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Lightweight searchable encryption protocol for industrial internet of things

- Zhang, Ke, Long, Jiahuan, Wang, Xiaofen, Dai, Hong-Ning, Liang, Kaitai, Imran, Muhammad

  • Authors: Zhang, Ke , Long, Jiahuan , Wang, Xiaofen , Dai, Hong-Ning , Liang, Kaitai , Imran, Muhammad
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Informatics Vol. 17, no. 6 (2021), p. 4248-4259
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  • Description: Industrial Internet of Things (IoT) has suffered from insufficient identity authentication and dynamic network topology, thereby resulting in vulnerabilities to data confidentiality. Recently, the attribute-based encryption (ABE) schemes have been regarded as a solution to ensure data transmission security and the fine-grained sharing of encrypted IoT data. However, most of existing ABE schemes that bring tremendous computational cost are not suitable for resource-constrained IoT devices. Therefore, lightweight and efficient data sharing and searching schemes suitable for IoT applications are of great importance. To this end, In this article, we propose a light searchable ABE scheme (namely LSABE). Our scheme can significantly reduce the computing cost of IoT devices with the provision of multiple-keyword searching for data users. Meanwhile, we extend the LSABE scheme to multiauthority scenarios so as to effectively generate and manage the public/secret keys in the distributed IoT environment. Finally, the experimental results demonstrate that our schemes can significantly maintain computational efficiency and save the computational cost at IoT devices, compared to other existing schemes. © 2005-2012 IEEE.

Machine learning for 5G security : architecture, recent advances, and challenges

- Afaq, Amir, Haider, Noman, Baig, Muhammad, Khan, Komal, Imran, Muhammad, Razzak, Imran

  • Authors: Afaq, Amir , Haider, Noman , Baig, Muhammad , Khan, Komal , Imran, Muhammad , Razzak, Imran
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Ad Hoc Networks Vol. 123, no. (2021), p.
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  • Description: The granularization of crucial network functions implementation using software-centric, and virtualized approaches in 5G networks have brought forth unprecedented security challenges in general and privacy concerns. Moreover, these software components’ premature deployment and compromised supply chain put the individual network components at risk and have a ripple effect for the rest of the network. Some of the novel threats to 5G assets include tampering in identity and access management, supply-chain poisoning, masquerade and bot attacks, loop-holes in source codes. Machine learning (ML) in this context can help to provide heavily dynamic and robust security mechanisms for the software-centric architecture of 5G Networks. ML models’ development and implementation also rely on programmable environments; hence, they can play a vital role in designing, modelling, and automating efficient security protocols. This article presents the threat landscape across 5G networks and discusses the feasibility and architecture of different ML-based models to counter these threats. Also, we present the architecture for automated threat intelligence using cooperative and coordinated ML to secure 5G assets and infrastructure. We also present the summary of closely related existing works along with future research challenges. © 2021 Elsevier B.V.

Machine Learning Techniques for 5G and beyond

- Kaur, Jasneet, Khan, M. Arif, Iftikhar, Mohsin, Imran, Muhammad, Emad Ul Haq, Qazi

  • Authors: Kaur, Jasneet , Khan, M. Arif , Iftikhar, Mohsin , Imran, Muhammad , Emad Ul Haq, Qazi
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 9, no. (2021), p. 23472-23488
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  • Description: Wireless communication systems play a very crucial role in modern society for entertainment, business, commercial, health and safety applications. These systems keep evolving from one generation to next generation and currently we are seeing deployment of fifth generation (5G) wireless systems around the world. Academics and industries are already discussing beyond 5G wireless systems which will be sixth generation (6G) of the evolution. One of the main and key components of 6G systems will be the use of Artificial Intelligence (AI) and Machine Learning (ML) for such wireless networks. Every component and building block of a wireless system that we currently are familiar with from our knowledge of wireless technologies up to 5G, such as physical, network and application layers, will involve one or another AI/ML techniques. This overview paper, presents an up-to-date review of future wireless system concepts such as 6G and role of ML techniques in these future wireless systems. In particular, we present a conceptual model for 6G and show the use and role of ML techniques in each layer of the model. We review some classical and contemporary ML techniques such as supervised and un-supervised learning, Reinforcement Learning (RL), Deep Learning (DL) and Federated Learning (FL) in the context of wireless communication systems. We conclude the paper with some future applications and research challenges in the area of ML and AI for 6G networks. © 2013 IEEE.

