ieee research paper on sensor network

Prediction of Re-Occurrences of Spoofed ACK Packets Sent to Deflate a Target Wireless Sensor Network Node by DDOS

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The Wireless Sensor Network (WSN) has evolved into a new IoT scheme, and its adoption has no restrictions at present. Sadly, security has an impact on the network of wireless sensors, and Denial-of-Service (DOS) categories of attacks are security concerns. This study therefore focuses on the distributed denial of service (DDOS), especially on DDoS-PSH-ACK (ACK & PUSH ACK Flood) in WSN. An experimental analysis was developed to predict that many spoofed ACK packets were reoccurring in order to deflate the target node. In the proposed approach, several experimental scenarios for the DDOS detection function were established and implemented. The experimental analysis draws traffic flow within the several transmission sessions involving “the normal transmission within sensor nodes and cluster head”, as well as the “transmission and retransmission scenarios within the sensor nodes and cluster head” at same time with different signal sizes. The main contribution of the paper is predicting DDoS attack by variability of transmission behavior with high degree accuracy. It was established that the most ideal delay between transmissions is 23 milliseconds in order to ensure that the receiving end is not overwhelmed. The result of the current study highlighted that when transmission session gets overwhelmed, that influence DDOS success.

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Reinforcement Learning Based MAC Protocol (UW-ALOHA-Q) for Underwater Acoustic Sensor Networks

The demand for regular monitoring of the marine environment and ocean exploration is rapidly increasing, yet the limited bandwidth and slow propagation speed of acoustic signals leads to low data throughput for underwater networks used for such purposes. This study describes a novel approach to medium access control that engenders efficient use of an acoustic channel. ALOHA-Q is a medium access protocol designed for terrestrial radio sensor networks and reinforcement learning is incorporated into the protocol to provide efficient channel access. In principle, it potentially offers opportunities for underwater network design, due to its adaptive capability and its responsiveness to environmental changes. However, preliminary work has shown that the achievable channel utilisation is much lower in underwater environments compared with the terrestrial environment. Three improvements are proposed in this paper to address key limitations and establish a new protocol (UW-ALOHA-Q). The new protocol includes asynchronous operation to eliminate the challenges associated with time synchronisation under water, offer an increase in channel utilisation through a reduction in the number of slots per frame, and achieve collision free scheduling by incorporating a new random back-off scheme. Simulations demonstrate that UW-ALOHA-Q provides considerable benefits in terms of achievable channel utilisation, particularly when used in large scale distributed networks.

A Novel Design Approach for 5G Massive MIMO and NB-IoT Green Networks Using a Hybrid Jaya-Differential Evolution Algorithm

Our main objective is to reduce power consumption by responding to the instantaneous bit rate demand by the user for 4th Generation (4G) and 5th Generation (5G) Massive MIMO network configurations. Moreover, we present and address the problem of designing green LTE networks with the Internet of Things (IoT) nodes. We consider the new NarrowBand-IoT (NB-IoT) wireless technology that will emerge in current and future access networks. In this context, we apply emerging evolutionary algorithms in the context of green network design. We investigate three different cases to show the performance of the new proposed algorithm, namely the 4G, 5G Massive MIMO, and the NB-IoT technologies. More specifically, we investigate the Teaching-Learning-Optimization (TLBO), the Jaya algorithm, the self-adaptive differential evolution jDE algorithm, and other hybrid algorithms. We introduce a new hybrid algorithm named Jaya-jDE that uses concepts from both Jaya and jDE algorithms in an effective way. The results show that 5G Massive MIMO networks require about 50% less power consumption than the 4G ones, and the NB-IoT in-band deployment requires about 10% less power than guard-band deployment. Moreover, Jaya-jDE emerges as the best algorithm based on the results.

