Real-world Wireless Sensor Networks Research Articles

A Novel Method for Energy Efficient Clustering in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) play a crucial role in various applications, including environmental monitoring, industrial automation, and healthcare. However, optimizing WSNs for efficient resource utilization, energy conservation, and reliable data transmission remains a challenging task due to the dynamic nature of the network environment and resource-constrained sensor nodes. In this study, we propose a Hybrid Firefly Genetic Algorithm (HFGA) for optimizing WSN performance. The HFGA combines the strengths of the firefly algorithm's global search capabilities and the genetic algorithm's local search and optimization efficiency. By integrating these two evolutionary algorithms, the HFGA aims to achieve superior performance in terms of energy efficiency, network coverage, and convergence speed. We evaluate the effectiveness of the proposed HFGA through extensive simulation experiments in various WSN scenarios. The results demonstrate that the HFGA outperforms traditional optimization approaches and baseline algorithms in optimizing WSN performance metrics. Furthermore, we discuss the practical implications and future research directions for deploying the HFGA in real-world WSN applications. Overall, this study contributes to advancing WSN optimization techniques and enhancing the reliability and efficiency of WSN deployments. Keywords: Clustering, Genetic algorithm, Firefly algorithm, FAG algorithm.

Fault detection in wireless sensor networks with deep neural networks

This paper addresses the challenge of fault detection in Wireless Sensor Networks (WSNs), commonly used in fields like environmental monitoring and healthcare. WSNs, prone to various faults due to their deployment in unpredictable environments, require effective solutions for fault detection. Traditional machine learning approaches show limitations such as unsuitability for streaming data and the detection of a single fault type. We propose the use of deep neural networks, particularly Recurrent Neural Networks (RNNs), for fault detection in WSNs, focusing on temperature and humidity data. The paper emphasizes the importance of careful model selection, tuning, and thorough evaluation to enhance the accuracy and robustness of fault detection in real-world WSN applications.

Sink Node Placement and Partial Connectivity in Wireless Sensor Networks.

This research delves into the aspects of communication and connectivity problems within random Wireless Sensor Networks (WSNs). It takes into account the distinctive role of the sink node, its placement, and application-specific requirements for effective communication while conserving valuable network resources. Through mathematical modeling, theoretical analysis, and simulation evaluations, we derive, compare, and contrast the probabilities of partial and full connectivity within a random WSN, factoring in network parameters and the maximum allowable hop distance/count hmax. hmax captures the diverse range of delay-sensitive requirements encountered in practical scenarios. Our research underscores the significant impact of the sink node and its placement on network connectivity and the sensor connection rate. The results exemplify a noteworthy decline in the sensor connection rate, dropping from 98.8% to 72.5%, upon relocating the sink node from the network center to the periphery. Moreover, as compared with full connectivity, partial connectivity and the sensor connection rate are more suitable metrics for assessing the communication capability of random WSNs. The results illustrate that 1.367 times more energy is required to connect less than 4% of the remote sensors, based on the examined network settings. Additionally, to increase the sensor connection rate slightly from 96% to 100%, an additional 538% more energy is required in multipath fading based on the widely adopted energy consumption model. This research and its outcomes contribute to establishing appropriate performance metrics and determining critical network parameters for the practical design and implementation of real-world wireless sensor networks.

Kalman filter based sensor fault detection in wireless sensor network for smart irrigation

Ensuring the reliability of data obtained from Wireless Sensor Networks (WSNs) deployed in farmland to monitor soil parameters is crucial for optimizing smart irrigation. These distributed sensor nodes encounter various challenges and are vulnerable to sensor faults that can significantly degrade the network's service quality. In this article, we present an innovative approach for detecting sensor faults within WSNs for smart irrigation by combining an autoregressive model with a Kalman filter. Integrating the Kalman filter and the autoregressive model combines their strengths in a synergistic manner. The algorithm is developed with consideration for the resource constraints of the sensor nodes and addresses the challenge of lacking ground truth information for the monitored area. The primary advantage of this proposed technique lies in its simplicity of implementation, requiring minimal computational complexity while enhancing the application's reliability. Through experimentation and validation, we demonstrate the effectiveness of this combined approach in detecting sensor fault detection in real-world WSNs scenarios.