Network slicing : a next generation 5G perspective

- Subedi, Prashant, Alsadoon, Abeer, Prasad, Prasad, Rehman, Sabih, Giweli, Nabil, Imran, Muhammad, Arif, Samrah

  • Authors: Subedi, Prashant , Alsadoon, Abeer , Prasad, Prasad , Rehman, Sabih , Giweli, Nabil , Imran, Muhammad , Arif, Samrah
  • Date: 2021
  • Type: Text , Journal article , Review
  • Relation: Eurasip Journal on Wireless Communications and Networking Vol. 2021, no. 1 (2021), p.
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  • Description: Fifth-generation (5G) wireless networks are projected to bring a major transformation to the current fourth-generation network to support the billions of devices that will be connected to the Internet. 5G networks will enable new and powerful capabilities to support high-speed data rates, better connectivity and system capacity that are critical in designing applications in virtual reality, augmented reality and mobile online gaming. The infrastructure of a network that can support stringent application requirements needs to be highly dynamic and flexible. Network slicing can provide these dynamic and flexible characteristics to a network architecture. Implementing network slicing in 5G requires domain modification of the preexisting network architecture. A network slicing architecture is proposed for an existing 5G network with the aim of enhancing network dynamics and flexibility to support modern network applications. To enable network slicing in a 5G network, we established the virtualisation of the underlying physical 5G infrastructure by utilising technological advancements, such as software-defined networking and network function virtualisation. These virtual networks can fulfil the requirement of multiple use cases as required by creating slices of these virtual networks. Thus, abstracting from the physical resources to create virtual networks and then applying network slicing on these virtual networks enable the 5G network to address the increased demands for high-speed communication. © 2021, The Author(s).

Resource optimization‐based software risk reduction model for large‐scale application development

- Shahzad, Basit, Amin, Fazal-e, Abro, Ahsanullah, Imran, Muhammad, Shoaib, Muhammad

  • Authors: Shahzad, Basit , Amin, Fazal-e , Abro, Ahsanullah , Imran, Muhammad , Shoaib, Muhammad
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Sustainability (Switzerland) Vol. 13, no. 5 (2021), p. 1-17
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  • Description: Software risks are a common phenomenon in the software development lifecycle, and risks emerge into larger problems if they are not dealt with on time. Software risk management is a strategy that focuses on the identification, management, and mitigation of the risk factors in the software development lifecycle. The management itself depends on the nature, size, and skill of the project under consideration. This paper proposes a model that deals with identifying and dealing with the risk factors by introducing different observatory and participatory project factors. It is as-sumed that most of the risk factors can be dealt with by doing effective business processing that in response deals with the orientation of risks and elimination or reduction of those risk factors that emerge over time. The model proposes different combinations of resource allocation that can help us conclude a software project with an extended amount of acceptability. This paper presents a Risk Reduction Model, which effectively handles the application development risks. The model can syn-chronize its working with medium to large‐scale software projects. The reduction in software failures positively affects the software development environment, and the software failures shall re-duce consequently. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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.

Secure big data ecosystem architecture : challenges and solutions

- Anwar, Memoona, Gill, Asif, Hussain, Farookh, Imran, Muhammad

  • Authors: Anwar, Memoona , Gill, Asif , Hussain, Farookh , Imran, Muhammad
  • Date: 2021
  • Type: Text , Journal article , Review
  • Relation: Eurasip Journal on Wireless Communications and Networking Vol. 2021, no. 1 (2021), p.
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  • Description: Big data ecosystems are complex data-intensive, digital–physical systems. Data-intensive ecosystems offer a number of benefits; however, they present challenges as well. One major challenge is related to the privacy and security. A number of privacy and security models, techniques and algorithms have been proposed over a period of time. The limitation is that these solutions are primarily focused on an individual or on an isolated organizational context. There is a need to study and provide complete end-to-end solutions that ensure security and privacy throughout the data lifecycle across the ecosystem beyond the boundary of an individual system or organizational context. The results of current study provide a review of the existing privacy and security challenges and solutions using the systematic literature review (SLR) approach. Based on the SLR approach, 79 applicable articles were selected and analyzed. The information from these articles was extracted to compile a catalogue of security and privacy challenges in big data ecosystems and to highlight their interdependencies. The results were categorized from theoretical viewpoint using adaptive enterprise architecture and practical viewpoint using DAMA framework as guiding lens. The findings of this research will help to identify the research gaps and draw novel research directions in the context of privacy and security in big data-intensive ecosystems. © 2021, The Author(s).

