Real Wireless Sensor Networks Research Articles

Hybrid Interval Type-2 Fuzzy AHP and COPRAS-G-based trusted neighbour node Discovery in Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), reliable and rapid neighbour node discovery is considered as the crucial operation which frequently needs to be executed over the entire lifecycle. Several neighbour node discovery mechanisms are proposed for reducing the latency or extending the sensor nodes’ lifetime. But majority of the existing neighbour node discovery mechanisms failed in addressing the critical issues of real WSNs related to energy consumptions, constraints of latency, uncertainty of node behaviors, and communication collisions. In this paper, Hybrid Interval Type-2 Fuzzy Analytical Hierarchical Process (AHP) and Complex Proportional Assessment using Grey Theory (COPRAS-G)-based trusted neighbour node discovery scheme (FAHPCG) is proposed for better data dissemination process. In specific, Interval Type 2 Fuzzy AHP is applied for determining the weight of the evaluation criteria considered for neighbour node discovery, and then Grey COPRAS method is adopted for prioritizing the sensor nodes of the routing path established between the source and destination. It adopted the merits of fuzzy theory for handling the uncertainty and vagueness involved in the change in the behavior of sensor nodes during the process of neighbour discovery. It is proposed with the capability of exploring maximized number of factors that aids in exploring the possible dimensions of sensor nodes packet forwarding potential during the process of neighbour node discovery. The simulation results of the proposed FAHPCG scheme confirmed an improved neighbour node discovery rate of 23.18% and prolonged the sensor nodes lifetime to the maximum of 7.12 times better than the baseline approaches used for investigation.

IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management

The Internet of Things (IoT) has the potential to revolutionize agriculture by providing real-time data on crop and livestock conditions. This study aims to evaluate the performance scalability of wireless sensor networks (WSNs) in agriculture, specifically in two scenarios: monitoring olive tree farms and stables for horse training. The study proposes a new classification approach of IoT in agriculture based on several factors and introduces performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The study utilizes COOJA, a realistic WSN simulator, to model and simulate the performance of the 6LowPAN and Routing protocol for low-power and lossy networks (RPL) in the two farming scenarios. The simulation settings for both fixed and mobile nodes are shared, with the main difference being node mobility. The study characterizes different aspects of the performance requirements in the two farming scenarios by comparing the average power consumption, radio duty cycle, and sensor network graph connectivity degrees. A new approach is proposed to model and simulate moving animals within the COOJA simulator, adopting the random waypoint model (RWP) to represent horse movements. The results show the advantages of using the RPL protocol for routing in mobile and fixed sensor networks, which supports dynamic topologies and improves the overall network performance. The proposed framework is experimentally validated and tested through simulation, demonstrating the suitability of the proposed framework for both fixed and mobile scenarios, providing efficient communication performance and low latency. The results have several practical implications for precision agriculture by providing an efficient monitoring and management solution for agricultural and livestock farms. Overall, this study provides a comprehensive evaluation of the performance scalability of WSNs in the agriculture sector, offering a new classification approach and performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The results demonstrate the suitability of the proposed framework for precision agriculture, providing efficient communication performance and low latency.

A Timestamp-Free Time Synchronization Scheme Based on Reverse Asymmetric Framework for Practical Resource-Constrained Wireless Sensor Networks

Energy-efficient time synchronizations for wireless sensor networks (WSNs) have been put under the spotlight for years. A promising technique among which is the timestamp-free approach where no timestamps are required to establish the synchronization, thereby sparing the transmissions of the timing messages for conserving significant transmission energy. In this paper, we first investigate the feasibility of adopting timestamp-free time synchronization in practical resource-constrained WSNs; we then identify the issue of inaccuracy in maintaining the pre-defined response interval which affects the foundations of most existing timestamp-free schemes. Based on the investigation and our previously proposed reverse asymmetric time synchronization framework, we further propose an asymmetric timestamp-free time synchronization scheme with two estimation methods tailored for resource-constrained WSNs. We as well introduce the centralized and distributed multi-hop extension methods for the proposed scheme to cover diverse multi-hop scenarios. Experimental results on a real WSN testbed consisting of TelosB motes running TinyOS demonstrate that the proposed scheme achieves high energy efficiency while maintaining microsecond-level time synchronization accuracy compared to three other conventional schemes.