A new approach for solving the minimum vertex cover problem using artificial bee colony algorithm

The minimum vertex cover problem is a well-known problem in classical graph theory. It is known as one of the NP-Hard optimization problems, meaning that the best solution cannot be found within an acceptable time. Therefore, to handle this optimization problem effectively, various alternative approaches based on either approximation or metaheuristic techniques have been considered and proposed in the previous literature. However, developing more effective methods to deal with the problem of finding the minimum vertex cover is still challenging. This study proposes a strategy for tackling the minimum vertex cover problem based on the artificial bee colony (ABC) algorithm. The main novelty and contribution of this research are to show that the ABC algorithm can be used as another useful and efficient way to solve the minimum vertex cover problem and also to demonstrate the application of the minimum vertex cover solutions to real-world wireless sensor network problems. The proposed method is evaluated by assessing the algorithm’s performance based on an obtained set of optimal vertices and the amount of computation time in terms of the number of iterations. The empirical results show that the proposed approach can yield satisfactory results and outperform other existing algorithms in finding a set of optimal vertices.

Many-objective optimization of wireless sensor network deployment

Recently, the efficient deployment of wireless sensor networks (WSNs) has become a leading field of research in WSN design optimization. Practical scenarios related to WSN deployment are often considered as optimization models with multiple conflicting objectives that are simultaneously enhanced. In the related literature, it had been shown that moving from mono-objective to multi-objective resolution of WSN deployment is beneficial. However, since the deployment of real-world WSNs encompasses more than three objectives, a multi-objective optimization may harm other deployment criteria that are conflicting with the already considered ones. Thus, our aim is to go further, explore the modeling and the resolution of WSN deployment in a many-objective (i.e., optimization with more than three objectives) fashion and especially, exhibit its added value. In this context, we first propose a many-objective deployment model involving seven conflicting objectives, and then we solve it using an adaptation of the Decomposition-based Evolutionary Algorithm “ $$\theta$$ -DEA”. The developed adaptation is named “WSN- $$\theta$$ -DEA” and is validated through a detailed experimental study.

Light Gradient Boosting Machine-Based Link Quality Prediction for Wireless Sensor Networks

Link quality prediction is a fundamental component of the wireless network protocols and is essential for routing protocols in wireless sensor networks (WSNs). Effective link quality prediction can select high-quality links for communication and improve the reliability of data transmission. In order to improve the accuracy of the link quality prediction model and reduce the model complexity, the link quality prediction model based on the light gradient boosting machine (LightGBM-LQP) is proposed in this paper. Specifically, agglomerative hierarchical clustering and manual division are combined to grade the link quality and obtain the labels of samples. Then, light gradient boosting machine (LightGBM) classification algorithm and Focal Loss are used to estimate the link quality grades. In order to reduce the impact of data imbalance, Borderline-SMOTE is employed to oversample the minority link quality samples. Finally, LightGBM-LQP predicts link quality grade at the next moment with historical link quality information. The experimental results on data collected from a real-world WSNs show that the proposed model has better prediction accuracy and shorter predicting time compared to related models.

Plug-and-play modular biobatteries with microbial consortia

Recently, a bacteria-powered biobattery containing multiple species demonstrated long-lasting and fully self-sustainable power generation through their synergistic interaction. Confining individual species in separate spaces avoids unbalanced competition between neighboring species, maximizing their cooperative interaction to extend battery life. Despite the vast potential and promise, however, a spatially engineered microbial consortium has never been sufficiently scaled in a systematic and controllable manner for immediate power applications. Moreover, the spatial organization of living microorganisms having their seamless and effective electrical coupling with the external electrode remains a significant challenge. In this work, we establish the groundwork for creating a microfabricable and scalable biobattery platform that allows control of a 3D multispecies microbial consortium. A layer-by-layer electropolymerization deposition of microbial-infused polymer solutions creates a vertically multi-layered, conductive, microbial structure where individual species are separately confined in quasi-solid-state polymer layers, ultimately providing effective coupling at the biotic-abiotic interface and efficient ionic environments for cross-feeding interactions between species. An integrated modular “plug-and-play” biobattery platform provides a simple and practical approach for its serial and parallel connections. By connecting multiple biobattery modules, an actual wireless telemetry system was successfully operated, ensuring the practical efficacy of this power supply for real-world wireless sensor network applications.

