Intrusion Prevention Framework for Secure Routing in WSN-Based Mobile Internet of Things

Haseeb, Khalid; Islam, Naveed; Almogren, Ahmad; Din, Ikram Ud

doi:10.1109/access.2019.2960633

Cited by 115 publications

(69 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In (10), grad indicates the gradient of the cost function (9). Because the minimum value of Z − z 0 W is less than zero and its maximum value is greater than zero, we can derive that there exists an interval to make the directional derivative ∂Cost/∂ l < 0, which means we can get a new path whose cost is smaller than the current shortest path.…”

Section: E Resilient Routing Mechanismmentioning

confidence: 99%

“…Some papers have studied energy-efficient and reliable routing for various sensor networks. K. Haseeb et al [10] presented a framework which utilize the end-to-end secure and multi-hop routing paths based on blockchain architecture to ensure the safe and efficient data delivery in sensor networks. Al-Qarniet al [11] used a modified LEACH protocol specially designed for MANET to handle the problem of transmitting multimedia big data that can easily cause node failures because of the limited node energy in wireless networks.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Resilient Routing Mechanism for Wireless Sensor Networks With Deep Learning Link Reliability Prediction

Huang

Zhai

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Wireless sensor networks play an important role in Internet of Things systems and services but are prone and vulnerable to poor communication channel quality and network attacks. In this paper we are motivated to propose resilient routing algorithms for wireless sensor networks. The main idea is to exploit the link reliability along with other traditional routing metrics for routing algorithm design. We proposed firstly a novel deep-learning based link prediction model, which jointly exploits Weisfeiler-Lehman kernel and Dual Convolutional Neural Network (WL-DCNN) for lightweight subgraph extraction and labelling. It is leveraged to enhance self-learning ability of mining topological features with strong generality. Experimental results demonstrate that WL-DCNN outperforms all the studied 9 baseline schemes over 6 open complex networks datasets. The performance of AUC (Area Under the receiver operating characteristic Curve) is improved by 16% on average. Furthermore, we apply the WL-DCNN model in the design of resilient routing for wireless sensor networks, which can adaptively capture topological features to determine the reliability of target links, especially under the situations of routing table suffering from attack with varying degrees of damage to local link community. It is observed that, compared with other classical routing baselines, the proposed routing algorithm with link reliability prediction module can effectively improve the resilience of sensor networks while reserving high-energy-efficiency.

show abstract

Section: E Resilient Routing Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Resilient Routing Mechanism for Wireless Sensor Networks With Deep Learning Link Reliability Prediction

Huang

Zhai

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Internet Things (IoT) [1] provides a diverse opportunity to automate distinct domains, which include wireless sensor networks [2,3], home appliances [4,5], smart cities [6][7][8], healthcare [9][10][11][12][13], security and surveillance [14][15][16], energy consumption [17][18][19], agriculture automation [20], and many more [21][22][23][24][25]. The concept of a smart irrigation system [26] is proposed in the Cloud-based Internet of Agriculture Things (IoAT) [27] in which sensors gather the findings of soil and transmit it towards the base station (BS) to take the required actions [28].…”

Section: Introductionmentioning

confidence: 99%

AgriTrust—A Trust Management Approach for Smart Agriculture in Cloud-based Internet of Agriculture Things

Awan

Din

Almogren

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Internet of Things (IoT) provides a diverse platform to automate things where smart agriculture is one of the most promising concepts in the field of Internet of Agriculture Things (IoAT). Due to the requirements of more processing power for computations and predictions, the concept of Cloud-based smart agriculture is proposed for autonomic systems. This is where digital innovation and technology helps to improve the quality of life in the area of urbanization expansion. For the integration of cloud in smart agriculture, the system is shown to have security and privacy challenges, and most significantly, the identification of malicious and compromised nodes along with a secure transmission of information between sensors, cloud, and base station (BS). The identification of malicious and compromised node among soil sensors communicating with the BS is a notable challenge in the BS to cloud communications. The trust management mechanism is proposed as one of the solutions providing a lightweight approach to identify these nodes. In this article, we have proposed a novel trust management mechanism to identify malicious and compromised nodes by utilizing trust parameters. The trust mechanism is an event-driven process that computes trust based on the pre-defined time interval and utilizes the previous trust degree to develop an absolute trust degree. The system also maintains the trust degree of a BS and cloud service providers using distinct approaches. We have also performed extensive simulations to evaluate the performance of the proposed mechanism against several potential attacks. In addition, this research helps to create friendlier environments and efficient agricultural productions for the migration of people to the cities.

show abstract

“…In recent decades, wireless technologies played an important role in the growth and development of different fields such as the Internet of Things (IoT), wireless sensor networks (WSNs), cloud computing, and smart heterogeneous systems [1][2][3][4]. Due to automated, self-configure, and lightweight infrastructures, different researchers exploit them in the domain of academics and industries.…”

Section: Introductionmentioning

confidence: 99%

DDR-ESC: A Distributed and Data Reliability Model for Mobile Edge-Based Sensor-Cloud

et al. 2020

Self Cite

View full text Add to dashboard Cite

Cloud computing gained a lot of growth in the development of scientific and industrial domains due to its scalability, data analysis, portability, and storage abilities. The sensors and actuators facilitate wireless communication with real-time data gathering and local preprocessing. However, the huge amount of data collection and storage for such low powered nodes incurs the problem of reliability and efficient resource management. The technology of cloud computing provides an emerging paradigm for the sensors-based applications to improve processing, management, and storage of massive data. However, sensor nodes are organized and communicate in a distributed environment, which is harmful to network reliability and privacy. Recently, many solutions are presented for data transmissions in distributed systems through link-aware routing, but efficient utilization of network resources is still an open research issue. Also, most of the proposed solutions provide secure transmissions on the cost of computation and routing overhead. In this work, we propose a distributed and data reliability model for mobile edge-based sensor-cloud (DDR-ESC) to grow the data delivery performance and preserve the control of security. Moreover, edge-based communications with the cloud systems make the transmission intelligently and decrease the threshold of packets level overhead with high dependability. The extensive experiments validate the proposed model as compared to the state-of-the-art schemes.

show abstract

Intrusion Prevention Framework for Secure Routing in WSN-Based Mobile Internet of Things

Cited by 115 publications

References 42 publications

Resilient Routing Mechanism for Wireless Sensor Networks With Deep Learning Link Reliability Prediction

Resilient Routing Mechanism for Wireless Sensor Networks With Deep Learning Link Reliability Prediction

AgriTrust—A Trust Management Approach for Smart Agriculture in Cloud-based Internet of Agriculture Things

DDR-ESC: A Distributed and Data Reliability Model for Mobile Edge-Based Sensor-Cloud

Contact Info

Product

Resources

About