Smart dynamic traffic monitoring and enforcement system

- El-Hansali, Youssef, Outay, Fatma, Yasar, Ansar, Farrag, Siham, Shoaib, Muhammad, Imran, Muhammad, Awan, Hammad

  • Authors: El-Hansali, Youssef , Outay, Fatma , Yasar, Ansar , Farrag, Siham , Shoaib, Muhammad , Imran, Muhammad , Awan, Hammad
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Computers, Materials and Continua Vol. 67, no. 3 (2021), p. 2797-2806
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  • Description: Enforcement of traffic rules and regulations involves a wide range of complex tasks, many of which demand the use of modern technologies. variable speed limits (VSL) control is to change the current speed limit according to the current traffic situation based on the observed traffic conditions. The aim of this study is to provide a simulation-based methodological framework to evaluate (VSL) as an effective Intelligent Transportation System (ITS) enforcement system. The focus of the study is on measuring the effectiveness of the dynamic traffic control strategy on traffic performance and safety considering various performance indicators such as total travel time, average delay, and average number of stops. United Arab Emirates (UAE) was selected as a case study to evaluate the effectiveness of this strategy. A micro simulation software package VISSIM with add-on module VisVAP is used to evaluate the impacts of VSL. It has been observed that VSL control strategy reduced the average delay time per vehicle to around 7%, travel time by 3.2%, and number of stops by 48.5%. Dynamic traffic control strategies also alleviated congestion by increasing the capacity of the bottleneck section and improving safety. Results of this study would act as a guidance for engineers and decision makers to new traffic control system implementation. © 2021 Tech Science Press. All rights reserved.

Software-defined networks for resource allocation in cloud computing : a survey

- Mohamed, Arwa, Hamdan, Mosab, Khan, Suleman, Abdelaziz, Abdelaziz, Babiker, Sharief, Imran, Muhammad, Marsono, M.

  • Authors: Mohamed, Arwa , Hamdan, Mosab , Khan, Suleman , Abdelaziz, Abdelaziz , Babiker, Sharief , Imran, Muhammad , Marsono, M.
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Computer Networks Vol. 195, no. (2021), p.
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  • Description: Cloud computing has a shared set of resources, including physical servers, networks, storage, and user applications. Resource allocation is a critical issue for cloud computing, especially in Infrastructure-as-a-Service (IaaS). The decision-making process in the cloud computing network is non-trivial as it is handled by switches and routers. Moreover, the network concept drifts resulting from changing user demands are among the problems affecting cloud computing. The cloud data center needs agile and elastic network control functions with control of computing resources to ensure proper virtual machine (VM) operations, traffic performance, and energy conservation. Software-Defined Network (SDN) proffers new opportunities to blueprint resource management to handle cloud services allocation while dynamically updating traffic requirements of running VMs. The inclusion of an SDN for managing the infrastructure in a cloud data center better empowers cloud computing, making it easier to allocate resources. In this survey, we discuss and survey resource allocation in cloud computing based on SDN. It is noted that various related studies did not contain all the required requirements. This study is intended to enhance resource allocation mechanisms that involve both cloud computing and SDN domains. Consequently, we analyze resource allocation mechanisms utilized by various researchers; we categorize and evaluate them based on the measured parameters and the problems presented. This survey also contributes to a better understanding of the core of current research that will allow researchers to obtain further information about the possible cloud computing strategies relevant to IaaS resource allocation. © 2021

The role of unmanned aerial vehicles and mmWave in 5G : recent advances and challenges

- Khan, Shah, Naseem, Usman, Siraj, Haris, Razzak, Imran, Imran, Muhammad

  • Authors: Khan, Shah , Naseem, Usman , Siraj, Haris , Razzak, Imran , Imran, Muhammad
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Transactions on Emerging Telecommunications Technologies Vol. 32, no. 7 (2021), p.
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  • Description: Next-generation wireless communication networks, in particular, the densified 5G will bring many developments to the existing telecommunications industry. The key benefits will be the higher throughput and very low latency. In this context, the usage of unmanned aerial vehicle (UAV) is becoming a feasible option for deploying 5G services on demand. At the same time, the immense bandwidth potential of mmWave has strengthened its performance in radio communication. In this article, we provide a consolidated synthesis on the role of UAVs and mmWave in 5G, emphasis on recent developments and challenges. The review focuses on UAV relay architectures, identifies the relevant problems and limitations in the deployment of UAVs using mmWave in both access and backhaul links simultaneously. There is a critical analysis of the optimum placement of the UAVs as a relay with a focus on the mmWave band. The distinctive rich characteristics of the mmWave propagation and scattering are presented. We also synthesis mmWave path loss models. Then, the scope of artificial intelligence and machine learning techniques as an efficient solution for combating the dynamic and complex nature of UAV-based cellular communication networks are discussed. In the end, security and privacy issues in UAV-based cellular network are spotlighted. It is believed that the literature discussed, and the findings reached in this article are of significant importance to researchers, application engineers and decision-makers in the designing and deployment of UAV-supported 5G network. © 2021 John Wiley & Sons, Ltd.