LoRa-LiSK: A Lightweight Shared Secret Key Generation Scheme for LoRa Networks

Physical-layer security (PLS) schemes use the randomness of the channel parameters, namely, channel state information (CSI) and received signal strength indicator (RSSI) to generate the secret keys. There has been limited work in PLS schemes in long-range (LoRa) wide-area networks (LoRaWANs) which hinder their widespread application. Limitations observed in existing studies include the requirement of high correlation between channel parameter measurements for secret key generation in the proposed schemes and the evaluation of the schemes has only been done in either fully indoor or outdoor environments. The real-world wireless sensor networks (WSNs) and LoRa use cases might not meet both requirements thus making the current PLS schemes inappropriate for these systems. By considering the limitations found in existing PLS schemes, this article proposes LoRA-LiSK, a practical and efficient shared secret key generation scheme for LoRa networks. Our proposed LoRa-LiSK scheme consists of several preprocessing techniques (timestamp matching, two sample Kolmogorov–Smirnov tests, and a Savitzky–Golay filter), multilevel quantization, information reconciliation using Bose–Chaudhuri–Hocquenghem (BCH) codes, and finally, privacy amplification using secure hash algorithm SHA-2. The LoRa-LiSK scheme is extensively evaluated on real WSN/IoT devices in practical application scenarios: 1) indoor to outdoor and 2) LoRa static and mobile outdoor links. It outperforms existing schemes by generating keys with channel parameter measurements of low correlation values (0.2–0.6), while still essentially achieving high key generation rates, and low key disagreement rates (10%–20%). The scheme updates a key in approximately 1 h using an application profile with high transmission rate compared to 3 h reported by existing works while still respecting the duty cycle regulation. It also incurs less communication overhead compared to the existing works.

Wireless Sensor Networks for Building Information Modeling

Building Information Modeling (BIM) is a critical element for the “digitalization” of the construction industry and can be exploited for energy-driven renovation procedures of existing residences. Advancing beyond a BIM with data-capturing capabilities that are limited to building static information only requires sensor data streams related to indoor/outdoor ambient conditions, as well as to energy-consumption parameters of the residences. The data streams require the deployment of robust Wireless Sensor Networks (WSNs) that are able to capture and transmit real-time data to appropriate cloud-based renovation toolkits. The technology and topology of such networks are addressed herein. The paper sets the lines for similar installations that are required by the construction industry for collecting dynamic data, since it is based on the outcome of real-world WSN installations in pilot sites in three European countries, carried out in the context of a major collaborative BIM research project. An application example of the WSN data is also provided in the context of training occupant behavior models in order to demonstrate the use of the measured data.

Robust Localization System Using Vector Combination in Wireless Sensor Networks

This paper proposes a vector-based localization system that uses both distance and angle information. In wireless sensor networks, the positions of nodes are commonly determined by a range-based localization system using distance information. If both distance and angle information are available, it is possible to improve the accuracy of estimating the positions of nodes compared to a positioning system with only distance information. Existing studies using distance and angle information assume that all the nodes are directly connected to one another and do not consider a method for measuring angle information between the nodes that are not directly connected. However, this assumption may not be valid for realworld wireless sensor networks especially with a large number of nodes having a limited communication range. The proposed localization algorithm solves this problem by a vector combination that transforms the vectors on the local coordinate system to the network-wide global coordinate system. The proposed algorithm is shown to be robust especially even in a network with 1-edge connectivity. Simulation results show that the proposed algorithm has up to 70% higher positioning accuracy compared to the existing iterative range-based algorithm such as MDS-MAP(C,R).

A Hybrid Model for Data Prediction in Real-World Wireless Sensor Networks

Data prediction is proposed in wireless sensor networks (WSNs) to extend system lifetime by avoiding transmissions of redundant messages. Existing prediction-based approaches can be classified into two types. One focuses on historical data reconstruction and proposes backward models, which incur uncontrollable delay. The other focuses on the future data prediction and proposes forward models, which require additional transmissions. This letter proposes a hybrid model with the capabilities of both historical data reconstruction and future data prediction to avoid additional transmission and control delay. Two algorithms are proposed to implement this model in real-world WSNs. One is a stagewise algorithm for sensor nodes to build optimal models. The other is for the sink to reconstruct and predict sensed values. Two WSN applications are simulated based on three real data sets to evaluate the performances of the hybrid model. Simulation results demonstrate that the proposed approach has high performance in terms of energy efficiency with controllable delay.

