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Shuja Ansari
James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK

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Short Biography

SHUJA ANSARI received the M.Sc. degree (distinction) in Telecommunications Engineering in 2015, and the Ph.D. degree in engineering in 2019 from Glasgow Caledonian University (GCU), UK. He is currently a Research Associate at University of Glasgow and 5G use case implementation lead at Scotland 5G Centre. He was an associate lecturer at GCU before joining the University of Glasgow. His research interests include wireless communications, Internet of things, intelligent transport systems, terrestrial/airborne mobile networks, healthcare and beyond 5G technologies.

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Review
Published: 05 August 2021 in Sensors
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The Internet of Things (IoT) and its applications in industrial settings are set to bring in the fourth industrial revolution. The industrial environment consisting of high profile manufacturing plants and a variety of equipment is inherently characterized by high reflectiveness, causing significant multi-path components that affect the propagation of wireless communications—a challenge among others that needs to be resolved. This paper provides a detailed insight into Narrow-Band IoT (NB-IoT), Industrial IoT (IIoT), and Wireless Sensor Networks (WSN) within the context of indoor industrial environments. It presents the applications of NB-IoT for industrial settings, such as the challenges associated with these applications. Furthermore, future research directions were put forth in the areas of NB-IoT network management using self-organizing network (SON) technology, edge computing for scalability enhancement, security in NB-IoT generated data, and proposing a suitable propagation model for reliable wireless communications.

ACS Style

Muhammad Dangana; Shuja Ansari; Qammer Abbasi; Sajjad Hussain; Muhammad Imran. Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights. Sensors 2021, 21, 5284 .

AMA Style

Muhammad Dangana, Shuja Ansari, Qammer Abbasi, Sajjad Hussain, Muhammad Imran. Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights. Sensors. 2021; 21 (16):5284.

Chicago/Turabian Style

Muhammad Dangana; Shuja Ansari; Qammer Abbasi; Sajjad Hussain; Muhammad Imran. 2021. "Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights." Sensors 21, no. 16: 5284.

Review
Published: 20 June 2021
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The race for the 6th generation of wireless networks (6G) has begun. Researchers around the world have started to explore the best solutions for the challenges that the previous generations have experienced. To provide the readers with a clear map of the current developments, several review papers shared their vision and critically evaluated the state of the art. However, most of the work is based on general observations and the big picture vision, and lack the practical implementation challenges of the Internet of Things (IoT) use cases. This paper takes a novel approach in the review, as we present a sample of IoT use cases that are representative of a wide variety of its implementations. The chosen use cases are from the most research-active sectors that can benefit from 6G and its enabling technologies. These sectors are healthcare, smart grid, transport, and Industry 4.0. Additionally, we identified some of the practical challenges and the lessons learned in the implementation of these use cases. The review highlights the cases’ main requirements and how they overlap with the key drivers for the future generation of wireless networks.

ACS Style

Basel Barakat; Ahmad Taha; Ryan Samson; Aiste Steponenaite; Shuja Ansari; Patrick M. Langdon; Ian J. Wassell; Qammer H. Abbasi; Muhammad Ali Imran; Simeon Keates. 6G Opportunities Arising from Internet of Things Use Cases: A Review Paper. 2021, 1 .

AMA Style

Basel Barakat, Ahmad Taha, Ryan Samson, Aiste Steponenaite, Shuja Ansari, Patrick M. Langdon, Ian J. Wassell, Qammer H. Abbasi, Muhammad Ali Imran, Simeon Keates. 6G Opportunities Arising from Internet of Things Use Cases: A Review Paper. . 2021; ():1.

Chicago/Turabian Style

Basel Barakat; Ahmad Taha; Ryan Samson; Aiste Steponenaite; Shuja Ansari; Patrick M. Langdon; Ian J. Wassell; Qammer H. Abbasi; Muhammad Ali Imran; Simeon Keates. 2021. "6G Opportunities Arising from Internet of Things Use Cases: A Review Paper." , no. : 1.

