Showing items 181 - 200 of 238

Your selections:

Performance analysis of machine learning classifiers for non-technical loss detection

- Ghori, Khawaja, Imran, Muhammad, Nawaz, Asad, Abbasi, Rabeeh, Ullah, Ata, Szathmary, Laszlo

  • Authors: Ghori, Khawaja , Imran, Muhammad , Nawaz, Asad , Abbasi, Rabeeh , Ullah, Ata , Szathmary, Laszlo
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Journal of Ambient Intelligence and Humanized Computing Vol. 14, no. 11 (2023), p. 15327-15342
  • Full Text:
  • Reviewed:
  • Description: Power companies are responsible for producing and transferring the required amount of electricity from grid stations to individual households. Many countries suffer huge losses in billions of dollars due to non-technical loss (NTL) in power supply companies. To deal with NTL, many machine learning classifiers have been employed in recent time. However, few has been studied about the performance evaluation metrics that are used in NTL detection to evaluate how good or bad the classifier is in predicting the non-technical loss. This paper first uses three classifiers: random forest, K-nearest neighbors and linear support vector machine to predict the occurrence of NTL in a real dataset of an electric supply company containing approximately 80,000 monthly consumption records. Then, it computes 14 performance evaluation metrics across the three classifiers and identify the key scientific relationships between them. These relationships provide insights into deciding which classifier can be more useful under given scenarios for NTL detection. This work can be proved to be a baseline not only for the NTL detection in power industry but also for the selection of appropriate performance evaluation metrics for NTL detection. © 2020, The Author(s).

Performance analysis of priority-based IEEE 802.15.6 protocol in saturated traffic conditions

- Ullah, Sana, Tovar, Eduardo, Kim, Ki, Kim, Kyong, Imran, Muhammad

  • Authors: Ullah, Sana , Tovar, Eduardo , Kim, Ki , Kim, Kyong , Imran, Muhammad
  • Date: 2018
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 6, no. (2018), p. 66198-66209
  • Full Text:
  • Reviewed:
  • Description: Recent advancement in internet of medical things has enabled deployment of miniaturized, intelligent, and low-power medical devices in, on, or around a human body for unobtrusive and remote health monitoring. The IEEE 802.15.6 standard facilitates such monitoring by enabling low-power and reliable wireless communication between the medical devices. The IEEE 802.15.6 standard employs a carrier sense multiple access with collision avoidance protocol for resource allocation. It utilizes a priority-based backoff procedure by adjusting the contention window bounds of devices according to user requirements. As the performance of this protocol is considerably affected when the number of devices increases, we propose an accurate analytical model to estimate the saturation throughput, mean energy consumption, and mean delay over the number of devices. We assume an error-prone channel with saturated traffic conditions. We determine the optimal performance bounds for a fixed number of devices in different priority classes with different values of bit error ratio. We conclude that high-priority devices obtain quick and reliable access to the error-prone channel compared to low-priority devices. The proposed model is validated through extensive simulations. The performance bounds obtained in our analysis can be used to understand the tradeoffs between different priority levels and network performance. © 2018 IEEE.

Pervasive blood pressure monitoring using Photoplethysmogram (PPG) sensor

- Riaz, Farhan, Azad, Muhammad, Arshad, Junaid, Imran, Muhammad, Hassan, Ali, Rehman, Saad

  • Authors: Riaz, Farhan , Azad, Muhammad , Arshad, Junaid , Imran, Muhammad , Hassan, Ali , Rehman, Saad
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Future Generation Computer Systems Vol. 98, no. (2019), p. 120-130
  • Full Text:
  • Reviewed:
  • Description: Preventive healthcare requires continuous monitoring of the blood pressure (BP) of patients, which is not feasible using conventional methods. Photoplethysmogram (PPG) signals can be effectively used for this purpose as there is a physiological relation between the pulse width and BP and can be easily acquired using a wearable PPG sensor. However, developing real-time algorithms for wearable technology is a significant challenge due to various conflicting requirements such as high accuracy, computationally constrained devices, and limited power supply. In this paper, we propose a novel feature set for continuous, real-time identification of abnormal BP. This feature set is obtained by identifying the peaks and valleys in a PPG signal (using a peak detection algorithm), followed by the calculation of rising time, falling time and peak-to-peak distance. The histograms of these times are calculated to form a feature set that can be used for classification of PPG signals into one of the two classes: normal or abnormal BP. No public dataset is available for such study and therefore a prototype is developed to collect PPG signals alongside BP measurements. The proposed feature set shows very good performance with an overall accuracy of approximately 95%. Although the proposed feature set is effective, the significance of individual features varies greatly (validated using significance testing) which led us to perform weighted voting of features for classification by performing autoregressive modeling. Our experiments show that the simplest linear classifiers produce very good results indicating the strength of the proposed feature set. The weighted voting improves the results significantly, producing an overall accuracy of about 98%. Conclusively, the PPG signals can be effectively used to identify BP, and the proposed feature set is efficient and computationally feasible for implementation on standalone devices. © 2019 Elsevier B.V.