Traffic accident detection and condition analysis based on social networking data

- Ali, Farman, Ali, Amjad, Imran, Muhammad, Naqvi, Rizwan, Siddiqi, Muhammad, Kwak, Kyung-Sup

  • Authors: Ali, Farman , Ali, Amjad , Imran, Muhammad , Naqvi, Rizwan , Siddiqi, Muhammad , Kwak, Kyung-Sup
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Accident Analysis and Prevention Vol. 151, no. (2021), p.
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  • Description: Accurate detection of traffic accidents as well as condition analysis are essential to effectively restoring traffic flow and reducing serious injuries and fatalities. This goal can be obtained using an advanced data classification model with a rich source of traffic information. Several systems based on sensors and social networking platforms have been presented recently to detect traffic events and monitor traffic conditions. However, sensor-based systems provide limited information, and may fail owing to the long detection times and high false-alarm rates. In addition, social networking data are unstructured, unpredictable, and contain idioms, jargon, and dynamic topics. The machine learning algorithms utilized for traffic event detection might not extract valuable information from social networking data. In this paper, a social network–based, real-time monitoring framework is proposed for traffic accident detection and condition analysis using ontology and latent Dirichlet allocation (OLDA) and bidirectional long short-term memory (Bi-LSTM). First, the query-based search engine effectively collects traffic information from social networks, and the data preprocessing module transforms it into structured form. Second, the proposed OLDA-based topic modeling method automatically labels each sentence (e.g., traffic or non-traffic) to identify the exact traffic information. In addition, the ontology-based event recognition approach detects traffic events from traffic-related data. Next, the sentiment analysis technique identifies the polarity of traffic events employing user's opinions, which helps determine accurate conditions of traffic events. Finally, the FastText model and Bi-LSTM with softmax regression are trained for traffic event detection and condition analysis. The proposed framework is evaluated using traffic-related data, comparing OLDA and Bi-LSTM with existing topic modeling methods and traditional classifiers using word embedding models, respectively. Our system outperforms state-of-the-art methods and achieves accuracy of 97 %. This finding demonstrates that the proposed system is more efficient for traffic event detection and condition analysis, in comparison to other existing systems. © 2021 Elsevier Ltd

Treating class imbalance in non-technical loss detection : an exploratory analysis of a real dataset

- Ghori, Khawaja, Awais, Muhammad, Khattak, Akmal, Imran, Muhammad, Amin, Fazal, Szathmary, Laszlo

  • Authors: Ghori, Khawaja , Awais, Muhammad , Khattak, Akmal , Imran, Muhammad , Amin, Fazal , Szathmary, Laszlo
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 9, no. (2021), p. 98928-98938
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  • Description: Non-Technical Loss (NTL) is a significant concern for many electric supply companies due to the financial impact caused as a result of suspect consumption activities. A range of machine learning classifiers have been tested across multiple synthesized and real datasets to combat NTL. An important characteristic that exists in these datasets is the imbalance distribution of the classes. When the focus is on predicting the minority class of suspect activities, the classifiers' sensitivity to the class imbalance becomes more important. In this paper, we evaluate the performance of a range of classifiers with under-sampling and over-sampling techniques. The results are compared with the untreated imbalanced dataset. In addition, we compare the performance of the classifiers using penalized classification model. Lastly, the paper presents an exploratory analysis of using different sampling techniques on NTL detection in a real dataset and identify the best performing classifiers. We conclude that logistic regression is the most sensitive to the sampling techniques as the change of its recall is measured around 50% for all sampling techniques. While the random forest is the least sensitive to the sampling technique, the difference in its precision is observed between 1% - 6% for all sampling techniques. © 2013 IEEE.