Review
Published: 18 June 2021 in Future Internet
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The race for the 6th generation of wireless networks (6G) has begun. Researchers around the world have started to explore the best solutions for the challenges that the previous generations have experienced. To provide the readers with a clear map of the current developments, several review papers shared their vision and critically evaluated the state of the art. However, most of the work is based on general observations and the big picture vision, and lack the practical implementation challenges of the Internet of Things (IoT) use cases. This paper takes a novel approach in the review, as we present a sample of IoT use cases that are representative of a wide variety of its implementations. The chosen use cases are from the most research-active sectors that can benefit from 6G and its enabling technologies. These sectors are healthcare, smart grid, transport, and Industry 4.0. Additionally, we identified some of the practical challenges and the lessons learned in the implementation of these use cases. The review highlights the cases’ main requirements and how they overlap with the key drivers for the future generation of wireless networks.

ACS Style

Basel Barakat; Ahmad Taha; Ryan Samson; Aiste Steponenaite; Shuja Ansari; Patrick Langdon; Ian Wassell; Qammer Abbasi; Muhammad Imran; Simeon Keates. 6G Opportunities Arising from Internet of Things Use Cases: A Review Paper. Future Internet 2021, 13, 159 .

AMA Style

Basel Barakat, Ahmad Taha, Ryan Samson, Aiste Steponenaite, Shuja Ansari, Patrick Langdon, Ian Wassell, Qammer Abbasi, Muhammad Imran, Simeon Keates. 6G Opportunities Arising from Internet of Things Use Cases: A Review Paper. Future Internet. 2021; 13 (6):159.

Chicago/Turabian Style

Basel Barakat; Ahmad Taha; Ryan Samson; Aiste Steponenaite; Shuja Ansari; Patrick Langdon; Ian Wassell; Qammer Abbasi; Muhammad Imran; Simeon Keates. 2021. "6G Opportunities Arising from Internet of Things Use Cases: A Review Paper." Future Internet 13, no. 6: 159.

Journal article
Published: 20 April 2021 in Physical Communication
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We consider futuristic, intelligent reflecting surfaces (IRS)-aided communication between a base station (BS) and a user equipment (UE) for two distinct scenarios: a single-input, single-output (SISO) system whereby the BS has a single antenna, and a multi-input, single-output (MISO) system whereby the BS has multiple antennas. For the considered IRS-assisted downlink, we compute the effective capacity (EC), which is a quantitative measure of the statistical quality-of-service (QoS) offered by a communication system experiencing random fading. For our analysis, we consider the two widely-known assumptions on channel state information (CSI)—i.e., perfect CSI and no CSI, at the BS. Thereafter, we first derive the distribution of the signal-to-noise ratio (SNR) for both SISO and MISO scenarios, and subsequently derive closed-form expressions for the EC under perfect CSI and no CSI cases, for both SISO and MISO scenarios. Furthermore, for the SISO and MISO systems with no CSI, it turns out that the EC could be maximized further by searching for an optimal transmission rate r∗, which is computed by exploiting the iterative gradient-descent method. We provide extensive simulation results which investigate the impact of the various system parameters, e.g., QoS exponent, power budget, number of transmit antennas at the BS, number of reflective elements at the IRS etc., on the EC of the system.

ACS Style

Waqas Aman; M. Mahboob Ur Rahman; Shuja Ansari; Ali Arshad Nasir; Khalid Qaraqe; M. Ali Imran; Qammer H. Abbasi. On the effective capacity of IRS-assisted wireless communication. Physical Communication 2021, 47, 101339 .

AMA Style

Waqas Aman, M. Mahboob Ur Rahman, Shuja Ansari, Ali Arshad Nasir, Khalid Qaraqe, M. Ali Imran, Qammer H. Abbasi. On the effective capacity of IRS-assisted wireless communication. Physical Communication. 2021; 47 ():101339.

Chicago/Turabian Style

Waqas Aman; M. Mahboob Ur Rahman; Shuja Ansari; Ali Arshad Nasir; Khalid Qaraqe; M. Ali Imran; Qammer H. Abbasi. 2021. "On the effective capacity of IRS-assisted wireless communication." Physical Communication 47, no. : 101339.