SLES: Scheduling-Based Low Energy Synchronization for Industrial Internet of Things

The growth in the Industrial Internet of Things (IIoT) drives the development of large-scale wireless sensor networks (WSN), such as smart cities. Time synchronization is essential and primary for enabling collaborative operations among sensor nodes. Generally, all sensor nodes in the network synchronize through exchanging messages, increasing energy consumption. This study introduces scheduling-based low-energy synchronization (SLES) for IIoT. SLES aims to reduce the number of transmitted messages, which in turn reduces the overall energy consumption. In addition, SLES establishes a schedule of distinct synchronization messaging times for each reference node to alleviate the collision problem among the sensor nodes. SLES is constructed and evaluated using real WSNs with different configurations. The experimental results showed that SLES achieves significantly less message traffic than the previous algorithms: FADS – fast scheduling and accurate drift compensation for time synchronization, EERS – energy-efficient reference node selection, and R-sync – robust time synchronization. In this study, we used a simulation to evaluate the performance of SLES in large-scale networks. For the 4-way grid network, the experiential results show that SLES reduced the number of transmitted messages by approximately 22% compared to EERS and FADS. Moreover, SLES outperformed the R-sync algorithm by a factor of 2.5.

An energy‐efficient technique for mobile‐wireless‐sensor‐network‐based IoT

Wireless sensor networks (WSNs) are one of the basic building blocks of Internet of Things (IoT) systems. However, the wireless sensing nodes in WSNs suffer from energy constraint issues because the replacement/recharging of the batteries of the nodes tends to be difficult. Furthermore, a number of realistic IoT scenarios, such as habitat and battlefield monitoring, contain mobile sensing elements, which makes the energy issues more critical. This research paper focuses on realistic WSN scenarios that involve mobile sensing elements with the aim of mitigating the attendant energy constraint issues using the concept of radio-frequency (RF) energy extraction. The proposed technique incorporates a cluster head election workflow for WSNs that includes mobile sensing elements capable of RF energy harvesting. The extensive simulation analysis demonstrated the higher efficacy of the proposed technique compared with the existing techniques in terms of residual energy, number of functional nodes, and network lifetime, with approximately 50% of the nodes found to be functional at the 4000th, 5000th, and 6000th rounds for the proposed technique with initial energies of 0.25, 0.5 and 1 J, respectively.

Missing and Corrupted Data Recovery in Wireless Sensor Networks Based on Weighted Robust Principal Component Analysis.

Although wireless sensor networks (WSNs) have been widely used, the existence of data loss and corruption caused by poor network conditions, sensor bandwidth, and node failure during transmission greatly affects the credibility of monitoring data. To solve this problem, this paper proposes a weighted robust principal component analysis method to recover the corrupted and missing data in WSNs. By decomposing the original data into a low-rank normal data matrix and a sparse abnormal matrix, the proposed method can identify the abnormal data and avoid the influence of corruption on the reconstruction of normal data. In addition, the low-rankness is constrained by weighted nuclear norm minimization instead of the nuclear norm minimization to preserve the major data components and ensure credible reconstruction data. An alternating direction method of multipliers algorithm is further developed to solve the resultant optimization problem. Experimental results demonstrate that the proposed method outperforms many state-of-the-art methods in terms of recovery accuracy in real WSNs.

