List of Titles

Process migration-based computational offloading framework for IoT-supported mobile edge/cloud computing

- Yousafzai, Abdullah, Yaqoob, Ibrar, Imran, Muhammad, Gani, Abdullah, Md Noor, Rafidah

Authors: Yousafzai, Abdullah , Yaqoob, Ibrar , Imran, Muhammad , Gani, Abdullah , Md Noor, Rafidah
Date: 2020
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 7, no. 5 (2020), p. 4171-4182
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Description: Mobile devices have become an indispensable component of Internet of Things (IoT). However, these devices have resource constraints in processing capabilities, battery power, and storage space, thus hindering the execution of computation-intensive applications that often require broad bandwidth, stringent response time, long-battery life, and heavy-computing power. Mobile cloud computing and mobile edge computing (MEC) are emerging technologies that can meet the aforementioned requirements using offloading algorithms. In this article, we analyze the effect of platform-dependent native applications on computational offloading in edge networks and propose a lightweight process migration-based computational offloading framework. The proposed framework does not require application binaries at edge servers and thus seamlessly migrates native applications. The proposed framework is evaluated using an experimental testbed. Numerical results reveal that the proposed framework saves almost 44% of the execution time and 84% of the energy consumption. Hence, the proposed framework shows profound potential for resource-intensive IoT application processing in MEC. © 2014 IEEE.

ESMLB : efficient switch migration-based load balancing for multicontroller SDN in IoT

- Sahoo, Kshira, Puthal, Deepak, Tiwary, Mayank, Usman, Muhammad, Sahoo, Bibhudatta, Wen, Zhenyu, Sahoo, Biswa, Ranjan, Rajiv

Authors: Sahoo, Kshira , Puthal, Deepak , Tiwary, Mayank , Usman, Muhammad , Sahoo, Bibhudatta , Wen, Zhenyu , Sahoo, Biswa , Ranjan, Rajiv
Date: 2020
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 7, no. 7 (2020), p. 5852-5860
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Description: In software-defined networks (SDNs), the deployment of multiple controllers improves the reliability and scalability of the distributed control plane. Recently, edge computing (EC) has become a backbone to networks where computational infrastructures and services are getting closer to the end user. The unique characteristics of SDN can serve as a key enabler to lower the complexity barriers involved in EC, and provide better quality-of-services (QoS) to users. As the demand for IoT keeps growing, gradually a huge number of smart devices will be connected to EC and generate tremendous IoT traffic. Due to a huge volume of control messages, the controller may not have sufficient capacity to respond to them. To handle such a scenario and to achieve better load balancing, dynamic switch migrating is one effective approach. However, a deliberate mechanism is required to accomplish such a task on the control plane, and the migration process results in high network delay. Taking it into consideration, this article has introduced an efficient switch migration-based load balancing (ESMLB) framework, which aims to assign switches to an underutilized controller effectively. Among many alternatives for selecting a target controller, a multicriteria decision-making method, i.e., the technique for order preference by similarity to an ideal solution (TOPSIS), has been used in our framework. This framework enables flexible decision-making processes for selecting controllers having different resource attributes. The emulation results indicate the efficacy of the ESMLB. © 2014 IEEE.

Adaptive transmission optimization in SDN-based industrial internet of things with edge computing

- Li, Xiaomin, Li, Di, Wan, Jiafu, Liu, Chengliang, Imran, Muhammad

Authors: Li, Xiaomin , Li, Di , Wan, Jiafu , Liu, Chengliang , Imran, Muhammad
Date: 2018
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 5, no. 3 (2018), p. 1351-1360
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Description: In recent years, smart factory in the context of Industry 4.0 and industrial Internet of Things (IIoT) has become a hot topic for both academia and industry. In IIoT system, there is an increasing requirement for exchange of data with different delay flows among different smart devices. However, there are few studies on this topic. To overcome the limitations of traditional methods and address the problem, we seriously consider the incorporation of global centralized software defined network (SDN) and edge computing (EC) in IIoT with EC. We propose the adaptive transmission architecture with SDN and EC for IIoT. Then, according to data streams with different latency constrains, the requirements can be divided into two groups: 1) ordinary and 2) emergent stream. In the low-deadline situation, a coarse-grained transmission path algorithm provided by finding all paths that meet the time constrains in hierarchical Internet of Things (IoT). After that, by employing the path difference degree (PDD), an optimum routing path is selected considering the aggregation of time deadline, traffic load balances, and energy consumption. In the high-deadline situation, if the coarse-grained strategy is beyond the situation, a fine-grained scheme is adopted to establish an effective transmission path by an adaptive power method for getting low latency. Finally, the performance of proposed strategy is evaluated by simulation. The results demonstrate that the proposed scheme outperforms the related methods in terms of average time delay, goodput, throughput, PDD, and download time. Thus, the proposed method provides better solution for IIoT data transmission. © 2018 IEEE.