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
  • Full Text: false
  • Reviewed:
  • 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.

Process state synchronization-based application execution management for mobile edge/cloud computing

- Ahmed, Ejaz, Naveed, Anjum, Gani, Abdullah, Hamid, Siti, Imran, Muhammad, Guizani, Mohsen

  • Authors: Ahmed, Ejaz , Naveed, Anjum , Gani, Abdullah , Hamid, Siti , Imran, Muhammad , Guizani, Mohsen
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Future Generation Computer Systems Vol. 91, no. (2019), p. 579-589
  • Full Text: false
  • Reviewed:
  • Description: Mobile cloud computing (MCC) and mobile edge computing (MEC) facilitate the mobile devices to augment their capabilities by utilizing the resources and services offered by Cloud and Edge Cloud, respectively. However, due to mobility, network connection becomes unstable that causes application execution disruption. Such disruption increases the execution time and in some cases restrain the mobile devices from getting execution results from the cloud. This research work analyzes the impact of user mobility on the execution of cloud-based mobile applications. We propose a Process State Synchronization (PSS)-based execution management to solve the aforementioned problem. We analytically compute a sufficient condition on synchronization interval that ensure reduction in mobile application execution time under PSS in case of disconnection. Similarly, we compute the upper bound on synchronization interval whereby a larger synchronization interval did not result in significant benefits in terms of execution time for the mobile application. The analytical results were confirmed by the sample implementation of PSS with the computed synchronization intervals. Moreover, we also compare the performance of proposed solution with the state-of-the-art solutions. The results show that the PSS-based execution outperforms the other contemporary solutions. © 2018 Elsevier B.V.

Protection of records and data authentication based on secret shares and watermarking

- Ali, Zulfiqar, Imran, Muhammad, McClean, Sally, Khan, Naveed, Shoaib, Muhammad

  • Authors: Ali, Zulfiqar , Imran, Muhammad , McClean, Sally , Khan, Naveed , Shoaib, Muhammad
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Future Generation Computer Systems Vol. 98, no. (2019), p. 331-341
  • Full Text:
  • Reviewed:
  • Description: The rapid growth in communication technology facilitates the health industry in many aspects from transmission of sensor's data to real-time diagnosis using cloud-based frameworks. However, the secure transmission of data and its authenticity become a challenging task, especially, for health-related applications. The medical information must be accessible to only the relevant healthcare staff to avoid any unfortunate circumstances for the patient as well as for the healthcare providers. Therefore, a method to protect the identity of a patient and authentication of transmitted data is proposed in this study. The proposed method provides dual protection. First, it encrypts the identity using Shamir's secret sharing scheme without the increase in dimension of the original identity. Second, the identity is watermarked using zero-watermarking to avoid any distortion into the host signal. The experimental results show that the proposed method encrypts, embeds and extracts identities reliably. Moreover, in case of malicious attack, the method distorts the embedded identity which provides a clear indication of fabrication. An automatic disorder detection system using Mel-frequency cepstral coefficients and Gaussian mixture model is also implemented which concludes that malicious attacks greatly impact on the accurate diagnosis of disorders. © 2019 Elsevier B.V.

Q-Learning for energy balancing and avoiding the void hole routing protocol in underwater sensor networks

- Javaid, Nadeem, Karim, Obaida, Sher, Arshad, Imran, Muhammad, Yasar, Ansar, Guizani, Mohsen

