Robust estimation of incorrect data using relative correlation clustering technique in wireless sensor networks

Random deployment, the absence of central authority, and the autonomous nature of the network make wireless sensor networks (WSNs) prone to security threats. Security, bandwidth, poor connectivity, intrusion, energy constraints, and other challenges are critical and could affect the performance of the WSN while considering the energy-efficient and secure routing protocols in WSNs. Security threats to WSNs are gradually being expanded. Thus, to improve the network’s performance, detection of anomalies (malicious and suspicious nodes, redundant data, bad connections, etc.) is important. This paper is aimed at introducing the malicious node detection algorithm based on the DBSCAN algorithm, which is a density-based unsupervised learning method for enabling wireless sensor networks to be much more secure and reliable. The prime objective of this algorithm is to develop a routing algorithm capable of detecting malicious nodes and having a prolonged network lifespan and higher stability period. Clustering and classification are two well-known methods in the field of machine learning that can be successfully used in various domains. Density-based clustering is a popular and extensively used approach in various domains. The DBSCAN is the utmost popular and best-known density-based clustering algorithm and is capable of determining arbitrary-shaped clusters. This paper addresses the two anomalies in the WSN, namely, spatial redundancy and malicious node identification. In this article, an algorithm has been suggested to reduce redundant data transmission along with the identification of suspicious nodes to conserve energy and to avoid falsification of data through malicious nodes. The analysis of simulation results and comparison of other algorithms that are in the same class shows that the SDBMND performs significantly better than EAMMH, TEEN, IC-ACO, and LEACH in dense networks.

In wireless sensor networks (WSNs), ensuring secure data transmission while preventing malicious activity is a critical challenge. This paper presents a novel approach for the identification of malicious nodes in WSNs by integrating directed acyclic graphs (DAGs) with the RC4 encryption algorithm. DAGs are employed to establish a hierarchical structure that enables efficient data flow and tracking of communication patterns across the network. By utilizing DAGs, the system can monitor the consistency and integrity of data transmission, making it easier to detect anomalies caused by malicious nodes. The RC4 encryption algorithm further strengthens the approach by securing the communication between nodes, preventing unauthorized access and tampering. In combination, DAGs and RC4 provide a robust framework for both detecting malicious nodes and securing data exchanges. Experimental simulations demonstrate that the proposed approach enhances network security by identifying compromised nodes with high accuracy while maintaining efficient communication and low overhead. This method offers a scalable and secure solution for protecting WSNs from malicious threats.

Wireless Sensor Networks (WSN) are often deployed to sample the desired environmental attributes and deliver the acquired samples to the sink for processing, analysis or simulations as per the application needs. Many applications stipulate high granularity and data accuracy that results in high data volumes. Sensor nodes are battery powered and sending the requested large amount of data rapidly depletes their energy. Fortunately, the environmental attributes (e.g., temperature, pressure) often exhibit spatial and temporal correlations. Moreover, a large class of applications such as scientific measurement and forensics tolerate high latencies for sensor data collection. Accordingly, we develop a fully distributed adaptive technique for spatial and temporal in-network data compression with accuracy guarantees. We exploit the spatio-temporal correlation of sensor readings while benefiting from possible data delivery latency tolerance to further minimize the amount of data to be transported to the sink. Using real data, we demonstrate that our proposed scheme can provide significant communication/energy savings without sacrificing the accuracy of collected data. In our simulations, we achieved data compression of up to 95% on the raw data requiring around 5% of the original data to be transported to the sink.

Personal health status is becoming a matter of concern while pressure of work growing gradually in everyday life, in the meantime, the ageing population needs more attention. Thanks to many challenging problems of wireless sensor networks (WSNs) solved, exploring work of human body monitoring based on WSNs achieved great progress, we called it wireless body sensor networks(BSNs). In this paper, we propose a novel health-monitoring system based on BSNs; we named it SENS-U. It consists of wireless multi-parameters monitoring terminals and two medical care centers; with human body, they form a remote human in loop 4 vital signs and ECG monitoring system.

Localization with low cost and high accuracy is of utmost importance for most applications in wireless sensor networks (WSNs). Most existing range-free schemes have limitations in one or more areas, such as low accuracy, low scalability and threat from the malicious nodes. Secure sensor localization in WSNs has received considerable attention with the rise of Internet of things. In this paper, we propose an efficient and secure range-free localization scheme based on the outlier elimination and the vector refinement process. An outlier elimination method is introduced to the localization process by filtering inaccurate beacon nodes in the presence of the malicious beacon nodes, which can prevent the location attacks and improve the localization accuracy. A vector refinement feedback process is introduced to further improve the localization accuracy. Parameters that affect the localization accuracy are discussed. Simulation and experiment are carried out to evaluate the performance of the proposed outlier elimination vector refinement DV-hop with the presence of the malicious beacon nodes. All the results show that the localization accuracy is improved significantly compared to the DV-hop algorithm and the vector refinement DV-hop algorithm, for both of the cases of with and without the malicious beacon nodes.

