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VANET–LTE based heterogeneous vehicular clustering for driving assistance and route planning applications

- Ahmad, Iftikhar, Noor, Rafidah, Ahmedy, Ismail, Shah, Syed, Imran, Muhammad

  • Authors: Ahmad, Iftikhar , Noor, Rafidah , Ahmedy, Ismail , Shah, Syed , Imran, Muhammad
  • Date: 2018
  • Type: Text , Journal article
  • Relation: Computer Networks Vol. 145, no. (2018), p. 128-140
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  • Description: The Internet of vehicles incorporates multiple access networks and technologies to connect vehicles on roads. These vehicles usually require the use of individual long-term evolution (LTE) connections to send/receive data to/from a remote server to make smart decisions regarding route planning and driving. An increasing number of vehicles on the roads may not only overwhelm LTE network usage but also incur added cost. Clustering helps minimize LTE usage, but the high speed of vehicles renders connections unstable and unreliable not only among vehicles but also between vehicles and the LTE network. Moreover, non-cooperative behavior among vehicles within a cluster is a bottleneck in sharing costly data acquired from the Internet. To address these issues, we propose a novel destination- and interest-aware clustering (DIAC) mechanism. DIAC primarily incorporates a strategic game-theoretic algorithm and a self-location calculation algorithm. The former allows vehicles to participate/cooperate and enforces a fair-use policy among the cluster members (CMs), whereas the latter enables CMs to calculate their location coordinates in the absence of a global positioning system under an urban topography. DIAC strives to reduce the frequency of link failures not only among vehicles but also between each vehicle and the 3G/LTE network. The mechanism also considers vehicle mobility and LTE link quality and exploits common interests among vehicles in the cluster formation phase. The performance of the DIAC mechanism is validated through extensive simulations, whose results demonstrate that the performance of the proposed mechanism is superior to that of similar and existing approaches. © 2018 Elsevier B.V.

Characterizing the role of vehicular cloud computing in road traffic management

- Ahmad, Iftikhar, Noor, Rafidah, Ali, Ihsan, Imran, Muhammad, Vasilakos, Athanasios

  • Authors: Ahmad, Iftikhar , Noor, Rafidah , Ali, Ihsan , Imran, Muhammad , Vasilakos, Athanasios
  • Date: 2017
  • Type: Text , Journal article
  • Relation: International Journal of Distributed Sensor Networks Vol. 13, no. 5 (2017), p.
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  • Description: Vehicular cloud computing is envisioned to deliver services that provide traffic safety and efficiency to vehicles. Vehicular cloud computing has great potential to change the contemporary vehicular communication paradigm. Explicitly, the underutilized resources of vehicles can be shared with other vehicles to manage traffic during congestion. These resources include but are not limited to storage, computing power, and Internet connectivity. This study reviews current traffic management systems to analyze the role and significance of vehicular cloud computing in road traffic management. First, an abstraction of the vehicular cloud infrastructure in an urban scenario is presented to explore the vehicular cloud computing process. A taxonomy of vehicular clouds that defines the cloud formation, integration types, and services is presented. A taxonomy of vehicular cloud services is also provided to explore the object types involved and their positions within the vehicular cloud. A comparison of the current state-of-the-art traffic management systems is performed in terms of parameters, such as vehicular ad hoc network infrastructure, Internet dependency, cloud management, scalability, traffic flow control, and emerging services. Potential future challenges and emerging technologies, such as the Internet of vehicles and its incorporation in traffic congestion control, are also discussed. Vehicular cloud computing is envisioned to have a substantial role in the development of smart traffic management solutions and in emerging Internet of vehicles. © The Author(s) 2017.

