Security and Privacy in Wireless Sensor Networks: Advances and Challenges

Lee, Cheng‐Chi

doi:10.3390/s20030744

Cited by 33 publications

(25 citation statements)

References 12 publications

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“…To reduce the security requirements, the data must be compromised. Consequently, a suitable solution to use robust and lightweight security algorithms leads to propose different encryption technologies [91].…”

Section: Perception Layer's Limitationsmentioning

confidence: 99%

Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview

Wazirali

Ahmad

Al-Amayreh³

et al. 2021

Electronics

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Information security is considered one of the most important issues in various infrastructures related to the field of data communication where most of the modern studies focus on finding effective and low-weight secure approaches. Digital watermarking is a trend in security techniques that hides data by using data embedding and data extraction processes. Watermarking technology is integrated into different frames without adding an overheard as in the conventional encryption. Therefore, it is efficient to be used in data encryption for applications that run over limited resources such as the Internet of Things (IoT). In this paper, different digital watermarking algorithms and approaches are presented. Additionally, watermarking requirements and challenges are illustrated in detail. Moreover, the common architecture of the watermarking system is described. Furthermore, IoT technology and its challenges are highlighted. Finally, the paper provides the motivations, objectives and applications of the recent secure watermarking techniques in IoT and summarises them into one table. In addition, the paper highlights the potential to apply the modified watermark algorithms to secure IoT networks.

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Section: Perception Layer's Limitationsmentioning

confidence: 99%

Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview

Wazirali

Ahmad

Al-Amayreh³

et al. 2021

Electronics

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“…There have been nearly 40 years of research on distributed sensor detection networks [16], and the research on distributed detection-based massive MIMO networks has been studied in [17,18]. Typical applications include spectrum sensing in cognitive radio networks (CRNs) [19] and event detection in wireless sensor networks (WSNs) [20,21]. With the rapid development of massive MIMO technology, the use of multi-antenna sensors should be more considered in a distributed detection network.…”

Section: Related Workmentioning

confidence: 99%

“…The system has a limited upper limit of detection performance when the number of antennas is large, and depends on the signal variance σ 2 θ , the variance of the receiver noise in the sensor σ 2 v,ik and the number of sensors N. It can be seen that the prerequisite for reaching the upper limit of the detection performance is the infinite M and P, at which time the distribution of the perceived report power becomes unimportant. However, for the actual system, the values of M and P cannot be infinite, so optimizing g 2 ik will bring significant benefits to the performance of Equation (21). To reduce the complexity of subsequent formula derivation, we use new symbols to define…”

Section: Detection Performance Analysismentioning

confidence: 99%

Massive MIMO-Based Distributed Signal Detection in Multi-Antenna Wireless Sensor Networks

Wei

Zhang

Ding

et al. 2020

Sensors

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For massive multiple-input multiple-output (MIMO) distributed wireless sensor networks, this paper investigates the role of multi-antenna sensors in improving network perception performance. First, we construct a distributed multi-antenna sensor network based on massive MIMO. By using the anti-fading characteristics of multi-antennas, it is better to achieve accurate detection than the single-antenna sensor network. Based on this, we derive a closed-loop expression for the detection probability of the best detector. Then, we consider the case that the sensor power resources are limited, and thus we want to use finite power to achieve higher detection probability. For this reason, the power was optimized by the alternating direction method of multipliers (ADMM). Moreover, we also prove that only statistical channel state is needed in large-scale antenna scenarios, which avoid the huge overhead of channel state information. Finally, according to the simulation results, the multi-antenna sensor network has better detection performance than the single-antenna sensor network which demonstrates the improved performance of the proposed schemes and also validates the theoretical findings.

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“…One of these issues applies to security, privacy, and availability within the perception layer [ 4 ]. Attackers can listen in on radio transmissions, send fake messages over communication channels, and alter received data packets [ 5 , 6 ]. Moreover, they can use compromised WSN nodes with similar hardware resources to legitimate network nodes [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

Ahmad

Wazirali

Bsoul

et al. 2021

Sensors

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Wireless Sensor Networks (WSNs) continue to face two major challenges: energy and security. As a consequence, one of the WSN-related security tasks is to protect them from Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Machine learning-based systems are the only viable option for these types of attacks, as traditional packet deep scan systems depend on open field inspection in transport layer security packets and the open field encryption trend. Moreover, network data traffic will become more complex due to increases in the amount of data transmitted between WSN nodes as a result of increasing usage in the future. Therefore, there is a need to use feature selection techniques with machine learning in order to determine which data in the DoS detection process are most important. This paper examined techniques for improving DoS anomalies detection along with power reservation in WSNs to balance them. A new clustering technique was introduced, called the CH_Rotations algorithm, to improve anomaly detection efficiency over a WSN’s lifetime. Furthermore, the use of feature selection techniques with machine learning algorithms in examining WSN node traffic and the effect of these techniques on the lifetime of WSNs was evaluated. The evaluation results showed that the Water Cycle (WC) feature selection displayed the best average performance accuracy of 2%, 5%, 3%, and 3% greater than Particle Swarm Optimization (PSO), Simulated Annealing (SA), Harmony Search (HS), and Genetic Algorithm (GA), respectively. Moreover, the WC with Decision Tree (DT) classifier showed 100% accuracy with only one feature. In addition, the CH_Rotations algorithm improved network lifetime by 30% compared to the standard LEACH protocol. Network lifetime using the WC + DT technique was reduced by 5% compared to other WC + DT-free scenarios.

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Security and Privacy in Wireless Sensor Networks: Advances and Challenges

Abstract: Wireless sensor networks (WSNs) have evolved over the last few decades due to the availability of low-cost, short-range and easy deployed sensors [...]

Cited by 33 publications

References 12 publications

Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview

Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview

Massive MIMO-Based Distributed Signal Detection in Multi-Antenna Wireless Sensor Networks

Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

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