Effective Error Control Scheme in Real-Time Wireless Sensor Networks

Wireless Sensor Networks (WSNs) subsist on network of huge numbers of hubs that are distributed in unfriendly environments used for habitat monitoring and to observe changes in phenomena. These hubs are susceptible to faults such as energy exhaustion, hardware glitches, communication link errors, malicious attacks, which may lead to errors in message delivery. However, these systems are typically utilized to convey vital information in remote places. Thus, efficient error identification and correction is thereby required in order to maintain the accuracy of the received message. This paper aimed at designing high speed reverse converter for effective error control codes using Redundant Residue Number System in order to enhance the accuracy of messages delivered for real-time WSNs. In order to attain a greater error correction, a reverse converter which was based on Mixed Radix Conversion was designed for the moduli set {2n+1-1, 2n, 2n-1}. In addition to this moduli, an extra redundant moduli set{2n+1+3, 22n - 3} is also designed for higher correctness. For correction and identification of corrupted values in the original data, maximum likelihood was used. The results obtained show that the suggested moduli set offers low energy utilization and consistency of the received message in real time WSNs.

RODENT: a flexible TOPSIS based routing protocol for multi-technology devices in wireless sensor networks

Wireless Sensor Networks (WSN) are efficient tools for many use cases, such as environmental monitoring. However WSN deployment is sometimes limited by the characteristics of the Radio Access Technologies (RATs) they use. To overcome some of these limitations, we propose to leverage the use of a Multiple Technologies Network (MTN). What we refer to as MTN is a network composed of nodes which are able to use several RAT and communicating wirelessly through multi-hop paths. The management of the RAT and routes must be handled by the nodes themselves, in a local and distributed way, with a
 suitable communication protocol stack. Nodes may reach multiple neighbors over multiple RAT. Therefore, each stack's layer has to take the technologies' heterogeneity of the devices into account. In this article, we introduce our custom Routing Over Different Existing Network Technologies protocol (RODENT), designed for MTN. It enables dynamic (re)selection of the best route and RAT based on the data type and requirements that may evolve over time, potentially mixing each technology over a single path. RODENT relies on a multi-criteria route selection performed with a custom lightweight TOPSIS method. To assess
 RODENT's performances, we implemented a functional prototype on real WSN hardware, Pycom FiPy devices. Unlike related prototypes, ours has the advantage not to rely on specific infrastructure on the operator's side. Results show that RODENT enables energy savings, an increased coverage as well as multiple data requirements support.

NISA: Node Identification and Spoofing Attack Detection Based on Clock Features and Radio Information for Wireless Sensor Networks

Node identification based on unique hardware features like clock skews has been considered an efficient technique in wireless sensor networks (WSNs). Spoofing attacks imitating unique hardware features, however, could significantly impair or break down conventional clock-skew-based node identification due to exposed clock information through broadcasting. To defend against Spoofing attacks, we propose a new node identification scheme called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">node identification against Spoofing attack</i> (NISA). It utilizes the reverse time synchronization framework, where sensor nodes’ clock skews are estimated at the head of a WSN, and the spatially-correlated radio link information to achieve simultaneous node identification and attack detection. We further provide centralized and distributed NISA for covering both single-hop and multi-hop scenarios, the former of which employs a single-input and multiple-output convolutional neural network. With a real WSN testbed consisting of TelosB sensor nodes running TinyOS, we investigate the identifiability of clock skews under temperature and voltage variations and evaluate the performance of both centralized and distributed NISA. Experimental results demonstrate that both centralized and distributed NISA could provide accurate node identification and Spoofing attack detection.

CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning.

Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.

Investigation & Mitigation of the Energy Efficiency Impact of Node Resets in RPL

Dependability is a keyword within the recent discussion about the operation of Wireless Sensor Network (WSN) as a vital part of the ever-growing Internet of Things (IoT). However, while focusing on, e.g., reliable routing it is often neglected that the energy supply is an important factor to enable dependable WSN, and thus IoT operation. Using the example of the Routing Protocol for Low power and Lossy Networks (RPL) we will show that a high operational capability is often at the expense of energy resources. Nevertheless, we will also show that this issue can be mitigated. While the resilience of RPL has been verified in a plethora of existing evaluations, the impact of simple node resets on the energy efficiency has not been investigated so far. However, resets may happen for a variety of reasons which are stated in this work as well. Thus, in the first part of this paper we examine the energy costs of resets in a RPL network. We show that such temporary node failures can have a serious impact on the network’s total energy usage depending on failure location and topology. As resets are common in real WSNs this work reveals an important weakness of RPL which can even be exploited for attacks.To mitigate this issue, in the second part of this paper we propose a hardening of RPL by maintaining a backup of the RPL state information in a non-volatile memory. Improvements as well as limitations of this approach and a mechanism for a seamless recovery are discussed and described in detail. Finally, the effectiveness and the characteristics of the proposed approach to harden RPL are evaluated extensively.

