A Distributed Trust Based Secure Communication Framework for Wireless Sensor Network

Wireless sensor networks (WSNs) consist of many sensor nodes that are densely deployed throughout a randomized geographical area to monitor, detect, and analyze various physical phenomena. The primary obstacle encountered in WSNs pertains to the significant reliance of sensor nodes on finite battery power for wireless data transfer. Sensors as a crucial element inside Cyber-Physical Systems (CPS) renders them vulnerable to failures arising from intricate surroundings, substandard manufacturing, and the passage of time. Various anomalies can appear within WSNs, mostly attributed to defects such as hardware and software malfunctions and anomalies and assaults initiated by unauthorized individuals. These anomalies significantly impact the overall integrity and completeness of the data gathered by the networks. Therefore, it is imperative to provide a critical mechanism for the early detection of faults, even in the presence of constraints imposed by the sensor nodes. Machine Learning (ML) techniques encompass a range of approaches that may be employed to identify and diagnose sensor node faults inside a network. This paper presents a novel Energy-aware and Context-aware fault detection framework (ECFDF) that utilizes the Extra-Trees algorithm (ETA) for fault detection in WSNs. To assess the effectiveness of the suggested methodology for identifying context-aware faults (CAF), a simulated WSN scenario is created. This scenario consists of data from humidity and temperature sensors and is designed to emulate severe low-intensity problems. This study examines six often-seen categories of sensor fault, including drift, hard-over/bias, spike, erratic/precision, stuck, and data loss. The ECFDF approach utilizes an Energy-Efficient Fuzzy Logic Adaptive Clustering Hierarchy (EE-FLACH) algorithm to select a Super Cluster Head (SCH) within WSNs. The SCH is responsible for achieving optimal energy consumption within the network, and this selection process facilitates the early detection of faults. The results of the simulation indicate that the ECFDF technique has superior performance in terms of Fault Detection Accuracy (FDA), False-Positive Rate (FPR), and Mean Residual Energy (MRE) when compared to other detection and classification methods.

Precision agriculture and agricultural decision making assistance have attracted researchers of engineering and agronomy as well as industries as a promising application of wireless sensor network (WSN) technology. However, agriculture is an industry with severe cost-cutting pressure and there is little advance guarantee that the WSN increases profit of farmers. Compared with other applications of the WSN such as power grid, civil engineering, etc., investment to the WSN cannot be expected so much. Nevertheless, if the problem of the cost is resolved, the WSN can be used with wide variety in the farm. The ready-made system satisfying requirements and constraints fully and generally of different farmers increases size and complexity of the system and finally the required investment. On the other hand, the full custom-made system for the individual farmer is not practical. This paper presents a concept of generative WSN framework enabling generation of semi custom-made WSN systems that satisfies requirements and constraints of individual farmers within a prescribed scope and also its fundamental technologies. The framework introduces a paradigm of software product line and manages core assets which are basis of the WSN system to be generated. Two tiered feature modeling, variation point mechanisms for hardware, and integer programming based optimization of system configuration are additionally developed technologies to realize the generative WSN framework.

Outlier detection is an important task for intrusion detection and fault diagnosis in wireless sensor networks (WSNs). Outliers in sensed data may be caused due to compromised or malfunctioning sensor nodes. In this paper, we propose a centralized and a distributed approach based on the principal component analysis (PCA) for outlier detection in WSNs. In the distributed approach, we partition the network into multiple groups of sensor nodes. Each group has a group head and several member nodes. Every member node uses a fixed- width clustering algorithm and sends a description of its local sensed data to the group head. The group head then applies a distributed PCA to establish a global normal pattern and detect outliers. This pattern is periodi- cally updated using weighted coefficients. We compare the performance of the centralized and distributed approaches based on the real sensed data collected by 54 Mica2Dot sensors deployed in Intel Berkeley Re- search Lab. The experimental results show that the distributed approach reduces both communication over- head and energy consumption, while achieving comparable accuracy.

Rare events detection is one of the main applications in Wireless Sensor Networks (WSN) and is currently a central concern of a vast literature. Compressed Sensing (CS) theory has been proved to be quite adapted to this objective. Although this is not the first work on applying CS to sparse events detection in WSN, it is the first to highly justify the validity of the targets detection and counting problem formulation. In order to enhance the CS recovery capacity in WSN, this work considers an approach based on a coherence reduction of the sensing matrix premised on the transmitted power control (PC). Simulation results prove that, under the constraint of equal power consumption, the detection and counting performance is improved when the proposed power control scheme is employed compared to the case without PC.

Designing hardware for wireless sensor network (WSN) systems is a time consuming process. Quite often new systems are designed from scratch and there is lack of support for the design, prototyping and debugging beyond the CAD tools. Therefore, many WSN systems are based on available platforms such as MicaZ, TelosB, EPIC [1] [7] [4]. Although these platforms offer a degree of flexibility, the hardware setup is limited to the predesigned sensor or extension modules. However, many WSN applications have specific requirements regarding the sensing types and fidelity while are very sensitive to the current draw by these sensors and waste by the unused components. Therefore, we propose a WSN hardware prototyping test bench EdiMote developed with the flexibility of prototyping, performance monitoring and hardware and software debug assistance in mind (Figure 1).

