Privacy and Energy Co-Aware Data Aggregation Computation Offloading for Fog-Assisted IoT Networks

Chen, Siguang; You, Zihui; Ruan, Xiukai

doi:10.1109/access.2020.2987749

Cited by 20 publications

(11 citation statements)

References 36 publications

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“…It can be present BDPS dependent upon Spark-RDD from the fog-IoT overlay structure for addressing the efficacy issue across the network orchestration. Chen et al [16] examined the privacy and energy co-aware data aggregation compute offloading to fog assists IoT network. In detail, a fog supported 3-layer security compute structure was established for counteracting security threats and allowing the aggregation function is executed from ciphertext.…”

Section: Literature Reviewmentioning

confidence: 99%

Hierarchical Data Aggregation with Data Offloading Scheme for Fog Enabled IoT Environment

Nalayini¹,

Prakash²

2023

Computer Systems Science and Engineering

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Fog computing is a promising technology that has been emerged to handle the growth of smart devices as well as the popularity of latency-sensitive and location-awareness Internet of Things (IoT) services. After the emergence of IoT-based services, the industry of internet-based devices has grown. The number of these devices has raised from millions to billions, and it is expected to increase further in the near future. Thus, additional challenges will be added to the traditional centralized cloud-based architecture as it will not be able to handle that growth and to support all connected devices in real-time without affecting the user experience. Conventional data aggregation models for Fog enabled IoT environments possess high computational complexity and communication cost. Therefore, in order to resolve the issues and improve the lifetime of the network, this study develops an effective hierarchical data aggregation with chaotic barnacles mating optimizer (HDAG-CBMO) technique. The HDAG-CBMO technique derives a fitness function from many relational matrices, like residual energy, average distance to neighbors, and centroid degree of target area. Besides, a chaotic theory based population initialization technique is derived for the optimal initial position of barnacles. Moreover, a learning based data offloading method has been developed for reducing the response time to IoT user requests. A wide range of simulation analyses demonstrated that the HDAG-CBMO technique has resulted in balanced energy utilization and prolonged lifetime of the Fog assisted IoT networks.

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Section: Literature Reviewmentioning

confidence: 99%

Hierarchical Data Aggregation with Data Offloading Scheme for Fog Enabled IoT Environment

Nalayini¹,

Prakash²

2023

Computer Systems Science and Engineering

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“…This method helps them to maintain privacy and charge less amount of fees from the users. In the paper, Chen et al [13], to provide more resources and security in IoT devices a data aggregation method has been proposed. In this method, a three-layer security framework using fog computing has been developed to provide integrity and confidentiality.…”

Section: Figure 1 Data Aggregation In Wsnsmentioning

confidence: 99%

“…to evaluate the attack risk, consider an energy constraint 𝐸 which consists of all the absolute nodes given as 𝕀 1 and consider the dishonest nodes as 𝕀 0 . These considerations are expressed as (13).…”

Section: 𝑦 ̃𝑗 = 𝑁(𝑦 𝑗 ) = 𝑦 𝑗 + 𝛿 𝑗mentioning

confidence: 99%

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Two phase secure data collection technique for wireless sensor networks

Thahniyath

Mishra

Moorthy

2022

IJEECS

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Wireless <span>sensor networks (WSNs) are the sensors that are dispersed in a different location that can sense the accumulated data in real-time and send it to the central location for the process of data aggregation. During the transfer of the information using the data nodes from the WSNs to the central location, there may be chances that the data node could be compromised by sensor failure or by an attack from a malicious user. To overcome this problem, we propose a two-phase secure data collection (TPSDC) technique for wireless sensor networks which provides confidentiality and integrity for the data nodes that are being sent to the central location and also during the data aggregation in the central location. Various existing methods have been proposed to secure the data when sent from the WSNs to the internet of things (IoT) devices but they lack to provide both confidentiality and integrity at the same time. Hence our model provides both integrity and confidentiality by providing security to the data nodes. Experimental results show that our model TPSDC performs better in terms of misclassification rate, detection rate, throughput, network lifetime analysis of the node, and communication overhead of the node when compared with the existing methods.</span>

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“…Nowadays cloud computing [1] plays the role of backbone in industry, education systems [2] and its popularity causes an explosive growth in cloud data and network traffic [3]. The exponential increase in the data and network traffic cause challenges in cloud computing [4] for instance, overloading of data centers and delays in task processing.…”

Section: Introductionmentioning

confidence: 99%

Cost Optimization in Cloud Environment based on Task Deadline

Ahmad

Munir

Ramzan

2022

Preprint

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The task of a service broker in a cloud computing environment is vital and challenging. Several sophisticated techniques have been proposed to improve the overall performance and minimize the cost. Most of the approaches optimize either performance or the cost; however, the users of cloud and fog need the best compromise for both. In this paper, we propose a hybrid technique that is Cost Optimization in the cloud/fog environment based on Task Deadline (COTD) that focuses on both parameters simultaneously and maintains the Quality of Service. The proposed algorithm is based on the task deadline and selects the most appropriate data center for task processing. We have implemented and tested COTD on a well-known simulation tool Cloud Analyst. Our comprehensive testbed experiments and simulations show that COTD outperforms comparable with Service Proximity Based Routing and Performance-Optimized Routing, and successfully minimizes an average of 35% cost while maintaining the same response time.

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Privacy and Energy Co-Aware Data Aggregation Computation Offloading for Fog-Assisted IoT Networks

Cited by 20 publications

References 36 publications

Hierarchical Data Aggregation with Data Offloading Scheme for Fog Enabled IoT Environment

Hierarchical Data Aggregation with Data Offloading Scheme for Fog Enabled IoT Environment

Two phase secure data collection technique for wireless sensor networks

Cost Optimization in Cloud Environment based on Task Deadline

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