A New Regularized Recursive Dynamic Factor Analysis With Variable Forgetting Factor and Subspace Dimension for Wireless Sensor Networks With Missing Data

The dynamic factor analysis (DFA) is an effective method for reducing the dimension of multivariate time series measurements in wireless sensor networks (WSNs) for prediction, monitoring, and anomaly detection. In large-scale systems, it is crucial to be able to track the time-varying loadings (or subspace) and the underlying factor signals, identify their dimensions, and impute missing or outlier samples, which can arise from transmission loss, hardware failure, and other factors. This article proposes a new regularized recursive DFA algorithm with a variable forgetting factor (FF) and subspace dimension, which is computationally efficient and capable of missing data imputation. A novel multiple deflation (MD) and rank-one-modification-based approach is proposed to recursively estimate the eigenvalues and associated eigenvectors from the progressively extracted subspace via MD. This allows us to update efficiently the subspace dimension and loading (eigen) vectors. By modeling the factor signals as independent univariate autoregressive (AR) processes, one can utilize the forecast value for outlier detection and missing samples' imputation. Furthermore, variable FF and regularization schemes are proposed to improve the tracking capability and reduce the variance of the proposed recursive DFA algorithm. Experimental results using real WSN datasets show that the proposed algorithm achieves better tracking performance and accuracy than other conventional approaches, which may serve as a useful tool for prediction, monitoring, and anomaly detection in WSNs.

Characterization of the Log-Normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks

Using the classical received signal strength (RSS)-distance log-normal model in wireless sensor network (WSN) applications poses a series of characteristic challenges derived from (a) the model’s structural limitations when it comes to explaining real observations, (b) the inherent hardware (HW) variability typically encountered in the low-cost nodes of WSNs, and (c) the inhomogeneity of the deployment environment. The main goal of this article is to better characterize how these factors impact the model parameters, an issue that has received little attention in the literature. For that matter, I qualitatively elaborate on their effects and interplay, and present the results of two quantitative empirical studies showing how much the parameters can vary depending on (a) the nodes used in the model identification and their position in the environment, and (b) the antenna directionality. I further show that the path loss exponent and the reference power can be highly correlated. In view of all this, I argue that real WSN deployments are better represented by random model parameters jointly accounting for HW and local environmental characteristics, rather than by deterministic independent ones. I further argue that taking this variability into account results in more realistic models and plausible results derived from their usage. The article contains example values of the mean and standard deviation of the model parameters, and of the correlation between the path loss exponent and the reference power. These can be used as a guideline in other studies. Given the sensitivity of localization algorithms to the proper model selection and identification demonstrated in the literature, the structural limitations of the log-normal model, the variability of its parameters and their interrelation are all relevant aspects that practitioners need to be aware of when devising optimal localization algorithms for real WSNs that rely on this popular model.

SPREAD - A Routing Protocol to Meet End-to-end Adjusted Deadline in Real Time Wireless Sensor Networks

Background: Real Time Wireless Sensor Networks (RT-WSN) have hard real time packet delivery requirements. Due to resource constraints of sensors, these networks need to trade-off energy and latency. Objective: In this paper, a routing protocol for RT-WSN named “SPREAD” has been proposed. The underlying idea is to reserve laxity by assuming tighter packet deadline than actual. This reserved laxity is used when no deadline-meeting next hop is available. Objective: As a result, if due to repeated transmissions, energy of nodes on shortest path is drained out, then time is still left to route the packet dynamically through other path without missing the deadline. Results: Congestion scenarios have been addressed by dynamically assessing 1-hop delays and avoiding traffic on congested paths. Conclusion: Through extensive simulations in Network Simulator NS2, it has been observed that SPREAD algorithm not only significantly reduces miss ratio as compared to other similar protocols but also keeps energy consumption under control. It also shows more resilience towards high data rate and tight deadlines than existing popular protocols.