  • Authors: Javaid, Nadeem , Karim, Obaida , Sher, Arshad , Imran, Muhammad , Yasar, Ansar , Guizani, Mohsen
  • Date: 2018
  • Type: Text , Conference paper
  • Relation: 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018 p. 702-706
  • Full Text: false
  • Reviewed:
  • Description: In energy constraint networks, the utilization of limited node battery is very crucial to enhance the network lifespan. The imbalanced node battery dissipation greatly effects the performance of the network. In this paper, we propose QLearning based energy-efficient and balanced data gathering routing protocol (QL-EEBDG). The effectiveness of a forwarder node is computed based on; residual energy of the source node and group energies of the neighbour nodes. The consideration of energy parameters provides complete control on the forwarder node selection and ensures efficient energy consumptions in the network. Still, due to topology changes, void node occurs which is avoided through adjacent node technique (QL-EEBDG-ADN). This scheme finds an alternate route via neighbor nodes to provide continuous communication among the network nodes. Simulations are performed to validate the effectiveness of proposed schemes against existing scheme based on energy tax, network lifetime. © 2018 IEEE.

Randomized nonlinear one-class support vector machines with bounded loss function to detect of outliers for large scale IoT data

- Razzak, Imran, Zafar, Khurram, Imran, Muhammad, Xu, Guandong

  • Authors: Razzak, Imran , Zafar, Khurram , Imran, Muhammad , Xu, Guandong
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Future Generation Computer Systems Vol. 112, no. (2020), p. 715-723
  • Full Text: false
  • Reviewed:
  • Description: Exponential growth of large scale data industrial internet of things is evident due to the enormous deployment of IoT data acquisition devices. Detection of unusual patterns from large scale IoT data is important though challenging task. Recently, one-class support vector machines is extensively being used for anomaly detection. It tries to find an optimal hyperplane in high dimensional data that best separates the data from anomalies with maximum margin. However, the hinge loss of traditional one-class support vector machines is unbounded, which results in larger loss caused by outliers affecting its performance for anomaly detection. Furthermore, existing methods are computationally complex for larger data. In this paper, we present novel anomaly detection for large scale data by using randomized nonlinear features in support vector machines with bounded loss function rather than finding optimized support vectors with unbounded loss function. Extensive experimental evaluation on ten benchmark datasets shows the robustness of the proposed approach against outliers such as 0.8239, 0.7921, 0.7501, 0.6711, 0.6692, 0.4789, 0.6462, 0.6812, 0.7271 and 0.7873 accuracy for Gas Sensor Array, Human Activity Recognition, Parkinson's, Hepatitis, Breast Cancer, Blood Transfusion, Heart, ILPD and Wholesale Customers datasets respectively. In addition to this, introduction of randomized nonlinear feature helps to considerably decrease the computational complexity and space complexity from O(N3) to O(Bkn) and O(N2) to O(Bkn). Thus, very attractive for larger datasets. © 2020 Elsevier B.V.

Real-time big data processing for anomaly detection : a survey

- Ariyaluran Habeeb, Riyaz, Nasaruddin, Fariza, Gani, Abdullah, Targio Hashem, Ibrahim, Ahmed, Ejaz, Imran, Muhammad

  • Authors: Ariyaluran Habeeb, Riyaz , Nasaruddin, Fariza , Gani, Abdullah , Targio Hashem, Ibrahim , Ahmed, Ejaz , Imran, Muhammad
  • Date: 2019
  • Type: Text , Journal article , Review
  • Relation: International Journal of Information Management Vol. 45, no. (2019), p. 289-307
  • Full Text:
  • Reviewed:
  • Description: The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed. © 2018 Elsevier Ltd

Recent advances and challenges in mobile big data

- Ahmed, Ejaz, Yaqoob, Ibrar, Hashem, Ibrahim, Shuja, Junaid, Imran, Muhammad

  • Authors: Ahmed, Ejaz , Yaqoob, Ibrar , Hashem, Ibrahim , Shuja, Junaid , Imran, Muhammad
  • Date: 2018
  • Type: Text , Journal article
  • Relation: IEEE Communications Magazine Vol. 56, no. 2 (2018), p. 102-108
  • Full Text: false
  • Reviewed:
  • Description: The unabated flurry of research activities dedicated to gaining business insights from a flood of data generated by heterogeneous mobile sources, such as the Internet of Vehicles, sensors, and smartphones, has instigated a new research domain called MBD. At the core of this mobile environment, scalability, cost effectiveness, reliability, analytics, and security are important concerns. Coping with these issues in handling MBD requires understanding the challenges associated with it. Mobile computing and big data have been widely studied separately; however, very few studies have explored the convergence of these two domains. In this article, we critically review recent research efforts directed at MBD. We also classify the MBD by devising a thematic taxonomy that is based on source, analytics, applications, characteristics, security, and data type. Furthermore, we discuss the opportunities offered by MBD in terms of analytics. Some potential uses of MBD in healthcare, telecommunication, digital advertising, and transportation are also presented. Several open research challenges are discussed as future research directions. © 1979-2012 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**