An application development framework for internet-of-things service orchestration

- Rafique, Wajid, Zhao, Xuan, Yu, Shui, Yaqoob, Ibrar, Imran, Muhammad, Dou, Wanchun

Authors: Rafique, Wajid , Zhao, Xuan , Yu, Shui , Yaqoob, Ibrar , Imran, Muhammad , Dou, Wanchun
Date: 2020
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 7, no. 5 (2020), p. 4543-4556
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Description: Application development for the Internet of Things (IoT) poses immense challenges due to the lack of standard development frameworks, tools, and techniques to assist end users in dealing with the complexity of IoT systems during application development. These challenges invoke the use of model-driven development (MDD) along with the representational state transfer (REST) architecture to develop IoT applications, supporting model generation at different abstraction levels while generating software implementation artifacts for heterogeneous platforms and ensuring loose coupling in complex IoT systems. This article proposes an IoT application development framework, named IADev, which uses attribute-driven design and MDD to address the above-mentioned challenges. This framework is composed of two major steps, including iterative architecture development using attribute-driven design and generating models to guide the transformation using MDD. IADev uses attribute-driven design to transform the requirements into a solution architecture by considering the concerns of all involved stakeholders, and then, MDD metamodels are generated to hierarchically transform the design components into the software artifacts. We evaluate IADev for a smart vehicle scenario in an intelligent transportation system to generate an executable implementation code for a real-world system. The case study experiments proclaim that IADev achieves higher satisfaction of the participants for the IoT application development and service orchestration, as compared to conventional approaches. Finally, we propose an architecture that uses IADev with the Siemens IoT cloud platform for service orchestration in industrial IoT. © 2014 IEEE.

A reconfigurable method for intelligent manufacturing based on industrial cloud and edge intelligence

- Tang, Hao, Li, Di, Wan, Jiafu, Imran, Muhammad, Shoaib, Muhammad

Authors: Tang, Hao , Li, Di , Wan, Jiafu , Imran, Muhammad , Shoaib, Muhammad
Date: 2020
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 7, no. 5 (2020), p. 4248-4259
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Description: The development of Industry 4.0 has provided the possibility to meet frequent changes in product type and batches, a sharp decline in the delivery cycle, constraints of quality cost, and other relevant parameters of customized production mode. Intelligent manufacturing, as a core of Industry 4.0, represents a deep integration of new IT technologies, such as the industrial Internet of Things and service-oriented architecture, and manufacturing process. To realize intelligent manufacturing, this article introduces a cloud-assisted and edge-decision-making manufacturing architecture that contains a cloud and production edges. An intelligent production edge is designed to provide the traditional devices the abilities of data access and self-decision making. Besides, the proposed architecture is modeled as a multiagent system with the edge intelligence support, describing the agent-based reconfiguration mechanism from the three aspects, namely, agent interaction, agent behavior, and negotiation mechanism. The experimental results show that the reconfigurable method based on the proposed architecture can be used in the mixed-flow production scenario based on random orders, to improve the adaptability and robustness. © 2014 IEEE.

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PAAL : a framework based on authentication, aggregation, and local differential privacy for internet of multimedia things

- Usman, Muhammad, Jan, Mian, Puthal, Deepak

Authors: Usman, Muhammad , Jan, Mian , Puthal, Deepak
Date: 2020
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 7, no. 4 (2020), p. 2501-2508
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Description: Internet of Multimedia Things (IoMT) applications generate huge volumes of multimedia data that are uploaded to cloud servers for storage and processing. During the uploading process, the IoMT applications face three major challenges, i.e., node management, privacy-preserving, and network protection. In this article, we propose a multilayer framework (PAAL) based on a multilevel edge computing architecture to manage end and edge devices, preserve the privacy of end-devices and data, and protect the underlying network from external attacks. The proposed framework has three layers. In the first layer, the underlying network is partitioned into multiple clusters to manage end-devices and level-one edge devices (LOEDs). In the second layer, the LOEDs apply an efficient aggregation technique to reduce the volumes of generated data and preserve the privacy of end-devices. The privacy of sensitive information in aggregated data is protected through a local differential privacy-based technique. In the last layer, the mobile sinks are registered with a level-two edge device via a handshaking mechanism to protect the underlying network from external threats. Experimental results show that the proposed framework performs better as compared to existing frameworks in terms of managing the nodes, preserving the privacy of end-devices and sensitive information, and protecting the underlying network. © 2014 IEEE.