An efficient network intrusion detection and classification system

- Ahmad, Iftikhar, Haq, Qazi, Imran, Muhammad, Alassafi, Madini, Alghamdi, Rayed

  • Authors: Ahmad, Iftikhar , Haq, Qazi , Imran, Muhammad , Alassafi, Madini , Alghamdi, Rayed
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Mathematics Vol. 10, no. 3 (2022), p.
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  • Description: Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

An optimized hybrid deep intrusion detection model (HD-IDM) for enhancing network security

- Ahmad, Iftikhar, Imran, Muhammad, Qayyum, Abdul, Ramzan, Muhammad, Alassafi, Madini

  • Authors: Ahmad, Iftikhar , Imran, Muhammad , Qayyum, Abdul , Ramzan, Muhammad , Alassafi, Madini
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Mathematics Vol. 11, no. 21 (2023), p.
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  • Description: Detecting cyber intrusions in network traffic is a tough task for cybersecurity. Current methods struggle with the complexity of understanding patterns in network data. To solve this, we present the Hybrid Deep Learning Intrusion Detection Model (HD-IDM), a new way that combines GRU and LSTM classifiers. GRU is good at catching quick patterns, while LSTM handles long-term ones. HD-IDM blends these models using weighted averaging, boosting accuracy, especially with complex patterns. We tested HD-IDM on four datasets: CSE-CIC-IDS2017, CSE-CIC-IDS2018, NSL KDD, and CIC-DDoS2019. The HD-IDM classifier achieved remarkable performance metrics on all datasets. It attains an outstanding accuracy of 99.91%, showcasing its consistent precision across the dataset. With an impressive precision of 99.62%, it excels in accurately categorizing positive cases, crucial for minimizing false positives. Additionally, maintaining a high recall of 99.43%, it effectively identifies the majority of actual positive cases while minimizing false negatives. The F1-score of 99.52% emphasizes its robustness, making it the top choice for classification tasks requiring precision and reliability. It is particularly good at ROC and precision/recall curves, discriminating normal and harmful network activities. While HD-IDM is promising, it has limits. It needs labeled data and may struggle with new intrusion methods. Future work should find ways to handle unlabeled data and adapt to emerging threats. Also, making HD-IDM work faster for real-time use and dealing with scalability challenges is key for its broader use in changing network environments. © 2023 by the authors.

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.

Performance evaluation of multivariate non-normal process using metaheuristic approaches

- Ahmad, S., Abdollahian, Mali, Bhatti, M.I., Huda, Shamsul, Yearwood, John

  • Authors: Ahmad, S. , Abdollahian, Mali , Bhatti, M.I. , Huda, Shamsul , Yearwood, John
  • Date: 2014
  • Type: Text , Journal article
  • Relation: Journal of Applied Statistical Science Vol. 20, no. 3 (2014), p. 299-315
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  • Description: Multivariate process performance indices generally rely on the assumption that the process follow normal distribution but in practice its non-normal with correlated characteristics patterns. This paper proposes two metaheuristic-based approaches to fit Burr distribution to such data; a single candidate model based approach using a Simulated Annealing (SA) technique and a population based approach using a constraint-based Evolutionary Alogorithn (EA). The fitted Burr distribution is then used to estimate the proportion of Non-conforming (PNC) which is then used to fit an appropiate Burr distribution to individual Geometric distance variables. Empirical performance of the proposed methods have been evaluated on real industrial data set using PNC criterion. Experimental results demonstrate that the new approach perform well than the existing.

Development of a unified railway track stability management tool to enhance track safety

- Ahmad, Shah, Mandal, Nirmal, Chattopadhyay, Gopinath, Powell, John

  • Authors: Ahmad, Shah , Mandal, Nirmal , Chattopadhyay, Gopinath , Powell, John
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit Vol. 227, no. 5 (2013), p. 493-516
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  • Description: Track buckling is a serious problem for railways. High longitudinal rail stresses contribute to problems such as track buckling, rail joint failure, rail breakage and failure of turnouts. The direct and indirect costs of track buckling problems are very high. The influences of rail temperature, stress-free temperature (SFT) and lateral misalignment of track on track buckling need comprehensive investigation. In this paper, an experimental design comprising strain gauges, thermocouples and rail stress sensors has been implemented on the Queensland Rail heavy haul 60 kg/m rail network. A new creep measurement technique using internal rail stress has been developed. The changes in rail neutral temperature due to the variation of actual rail temperature and the occurrence of rail creep in straight and curved track is quantified. Modes of differences of SFT in the two rails at a location, and of SFT in straight track and in curved track are discussed. The relationship of SFT to rail temperature is also presented. Daily variation in rail temperature due to ambient air temperature is presented. Field trials showed that SFT can vary by 2-3 °C during the day. Based on this finding and the derivation of an equation for change of SFT, an improvement in utilising rail creep measurements for assessing track condition has resulted. This finding suggests that it is possible to determine the SFT throughout a day rather than just a single SFT value. This paper also presents a simple track stability management tool that is based on two major parameters, namely rail stress and track resistance. Each parameter in the tool has been given three levels of value to determine the required preventive measures. Overall, the tool decides the need for speed restriction during hot weather based on the quantified parameters from the field trials and rail standards. © IMechE 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Pattern recognition in bioinformatics : Girls lose out