Wireless Sensor Network (WSN) is a promising approach that is developed for a wide range of applications due to its low installation cost. However, the nodes in the WSN are susceptible to different security threats, because these nodes are located in hostile or harsh environments. Moreover, an inappropriate selection of routing path affects the data delivery of the WSN. The important goal of this paper is to obtain secure data transmission while minimizing energy consumption. In this paper, Trust Centric - Multiobjective Black Widow Optimization (TC-MBWO) is proposed for selection of Secure Cluster Head (SCH) from the large-scale WSN. Moreover, the secure routing path is generated by using the TC-MBWO, in which the factors considered for the cost function are: residual energy, distance, trust and node degree. Therefore, the secured clustering and routing achieved by using TC-MBWO, provides the resistance against malicious nodes and simultaneously the energy consumption is also minimized by identifying the shortest path. The proposed TC-MBWO method is analyzed in terms of alive nodes, dead nodes, energy consumption, throughput, and network lifetime. Here, the TC-MBWO method is compared with different existing methods such as Low Energy Adaptive Clustering Hierarchy (LEACH), Particle Swarm Optimization - Grey Wolf Optimizer (PSO-GWO), Particle-Water Wave Optimization (P-WWO) and Particle-based Spider Monkey Optimization (P-SMO). The alive nodes of the TC-MBWO are 70 for 2800 rounds which are higher in number when compared to the PSO-GWO, P-WWO and P-SMO.

Proposing a method to solve energy hole problem in wireless sensor networks

<p>Wireless sensor networks (WSNs) are susceptible to a variety of attacks, such as data tampering attacks, blackhole attacks, and grayhole attacks, that can affect the reliability of communication. We proposed a reputationenhanced two-way hybrid algorithm (RCHA) that uses cryptographic hash functions and reputation-based trust management to detect and de-escalate attacks accurately. The RCHA algorithm implements two hash functions RACE integrity primitives’ evaluation message digest (RIPEMD) and secure hash algorithm (SHA-3), to initiate the integrity check for the entire packet sent across the network. Every node in the WSN tracks a reputation score for each neighbor the node is connected to, and this score is dynamically updated based on the behavior of each neighbor. If a neighboring node’s reputation drops below a threshold, the node is sent a maliciousness designation. At that time, the node will broadcast an alert message to its neighboring nodes and begin to reroute its data through one of its trusted neighbors to ensure the reliability of the communication. The simulation results reported that the RCHA algorithm improved the accuracy of the attack detection rate and the number of packets delivered compared to traditional attack detection methods. The RCHA algorithm was able to maintain low computational and energy overhead for the WSN, making it an attractive option for a resource-constrained application in a WSN. Given the trends towards more collaborative networks, the reputation mechanism in the RCHA algorithm improves the overall reliability and capabilities of the WSN, regardless of adversaries.</p>

Limited energy supply (battery-powered) is a crucial problem in wireless sensor networks (WSNs). Sensor node placement schemes and routing protocols are mostly proposed to address this problem. In this paper, we first present how to place sensor nodes by use of a minimal number of them to maximize the coverage area when the communication radius of the sensor node is different from the sensing radius, which results in the application of regular topology to WSNs deployment. With nodes placed at an equal distance and equipped with an equal power supply, the problem of unbalanced energy consumption in 2-D regular topologies becomes more severe and much more difficult to tackle than that in 1-D chains, though the latter is known as an already quite hard problem. We address this problem and propose an adaptive data collection scheme by employing different communication radii for nodes in different locations to balance the energy consumption in WSNs. In order to achieve the ultimate goal of maximizing network lifetime in grid-based WSNs, we give a mathematical formulation, which shows the problem of maximizing network lifetime is a nonlinear programming problem and NP-hard even in the 1-D case. We discuss several heuristic solutions and show that the halving shift data collection scheme is the best solution among them. We also generalize the maximizing network lifetime problem to the randomly-deployed WSNs, which shows the significance of our mathematical formulation for this crucial problem in WSNs.

Wireless sensor networks consist of a large number of low-cost, low-power, and multifunctional sensor nodes that communicate over short distances through wireless links. Such sensor networks are ideal candidates for a wide range of applications such as monitoring of critical infrastructures, data acquisition in hazardous environments, industry control systems, vehicular networks, and military operations. Wireless sensor networks introduce new security challenges due to their dynamic topology, severe resource constraints, and absence of a trusted infrastructure. The purpose of this special issue is to publish high-quality research papers as well as review articles addressing recent advances of wireless sensor network security. In this special issue, we will explore the topic of security challenges and solutions for the wide application of wireless sensor networks in different areas, such as RFID and smart grid. RFID has been widely used in logistics and internet of things. Smart grid leverages sensor networks to better balance the load of the power grid. Both of them have great impact on the daily life and areweak points of thewhole economy,which attract the attention of terrorists. The feasible attacks and defense solutions are still not clear.The papers in this volume also cover several important foundational security services for wireless sensor networks, such as key management, key distribution, secure cluster formation, secure architecture for multihop communication, intrusion prediction, and secure localization. All these foundational security services are critical bases for application of wireless sensor networks. We hope that the papers in this volume engender further thinking about new security challenges for wireless sensor networks and even further “out-of-the-box” ideas about how to solve new security problems for wireless sensor networks.