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Contention resolution in wi-fi 6-enabled internet of things based on deep learning

- Chen, Chen, Li, Junchao, Balasubramanian, Venki, Wu, Yongqiang, Zhang, Yongqiang, Wan, Shaohua

Authors: Chen, Chen , Li, Junchao , Balasubramanian, Venki , Wu, Yongqiang , Zhang, Yongqiang , Wan, Shaohua
Date: 2021
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 8, no. 7 (2021), p. 5309-5320
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Description: Internet of Things (IoT) is expected to vastly increase the number of connected devices. As a result, a multitude of IoT devices transmit various information through wireless communication technology, such as the Wi-Fi technology, cellular mobile communication technology, low-power wide-area network (LPWAN) technology. However, even the latest Wi-Fi technology is still ready to accommodate these large amounts of data. Accurately setting the contention window (CW) value significantly affects the efficiency of the Wi-Fi network. Unfortunately, the standard collision resolution used by IEEE 802.11ax networks is nonscalable; thus, it cannot maintain stable throughput for an increasing number of stations, even when Wi-Fi 6 has been designed to improve performance in dense scenarios. To this end, we propose a CW control strategy for Wi-Fi 6 systems. This strategy leverages deep learning to search for optimal configuration of CW under different network conditions. Our deep neural network is trained by data generated from a Wi-Fi 6 simulation system with some varying key parameters, e.g., the number of nodes, short interframe space (SIFS), distributed interframe space (DIFS), and data transmission rate. Numerical results demonstrated that our deep learning scheme could always find the optimal CW adjustment multiple by adaptively perceiving the channel competition status. The finalized performance of our model has been significantly improved in terms of system throughput, average transmission delay, and packet retransmission rate. This makes Wi-Fi 6 better adapted to the access of a large number of IoT devices. © 2014 IEEE.

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Robust spammer detection using collaborative neural network in internet-of-things applications

- Guo, Zhiwei, Shen, Yu, Bashir, Ali, Imran, Muhammad, Kumar, Neeraj, Zhang, Di, Yu, Keping

Authors: Guo, Zhiwei , Shen, Yu , Bashir, Ali , Imran, Muhammad , Kumar, Neeraj , Zhang, Di , Yu, Keping
Date: 2021
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 8, no. 12 (2021), p. 9549-9558
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Description: Spamming is emerging as a key threat to the Internet of Things (IoT)-based social media applications. It will pose serious security threats to the IoT cyberspace. To this end, artificial intelligence-based detection and identification techniques have been widely investigated. The literature works on IoT cyberspace can be categorized into two categories: 1) behavior pattern-based approaches and 2) semantic pattern-based approaches. However, they are unable to effectively handle concealed, complicated, and changing spamming activities, especially in the highly uncertain environment of the IoT. To address this challenge, in this article, we exploit the collaborative awareness of both patterns, and propose a Collaborative neural network-based spammer detection mechanism (Co-Spam) in social media applications. In particular, it introduces multisource information fusion by collaboratively encoding long-term behavioral and semantic patterns. Hence, a more comprehensive representation of the feature space can be captured for further spammer detection. Empirically, we implement a series of experiments on two real-world data sets under different scenarios and parameter settings. The efficiency of the proposed Co-Spam is compared with five baselines with respect to several evaluation metrics. The experimental results indicate that the Co-Spam has an average performance improvement of approximately 5% compared to the baselines. © 2014 IEEE.

IoT Sensor Numerical Data Trust Model Using Temporal Correlation

- Karmakar, Gour, Das, Rajkumar, Kamruzzaman, Joarder

Authors: Karmakar, Gour , Das, Rajkumar , Kamruzzaman, Joarder
Date: 2020
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 7, no. 4 (2020), p. 2573-2581
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Description: Internet of Things (IoT) applications are increasingly being adopted for innovative and cost-effective services. However, the IoT devices and data are susceptible to various attacks, including cyberattacks, which emphasizes the need for pervasive security measure like trust evaluation on the fly. There exist several IoT numerical data trustworthiness measures which are based on the quality of information (QoI) and correlations. The QoI measurement techniques excessively exploit heuristics, while the correlation-based approaches predict temporal correlation using an average or moving average, which limits their efficacy. To improve accuracy and reliability, we propose a model for assessing trust of IoT sensor numerical data by representing the temporal correlation using temporal relationship. We represent the temporal relationship between data within a time window in two ways: first, using the discrete cosine transform (DCT) coefficients of daily data; and second, to obtain the impact of shuttle variation, we further divide the daily data into some time windows and calculate the average of each DCT coefficient over all time windows. These two feature sets are then used to develop two independent deep neural network models. The model outcomes are fused by the Dempster-Shepard theory to calculate trust scores. The strength of our model is evaluated using both trustworthy and untrustworthy data - the former are collected from sensors under controlled supervision in a smart city project in Melbourne, Australia and the latter are generated either by simulating breached sensors or perturbing real data. Our proposed approach outperforms a contemporary correlation-based approach in terms of trust score accuracy and consistency. © 2014 IEEE.

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