15,811 research outputs found
Prevention of cyberattacks in WSN and packet drop by CI framework and information processing protocol using AI and Big Data
As the reliance on wireless sensor networks (WSNs) rises in numerous sectors,
cyberattack prevention and data transmission integrity become essential
problems. This study provides a complete framework to handle these difficulties
by integrating a cognitive intelligence (CI) framework, an information
processing protocol, and sophisticated artificial intelligence (AI) and big
data analytics approaches. The CI architecture is intended to improve WSN
security by dynamically reacting to an evolving threat scenario. It employs
artificial intelligence algorithms to continuously monitor and analyze network
behavior, identifying and mitigating any intrusions in real time. Anomaly
detection algorithms are also included in the framework to identify packet drop
instances caused by attacks or network congestion. To support the CI
architecture, an information processing protocol focusing on efficient and
secure data transfer within the WSN is introduced. To protect data integrity
and prevent unwanted access, this protocol includes encryption and
authentication techniques. Furthermore, it enhances the routing process with
the use of AI and big data approaches, providing reliable and timely packet
delivery. Extensive simulations and tests are carried out to assess the
efficiency of the suggested framework. The findings show that it is capable of
detecting and preventing several forms of assaults, including as
denial-of-service (DoS) attacks, node compromise, and data tampering.
Furthermore, the framework is highly resilient to packet drop occurrences,
which improves the WSN's overall reliability and performanc
A Low-Delay MAC for IoT Applications: Decentralized Optimal Scheduling of Queues without Explicit State Information Sharing
We consider a system of several collocated nodes sharing a time slotted
wireless channel, and seek a MAC (medium access control) that (i) provides low
mean delay, (ii) has distributed control (i.e., there is no central scheduler),
and (iii) does not require explicit exchange of state information or control
signals. The design of such MAC protocols must keep in mind the need for
contention access at light traffic, and scheduled access in heavy traffic,
leading to the long-standing interest in hybrid, adaptive MACs.
Working in the discrete time setting, for the distributed MAC design, we
consider a practical information structure where each node has local
information and some common information obtained from overhearing. In this
setting, "ZMAC" is an existing protocol that is hybrid and adaptive. We
approach the problem via two steps (1) We show that it is sufficient for the
policy to be "greedy" and "exhaustive". Limiting the policy to this class
reduces the problem to obtaining a queue switching policy at queue emptiness
instants. (2) Formulating the delay optimal scheduling as a POMDP (partially
observed Markov decision process), we show that the optimal switching rule is
Stochastic Largest Queue (SLQ).
Using this theory as the basis, we then develop a practical distributed
scheduler, QZMAC, which is also tunable. We implement QZMAC on standard
off-the-shelf TelosB motes and also use simulations to compare QZMAC with the
full-knowledge centralized scheduler, and with ZMAC. We use our implementation
to study the impact of false detection while overhearing the common
information, and the efficiency of QZMAC. Our simulation results show that the
mean delay with QZMAC is close that of the full-knowledge centralized
scheduler.Comment: 28 pages, 19 figure
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
A fuzzy set theory-based fast fault diagnosis approach for rotators of induction motors
Induction motors have been widely used in industry, agriculture, transportation, national defense engineering, etc. Defects of the motors will not only cause the abnormal operation of production equipment but also cause the motor to run in a state of low energy efficiency before evolving into a fault shutdown. The former may lead to the suspension of the production process, while the latter may lead to additional energy loss. This paper studies a fuzzy rule-based expert system for this purpose and focuses on the analysis of many knowledge representation methods and reasoning techniques. The rotator fault of induction motors is analyzed and diagnosed by using this knowledge, and the diagnosis result is displayed. The simulation model can effectively simulate the broken rotator fault by changing the resistance value of the equivalent rotor winding. And the influence of the broken rotor bar fault on the motors is described, which provides a basis for the fault characteristics analysis. The simulation results show that the proposed method can realize fast fault diagnosis for rotators of induction motors
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Effects of Particle Swarm Optimisation on a Hybrid Load Balancing Approach for Resource Optimisation in Internet of Things
This article belongs to the Special Issue Emerging Machine Learning Techniques in Industrial Internet of ThingsCopyright © 2023 by the authors. The internet of things, a collection of diversified distributed nodes, implies a varying choice of activities ranging from sleep monitoring and tracking of activities, to more complex activities such as data analytics and management. With an increase in scale comes even greater complexities, leading to significant challenges such as excess energy dissipation, which can lead to a decrease in IoT devices’ lifespan. Internet of things’ (IoT) multiple variable activities and ample data management greatly influence devices’ lifespan, making resource optimisation a necessity. Existing methods with respect to aspects of resource management and optimisation are limited in their concern of devices energy dissipation. This paper therefore proposes a decentralised approach, which contains an amalgamation of efficient clustering techniques, edge computing paradigms, and a hybrid algorithm, targeted at curbing resource optimisation problems and life span issues associated with IoT devices. The decentralised topology aimed at the resource optimisation of IoT places equal importance on resource allocation and resource scheduling, as opposed to existing methods, by incorporating aspects of the static (round robin), dynamic (resource-based), and clustering (particle swarm optimisation) algorithms, to provide a solid foundation for an optimised and secure IoT. The simulation constructs five test-case scenarios and uses performance indicators to evaluate the effects the proposed model has on resource optimisation in IoT. The simulation results indicate the superiority of the PSOR2B to the ant colony, the current centralised optimisation approach, LEACH, and C-LBCA.This research received no external funding
Fast and Low-Overhead Time Synchronization for Industrial Wireless Sensor Networks with Mesh-Star Architecture