- Ahmad, Shandar, Chetty, Madhu, Schmidt, Bertil

  • Authors: Ahmad, Shandar , Chetty, Madhu , Schmidt, Bertil
  • Date: 2010
  • Type: Text , Journal article
  • Relation: Pattern Recognition Letter Vol. 31, no. 14 (2010), p. 2071-2072
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  • Description: Editorial- With the advent of high speed computers, in-silico studies on biological patterns in recent years have been significantly impacted by the pattern recognition techniques. In this special issue, ‘Pattern Recognition in Bioinformatics’, we present various sophisticated algorithms for a wide range of pattern recognition problems from the world of complex biological systems, whether these are specific sequence signatures – motifs that stand out in discovering its partner – or substructures in an interaction network that determines an organisms’ response to external stimuli. The 12 high-quality articles included in this special issue are essentially based on significant extensions of the selected papers presented at the Third International Conference on Pattern Recognition in Bioinformatics (PRIB 2008) held in Melbourne, Australia. All these selected papers for special issue have again undergone a thorough review by at least three reviewers who are experts in the field. The fresh review process was followed to ensure that the papers met the high standards of scientific and technical merit of the Pattern Recognition Letters journal. The issue is broadly divided into three sections of four papers each, namely (1) Section 1: Interaction Networks and Feature-based Predictions (2) Section 2: Microarray and Transcription Data Analysis (3) Section 3: Sequence Analysis and Motif Discovery

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
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  • 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.

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
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  • 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**

Bringing Computation Closer toward the User Network: Is Edge Computing the Solution?

- Ahmed, Ejaz, Ahmed, Arif, Yaqoob, Ibrar, Shuja, Junaid, Imran, Muhammad

  • Authors: Ahmed, Ejaz , Ahmed, Arif , Yaqoob, Ibrar , Shuja, Junaid , Imran, Muhammad
  • Date: 2017
  • Type: Text , Journal article
  • Relation: IEEE Communications Magazine Vol. 55, no. 11 (2017), p. 138-144
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  • Description: The virtually unlimited available resources and wide range of services provided by the cloud have resulted in the emergence of new cloud-based applications, such as smart grids, smart building control, and virtual reality. These developments, however, have also been accompanied by a problem for delay-sensitive applications that have stringent delay requirements. The current cloud computing paradigm cannot realize the requirements of mobility support, location awareness, and low latency. Hence, to address the problem, an edge computing paradigm that aims to extend the cloud resources and services and enable them to be nearer the edge of an enterprise's network has been introduced. In this article, we highlight the significance of edge computing by providing real-life scenarios that have strict constraint requirements on application response time. From the previous literature, we devise a taxonomy to classify the current research efforts in the domain of edge computing. We also discuss the key requirements that enable edge computing. Finally, current challenges in realizing the vision of edge computing are discussed. © 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**

The role of big data analytics in internet of things

- Ahmed, Ejaz, Yaqoob, Ibrar, Hashem, Ibrahim, Khan, Imran, Imran, Muhammad

  • Authors: Ahmed, Ejaz , Yaqoob, Ibrar , Hashem, Ibrahim , Khan, Imran , Imran, Muhammad
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Computer Networks Vol. 129, no. (2017), p. 459-471
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  • Description: The explosive growth in the number of devices connected to the Internet of Things (IoT) and the exponential increase in data consumption only reflect how the growth of big data perfectly overlaps with that of IoT. The management of big data in a continuously expanding network gives rise to non-trivial concerns regarding data collection efficiency, data processing, analytics, and security. To address these concerns, researchers have examined the challenges associated with the successful deployment of IoT. Despite the large number of studies on big data, analytics, and IoT, the convergence of these areas creates several opportunities for flourishing big data and analytics for IoT systems. In this paper, we explore the recent advances in big data analytics for IoT systems as well as the key requirements for managing big data and for enabling analytics in an IoT environment. We taxonomized the literature based on important parameters. We identify the opportunities resulting from the convergence of big data, analytics, and IoT as well as discuss the role of big data analytics in IoT applications. Finally, several open challenges are presented as future research directions. © 2017 Elsevier B.V. **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**