Nowadays, the efficiency of multiagent systems in wireless sensor networks prompts the researchers to use these emerging mobile software packets in different simulated approaches or real-world applications. Heterogeneous and distributed wireless sensor networks could be integrated with the multiagent systems to map the real-world challenges into the artificial intelligence world. The multiagent systems have been applied from simulated approaches like object detection/tracking, healthcare, control/assistant, and security systems to real-world applications, including medical/human-care systems and unmanned aerial vehicles. Furthermore, the integration of wireless sensor networks with multiagent systems have emerged novel application, which is known as mobile robots. However, the extensive use of mobile agents in wireless sensor networks has posed different challenges for researchers, including security, resource, and timing limitation. In this work, we review recent simulated approaches and real-world applications of multiagent systems in wireless sensor networks, in which a set of common factors about the things that have been studied are extracted and compared to analyze the performance of mobile agent–based systems in the wireless sensor networks, as well. This analysis provides new research directions about multiagent systems in wireless sensor networks for interested researchers. Finally, a novel framework for dealing with the challenges of multiagent-based applications in the wireless sensor networks which have been mentioned is suggested.

Security problems have become obstacles in the practical application of wireless sensor networks (WSNs), and intrusion detection is the second line of defense. In this paper, an intrusion detection based on dynamic state context and hierarchical trust in WSNs is proposed, which is flexible and suitable for constantly changing WSNs characterized by changes in the perceptual environment, transitions of states of nodes, and variations in trust value. A multidimensional two-tier hierarchical trust mechanism in the level of sensor nodes (SNs) and cluster heads (CHs) considering interactive trust, honesty trust, and content trust is put forward, which combines direct evaluation and feedback-based evaluation in the fixed hop range. This means that the trust of SNs is evaluated by CHs, and the trust of CHs is evaluated by neighbor CHs and BS; in this way, the complexity of evaluation is reduced without evaluations by all other CHs in networks. Meanwhile, the intrusion detection mechanism based on a self-adaptive dynamic trust threshold is described, which improves the flexibility and applicability and is suitable for cluster-based WSNs. The experiment simulation and evaluation indicate that the mechanism we proposed outperforms the existing typical system in malicious detection and resource overhead.

Data aggregation processes aim to reduce the amount of exchanged data in wireless sensor networks and consequently minimize the packet overhead and optimize energy efficiency. Securing the data aggregation process is a real challenge since the aggregation nodes must access the relayed data to apply the aggregation functions. The data aggregation security problem has been widely addressed in classical homogeneous wireless sensor networks, however, most of the proposed security protocols cannot guarantee a high level of security since the sensor node resources are limited. Heterogeneous wireless sensor networks have recently emerged as a new wireless sensor network category which expands the sensor nodes’ resources and capabilities. These new kinds of WSNs have opened new research opportunities where security represents a most attractive area. Indeed, robust and high security level algorithms can be used to secure the data aggregation at the heterogeneous aggregation nodes which is impossible in classical homogeneous WSNs. Contrary to the homogeneous sensor networks, the data aggregation security problem is still not sufficiently covered and the proposed data aggregation security protocols are numberless. To address this recent research area, this paper describes the data aggregation security problem in heterogeneous wireless sensor networks and surveys a few proposed security protocols. A classification and evaluation of the existing protocols is also introduced based on the adopted data aggregation security approach.

Many wireless sensor network (WSN) applications require privacy of the sampled data during transmission from the source nodes to a data collecting device, say a query server. Providing an efficient data aggregation scheme with preserving data privacy is a challenging problem in WSNs. Although the secure data aggregation in WSNs has been well studied in the recent years, there exists a little work, for instance PDA (Privacy-preserving Data Aggregation), which focuses on protecting sensor data not only from adversaries but also from the participating trusted sensor nodes. However, PDA suffers from two problems. The first one is high communication cost due to unnecessary traffics in the network during data transmissions. The second one is high computation cost due to the use of expensive technique to customize sensor data. To resolve the problems, we, in this paper, propose a new private data aggregation scheme for WSNs. The proposed scheme applies the additive property of complex numbers in order to combine sensor data and preserve data privacy during transmissions to the query server. We show from our analytical performance evaluations that our scheme is more efficient than the PDA scheme in terms of both communication and computation costs.

Over the past decades, many WSN (Wireless Sensor Network) systems have emerged. Today, the WSN systems with different technologies have started to become parts of Internet of Things (IoT) and Cyber-Physical Systems(CPSs). This situation has also led to the emergence of heterogeneous and autonomous WSN structures. In these types of complex projects, involving different WSN systems, it is necessary to well define the structure of WSN heterogeneity in order to perform some important operations such as interoperability and resource-sharing. To this end, the paper presents a WSN heterogeneity classification model. In the classification, WSN heterogeneity is addressed from a general perspective. The classification considers not only the heterogeneity within a WSN but also the heterogeneity which can be occurred in the scope of IoT/CPS projects. As far as is known, this type of classification on WSN heterogeneity has been proposed for the first time in literature.