In a wireless sensor network, the size of radio nodes has direct relation to the cost of total wireless sensor networks, and at the same time, the problem is closely connected to wireless sensor networks' performance, such as robust, fault-tolerance, and further more, it is considered at first as wireless sensor networks are designed. Therefore, the research on the size of radio nodes has significant meanings of theory and practice to design of wireless sensor networks. The paper simplifies complex coverage problem step by step. By means of math modeling, theoretical analysis and formula deducting, classical geometric theories and the method of mathematics induction are adopted, at last, the analysis formula of minimum number of nodes is theoretically educed under circumstances of entire and seamless coverage in WSN. In addition, it is worth mentioning that the conclusion is the full same with computing the number of communication nodes of wireless sensor networks.

Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this article, we aim to study the multi-objective differential evolution and use the improved algorithm to solve a two-objective coverage problem of wireless sensor networks (WSNs). It is found that there are some redundant operations in the classic multi-objective evolution based on the Pareto sorting. Hence, we introduce a sorting method which only handles the highest rank individual in current population. The introduced method sorts the individuals meanwhile chooses some of them into next generation. When the next generation is achieved, the method will finish the evolution of the current population. We incorporate the proposed method into differential evolution to solve the two-objective coverage problem of WSN. We provide the optimization model of this two-objective coverage problem and offer the encoding style of chromosomes. Simulation result shows that the improved algorithm has better performance than the classic multi-objective algorithms.

This paper is a review on energy conservation in wireless sensor networks (WSNs). Due to the nature of wireless sensor nodes in terms of deployment and their common usage in terrains with limited access, recharging or replacing sensor nodes batteries may be difficult. This paper examined various sources of energy in WSNs Battery, energy harvesting and energy transference. Also, various energy usage operations and energy wastage activities in WSNs were examined, and comparisons of different routing protocols based on network structure, energy dissipation, data communication cost, and entire energy usage in WSNs were itemized. The prospects of the machine learning (ML) approach in addressing energy constraint issues in WSNs were reviewed. This paper recommends a compound approach in routing decisions to maximize energy usage operation and minimize energy wastage activities, consideration for energy harvesting and transference mechanisms, and exploring the potentials in ML algorithms to resolve energy problem in wireless sensor networks.

An outlier in the Internet of Things is an immediate change in data induced by a significant difference in the atmosphere (Event) or sensor malfunction (Error). Outliers in the data cause the decision-maker to make incorrect judgments about data analysis. Hence it is essential to detect outliers in any discipline. The detection of outliers becomes the most crucial task to improve sensor data quality and ensure accuracy, reliability and robustness. In this research, a novel outlier detection technique based on spatial, temporal correlations and attribute correlations is proposed to detect outliers (both Errors and Events). This research uses a correlation measure in the temporal correlation algorithm to determine outliers and the spatial correlation algorithm to classify the outliers, whether the outliers are events or errors. This research uses optimal clusters to improve network lifetime, and malicious nodes were also detected based on spatial–temporal correlations and attribute correlations in these clusters. The experimental results proved that the proposed method in this research outperforms some other models in terms of accuracy against the percentage of outliers infused and detection rate against the false alarm rate.

Wireless Sensor Network (WSN), which finds as one of the major components of modern electronic and wireless systems. A WSN consists of numerous sensor nodes for the discovery of sensor networks to leverage features like data sensing, data processing, and communication. In the field of medical health care, these network plays a very vital role in transmitting highly sensitive data from different geographic regions and collecting this information by the respective network. But the fear of different attacks on health care data typically increases day by day. In a very short period, these attacks may cause adversarial effects to the WSN nodes. Furthermore, the existing Intrusion Detection System (IDS) suffers from the drawbacks of limited resources, low detection rate, and high computational overhead and also increases the false alarm rates in detecting the different attacks. Given the above-mentioned problems, this paper proposes the novel MegaBAT optimized Long Short Term Memory (MBOLT)-IDS for WSNs for the effective detection of different attacks. In the proposed framework, hyperparameters of deep Long Short-Term Memory (LSTM) were optimized by the meta-heuristic megabat algorithm to obtain a low computational overhead and high performance. The experimentations have been carried out using (Wireless Sensor NetworkDetection System)WSN-DS datasets and performance metrics such as accuracy, recall, precision, specificity, and F1-score are calculated and compared with the other existing intelligent IDS. The proposed framework provides outstanding results in detecting the black hole, gray hole, scheduling, flooding attacks and significantly reduces the time complexity, which makes this system suitable for resource-constraint WSNs.