PFARS : Enhancing throughput and lifetime of heterogeneous WSNs through power-aware fusion, aggregation, and routing scheme
- Khan, Rahim, Zakarya, Muhammad, Tan, Zhiyuan, Usman, Muhammad, Jan, Mian, Khan, Mukhtaj
- Authors: Khan, Rahim , Zakarya, Muhammad , Tan, Zhiyuan , Usman, Muhammad , Jan, Mian , Khan, Mukhtaj
- Date: 2019
- Type: Text , Journal article
- Relation: International Journal of Communication Systems Vol. 32, no. 18 (Dec 2019), p. 21
- Full Text:
- Reviewed:
- Description: Heterogeneous wireless sensor networks (WSNs) consist of resource-starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy-efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application-specific or too complex that make their implementation unrealistic, specifically, in a resource-constrained environment. In this paper, we propose a novel node-level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in-network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real-time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.
- Authors: Khan, Rahim , Zakarya, Muhammad , Tan, Zhiyuan , Usman, Muhammad , Jan, Mian , Khan, Mukhtaj
- Date: 2019
- Type: Text , Journal article
- Relation: International Journal of Communication Systems Vol. 32, no. 18 (Dec 2019), p. 21
- Full Text:
- Reviewed:
- Description: Heterogeneous wireless sensor networks (WSNs) consist of resource-starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy-efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application-specific or too complex that make their implementation unrealistic, specifically, in a resource-constrained environment. In this paper, we propose a novel node-level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in-network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real-time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.
SmartEdge : An end-to-end encryption framework for an edge-enabled smart city application
- Jan, Mian, Zhang, Wenjing, Usman, Muhammad, Tan, Zhiyuan, Khan, Fazlullah, Luo, Entao
- Authors: Jan, Mian , Zhang, Wenjing , Usman, Muhammad , Tan, Zhiyuan , Khan, Fazlullah , Luo, Entao
- Date: 2019
- Type: Text , Journal article
- Relation: Journal of Network and Computer Applications Vol. 137, no. (2019), p. 1-10
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- Description: The Internet of Things (IoT) has the potential to transform communities around the globe into smart cities. The massive deployment of sensor-embedded devices in the smart cities generates voluminous amounts of data that need to be stored and processed in an efficient manner. Long-haul data transmission to the remote cloud data centers leads to higher delay and bandwidth consumption. In smart cities, the delay-sensitive applications have stringent requirements in term of response time. To reduce latency and bandwidth consumption, edge computing plays a pivotal role. The resource-constrained smart devices at the network core need to offload computationally complex tasks to the edge devices located in their vicinity and have relatively higher resources. In this paper, we propose an end-to-end encryption framework, SmartEdge, for a smart city application by executing computationally complex tasks at the network edge and cloud data centers. Using a lightweight symmetric encryption technique, we establish a secure connection among the smart core devices for multimedia streaming towards the registered and verified edge devices. Upon receiving the data, the edge devices encrypts the multimedia streams, encodes them, and broadcast to the cloud data centers. Prior to the broadcasting, each edge device establishes a secured connection with a data center that relies on the combination of symmetric and asymmetric encryption techniques. In SmartEdge, the execution of a lightweight encryption technique at the resource-constrained smart devices, and relatively complex encryption techniques at the network edge and cloud data centers reduce the resource utilization of the entire network. The proposed framework reduces the response time, security overhead, computational and communication costs, and has a lower end-to-end encryption delay for participating entities. Moreover, the proposed scheme is highly resilient against various adversarial attacks.