Data tampering attacks diagnosis in dynamic wireless sensor networks

Real-world wireless sensor networks (WSNs) usually have dynamic topologies and are vulnerable to data tampering attacks, which may possibly lead to serious consequences. This paper addresses the issue of defending against data tampering attacks to dynamic WSNs. First, a hybrid diagnosis algorithm is proposed, which consists of three phases: data comparison phase, distributed diagnosis phase, and global diagnosis phase. In the distributed diagnosis phase, a majority voting mechanism and a multi-round diagnosis mechanism are introduced. As the phase may lead to inconsistent diagnosis results among different sensor nodes, we introduce a global diagnosis phase to overcome the shortcoming. Next, we evaluate the performance of the diagnosis algorithm, accompanied with a few examples. Finally, to achieve near perfect performance, some experiments on how to choose the parameters of the algorithm are provided. This work provides guidance for dealing with data tampering attacks in dynamic WSNs.

When RSSI encounters deep learning: An area localization scheme for pervasive sensing systems

Localization has long been considered as a crucial research problem for pervasive sensing systems, especially with the arrival of big data era. Various techniques have been proposed to improve the localization accuracy by leveraging common wireless signals, such as radio signal strength indication (RSSI), collected from sensors placed in pervasive environments. However, the measured signal value can be easily affected by noise caused by physical obstacles in such sensing environment, which in turn compromises the localization performance. Hence, we present a novel RSSI-based area localization scheme using deep neural network (DNN) to explore the underlying correlation between the RSSI data and the respective sensor placement to achieve a superior localization performance. Moreover, to cope with the sensor data loss issue that commonly occurs during wireless sensor network (WSN) operation, an algorithm is designed to reconstruct the missing data for respective sensors in order to preserve the performance of DNN localization model. The effectiveness of the proposed scheme is verified with a real-world WSN testbed deployed inside an office building. The results demonstrate that the proposed scheme provides satisfactory prediction accuracy in area localization for pervasive sensing systems, regardless of the data loss issue that occurs with the respective sensors.

Development of an extended topology-based lightweight cryptographic scheme for IEEE 802.15.4 wireless sensor networks

Among the classes of wireless personal area networks, a wireless sensor network typically refers to a versatile and densely distributed sensing platform that enables the support of a wide variety of application domains. Among the various technical challenges addressed by more than one decade of research in wireless sensor networks, security across wireless links is by far one of the most critical ones and relates to the need of guaranteeing reliability and trustiness of the collected data. This article deals with the cryptographic aspects involved in securing wireless sensor networks, in terms of confidentiality and authentication. In particular, moving from some results previously achieved in our research activity, this article extends a cryptography scheme in order to better comply with the security requirements that arise from real-world wireless sensor network installations. The proposed scheme, called topology-authenticated key scheme 2, takes advantage of hybrid cryptography to provide security in the presence of resource-constrained sensor nodes using topology-authenticated keys to provide increased robustness to the scheme itself. The proposed extensions provide full practical support to star-topology wireless sensor networks and the article presents also some experimental results obtained by implementing the scheme on two different wireless sensor network platforms available for protocol stacks compliant with the IEEE 802.15.4 standard.

Transmit Power Reduction ≠ Proportional Power Savings: Applicability of Transmit Power Control in Large-Scale Wireless Sensor Networks

This article argues that reducing transmit power at low-power RF transceivers does not lead to proportional energy savings. This is due to the fact that the baseline operations of low-power transceivers are already optimized for very low power consumption. The article then claims that typical transmit power control protocols discussed in the context of wireless sensor networks (WSNs) are tested in small-scale settings, and thus do not reflect the actual real-world large-scale WSN or IoT scenarios. The article also discusses how a joint transmit power and routing scheme can help in reducing the overhearing effects on the nodes that have significantly low available energy.

Wireless Sensor Network Design Methodologies: A Survey

Wireless sensor networks (WSNs) have grown considerably in recent years and have a significant potential in different applications including health, environment, and military. Despite their powerful capabilities, the successful development of WSN is still a challenging task. In current real-world WSN deployments, several programming approaches have been proposed, which focus on low-level system issues. In order to simplify the design of the WSN and abstract from technical low-level details, high-level approaches have been recognized and several solutions have been proposed. In particular, the model-driven engineering (MDE) approach is becoming a promising solution. In this paper, we present a survey of existing programming methodologies and model-based approaches for the development of sensor networks. We recall and classify existing related WSN development approaches. The main objective of our research is to investigate the feasibility and the application of high-level-based approaches to ease WSN design. We concentrate on a set of criteria to highlight the shortcomings of the relevant approaches. Finally, we present our future directions to cope with the limits of existing solutions.

