Classification approach to avoid link failures in wireless sensor networks in mobile virtual communities and teleworking

For the past few years, centralized decision-making is being used for malicious node identification in wireless sensor networks (WSNs). Generally, WSN is the primary technology used to support operations, and security issues are becoming progressively worse. In order to detect malicious nodes in WSN, a blockchain-routing- and trust-model-based jellyfish search optimizer (BCR-TM-JSO) is created. Additionally, it provides the complete trust-model architecture before creating the blockchain data structure that is used to identify malicious nodes. For further analysis, sensor nodes in a WSN collect environmental data and communicate them to the cluster heads (CHs). JSO is created to address this issue by replacing CHs with regular nodes based on the maximum remaining energy, degree, and closeness to base station. Moreover, the Rivest–Shamir–Adleman (RSA) mechanism provides an asymmetric key, which is exploited for securing data transmission. The simulation outcomes show that the proposed BCR-TM-JSO model is capable of identifying malicious nodes in WSNs. Furthermore, the proposed BCR-TM-JSO method outperformed the conventional blockchain-based secure routing and trust management (BSRTM) and distance degree residual-energy-based low-energy adaptive clustering hierarchy (DDR-LEACH), in terms of throughput (5.89 Mbps), residual energy (0.079 J), and packet-delivery ratio (89.29%).

Due to their decentralized and autonomous nature, Wireless Sensor Networks (WSNs) have been widely deployed, and this makes both efficient collective sensing and data processing possible. In particular, these networks must be secure in terms of their ability to withstand different attacks. WSNs are particularly sensitive to various forms of attack in hostile environments. This paper aims to provide a novel design based on blockchain technology for detecting and isolating malicious nodes in WSNs. In WSNs, malicious activities can have eco-centric consequences in addition to spreading false data and disrupting normal operations. Therefore, an urgent need arises for Intrusion Detection Systems (IDS) that require few resources, are effective, and are able to operate within the computational abilities of WSN nodes. The method we propose surpasses current IDS techniques by reducing the requirements for communication and computation. Above all, in comparison to traditional blockchain frameworks. The objective is to detect and eliminate compromised nodes, which can seriously affect network performance and cause sensor data to be misreported for longer periods. Chaos theory and a simplified algorithm are used to generate a validation hash. Node verification made easier by chaos theory generates a hash for verification. The simulation results confirm that our approach can accurately discover compromised nodes while optimizing resource utilization.

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 Network (WSN) suffers from different malware attacks. Several traditional approaches are proposed for detecting fault nodes in WSN. It is necessary to redistribute the nodes and detect the fault nodes while modifying the WSN parameters. Faults can occur in the nodes interrupting the continuous communication process of the nodes in the WSN. The main reason for a fault is also the induction of the energy drain in a node to the maximum possible level, failure of links in communicating nodes due to bandwidth constraints, and attacks induced by the malicious nodes. To solve this problems, Extended Finite State Machine based Fault Tolerance (EFSM_FT) in the WSN is used to detect the fault sensor nodes in the WSN. In this approach, called the finite state machine (FSM), state prompted is modified while a set of situations are true. Every node can be absolutely to be faulty or not establishing on the sensor node states. Using sensor energy, drop rate, forward sensor rate, sensor obtained rate, bandwidth, and transmission delay, this method finds the node with the fault. As a result, this approach accurately detects the faulty nodes in the WSN. The simulation outcomes illustrate the proposed method has a better fault detection ratio and minimizes the false negative ratio in the WSN.

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.

Malicious nodes in wireless sensor networks will cause a great deviation in aggregation data. Such malicious nodes assigned with the aggregation task can forge and send a mass of false data. As a result, the network may consume a mass of resources and users may make wrong decisions. Concerning this problem, a secure data aggregation scheme based on node self-adaptive monitoring is proposed in this paper. According to the scheme, a master cluster head node and a vice cluster head node are selected, wherein, the vice cluster head node verifies the aggregation data produced by the master cluster head, the intra-cluster monitoring nodes monitor the behaviour of the master and vice cluster head nodes in a self-adaptive manner. Besides, the confidence interval theory is introduced to evaluate and calculate the credibility of nodes and highly credible node data are selected for data aggregation. Both theoretical analysis and experimental results show that our scheme can not only effectively recognise and shield malicious nodes, enhance accuracy of data aggregation but also prevent malicious master cluster head nodes from forging and sending a lot of false data.