- Authors: Jan, Mian , Zhang, Wenjing , Usman, Muhammad , Tan, Zhiyuan , Khan, Fazlullah , Luo, Entao
- Date: 2019
- Type: Text , Journal article
- Relation: Journal of Network and Computer Applications Vol. 137, no. (2019), p. 1-10
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) has the potential to transform communities around the globe into smart cities. The massive deployment of sensor-embedded devices in the smart cities generates voluminous amounts of data that need to be stored and processed in an efficient manner. Long-haul data transmission to the remote cloud data centers leads to higher delay and bandwidth consumption. In smart cities, the delay-sensitive applications have stringent requirements in term of response time. To reduce latency and bandwidth consumption, edge computing plays a pivotal role. The resource-constrained smart devices at the network core need to offload computationally complex tasks to the edge devices located in their vicinity and have relatively higher resources. In this paper, we propose an end-to-end encryption framework, SmartEdge, for a smart city application by executing computationally complex tasks at the network edge and cloud data centers. Using a lightweight symmetric encryption technique, we establish a secure connection among the smart core devices for multimedia streaming towards the registered and verified edge devices. Upon receiving the data, the edge devices encrypts the multimedia streams, encodes them, and broadcast to the cloud data centers. Prior to the broadcasting, each edge device establishes a secured connection with a data center that relies on the combination of symmetric and asymmetric encryption techniques. In SmartEdge, the execution of a lightweight encryption technique at the resource-constrained smart devices, and relatively complex encryption techniques at the network edge and cloud data centers reduce the resource utilization of the entire network. The proposed framework reduces the response time, security overhead, computational and communication costs, and has a lower end-to-end encryption delay for participating entities. Moreover, the proposed scheme is highly resilient against various adversarial attacks.
- Han, Tao, Jan, Syed Rooh Ullah, Tan, Zhiyuan, Usman, Muhammad, Jan, Mian Ahmad, Khan, Rahim, Xu, Yongzhao
- Authors: Han, Tao , Jan, Syed Rooh Ullah , Tan, Zhiyuan , Usman, Muhammad , Jan, Mian Ahmad , Khan, Rahim , Xu, Yongzhao
- Date: 2020
- Type: Text , Journal article
- Relation: Concurrency and computation Vol. 32, no. 16 (2020), p. n/a
- Full Text: false
- Reviewed:
- Description: Summary Software Defined Network (SDN) and Network Virtualization (NV) are emerged paradigms that simplified the control and management of the next generation networks, most importantly, Internet of Things (IoT), Cloud Computing, and Cyber‐Physical Systems. The Internet of Things (IoT) includes a diverse range of a vast collection of heterogeneous devices that require interoperable communication, scalable platforms, and security provisioning. Security provisioning to an SDN‐based IoT network poses a real security challenge leading to various serious security threats due to the connection of various heterogeneous devices having a wide range of access protocols. Furthermore, the logical centralized controlled intelligence of the SDN architecture represents a plethora of security challenges due to its single point of failure. It may throw the entire network into chaos and thus expose it to various known and unknown security threats and attacks. Security of SDN controlled IoT environment is still in infancy and thus remains the prime research agenda for both the industry and academia. This paper comprehensively reviews the current state‐of‐the‐art security threats, vulnerabilities, and issues at the control plane. Moreover, this paper contributes by presenting a detailed classification of various security attacks on the control layer. A comprehensive state‐of‐the‐art review of the latest mitigation techniques for various security breaches is also presented. Finally, this paper presents future research directions and challenges for further investigation down the line.
- Khan, Rahim, Zakarya, Muhammad, Tan, Zhiyuan, Usman, Muhammad, Jan, Mian Ahmad, Khan, Mukhtaj
- Authors: Khan, Rahim , Zakarya, Muhammad , Tan, Zhiyuan , Usman, Muhammad , Jan, Mian Ahmad , Khan, Mukhtaj
- Date: 2019
- Type: Text , Journal article
- Relation: International journal of communication systems Vol. 32, no. 18 (2019), p. e4144-n/a
- Full Text: false
- Reviewed:
- Description: Summary Heterogeneous wireless sensor networks (WSNs) consist of resource‐starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy‐efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application‐specific or too complex that make their implementation unrealistic, specifically, in a resource‐constrained environment. In this paper, we propose a novel node‐level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in‐network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real‐time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay. Data fusion approach to enhance the accuracy of gathered data in heterogeneous wireless sensor networks (WSNs). Systematic sampling‐based aggregation to enhance the lifetime of heterogeneous WSNs. Simplified outlier detection to fine‐tune gathered sensor data prior to processing.
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