Green industrial networking : recent advances, taxonomy, and open research challenges

- Ahmed, Ejaz, Yaqoob, Ibrar, Ahmed, Ahmed, Gani, Abdullah, Imran, Muhammad, Guizani, Sghaier

  • Authors: Ahmed, Ejaz , Yaqoob, Ibrar , Ahmed, Ahmed , Gani, Abdullah , Imran, Muhammad , Guizani, Sghaier
  • Date: 2016
  • Type: Text , Journal article
  • Relation: IEEE Communications Magazine Vol. 54, no. 10 (2016), p. 38-45
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  • Description: The consciousness of environmental problems has attracted the industry's attention toward the reduction of unnecessary energy emission by enabling green industrial networking. The reduction of unnecessary energy emitted by industrial networks can be a possible solution to many environmental issues. Green industrial networking is in its infancy, and an overview of the domain is still lacking. In this article, we discuss recent advances in industrial and green networking paradigms to investigate the impact on global communities. We also classify the literature by devising a taxonomy based on networking technologies, machines, network types, topologies, field bus types, transmission media, and hierarchical levels. Moreover, we identify and discuss key enablers (adaptive links, resource-based energy conservation, energy-efficient scheduling, energy-aware systems, energy-aware proxying, energy-conservative approaches, and low-power wireless protocols) for green industrial networking. Furthermore, we discuss challenges that remain to be addressed as future research directions. © 2016 IEEE.

Internet-of-things-based smart environments : state of the art, taxonomy, and open research challenges

- Ahmed, Ejaz, Yaqoob, Ibrar, Gani, Abdullah, Imran, Muhammad, Guizani, Mohsen

  • Authors: Ahmed, Ejaz , Yaqoob, Ibrar , Gani, Abdullah , Imran, Muhammad , Guizani, Mohsen
  • Date: 2016
  • Type: Text , Journal article
  • Relation: IEEE Wireless Communications Vol. 23, no. 5 (2016), p. 10-16
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  • Description: The rapid advancements in communication technologies and the explosive growth of the Internet of Things have enabled the physical world to invisibly interweave with actuators, sensors, and other computational elements while maintaining continuous network connectivity. The continuously connected physical world with computational elements forms a smart environment. A smart environment aims to support and enhance the abilities of its dwellers in executing their tasks, such as navigating through unfamiliar space and moving heavy objects for the elderly, to name a few. Researchers have conducted a number of efforts to use IoT to facilitate our lives and to investigate the effect of IoT-based smart environments on human life. This article surveys the state-of-the-art research efforts to enable IoT-based smart environments. We categorize and classify the literature by devising a taxonomy based on communication enablers, network types, technologies, local area wireless standards, objectives, and characteristics. Moreover, the article highlights the unprecedented opportunities brought about by IoT-based smart environments and their effect on human life. Some reported case studies from different enterprises are also presented. Finally, we discuss open research challenges for enabling IoT-based smart environments. © 2016 IEEE.