6G wireless systems : a vision, architectural elements, and future directions

- Khan, Latif, Yaqoob, Ibrar, Imran, Muhammad, Han, Zhu, Hong, Choong

  • Authors: Khan, Latif , Yaqoob, Ibrar , Imran, Muhammad , Han, Zhu , Hong, Choong
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 8, no. (2020), p. 147029-147044
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  • Description: Internet of everything (IoE)-based smart services are expected to gain immense popularity in the future, which raises the need for next-generation wireless networks. Although fifth-generation (5G) networks can support various IoE services, they might not be able to completely fulfill the requirements of novel applications. Sixth-generation (6G) wireless systems are envisioned to overcome 5G network limitations. In this article, we explore recent advances made toward enabling 6G systems. We devise a taxonomy based on key enabling technologies, use cases, emerging machine learning schemes, communication technologies, networking technologies, and computing technologies. Furthermore, we identify and discuss open research challenges, such as artificial-intelligence-based adaptive transceivers, intelligent wireless energy harvesting, decentralized and secure business models, intelligent cell-less architecture, and distributed security models. We propose practical guidelines including deep Q-learning and federated learning-based transceivers, blockchain-based secure business models, homomorphic encryption, and distributed-ledger-based authentication schemes to cope with these challenges. Finally, we outline and recommend several future directions. © 2013 IEEE.

A blockchain based privacy-preserving system for electric vehicles through local communication

- Yahaya, Adamu, Javaid, Nadeem, Khalid, Rabiya, Imran, Muhammad, Naseer, Nidal

A blockchain-based decentralized energy management in a P2P trading system

- Khalid, Rabiya, Javaid, Nadeem, Javaid, Sakeena, Imran, Muhammad, Naseer, Nidal

  • Authors: Khalid, Rabiya , Javaid, Nadeem , Javaid, Sakeena , Imran, Muhammad , Naseer, Nidal
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE International Conference on Communications, ICC 2020, Dublin, Ireland, 7 to 11 June, IEEE International Conference on Communications Vol. 2020-June
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  • Description: Local energy generation and peer to peer (P2P) energy trading in the local market can reduce energy consumption cost, emission of harmful gases (as renewable energy sources (RESs) are used to generate energy at user's premises) and increase smart grid resilience. In this paper, to implement a hybrid P2P energy trading market, a blockchain-based solution is proposed. A blockchain-based system is fully decentralized and it allows the market members to interact with each other and trade energy without involving any third party. Smart contracts play a very important role in the blockchain-based energy trading market. They contain all the necessary rules for energy trading. We have proposed three smart contracts to implement the hybrid electricity trading market. The market members interact with main smart contract which requests P2P smart contract and prosumer to grid (P2G) smart contract for further processing. The main objectives of this paper are to propose a model to implement an efficient hybrid energy trading market while reducing cost and peak to average ratio (PAR) of electricity. © 2020 IEEE.

A blockchain-based privacy-preserving mechanism with aggregator as common communication point

- Yahaya, Adamu, Javaid, Nadeem, Khalid, Rabiya, Imran, Muhammad, Guizani, Mohsen

  • Authors: Yahaya, Adamu , Javaid, Nadeem , Khalid, Rabiya , Imran, Muhammad , Guizani, Mohsen
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE International Conference on Communications, ICC 2020, Dublin, Ireland, 7 to 11 June, IEEE International Conference on Communications Vol. 2020-June
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  • Description: The high penetration of renewable energy resources into the distributed system and their intermittent behavior of the non-dispatchable generation causes issues of demand supply mismatch and serious security and privacy concerned in the system. It is believed that incorporating blockchain will reduce costs, enhance data security, and improve the system efficiency. However, privacy issues are not completely eliminated and can hinder the wide applications of blockchain. In the study, we present a Reputation Based Starvation Free Energy Allocation Policy (Reputation-SFEAP) in a decentralized and distributed blockchain-based energy trading; while keeping Aggregator as Common Communication Point. In addition, Identity-Based encryption (ID-Based encryption) technique is added that improves transactional information privacy. According to the research analysis, it is observed that the proposed system model has optimal and fair energy allocation algorithms, which prevent all the energy users from energy starvation and share the available energy accordingly. Moreover, the incorporated encryption system has greater security-privacy level, which protects passive attacker and disguises attacker from penetration. © 2020 IEEE.