Wireless Communications and Applications Above 100 GHz: Opportunities and Challenges for 6G and Beyond

Frequencies from 100 GHz to 3 THz are promising bands for the next generation of wireless communication systems because of the wide swaths of unused and unexplored spectrum. These frequencies also offer the potential for revolutionary applications that will be made possible by new thinking, and advances in devices, circuits, software, signal processing, and systems. This paper describes many of the technical challenges and opportunities for wireless communication and sensing applications above 100 GHz, and presents a number of promising discoveries, novel approaches, and recent results that will aid in the development and implementation of the sixth generation (6G) of wireless networks, and beyond. This paper shows recent regulatory and standard body rulings that are anticipating wireless products and services above 100 GHz and illustrates the viability of wireless cognition, hyper-accurate position location, sensing, and imaging. This paper also presents approaches and results that show how long distance mobile communications will be supported to above 800 GHz since the antenna gains are able to overcome air-induced attenuation, and present methods that reduce the computational complexity and simplify the signal processing used in adaptive antenna arrays, by exploiting the Special Theory of Relativity to create a cone of silence in over-sampled antenna arrays that improve performance for digital phased array antennas. Also, new results that give insights into power efficient beam steering algorithms, and new propagation and partition loss models above 100 GHz are given, and promising imaging, array processing, and position location results are presented. The implementation of spatial consistency at THz frequencies, an important component of channel modeling that considers minute changes and correlations over space, is also discussed. This paper offers the first in-depth look at the vast applications of THz wireless products and applications and provides approaches.

Most Cited Article of 2017: Lightweight three-factor authentication and key agreement protocol for internet-integrated wireless sensor networks

Wireless sensor networks (WSNs) will be integrated into the future Internet as one of the components of the Internet of Things, and will become globally addressable by any entity connected to the Internet. Despite the great potential of this integration, it also brings new threats, such as the exposure of sensor nodes to attacks originating from the Internet. In this context, lightweight authentication and key agreement protocols must be in place to enable end-to-end secure communication. Recently, Amin et al. proposed a three-factor mutual authentication protocol for WSNs. However, we identified several flaws in their protocol. We found that their protocol suffers from smart card loss attack where the user identity and password can be guessed using offline brute force techniques. Moreover, the protocol suffers from known session-specific temporary information attack, which leads to the disclosure of session keys in other sessions. Furthermore, the protocol is vulnerable to tracking attack and fails to fulfill user untraceability. To address these deficiencies, we present a lightweight and secure user authentication protocol based on the Rabin cryptosystem, which has the characteristic of computational asymmetry. We conduct a formal verification of our proposed protocol using ProVerif in order to demonstrate that our scheme fulfills the required security properties. We also present a comprehensive heuristic security analysis to show that our protocol is secure against all the possible attacks and provides the desired security features. The results we obtained show that our new protocol is a secure and lightweight solution for authentication and key agreement for Internet-integrated WSNs.

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Machine learning for wireless sensor networks security: an overview of challenges and issues.

1. Introduction

• We explain in detail the security requirements covered by ML algorithms in WSN security in current applications
• We present a systematic and comprehensive survey of current technologies in the literature related to improving the security of WSNs using machine learning techniques. The pros and cons of each technique are also highlighted.
• We describe the limitations of using ML in current security solutions for WSNs, the challenges open to ML algorithms in providing them with security, and future re-search solutions.

2. Background on WSN

2.1. wsn overview.

• Independent nodes without a central control
• Stationary or mobile WSN nodes
• The transmission range of WSN nodes is also limited
• The WSN network topology is constantly changing
• Multiple hop connections
• Limited bandwidth

2.2. WSN Applications

2.3. security in wsn, attacks on wsns.

• Eavesdropping

2.4. Why Is Machine Learning Needed in WSN Security?

3. machine learning techniques, 3.1. supervised learning, 3.1.1. k-nearest neighbor, 3.1.2. decision tree, 3.1.3. random forest, 3.1.4. supportive vector machine, 3.1.5. naïve bayes, 3.1.6. artificial neural network, 3.1.7. logistic regression, 3.1.8. least-mean-square, 3.1.9. bayesian, 3.2. unsupervised learning, 3.2.1. k-means, 3.2.2. fuzzy logic, 3.3. deep learning, 3.3.1. convolutional neural networks, 3.3.2. recurrent neural networks, 3.3.3. long-term short memory, 3.3.4. multi-layer perceptron, 3.3.5. backpropagation neural networks, 4. wsn security challenges, 4.1. challenges of wsn security, 4.1.1. absence of centralized control, 4.1.2. wsns topology changes, 4.1.3. scalable trust management, 4.1.4. limited resources, 4.2. challenges of using ml algorithms in wsn security.