Social-aware resource allocation and optimization for D2D communication

- Ahmed, Ejaz, Yaqoob, Ibrar, Gani, Abdullah, Imran, Muhammad, Guizani, Mohsen

  • Authors: Ahmed, Ejaz , Yaqoob, Ibrar , Gani, Abdullah , Imran, Muhammad , Guizani, Mohsen
  • Date: 2017
  • Type: Text , Journal article
  • Relation: IEEE Wireless Communications Vol. 24, no. 3 (2017), p. 122-129
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  • Description: The undiminished growth of research activities to converge social awareness with D2D communication has paved the way for facilitating and providing significant benefits to users. Realizing these benefits depends on efficiently addressing several main technical challenges associated with the convergence. Although there are many research studies related to social networks and D2D communication, convergence of these two areas leads to further research efforts to implement social-aware D2D communication. In this article, we discuss recent advances in the domain of D2D communication from the perspective of social-aware resource allocation and optimization. We also categorize and classify the literature by devising a taxonomy based on channel-centric attributes, objectives, solving approaches, networking technologies, characteristics, and communication types. Moreover, we also outline the key requirements with the aim of providing guidelines for the domain researchers and designers to enable the social-aware resource allocation for D2D communication. Several open research challenges are presented as future research directions. © 2002-2012 IEEE.

A novel collaborative IoD-assisted VANET approach for coverage area maximization

- Ahmed, Gamil, Sheltami, Tarek, Mahmoud, Ashraf, Imran, Muhammad, Shoaib, Muhammad

  • Authors: Ahmed, Gamil , Sheltami, Tarek , Mahmoud, Ashraf , Imran, Muhammad , Shoaib, Muhammad
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 9, no. (2021), p. 61211-61223
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  • Description: Internet of Drones (IoD) is an efficient technique that can be integrated with vehicular ad-hoc networks (VANETs) to provide terrestrial communications by acting as an aerial relay when terrestrial infrastructure is unreliable or unavailable. To fully exploit the drones' flexibility and superiority, we propose a novel dynamic IoD collaborative communication approach for urban VANETs. Unlike most of the existing approaches, the IoD nodes are dynamically deployed based on current locations of ground vehicles to effectively mitigate inevitable isolated cars in conventional VANETs. For efficiently coordinating IoD, we model IoD to optimize coverage based on the location of vehicles. The goal is to obtain an efficient IoD deployment to maximize the number of covered vehicles, i.e., minimize the number of isolated vehicles in the target area. More importantly, the proposed approach provides sufficient interconnections between IoD nodes. To do so, an improved version of succinct population-based meta-heuristic, namely Improved Particle Swarm Optimization (IPSO) inspired by food searching behavior of birds or fishes flock, is implemented for IoD assisted VANET (IoDAV). Moreover, the coverage, received signal quality, and IoD connectivity are achieved by IPSO's objective function for optimal IoD deployment at the same time. We carry out an extensive experiment based on the received signal at floating vehicles to examine the proposed IoDAV performance. We compare the results with the baseline VANET with no IoD (NIoD) and Fixed IoD assisted (FIoD). The comparisons are based on the coverage percentage of the ground vehicles and the quality of the received signal. The simulation results demonstrate that the proposed IoDAV approach allows finding the optimal IoD positions throughout the time based on the vehicle's movements and achieves better coverage and better quality of the received signal by finding the most appropriate IoD position compared with NIoD and FIoD schemes. © 2013 IEEE.

Tumour microenvironment and metabolic plasticity in cancer and cancer stem cells : Perspectives on metabolic and immune regulatory signatures in chemoresistant ovarian cancer stem cells

- Ahmed, Nuzhat, Escalona, Ruth, Leung, Dilys, Chan, Emily, Kannourakis, George

  • Authors: Ahmed, Nuzhat , Escalona, Ruth , Leung, Dilys , Chan, Emily , Kannourakis, George
  • Date: 2018
  • Type: Text , Journal article , Review
  • Relation: Seminars in Cancer Biology Vol. 53, no. (2018), p. 265-281
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  • Description: Cancer stem cells (CSCs) are a sub-population of tumour cells, which are responsible to drive tumour growth, metastasis and therapy resistance. It has recently been proposed that enhanced glucose metabolism and immune evasion by tumour cells are linked, and are modulated by the changing tumour microenvironment (TME) that creates a competition for nutrient consumption between tumour and different sub-types of cells attracted to the TME. To facilitate efficient nutrient distribution, oncogene-induced inflammatory milieu in the tumours facilitate adaptive metabolic changes in the surrounding non-malignant cells to secrete metabolites that are used as alternative nutrient sources by the tumours to sustain its increasing energy needs for growth and anabolic functions. This scenario also affects CSCs residing at the primary or metastatic niches. This review summarises recent advances in our understanding of the metabolic phenotypes of cancer cells and CSCs and how these processes are affected by the TME. We also discuss how the evolving TME modulates tumour cells and CSCs in cancer progression. Using previously described proteomic and genomic platforms, ovarian cancer cell lines and a mouse xenograft model we highlight the existence of metabolic and immune regulatory signatures in chemoresistant ovarian CSCs, and discuss how these processes may affect recurrence in ovarian tumours. We propose that progress in cancer control and eradication may depend not only on the elimination of highly chemoresistant CSCs, but also in designing novel strategies which would intervene with the tumour-promoting TME factors.