A cooperative heterogeneous vehicular clustering mechanism for road traffic management

- Ahmad, Iftikhar, Noor, Rafidah, Zaba, Muhammad, Qureshi, Muhammad, Imran, Muhammad, Shoaib, Muhammad

  • Authors: Ahmad, Iftikhar , Noor, Rafidah , Zaba, Muhammad , Qureshi, Muhammad , Imran, Muhammad , Shoaib, Muhammad
  • Date: 2020
  • Type: Text , Journal article
  • Relation: International Journal of Parallel Programming Vol. 48, no. 5 (2020), p. 870-889
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  • Description: The vehicular ad-hoc networks integrates with long-term evolution (LTE) forming a heterogeneous network, capable of providing seamless connectivity, which meets the communication requirements of intelligent transportation systems. However, heterogeneous network-based applications involve LTE resource (data and spectrum) usage cost and must be taken care while developing such a solution. One of the scenarios is the access of the information to/from remote server over the internet via LTE for road traffic management applications. Although clustering of the vehicle is significant to minimize the data and LTE network usage, however, the problem of non-cooperation of the vehicles in clustering process and within a cluster are major issues in sharing costly data acquired from the internet. Because, who and why one (vehicle) should pay the cost is the big question, proliferating the non-cooperative behavior among the cluster members. To solve these issues, strategic game-theoretic based clustering mechanism named as cooperative interest-aware clustering (CIAC) is developed. The proposed CIAC not only balance the cost of usage by controlling non-cooperative behavior among the vehicles within the cluster but at the same time motivate vehicles to participate in the clustering process to share the data and cost as well. It consists of a cluster head selection process based on the strategic game-theoretic approach and a fair-use policy. The implementation results show superiority in performance of our protocol over the existing approaches. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

A deep learning model based on concatenation approach for the diagnosis of brain tumor

- Noreen, Neelum, Palaniappan, Sellappan, Qayyum, Abdul, Ahmad, Iftikhar, Imran, Muhammad, Shoaib, M.uhammad

  • Authors: Noreen, Neelum , Palaniappan, Sellappan , Qayyum, Abdul , Ahmad, Iftikhar , Imran, Muhammad , Shoaib, M.uhammad
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 8, no. (2020), p. 55135-55144
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  • Description: Brain tumor is a deadly disease and its classification is a challenging task for radiologists because of the heterogeneous nature of the tumor cells. Recently, computer-aided diagnosis-based systems have promised, as an assistive technology, to diagnose the brain tumor, through magnetic resonance imaging (MRI). In recent applications of pre-trained models, normally features are extracted from bottom layers which are different from natural images to medical images. To overcome this problem, this study proposes a method of multi-level features extraction and concatenation for early diagnosis of brain tumor. Two pre-trained deep learning models i.e. Inception-v3 and DensNet201 make this model valid. With the help of these two models, two different scenarios of brain tumor detection and its classification were evaluated. First, the features from different Inception modules were extracted from pre-trained Inception-v3 model and concatenated these features for brain tumor classification. Then, these features were passed to softmax classifier to classify the brain tumor. Second, pre-trained DensNet201 was used to extract features from various DensNet blocks. Then, these features were concatenated and passed to softmax classifier to classify the brain tumor. Both scenarios were evaluated with the help of three-class brain tumor dataset that is available publicly. The proposed method produced 99.34 %, and 99.51% testing accuracies respectively with Inception-v3 and DensNet201 on testing samples and achieved highest performance in the detection of brain tumor. As results indicated, the proposed method based on features concatenation using pre-trained models outperformed as compared to existing state-of-the-art deep learning and machine learning based methods for brain tumor classification. © 2013 IEEE.

A joint SLM and precoding based PAPR reduction scheme for 5G UFMC cellular networks

- Baig, Imran, Farooq, Umer, Hasan, Najam, Zghaibeh, Manaf, Arshad, Muhammad, Imran, Muhammad

A microservice recommendation mechanism based on mobile architecture

- Wang, Ru, Imran, Muhammad, Saleem, Kashif

  • Authors: Wang, Ru , Imran, Muhammad , Saleem, Kashif
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Journal of Network and Computer Applications Vol. 152, no. (2020), p.
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  • Description: The microservice recommendation mechanism, as a key supporting technology for providing users with intelligent and proactive application services, is one of the important research contents in mobile social networks. In this paper, a recommendation mechanism of mobile social network service based on user trajectory, trust network and collaborative filtering algorithm is proposed. The user trajectory prediction model based on Generative Adversarial Network (GAN) is introduced into the process of service recommendation, and combined with trust degree to constitute the 3D service recommendation model of user-service-trajectory prediction. This method improves the accuracy and reliability of service recommendation and avoids the blindness and randomness of service recommendation. The simulation experiments show that the service recommendation algorithm has higher accuracy than the traditional collaborative filtering algorithm. © 2019 Elsevier Ltd