Auto-Configuration in Wireless Sensor Networks: A Review.

Wireless sensor network (WSN) studies have been carried out for multiple years. At this stage, many real WSNs have been deployed. Therefore, configuration and updating are critical issues. In this paper, we discuss the issues of configuring and updating a wireless sensor network (WSN). Due to a large number of sensor nodes, in addition to the limited resources of each node, manual configuring turns out to be impossible. Therefore, various auto-configuration approaches have been proposed to address the above challenges. In this survey, we present a comprehensive review of auto-configuration mechanisms with the taxonomy of classifications of the existing studies. For each category, we discuss and compare the advantages and disadvantages of related schemes. Lastly, future works are discussed for the remaining issues in this topic.

Lossy Compression of Noisy Sparse Sources Based on Syndrome Encoding

Data originating from devices and sensors in Internet of Things scenarios can often be modeled as sparse signals. In this paper, we provide new source compression schemes for noisy sparse and non-strictly sparse sources, based on channel coding theory. Specifically, nonlinear excision filtering by means of model order selection or thresholding is first used to detect the support of the non-zero elements of sparse vectors in noise. Then, the sparse sources are quantized and compressed using syndrome-based encoders. The theoretical performance of the schemes is provided, accounting for the uncertainty in the support estimation. In particular, we derive the operational distortion-rate and operational distortion-energy of the encoders for noisy Bernoulli-uniform and Bernoulli-Gaussian sparse sources. It is found that the performance of the proposed encoders approaches the information-theoretic bounds for sources with low sparsity order. As a case study, the proposed encoders are used to compress signals gathered from a real wireless sensor network for environmental monitoring.

An Event-Aware Cluster-Head Rotation Algorithm for Extending Lifetime of Wireless Sensor Network with Smart Nodes.

Smart sensor nodes can process data collected from sensors, make decisions, and recognize relevant events based on the sensed information before sharing it with other nodes. In wireless sensor networks, the smart sensor nodes are usually grouped in clusters for effective cooperation. One sensor node in each cluster must act as a cluster head. The cluster head depletes its energy resources faster than the other nodes. Thus, the cluster-head role must be periodically reassigned (rotated) to different sensor nodes to achieve a long lifetime of wireless sensor network. This paper introduces a method for extending the lifetime of the wireless sensor networks with smart nodes. The proposed method combines a new algorithm for rotating the cluster-head role among sensor nodes with suppression of unnecessary data transmissions. It enables effective control of the cluster-head rotation based on expected energy consumption of sensor nodes. The energy consumption is estimated using a lightweight model, which takes into account transmission probabilities. This method was implemented in a prototype of wireless sensor network. During experimental evaluation of the new method, detailed measurements of lifetime and energy consumption were conducted for a real wireless sensor network. Results of these realistic experiments have revealed that the lifetime of the sensor network is extended when using the proposed method in comparison with state-of-the-art cluster-head rotation algorithms.

Optimal Message Bundling with Delay and Synchronization Constraints in Wireless Sensor Networks.