Intrusion detection in wireless sensor networks is crucial for ensuring network security. This study focuses on the problem of estimating the number of barriers necessary for effective intrusion detection in WSNs. The aim is to make accurate predictions to improve security optimization in WSNs. To this end, various regression models (Linear Regression, Ridge and Lasso Regression, Random Forest, Support Vector and Gradient Boosting) were applied on a dataset including parameters such as field size, sensing range, transmission range, and the number of sensor nodes. The performance of the models was evaluated with metrics such as R2, RMSE, MAE, and MSE, and validated with 5-fold cross-validation. The results show that the Linear Regression model achieved the best performance with the lowest error values (RMSE 0.0181, MAE 0.0136, and MSE 0.0003), followed closely by Ridge Regression. These findings highlight the effectiveness of simple linear models in accurately predicting barrier requirements, supporting the optimization of WSN security systems

Intrusion detection in Wireless Sensor Networks (WSNs) is an emerging area of research, given their extensive use in sensitive fields like military surveillance, healthcare, environmental monitoring, and smart cities. However, WSNs face several security challenges due to their limited computational capabilities and energy constraints. Their deployment in open, unattended environments makes them especially vulnerable to threats like eavesdropping, interference, and jamming. To address this problem, Random Forest (RF) is a popular machine learning model. The RF model can be tweaked because of its multiple hyperparameters. Tuning these parameters manually is tedious, as the combinations will be exponential. This work presents an enhanced intrusion detection approach by integrating Tabu Search (TS) optimization with a RF classifier. As a result, TS will help RF automatically search optimal hyperparameters and improve the generalization ability. This work integrates the pros of TS with RF. The model was tested on three different datasets, i.e., (a) the WSN-DS dataset, (b) CICIDS 2017, and (c) the CIC-IoT 2023 dataset, which shows better results on different metrics like precision, recall, F1-score, Cohen’s Kappa, and ROC AUC. Detection of Blackhole and Gray Hole attacks also improved, demonstrating the effectiveness of combining metaheuristic optimization with ensemble learning for stronger WSN security.

Outliers can contaminate the communication and measurement processes of many wireless sensor networks (WSNs) systems, which may be induced by environmental disturbances, model uncertainties or mismatches, sensor faults or errors, subnetwork faults, or malicious cyberattacks. Once the distributed Kalman filter (DKF) is used in such WSN systems with outliers for distributed state estimation, the estimation performance can be seriously degraded in each sensor node. To address this problem, this article proposes mean-shift-based outliers screening and fuzzy-trust-based information fusion mechanisms to modify the DKF toward improving resilience and enhancing robustness against outliers. The proposed mechanism is capable of screening out the outliers-contaminated local measurements and estimates and adaptively narrowing the outliers-contaminated information pairs without modifying the original local state estimates to guarantee the fused estimation consistency and accuracy. This design leads to the development of mean-shift-based outliers-robust DKF (MSRDKF) for the state estimation over WSN systems in a distributed manner. It is proved that our proposed MSRDKF can generate mean-square-bounded-error estimation in each sensor node under bounded outlier disturbances, system collective observability, and network connectivity, and the upper bound of estimation error increases with the occurrence probability of outliers. Simulation and experimental results demonstrate the practical validity and superiority of the proposed MSRDKF in suppressing the effects of outliers, as evidenced by significantly lower root-mean-square-error (RMSE) and average-RMSE (ARMSE) compared to the DKF.

Trust and reputation systems have been regarded as a powerful tool to defend against insider attacks caused by the captured nodes in wireless sensor networks (WSNs). However, trust systems are vulnerable to on-off attacks, in which malicious nodes can opportunistically behave good or bad, compromising the network with the hope that bad behavior will be undetected. Thus, malicious nodes can remain trusted while behaving badly. In this paper, we propose O2Trust, On-Off attack mitigation for Trust systems in wireless sensor networks. O2Trust adopts the penalty policy against the misbehavior history of each node in the network as a reliable factor that should influence on the calculation of the trust value. This punishment future helps to perceive malicious node that aim to launch intelligent attacks against trust-establishment and consequently on-off attack is mitigated efficiently.

Wireless sensor networks cannot be secured against internal attacker with only cryptographic techniques because their nodes are not physically protected and can be easily captured by the attacker. Therefore, in this work we consider intrusion detection systems that monitor behavior of sensor nodes and detect the malicious ones among them. Our work is exploratory in that we propose metrics for evaluation of these systems. There are evaluation metrics for intrusion detection systems for conventional wired and wireless networks. However, to our best knowledge there is no work that proposes evaluation metrics for intrusion detection systems for wireless sensor networks. We divide the proposed metrics into two groups. In the first group there are metrics for detection techniques without a response mechanism. In the second group there are metrics for detection techniques together with a response mechanism. The proposed metrics will help an administrator of a network to choose the best intrusion detection system from a set of systems or to optimize a configuration of a certain intrusion detection system for a given network with a particular topology, sensor nodes capabilities and anticipated types of attack.