Reconfigurable smart factory for drug packing in healthcare industry 4.0

- Wan, Jiafu, Tang, Shenglong, Li, Di, Imran, Muhammad, Zhang, Chunhua

  • Authors: Wan, Jiafu , Tang, Shenglong , Li, Di , Imran, Muhammad , Zhang, Chunhua
  • Date: 2019
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Informatics Vol. 15, no. 1 (2019), p. 507-516
  • Full Text: false
  • Reviewed:
  • Description: Industry 4.0, which exploits cyber-physical systems and represents digital transformation of manufacturing, is deeply affecting healthcare as well as other traditional production sector. To accommodate the increasing demand of agility, flexibility, and low cost in healthcare sector, a data-driven reconfigurable production mode of Smart Factory for pharmaceutical manufacturing is proposed in this paper. The architecture of the Smart Factory is consisted of three primary layers, namely perception layer, deployment layer, and executing layer. A Manufacturing's Semantics Ontology based knowledgebase is introduced in the perception layer, which is responsible for plan scheduling of pharmaceutical production. The reconfigurable plans are generated from the production demand of drugs as well as the information statement of low-level machine resources. To further functionality reconfiguration and low-level controlling, the IEC 61499 standard is also introduced for functionality modeling and machine controlling. We verify the proposed method with an experiment of demand-based drug packing production, which reflects the feasibility and adequate flexibility of the proposed method. © 2005-2012 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran" is provided in this record**

Refining Parkinson’s neurological disorder identification through deep transfer learning

- Naseer, Amina, Rani, Monai, Naz, Saeeda, Razzak, Muhammad, Imran, Muhammad, Xu, Guandong

  • Authors: Naseer, Amina , Rani, Monai , Naz, Saeeda , Razzak, Muhammad , Imran, Muhammad , Xu, Guandong
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 32, no. 3 (2020), p. 839-854
  • Full Text:
  • Reviewed:
  • Description: Parkinson’s disease (PD), a multi-system neurodegenerative disorder which affects the brain slowly, is characterized by symptoms such as muscle stiffness, tremor in the limbs and impaired balance, all of which tend to worsen with the passage of time. Available treatments target its symptoms, aiming to improve the quality of life. However, automatic diagnosis at early stages is still a challenging medicine-related task to date, since a patient may have an identical behavior to that of a healthy individual at the very early stage of the disease. Parkinson’s disease detection through handwriting data is a significant classification problem for identification of PD at the infancy stage. In this paper, a PD identification is realized with help of handwriting images that help as one of the earliest indicators for PD. For this purpose, we proposed a deep convolutional neural network classifier with transfer learning and data augmentation techniques to improve the identification. Two approaches like freeze and fine-tuning of transfer learning are investigated using ImageNet and MNIST dataset as source task independently. A trained network achieved 98.28% accuracy using fine-tuning-based approach using ImageNet and PaHaW dataset. Experimental results on benchmark dataset reveal that the proposed approach provides better detection of Parkinson’s disease as compared to state-of-the-art work. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

Region based cooperative routing in underwater wireless sensor networks

- Javaid, Nadeem, Hussain, Sheraz, Ahmad, Ashfaq, Imran, Muhammad, Khan, Abid, Guizani, Mohsen

  • Authors: Javaid, Nadeem , Hussain, Sheraz , Ahmad, Ashfaq , Imran, Muhammad , Khan, Abid , Guizani, Mohsen
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Journal of Network and Computer Applications Vol. 92, no. (2017), p. 31-41
  • Full Text: false
  • Reviewed:
  • Description: Cooperative routing is an appealing challenge in underwater wireless sensor networks (UWSNs). In this paper, we propose a region based cooperative routing protocol (RBCRP) for amplify and forward (AF) technique over Rayleigh faded channels in UWSNs. The source node sends the sensed signal to the destination and available relay nodes. At the destination node, bit error rate (BER) is checked on the basis of which, either positive or negative acknowledgement (ACK or NACK) is sent to the source and relay nodes. We also use mobile sinks (MSs) and energy harvesting techniques to further prolong the network lifetime and maximize the throughput. Our derived mathematical equations for the SNR gain and outage probability are verified by simulations. Results show that RBCRP performs better than the existing incremental best relay technique (IBRT) in terms of throughput, network lifetime and outage probability. © 2017 Elsevier Ltd

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
  • Full Text:
  • Reviewed:
  • 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.