Blockchain-machine learning fusion for enhanced malicious node detection in wireless sensor networks

Profiling environmental parameter using a large number of spatially distributed wireless sensor network (WSN) NODEs is an extensive illustration of advanced modern technologies, but high power requirement for WSN NODEs limits the widespread deployment of these technologies. Currently, WSN NODEs are extensively powered up using batteries, but the battery has limitation of lifetime, power density, and environmental concerns. To overcome this issue, energy harvester (EH) is developed and presented in this paper. Solar-based EH has been identified as the most viable source of energy to be harvested for autonomous WSN NODEs. Besides, a novel chemical-based EH is reported as the potential secondary source for harvesting energy because of its uninterrupted availability. By integrating both solar-based EH and chemical-based EH, a hybrid energy harvester (HEH) is developed to power up WSN NODEs. Experimental results from the real-time deployment shows that, besides supporting the daily operation of WSN NODE and Router, the developed HEH is capable of producing a surplus of 971 mA·hr equivalent energy to be stored inside the storage for NODE and 528.24 mA·hr equivalent energy for Router, which is significantly enough for perpetual operation of autonomous WSN NODEs used in environmental parameter profiling.

Herein, an Internet of Things (IoT) system with remote reconfigurable wireless sensor network (WSN) nodes is presented and its application to measure group work activity in a classroom is discussed. This IoT system consists of wiki pages, bots, (defined as remotely controlled devices), gateways, and remotely reconfigurable WSN Nodes. The bots/gateways and WSN nodes of the IoT system are controlled by scripts written on wiki pages that are read by the bot/gateways, which then forward the commands contained in the scripts to the WSN nodes. Each WSN node then executes the commands, obtains the resulting sensor values, and then returns those values to the bot/gateways. The bot/gateways relay the values and write them onto the wiki page. Each WSN node is flexible and reconfigurable because its behavior can be changed by the script commands written onto the wiki page. Using this IoT system, we created a measurement system to evaluate activity levels of group work in a classroom. After developing the measurement system, we found it was possible to adjust the sampling rate for obtaining sensor values simply by rewriting the script on the wiki page without directly manipulating the WSN.

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.

A scheme for WSNs (wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper.The sensing tetrahedron is divided into a number of small grids.All those sensor nodes, both ordinary sensor nodes and heterogeneous sensor nodes are distributed in the sensing tetrahedron.In a grid, all ordinary sensor nodes and heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.All the heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.At last, all sensor nodes establish their keys directly or indirectly in the whole sensing tetrahedron.Analysis and comparison demonstrate this scheme enhances the WSN security, has good network connectivity, saves node storage, reduces network computing load, and extends the network lifetime.

Wireless sensor network (WSN) with several sensors is used to group measurements of certain physical quantities or environmental conditions, including sound, temperature, pressure, vibration, motion, or pollution, at various locations and ranges. In WSNs, several protocols address the issue of routing. Sensor nodes in WSN usually have limited energy resources and storage capacities. Therefore, the issue of energy usage in sensors and protocols is very important. This paper analyzes and compares the quality of service (QoS) performances of three important routing protocols of the mobile ad-hoc network (MANET) including Ad-hoc on-demand distance vector (AODV), dynamic source routing (DSR), and destination sequenced distance vector (DSDV), in a smart building case study. The QoS evaluation metrics include residual energy of nodes, instant throughput (IT), average throughput (AT), packet delivery ratio (PDR), packet loss ratio (PLR), and route discovery latency (RDL) based on IEEE 802.15.4 MAC protocol standard. The simulation is carried out using NS2, and the WSN has 16 fixed and 4 mobility nodes with different speeds and paths. The simulation results illustrate that average throughput in AODV is 1.985118 (kbps), however, the figures for DSR and DSDV are 1.977780 and 1.720700 (kbps), respectively. PDR, also, in DSR stands at 1.0, but the figures for AODV and DSDV are lower with the range of 0.999572 and 0.997930, respectively. Overall, The DSR protocol provides a better performance compared to AODV and DSDV routing protocols in terms of PLR and PDR. Also, AODV has better efficiency in RDL and AT compared to other assumed protocols.

In Wireless Sensor Networks (WSNs) the existence of malicious nodes is a serious challenge. Malicious nodes increase the traffic by sending distrusted messages. In this paper, we present CCTF: a congestion control protocol based on trustworthiness of nodes in WSNs using fuzzy logic. Through the use of CCTF, each node reduces malfunctioning neighbour(s) effects via dropping valueless packets and increases the buffer capacity; consequently, the chance of network congestion reduces. Simulation results show that when 16% of all nodes are malicious, CCTF increases the packet delivery ratio up to 40% meanwhile it reduces the packet drop ratio nearly 27.5%.

Wireless sensor networks have gained attention over the last few years and have significant applications for example remote supervising and target watching. They can communicate with each other though wireless interface and configure a network. Wireless sensor networks are often deployed in an unfriendly location and most of time it works without human management; individual node may possibly be compromised by the adversary due to some constraints. In this manner, the security of a wireless sensor network is critical. This work will focus on evaluation of mining techniques that can be used to find malicious nodes. The detection mechanisms provide the accuracy of the classification using different algorithm to detect the malicious node. Pragmatically the detection accuracy of J48 is 99.17%, Random Forest is 80.83%, NF Tree is 81.67% and BF Tree is 72.33%. J48 have very high detection accuracy as compared with BF Tree, NF Tree Random Forest.

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.