Unique proteome signature of post-chemotherapy ovarian cancer ascites-derived tumor cells

- Ahmed, Nuzhat, Greening, David, Samardzija, Chantel, Escalona, Ruth, Chen, Maoshan, Findlay, Jock, Kannourakis, George

  • Authors: Ahmed, Nuzhat , Greening, David , Samardzija, Chantel , Escalona, Ruth , Chen, Maoshan , Findlay, Jock , Kannourakis, George
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Scientific Reports Vol. 6, no. (2016), p. 1-13
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  • Description: Eighty % of ovarian cancer patients diagnosed at an advanced-stage have complete remission after initial surgery and chemotherapy. However, most patients die within <5 years due to episodes of recurrences resulting from the growth of residual chemoresistant cells. In an effort to identify mechanisms associated with chemoresistance and recurrence, we compared the expression of proteins in ascites-derived tumor cells isolated from advanced-stage ovarian cancer patients obtained at diagnosis (chemonaive, CN) and after chemotherapy treatments (chemoresistant/at recurrence, CR) by using in-depth, high-resolution label-free quantitative proteomic profiling. A total of 2,999 proteins were identified. Using a stringent selection criterion to define only significantly differentially expressed proteins, we report identification of 353 proteins. There were significant differences in proteins encoding for immune surveillance, DNA repair mechanisms, cytoskeleton rearrangement, cell-cell adhesion, cell cycle pathways, cellular transport, and proteins involved with glycine/proline/arginine synthesis in tumor cells isolated from CR relative to CN patients. Pathway analyses revealed enrichment of metabolic pathways, DNA repair mechanisms and energy metabolism pathways in CR tumor cells. In conclusion, this is the first proteomics study to comprehensively analyze ascites-derived tumor cells from CN and CR ovarian cancer patients.

Ovarian cancer, cancer stem cells and current treatment strategies : a potential role of magmas in the current treatment methods

- Ahmed, Nuzhat, Kadife, Elif, Raza, Ali, Short, Mary, Jubinsky, Paul, Kannourakis, George

  • Authors: Ahmed, Nuzhat , Kadife, Elif , Raza, Ali , Short, Mary , Jubinsky, Paul , Kannourakis, George
  • Date: 2020
  • Type: Text , Journal article , Review
  • Relation: Cells Vol. 9, no. 3 (Mar 2020), p. 35
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  • Description: Epithelial ovarian cancer (EOC) constitutes 90% of ovarian cancers (OC) and is the eighth most common cause of cancer-related death in women. The cancer histologically and genetically is very complex having a high degree of tumour heterogeneity. The pathogenic variability in OC causes significant impediments in effectively treating patients, resulting in a dismal prognosis. Disease progression is predominantly influenced by the peritoneal tumour microenvironment rather than properties of the tumor and is the major contributor to prognosis. Standard treatment of OC patients consists of debulking surgery, followed by chemotherapy, which in most cases end in recurrent chemoresistant disease. This review discusses the different origins of high-grade serous ovarian cancer (HGSOC), the major sub-type of EOC. Tumour heterogeneity, genetic/epigenetic changes, and cancer stem cells (CSC) in facilitating HGSOC progression and their contribution in the circumvention of therapy treatments are included. Several new treatment strategies are discussed including our preliminary proof of concept study describing the role of mitochondria-associated granulocyte macrophage colony-stimulating factor signaling protein (Magmas) in HGSOC and its unique potential role in chemotherapy-resistant disease.

Texture as pixel feature for video object segmentation

- Ahmed, Rakib, Karmakar, Gour, Dooley, Laurence