Resource optimized federated learning-enabled cognitive internet of things for smart industries

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

  • Authors: Khan, Latif , Alsenwi, Madyan , Yaqoob, Ibrar , Imran, Muhammad , Han, Zhu , Hong, Choong
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 8, no. (2020), p. 168854-168864
  • Full Text:
  • Reviewed:
  • Description: Leveraging the cognitive Internet of things (C-IoT), emerging computing technologies, and machine learning schemes for industries can assist in streamlining manufacturing processes, revolutionizing operational analytics, and maintaining factory efficiency. However, further adoption of centralized machine learning in industries seems to be restricted due to data privacy issues. Federated learning has the potential to bring about predictive features in industrial systems without leaking private information. However, its implementation involves key challenges including resource optimization, robustness, and security. In this article, we propose a novel dispersed federated learning (DFL) framework to provide resource optimization, whereby distributed fashion of learning offers robustness. We formulate an integer linear optimization problem to minimize the overall federated learning cost for the DFL framework. To solve the formulated problem, first, we decompose it into two sub-problems: association and resource allocation problem. Second, we relax the association and resource allocation sub-problems to make them convex optimization problems. Later, we use the rounding technique to obtain binary association and resource allocation variables. Our proposed algorithm works in an iterative manner by fixing one problem variable (for example, association) and compute the other (for example, resource allocation). The iterative algorithm continues until convergence of the formulated cost optimization problem. Furthermore, we compare the proposed DFL with two schemes; namely, random resource allocation and random association. Numerical results show the superiority of the proposed DFL scheme. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

Review on variants of restricted boltzmann machines and autoencoders for cyber-physical systems

- Ul Haq, Qazi, Imran, Muhammad, Saleem, Kashif, Zia, Tanveer, Al Muhtadi, Jalal

  • Authors: Ul Haq, Qazi , Imran, Muhammad , Saleem, Kashif , Zia, Tanveer , Al Muhtadi, Jalal
  • Date: 2023
  • Type: Text , Book chapter
  • Relation: Internet of Things Security and Privacy: Practical and Management Perspectives Chapter 8 p. 188-207
  • Full Text: false
  • Reviewed:
  • Description: To understand the variants of Restricted Boltzmann Machines (RBMs) and Auto-encoders, we first need to outline what they are: RBMs are generative and unlike auto-encoders that are biased towards a limited set of data. RBMs are capable of generating new data with the joint set distribution. They are used to solve problems such as pattern recognition where there is a handwritten text that needs to be deciphered or a random pattern. It can also be used for recommendation engines where in collaboration with filtering techniques, recommendations are made to an end-user, and radar target recognition where it is used to detect intra-pulse with extremely low SNR and high noise. On the contrary, auto-encoders are not very commonly used in real-life applications; however, they are useful in reducing dimensionality and variational auto-encoders (VAE), where VAE learns the limitations of a probability distribution modeling the input data rather than learning the absolute function. RBMs and auto-encoders can be used for cyber-physical systems (CPS); RBMs are a dual-layer, two-part, erratic graphical model, allowing data to flow in two ways rather than one which forms the foundation of DBNs. CPS aims to use RBMs in making the model understand various functions, which will ultimately help to identify the hidden state and minimize the energy of the system. RBMs assign probabilities rather than definite values. Auto-encoders are unsupervised neural networks that use input vectors and try to match them to similar output vectors. These vectors are extremely skilled as they study compressed data encoding autonomously. This chapter will discuss in detail the breakthroughs with CPS and their findings. Previously CPS were only evaluated with techniques that did not differentiate the facts from the internal view, which ultimately resulted in a mismatch between the behavior of theoretical models and their real-life counterparts. This ultimately gave way to the question of how they could perform critical safety tasks. Another vital breakthrough related to CPS revolves around Intellectual Merit and Broader Impacts. They both revolve around the design and analysis of Artificial Intelligence as an integral part of CPS; the findings for these breakthroughs will be discussed in detail in the chapter. © 2024 selection and editorial matter, [Ali Ismail Awad, Atif Ahmad, Kim-Kwang Raymond Choo, Saqib Hakak]; individual chapters, the contributors.

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
  • Full Text:
  • Reviewed:
  • 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.