Energy efficiency and end-to-end delay are two of the major requirements for the monitoring and detection applications based on resource-constrained wireless sensor networks (WSNs). As new advanced technologies for accurate monitoring and detection—such as device-free wireless sensing schemes for human activity and gesture recognition—have been developed, time synchronization accuracy becomes an important requirement for those WSN applications too. Message bundling is considered one of the effective methods to reduce the energy consumption for message transmissions in WSNs, but bundling more messages increases the transmission interval of bundled messages and thereby their end-to-end delays; the end-to-end delays need to be maintained within a certain value for time-sensitive applications like factory monitoring and disaster prevention, while the message transmission interval affects time synchronization accuracy when the bundling includes synchronization messages as well. Taking as an example a novel WSN time synchronization scheme recently proposed for energy efficiency, we investigate an optimal approach for message bundling to reduce the number of message transmissions while maintaining the user-defined requirements on end-to-end delay and time synchronization accuracy. Formulating the optimal message bundling problem as integer linear programming, we compute a set of optimal bundling numbers for the sensor nodes to constrain their link-level delays, thereby achieving and maintaining the required end-to-end delay and synchronization accuracy. Extensive experimental results based on a real WSN testbed using TelosB sensor nodes demonstrate that the proposed optimal bundling could reduce the number of message transmissions about 70% while simultaneously maintaining the required end-to-end delay and time synchronization accuracy.

Ant Colony Based Data Aggregation in Real Time Wireless Sensor Networks

Sensing the natural phenomenon, sensing the eventual activities, sensing the abrupt change in human developed systems has a wide number of applications. To sense, one needs the tiny sensor devices called sensor nodes. To collect the data from these devices, one has to arrange these tiny devices into a intelligently managed network, called as sensor networks. Due to mostly unattended application areas and zones, these devices are supposed to be wireless which adds one more dimension to our discussion, known as wireless sensor networks. So basically these are an intelligent arrangement of small, tiny sensing devices with wide array of attributes, namely, sensing ability, low communication range mechanism, limited power sources, small processing ability, small memory. With these limitations, even handling a small data generated/sensed in the network becomes crucial to handle. Therefore there has to be an effective data aggregation technique. The prime goal of any data aggregation algorithms is to collect/retrieve/gather the data in an efficient possible way so that the effort to communicate the data over the network can be reduced. Less work from the network means less energy dissipated, thus lifetime can be enhanced. An effective data aggregation technique reduces the latency also. Small network latency is crucial for time critical applications in real time wireless sensor networks. Ant colony based optimization provides us such a possibility to find an optimal route for efficient data aggregation with significantly reduced network latency. In our work, Ant colony based Data Aggregation in Real Time wireless sensor networks (ACDAR), Ants use their pheromones to establish a new path or follow already established path by other ants. Similarly data packets in wireless sensor networks can take advantage of freshly established paths by Route Ants. As the results received, it is verified that number of packets successfully delivered to the sink have increased and the miss ratio, cumulative packets consumption have improved, network latency has decreased.

An Improved Cluster Head Selection in Routing for Solar Energy-Harvesting Multi-heterogeneous Wireless Sensor Networks

Wireless sensor network (WSN) is an effective and efficient technology for field information collection in Internet of Things arena. Generally, the lifetime of any WSN is restricted due to the limited battery capacities available with its sensor nodes. Replenishing the nodes’ batteries through energy-harvesting is becoming popular nowadays for improving the lifetime of the WSNs. Wireless communication activities of the sensor nodes take a major chunk of battery’s energy during WSN operations and to optimize the energy dissipation, energy-efficiency is given prime importance in routing decisions. The WSN heterogeneity (e.g., sensor nodes with heterogeneous sensing requirements, nodes with different energies, etc.) has become unavoidable and its effective exploitation further complicates the routing challenges. To fulfill the requirements of a realistic WSN system, this paper considers a multi-heterogeneity WSN scenario with sensors nodes having different initial energies and different traffic requirements along with solar energy-harvesting capabilities. An improved cluster-head selection based routing algorithm is proposed for the scenario, which exploits effectively the WSN heterogeneities in terms of energy, traffic and energy-harvesting. To highlight the performance of the proposed algorithm, the system is considered non energy-neutral, i.e. the energy dissipation of the system is higher than the system’s harvesting energy over a longer time period. The proposed algorithm, Energy-Harvesting, Traffic and Energy Aware Routing, improves the WSN stability period over existing routing algorithms under the scenario, where the stability period signifies the WSN lifetime till all the nodes are alive and represents the most reliable period of an operational WSN.