CBN-VAE: A Data Compression Model with Efficient Convolutional Structure for Wireless Sensor Networks.

Data compression is a useful method to reduce the communication energy consumption in wireless sensor networks (WSNs). Most existing neural network compression methods focus on improving the compression and reconstruction accuracy (i.e., increasing parameters and layers), ignoring the computation consumption of the network and its application ability in WSNs. In contrast, we pay attention to the computation consumption and application of neural networks, and propose an extremely simple and efficient neural network data compression model. The model combines the feature extraction advantages of Convolutional Neural Network (CNN) with the data generation ability of Variational Autoencoder (VAE) and Restricted Boltzmann Machine (RBM), we call it CBN-VAE. In particular, we propose a new efficient convolutional structure: Downsampling-Convolutional RBM (D-CRBM), and use it to replace the standard convolution to reduce parameters and computational consumption. Specifically, we use the VAE model composed of multiple D-CRBM layers to learn the hidden mathematical features of the sensing data, and use this feature to compress and reconstruct the sensing data. We test the performance of the model by using various real-world WSN datasets. Under the same network size, compared with the CNN, the parameters of CBN-VAE model are reduced by 73.88% and the floating-point operations (FLOPs) are reduced by 96.43% with negligible accuracy loss. Compared with the traditional neural networks, the proposed model is more suitable for application on nodes in WSNs. For the Intel Lab temperature data, the average Signal-to-Noise Ratio (SNR) value of the model can reach 32.51 dB, the average reconstruction error value is 0.0678 °C. The node communication energy consumption can be reduced by 95.83%. Compared with the traditional compression methods, the proposed model has better compression and reconstruction accuracy. At the same time, the experimental results show that the model has good fault detection performance and anti-noise ability. When reconstructing data, the model can effectively avoid fault and noise data.

A robust uncertainty-aware cluster-based deployment approach for WSNs: Coverage, connectivity, and lifespan

Predicting the expected performance of Wireless Sensor Networks (WSNs) is the key to their successful deployment. This paper investigates the following fundamental problem: how to judiciously plan the physical and the logical topologies of a WSN so that performance demands including network connectivity, sensing coverage quality, reliability, and lifetime are all satisfied with the least possible cost. To handle the uncertainty related to sensor connectivity and coverage, we devise a probabilistic-based communication cost model, and we exploit the belief functions theory to define a generic evidence fusion scheme that captures several characteristics of real-world applications. The uncertainty-aware cluster-based WSNs deployment problem is formulated as a multi-objective binary nonlinear and non-convex optimization problem, and an efficient heuristic using genetic algorithms is investigated. Using both simulations and testbed-based experiments, we show that the proposed deployment approach can fulfill the user performance needs, which confirms that the deployment of real-world fusion-based WSNs with predictable performance is possible.

Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks.

For monitoring the aquaculture parameters in pond with wireless sensor networks (WSN), high accuracy of fault detection and high precision of error correction are essential. However, collecting accurate data from WSN to server or cloud is a bottleneck because of the data faults of WSN, especially in aquaculture applications, limits their further development. When the data fault occurs, data fusion mechanism can help to obtain corrected data to replace abnormal one. In this paper, we propose a data fusion method using a novel function that is Dynamic Time Warping time series strategy improved support degree (DTWS-ISD) for enhancing data quality, which employs a Dynamic Time Warping (DTW) time series segmentation strategy to the improved support degree (ISD) function. We use the DTW distance to replace Euclidean distance, which can explore the continuity and fuzziness of data streams, and the time series segmentation strategy is adopted to reduce the computation dimension of DTW algorithm. Unlike Gauss support function, ISD function obtains mutual support degree of sensors without the exponent calculation. Several experiments were finished to evaluate the accuracy and efficiency of DTWS-ISD with different performance metrics. The experimental results demonstrated that DTWS-ISD achieved better fusion precision than three existing functions in a real-world WSN water quality monitoring application.