Robustness optimization of scale-free IoT Networks

- Usman, Muhammad, Javaid, Nadeem, Khalid, Adia, Nasser, Nidal, Imran, Muhammad

  • Authors: Usman, Muhammad , Javaid, Nadeem , Khalid, Adia , Nasser, Nidal , Imran, Muhammad
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020, Limassol, Cyrprus, 15 to 19 June 2020, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 p. 2240-2244
  • Full Text: false
  • Reviewed:
  • Description: In today's modern world, people are cultivating towards the Internet of Things (IoT) networks due to their various demands in health monitoring, smart homes, traffic management, and industrial optimization, etc., IoT networks comprise of sensor nodes that have multiple functionalities to fulfill the demands of individuals. With the advancement in technology, the need for IoT networks is increasing as the devices are getting smarter day by day. The scale-free topology is considered to be the best topology for IoT networks because it is more robust against the attacks. For a scale-free network, robustness optimization is essential. Therefore, in this paper, to enhance the robustness, we have optimized a scale-free network through proposed the Improved Scale-Free Network (ISFN) technique. In ISFN, the edges are swapped based on their degree and nodes distance operation. This technique does not change the degree of the nodes of original topology which makes the optimized topology remains scale-free. Through experiments, we have compared the ISFN with two existing techniques, i.e., ROSE and SA. The results prove that by increasing the number of nodes, ISFN outperforms these existing techniques. © 2020 IEEE.

RTRD: Real-time route discovery for urban scenarios using internet of things

- Din, Sadia, Ahmad, Awais, Paul, Anand, Anisetti, Marco, Imran, Muhammad

  • Authors: Din, Sadia , Ahmad, Awais , Paul, Anand , Anisetti, Marco , Imran, Muhammad
  • Date: 2019
  • Type: Text , Conference paper
  • Relation: 2019 IEEE Global Communications Conference, GLOBECOM 2019, Waikola, 9-13 December 2019
  • Full Text: false
  • Reviewed:
  • Description: A rapid development has been seen in the Vehicular ad hoc networks (VANETs) because of their applicability and significance in the fields of traffic management, road monitoring and safety, infotainment, and on-demand services. Route planning in vehicular networks based on efficient collection of real-time data can effectively mitigate traffic congestion problems in urban areas. Furthermore, real-time data is shared by using an effective sharing mechanism to avoid redundancy of the collected information. However, dynamic route replanning and effective sharing mechanisms based on real-time data are still challenging problems. Therefore, based on the aforementioned constraints, this paper describes a route discovery technique that uses real time data collected from various vehcicles using the Internet of Things. The proposed scheme is based on the novel data dissemination technique for information sharing among the roadside units. RTRD is comprised of VANETs, vehicular traffic servers, and a 5G-based cellular system of public transportation. By considering the traffic congestion in urban areas, the optimal path is calculated to re-plan routes based on the k shortest path algorithm, and a load balancing technique is adopted to avoid further congestion. © 2019 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**

SDN-Based load balancing service for cloud servers

- Abdelaziz, Ahmed, Ahmed, Ejaz, Fong, Ang, Gani, Abdullah, Imran, Muhammad

  • Authors: Abdelaziz, Ahmed , Ahmed, Ejaz , Fong, Ang , Gani, Abdullah , Imran, Muhammad
  • Date: 2018
  • Type: Text , Journal article
  • Relation: IEEE Communications Magazine Vol. 56, no. 8 (2018), p. 106-111
  • Full Text: false
  • Reviewed:
  • Description: With the continuous growth, heterogeneity, and ever increasing demand of services, load balancing of cloud servers is an emerging challenge to meet highly demanding requirements (e.g., data rates, latency, quality of service) of 5G network applications. Although various load balancing techniques have been proposed, some of these techniques either require installation of dedicated additional load balancers for each service, or manual reconfiguration of the device to handle new services is desired. These techniques are expensive, time-consuming, and impractical. Moreover, most of the existing load balancing schemes do not consider service types. This article presents an SDN-based load balancing (SBLB) service for cloud servers to maximize resource utilization and minimize response time of users. The constituents of the proposed scheme are an application module that runs on top of an SDN controller and server pools that connect to the controller through OpenFlow switches. The application module consists of a service classification module, a dynamic load balancing module, and a monitoring module. The controller handles all messages, manages host pools, and maintains the load of host in real time. Experimental results validate the performance of the proposed scheme. Through experimental results, SBLB demonstrates significant decrease in average response time and reply time. © 1979-2012 IEEE.