• Machine learning algorithms, which include learning from historical data, cannot make accurate real-time predictions. The amount of additional data determines the efficiency of the algorithm. When the amount of data is huge, the cost of energy required to process it is equally large. In other words, there is a trade-off between the power limitations of the WSN and the higher computing burden of the ML algorithm. ML algorithms must be implemented centrally to avoid this trade-off. Therefore, these algorithms pose a risk [ 27 ] for wireless sensor network environments.
• Machine learning techniques cannot be applied to all WSN’s security requirements. Sometimes it is difficult to apply them to some security domains, such as authentication and integrity [ 107 ]. Providing such operations between WSN nodes requires a high CPU and power. This can be represented by authentication between the vehicle and the driver, for example, but it is difficult to represent between one WSN node and another [ 108 ]. On the other hand, some studies have used ML algorithms for authentication through physical channel exploits [ 109 ]. These ML techniques are discussed in Section 5.2 .
• Most machine learning algorithms have a margin of error, even if this margin is small, it is there. Therefore, in secret data, its confidentiality should be close to perfect [ 110 ]. The authors worked in [ 111 ] by providing a Mathematical Encryption Standard (MES) to increase case-based risk monitoring of confidential healthcare data using ML technology. Decision-making regarding the risk control strategy in MES was enhanced based on a fuzzy inference system integrated with neural networks. Analysis of the results shows that the MES error rate is less than 0.05 and the accuracy rate is 97%, which indicates their desire to increase security risks. Despite the improvements made by the authors, there is still an error rate, even if it is close to zero.

5. Applications of ML to Secure WSN Networks

5.1. availability, 5.1.1. intrusion detection.

5.1.2. Error Detection

5.1.3. congestion control, 5.2. authentication.

5.3. ML-Based WSN Diversified Security

6. discussion and open issues, 6.1. location of the ml training process, 6.2. lightweight ml algorithms, 6.3. privacy concerns, 6.4. trust domain, 7. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Ahmad, R.; Wazirali, R.; Abu-Ain, T. Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues. Sensors 2022 , 22 , 4730. https://doi.org/10.3390/s22134730

Ahmad R, Wazirali R, Abu-Ain T. Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues. Sensors . 2022; 22(13):4730. https://doi.org/10.3390/s22134730

Ahmad, Rami, Raniyah Wazirali, and Tarik Abu-Ain. 2022. "Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues" Sensors 22, no. 13: 4730. https://doi.org/10.3390/s22134730

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A comprehensive review of wireless sensor networks: Applications, challenges, radio technologies, and protocols

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Fadhil Mohammad Salman; A comprehensive review of wireless sensor networks: Applications, challenges, radio technologies, and protocols. AIP Conf. Proc. 4 December 2023; 2834 (1): 020021. https://doi.org/10.1063/5.0161523

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Recently, significant advances in digital sensing technology and wireless communications have led to the production of small-scale smart sensors that have the ability to sense physical changes in different environments and convert them into digital signals that are collected in one central location. These networks are called wireless sensor networks (WSNs) because they have the ability to wirelessly communicate with each other for the purpose of passing data between them and deliver it to the central station. Wireless sensor networks have great importance in human environments, as they are used to monitor and record physical changes in environments that are not accessible to humans, such as environments with very high temperatures, or in cases of chemical environmental pollution, in forests and seas as well as in deep layers of earth to monitor seismic and volcanic activity. In view of the great importance of wireless sensor networks and their obvious impact on human life, and for the purpose of providing researchers with brief and complete information about these networks, we have presented this paper that briefly reviews what sensor networks are, their specifications, applications and the challenges they face. This paper also included most of the radio communication techniques used in sensor architectures. As well as touching on the most important routing algorithms in "WSNs" and their classifications with a comparison of their performance. Finally, this paper provided an overview of research trends that could lead to significant technological development and beneficial growth to overcome the challenges of wireless sensor networks.

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