Low-overhead, robust, and fast-convergent time synchronization is important for resource-constrained large-scale industrial wireless sensor networks (IWSNs). The consensus-based time synchronization method with strong robustness has been paid more attention in wireless sensor networks. However, high communication overhead and slow convergence speed are inherent drawbacks for consensus time synchronization due to inefficient frequent iterations. In this paper, a novel time synchronization algorithm for IWSNs with a mesh–star architecture is proposed, namely, fast and low-overhead time synchronization (FLTS). The proposed FLTS divides the synchronization phase into two layers: mesh layer and star layer. A few resourceful routing nodes in the upper mesh layer undertake the low-efficiency average iteration, and the massive low-power sensing nodes in the star layer synchronize with the mesh layer in a passive monitoring manner. Therefore, a faster convergence and lower communication overhead time synchronization is achieved. The theoretical analysis and simulation results demonstrate the efficiency of the proposed algorithm in comparison with the state-of-the-art algorithms, i.e., ATS, GTSP, and CCTS
Secure Routing Protocol To Mitigate Attacks By Using Blockchain Technology In Manet
MANET is a collection of mobile nodes that communicate through wireless
networks as they move from one point to another. MANET is an
infrastructure-less network with a changeable topology; as a result, it is very
susceptible to attacks. MANET attack prevention represents a serious
difficulty. Malicious network nodes are the source of network-based attacks. In
a MANET, attacks can take various forms, and each one alters the network's
operation in its unique way. In general, attacks can be separated into two
categories: those that target the data traffic on a network and those that
target the control traffic. This article explains the many sorts of assaults,
their impact on MANET, and the MANET-based defence measures that are currently
in place. The suggested SRA that employs blockchain technology (SRABC) protects
MANET from attacks and authenticates nodes. The secure routing algorithm (SRA)
proposed by blockchain technology safeguards control and data flow against
threats. This is achieved by generating a Hash Function for every transaction.
We will begin by discussing the security of the MANET. This article's second
section explores the role of blockchain in MANET security. In the third
section, the SRA is described in connection with blockchain. In the fourth
phase, PDR and Throughput are utilised to conduct an SRA review using
Blockchain employing PDR and Throughput. The results suggest that the proposed
technique enhances MANET security while concurrently decreasing delay. The
performance of the proposed technique is analysed and compared to the routing
protocols Q-AODV and DSR.Comment: https://aircconline.com/ijcnc/V15N2/15223cnc07.pd
PrimeTime: A Finite-Time Consensus Protocol for Open Networks
In distributed problems where consensus between agents is required but
average consensus is not desired, it can be necessary for each agent to know
not only the data of each other agent in the network, but also the origin of
each piece of data before consensus can be reached. However, transmitting large
tables of data with IDs can cause the size of an agent's message to increase
dramatically, while truncating down to fewer pieces of data to keep the message
size small can lead to problems with the speed of achieving consensus. Also,
many existing consensus protocols are not robust against agents leaving and
entering the network. We introduce PrimeTime, a novel communication protocol
that exploits the properties of prime numbers to quickly and efficiently share
small integer data across an open network. For sufficiently small networks or
small integer data, we show that messages formed by PrimeTime require fewer
bits than messages formed by simply tabularizing the data and IDs to be
transmitted
Multilink and AUV-Assisted Energy-Efficient Underwater Emergency Communications
Recent development in wireless communications has provided many reliable
solutions to emergency response issues, especially in scenarios with
dysfunctional or congested base stations. Prior studies on underwater emergency
communications, however, remain under-studied, which poses a need for combining
the merits of different underwater communication links (UCLs) and the
manipulability of unmanned vehicles. To realize energy-efficient underwater
emergency communications, we develop a novel underwater emergency communication
network (UECN) assisted by multiple links, including underwater light,
acoustic, and radio frequency links, and autonomous underwater vehicles (AUVs)
for collecting and transmitting underwater emergency data. First, we determine
the optimal emergency response mode for an underwater sensor node (USN) using
greedy search and reinforcement learning (RL), so that isolated USNs (I-USNs)
can be identified. Second, according to the distribution of I-USNs, we dispatch
AUVs to assist I-USNs in data transmission, i.e., jointly optimizing the
locations and controls of AUVs to minimize the time for data collection and
underwater movement. Finally, an adaptive clustering-based multi-objective
evolutionary algorithm is proposed to jointly optimize the number of AUVs and
the transmit power of I-USNs, subject to a given set of constraints on transmit
power, signal-to-interference-plus-noise ratios (SINRs), outage probabilities,
and energy, which achieves the best tradeoff between the maximum emergency
response time (ERT) and the total energy consumption (EC). Simulation results
indicate that our proposed approach outperforms benchmark schemes in terms of
energy efficiency (EE), contributing to underwater emergency communications.Comment: 15 page
Security and Privacy Problems in Voice Assistant Applications: A Survey
Voice assistant applications have become omniscient nowadays. Two models that
provide the two most important functions for real-life applications (i.e.,
Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR)
models and Speaker Identification (SI) models. According to recent studies,
security and privacy threats have also emerged with the rapid development of
the Internet of Things (IoT). The security issues researched include attack
techniques toward machine learning models and other hardware components widely
used in voice assistant applications. The privacy issues include technical-wise
information stealing and policy-wise privacy breaches. The voice assistant
application takes a steadily growing market share every year, but their privacy
and security issues never stopped causing huge economic losses and endangering
users' personal sensitive information. Thus, it is important to have a
comprehensive survey to outline the categorization of the current research
regarding the security and privacy problems of voice assistant applications.
This paper concludes and assesses five kinds of security attacks and three
types of privacy threats in the papers published in the top-tier conferences of
cyber security and voice domain.Comment: 5 figure
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