Authentication Framework by Using Lightweight Chaos-Driven Blockchain to Identify Malicious Nodes in WSNs

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%).

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.

Security issues are the primary issues in wireless sensor networks (WSN) due to its large network coverage and number of nodes. The attackers in WSN attack the particular node which has a low energy level and converts this node into malicious node. The formation of malicious node is the primary reason for link failures between nodes. This paper proposes an efficient methodology to detect the malicious nodes in WSN using feed forward back propagation neural network classifier. This classifier differentiates the malicious node from trusty node based on the extracted features of the test node. The performance of the proposed malicious node detection system is analysed in terms of detection rate, packet delivery ratio (PDR) and latency. The experimental results are compared with state-of-art methods.

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.

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.

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 networks are nowadays widely popular and has become an integral part in the military applications for human monitoring, thermal detection etc. Security of Wireless sensor network (WSN) becomes a very important issue with the rapid developme nt of WSN that is vulnerable to a wide range of attacks such as sinkhole attacks due to deployment in the hostile environment and having limited resources. Intrusion detection system is one of the major and efficient defensive methods against attacks in WS N. One such detection technique is black listing technology.But using only Black listing technology is not suitable for a mobile intruder since it was designed considering only a static intruding node in a WSN. So it is necessary to build an energy efficient Intrusion detection system for sinkhole attack by a mobile intruder in WSN. We are intended to design an energy efficient system for detection of sinkhole and elimination of a mobile intruder from WSN nodes using a technology called greylisting. This t echnology uses pre alarm packets to warn the neighboring nodes about the intruder and the energy consumed by the pre alarm packets for making an alarm is much lesser than that of the packets used in black listing technology. Thus this method will serve asthe solution for the dilemma in providing the security for WSN in sinkhole attack.

Recently, the technology of wireless sensor networks (WSNs) experiences a growing use in home automation or advanced industry infrastructure applications. Despite a strong interest of industries in this technology, key issues like miniaturization and security of WSN nodes has not been solved yet. State-of-the-art WSN nodes do not provide credible security nor satisfying configurability and miniaturized implementations. This publication deals with these limitations and presents a WSN node that provides security, configurability, and a miniaturized design. To show the sensor node feasibility, the WSN node is implemented within a smart home demonstrator. Additionally, a miniaturized pre-study WSN node design is presented using the novel embedded wafer level ball grid array (eWLB) packaging technology. Furthermore, an eWLB based WSN node design is proposed that further miniaturize the presented WSN node.

Over the past decades, hardware and software technologies for wireless sensor networks (WSNs) have significantly progressed, and WSNs are widely used in various areas including Internet of Things (IoT). In general, existing WSNs are mainly used for applications that require delay-tolerance and low-computation due to the poor resources of traditional sensor nodes in WSNs. However, compared to the traditional sensor nodes, today’s devices for WSNs have more powerful resource. Thus, sensor nodes these days not only conduct sensing and transmitting data to servers but also are able to process many operations, so more diverse applications can be applied to WSNs. Especially, many applications using audio data have been proposed because audio is one of the most widely used data types, and many mobile devices already have a built-in microphone. However, many of the applications have a requirement that heavy-operations should be done by a tight deadline, so it is difficult for a single node in WSNs to run relatively heavy applications by itself. In this paper, to overcome this limitation of WSNs, we propose a new emerging system, HeaLow, a cooperative computing system for heavy-computation and low-latency processing in WSNs. We designed HeaLow and carried out the practical implementation on real devices. We confirmed the effectiveness of HeaLow through various experiments using the real devices and simulations. Using HeaLow, nodes in WSNs are able to perform heavy-computation processes while satisfying a completion time requirement.

Self-powered wireless sensor networks based on the radioisotope thermoelectric generator for aquatic temperature monitoring

Over the last few years, security concern has a very important role over the wireless sensor networks (WSNs). There are lot of attacks on WSN due to its open deployment and wireless communication between the nodes. In this paper, trust-based intrusion detection system is proposed to calculate the trust by using MAC layer protocol. The trust value of each node in WSN is evaluated by deviation of key factor at the MAC layer. The ML-IDS is impressive system to detect the malicious nodes in wireless sensor network. The malicious nodes attack the MAC layer by Back-off manipulation attack. Mainly Back-off manipulation attack is used to reduce the back-off time to get the channel priority. We have implemented the back-off manipulation attack at MAC layer for performance evaluation. Results show that ML-IDS perform higher quality as compared to Wang et al. (Sensors 17(6):1227, 2017, [1]) based on detection accuracy and false alarm rate.

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.

Designing a low power Wireless Sensor Network (WSN) Node is highly important for getting longer lifetime. Radio transmission and data processing are energy consuming processes. No compensation can be done in the field of energy consumption in radio transmission. This paper concentrates on energy reduction in the field of On the node processing. Due to limited energy supply from batteries the node must trade communication for on the node computation. Many of the WSN data processing algorithms use parallel prefix operations as common denominator. Parallel Prefix Operations have the form of Binary Tree Architecture. Binary tree architecture uses more number of Processing Element (PE) s. Parallel Prefix operations using Binary Tree Architecture (BTA) uses more area and more power. Binary Tree Architecture can be modified in to area and power efficient Folded Tree Architecture (FTA). A Digital comparator is a hardware electronic device that has two binary inputs and determines whether one number is greater than, less than or equal to the other number. The comparators are widely used in Micro Controller Units (MCUs) of WSN nodes. This is a crucial data path element of image and signal processing architectures. By using Folded Tree Architecture Area and Power Efficient Comparator Architecture can be formed. Thus Power and Area efficient WSN node can be formed.