An IoT-Oriented Offloading Method with Privacy Preservation for Cloudlet-Enabled Wireless Metropolitan Area Networks

Xu, Zhezhuang; Gu, Renhao; Huang, Tao; Xiang, Haolong; Zhang, Xuyun; Qi, Lianyong; Xu, Xiaolong

doi:10.3390/s18093030

Cited by 19 publications

(11 citation statements)

References 52 publications

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“…us, privacy preservation is becoming more important [77][78][79]. In addition, intelligent IoT applications enhanced with cloud, edge, and fog computing increasingly deal with personal information to provide intelligent services, and many studies on personal information protection and data protection are being conducted [80][81][82][83].…”

Section: Keyword Clustering and Evolution Of Research On Iotmentioning

confidence: 99%

Current Research Trends in IoT Security: A Systematic Mapping Study

Lee

2021

Mobile Information Systems

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The smart mobile Internet-of-things (IoT) network lays the foundation of the fourth industrial revolution, the era of hyperconnectivity, hyperintelligence, and hyperconvergence. As this revolution gains momentum, the security of smart mobile IoT networks becomes an essential research topic. This study aimed to provide comprehensive insights on IoT security. To this end, we conducted a systematic mapping study of the literature to identify evolving trends in IoT security and determine research subjects. We reviewed the literature from January 2009 to August 2020 to identify influential researchers and trends of keywords. We additionally performed structural topic modeling to identify current research topics and the most promising ones via topic trend estimation. We synthesized and interpreted the results of the systematic mapping study to devise future research directions. The results obtained from this study are useful to understand current trends in IoT security and provide insights into research and development of IoT security.

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Section: Keyword Clustering and Evolution Of Research On Iotmentioning

confidence: 99%

Current Research Trends in IoT Security: A Systematic Mapping Study

Lee

2021

Mobile Information Systems

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“…As most of the IoT data is relevant to personal privacy, it is necessary to pay attention to data transmission security. The authors of [49] investigated an IoT-oriented offloading method (IOM) which is enclave with privacy preservation to solve the problem of privacy in Cloudlet-enabled Wireless Metropolitan Area Networks (CWMAN). Their research and that of [50] adopted the non-dominated sorting differential evolution algorithm (NSDE) in order to optimize the multi-objective problem.…”

Section: Related Workmentioning

confidence: 99%

Realizing Efficient Security and Privacy in IoT Networks

Anajemba

Tang

Iwendi

et al. 2020

Sensors

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In recent times, security and privacy at the physical (PHY) layer has been a major issue of several communication technologies which comprise the internet of things (IoT) and mostly, the emerging fifth-generation (5G) cellular network. The most real-world PHY security challenge stems from the fact that the passive eavesdropper’s information is unavailable to the genuine source and destination (transmitter/receiver) nodes in the network. Without this information, it is difficult to optimize the broadcasting parameters. Therefore, in this research, we propose an efficient sequential convex estimation optimization (SCEO) algorithm to mitigate this challenge and improve the security of physical layer (PHY) in a three-node wireless communication network. The results of our experiments indicate that by using the SCEO algorithm, an optimal performance and enhanced convergence is achieved in the transmission. However, considering possible security challenges envisaged when a multiple eavesdropper is active in a network, we expanded our research to develop a swift privacy rate optimization algorithm for a multiple-input, multiple-output, multiple-eavesdropper (MIMOME) scenario as it is applicable to security in IoT and 5G technologies. The result of the investigation show that the algorithm executes significantly with minimal complexity when compared with nonoptimal parameters. We further employed the use of rate constraint together with self-interference of the full-duplex transmission at the receiving node, which makes the performance of our technique outstanding when compared with previous studies.

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“…The use of differential evolution is motivated by the nature of the problems arising in this area which are often modeled as multi-objective, multimodal or non-convex optimization problems, of which DE can handle effectively [30]. An example of a multi-objective problem is the case where DE was employed to optimize resource utilization, energy consumption and data transmission with security controls as constraints [31]. The multi-objective problem is known as deployment issues which include variables such as connectivity, coverage, lifetime, clustering and reliability [32], construction cost and total head loss in the network [33].…”

Section: Differential Evolution In Wireless Communicationmentioning

confidence: 99%

Quantile Approximation of the Erlang Distribution using Differential Evolution Algorithm

Okagbue¹

2020

IJATCSE

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Erlang distribution is a particular case of the gamma distribution and is often used in modeling queues, traffic congestion in wireless sensor networks, cell residence duration and finding the optimal queueing model to reduce the probability of blocking. The application is limited because of the unavailability of closed-form expression for the quantile (inverse cumulative distribution) function of the distribution. The problem is primarily tackled using approximation since the inversion method cannot be applied. This paper extended a six parameter quantile model earlier proposed to the Nakagami distribution to the Erlang distributions. Consequently, the established relationship between the two distributions is now extended to their quantile functions. The quantile model was used to fit the machine (R software) values with their corresponding quartiles in two ways. Firstly, artificial neural network (ANN) was used to establish that a curve fitting can be achieved. Lastly, differential evolution (DE) algorithm was used to minimize the errors obtained from the curve fitting and hence estimate the values of the six parameters of the quantile model that will ensure the best possible fit, for different values of the parameters that characterize Erlang distribution. Hence, the problem is constrained optimization in nature and the DE algorithm was able to find the different values of the parameters of the quantile model. The simulation result corroborates theoretical findings. The work is a welcome result for the quest for a universal quantile model that can be applied to different distributions.

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An IoT-Oriented Offloading Method with Privacy Preservation for Cloudlet-Enabled Wireless Metropolitan Area Networks

Cited by 19 publications

References 52 publications

Current Research Trends in IoT Security: A Systematic Mapping Study

Current Research Trends in IoT Security: A Systematic Mapping Study

Realizing Efficient Security and Privacy in IoT Networks

Quantile Approximation of the Erlang Distribution using Differential Evolution Algorithm

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