LBR and LQI-BLR in a heterogeneous environment. In terms of the packet delivery ratio, our proposed protocol is able to achieve at least 35% and 20% of improvement when compared to LBR and LQI-BLR, respectively. Regarding the energy consumption and packet delivery delay, more than 20% improvement can be observed.