Journal article
Published: 27 January 2021 in IEEE Sensors Journal
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Energy consumption is a critical issue in the design of wireless underwater sensor networks (WUSNs). Data transfer in the harsh underwater channel requires higher transmission powers compared to an equivalent terrestrial-based network to achieve the same range. However, battery-operated underwater sensor nodes are energy-constrained and require that they transmit with low power to conserve power. Clustering is a technique for partitioning wireless networks into groups where a local base station (cluster head) is only one hop away. Due to the proximity to the cluster head, sensor nodes can lower their transmitting power, thereby improving the network energy efficiency. This paper describes the implementation of a new clustering algorithm to prolong the lifetime of WUSNs. We propose a new protocol called distance- and energy-constrained k-means clustering scheme (DEKCS) for cluster head selection. A potential cluster head is selected based on its position in the cluster and based on its residual battery level. We dynamically update the residual energy thresholds set for potential cluster heads to ensure that the network fully runs out of energy before it becomes disconnected. Also, we leverage the elbow method to dynamically select the optimal number of clusters according to the network size, thereby making the network scalable. Our evaluations show that the proposed scheme outperforms the conventional low-energy adaptive clustering hierarchy (LEACH) protocol by over 90% and an optimised version of LEACH based on k-means clustering by 42%.

ACS Style

Kenechi G. Omeke; Michael S. Mollel; Metin Ozturk; Shuja Ansari; Lei Zhang; Qammer H. Abbasi; Muhammad Ali Imran. DEKCS: A Dynamic Clustering Protocol to Prolong Underwater Sensor Networks. IEEE Sensors Journal 2021, 21, 9457 -9464.

AMA Style

Kenechi G. Omeke, Michael S. Mollel, Metin Ozturk, Shuja Ansari, Lei Zhang, Qammer H. Abbasi, Muhammad Ali Imran. DEKCS: A Dynamic Clustering Protocol to Prolong Underwater Sensor Networks. IEEE Sensors Journal. 2021; 21 (7):9457-9464.

Chicago/Turabian Style

Kenechi G. Omeke; Michael S. Mollel; Metin Ozturk; Shuja Ansari; Lei Zhang; Qammer H. Abbasi; Muhammad Ali Imran. 2021. "DEKCS: A Dynamic Clustering Protocol to Prolong Underwater Sensor Networks." IEEE Sensors Journal 21, no. 7: 9457-9464.

Journal article
Published: 17 December 2020 in Sensors
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With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, traditional machine-learning methods for data classification, especially in real time, are computationally intensive. The deployment of Artificial Intelligence algorithms on embedded hardware for fast data classification and accurate fall detection poses a huge challenge in achieving power-efficient embedded systems. Therefore, by exploiting the associative memory feature of Hopfield Neural Network, a hardware module has been designed to simulate the Neural Network algorithm which uses sensor data integration and data classification for recognizing the fall. By adopting the Hebbian learning method for training neural networks, weights of human activity features are obtained and implemented/embedded into the hardware design. Here, the neural network weight of fall activity is achieved through data preprocessing, and then the weight is mapped to the amplification factor setting in the hardware. The designs are checked with validation scenarios, and the experiment is completed with a Hopfield neural network in the analog module. Through simulations, the classification accuracy of the fall data reached 88.9% which compares well with some other results achieved by the software-based machine-learning algorithms, which verify the feasibility of our hardware design. The designed system performs the complex signal calculations of the hardware’s feedback signal, replacing the software-based method. A straightforward circuit design is used to meet the weight setting from the Hopfield neural network, which is maximizing the reusability and flexibility of the circuit design.

ACS Style

Zheqi Yu; Adnan Zahid; Shuja Ansari; Hasan Abbas; Amir M. Abdulghani; Hadi Heidari; Muhammad A. Imran; Qammer H. Abbasi. Hardware-Based Hopfield Neuromorphic Computing for Fall Detection. Sensors 2020, 20, 7226 .

AMA Style

Zheqi Yu, Adnan Zahid, Shuja Ansari, Hasan Abbas, Amir M. Abdulghani, Hadi Heidari, Muhammad A. Imran, Qammer H. Abbasi. Hardware-Based Hopfield Neuromorphic Computing for Fall Detection. Sensors. 2020; 20 (24):7226.

Chicago/Turabian Style

Zheqi Yu; Adnan Zahid; Shuja Ansari; Hasan Abbas; Amir M. Abdulghani; Hadi Heidari; Muhammad A. Imran; Qammer H. Abbasi. 2020. "Hardware-Based Hopfield Neuromorphic Computing for Fall Detection." Sensors 20, no. 24: 7226.

Journal article
Published: 18 November 2020 in Signals
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A paramount challenge of prohibiting increased CO2 emissions for network densification is to deliver the Fifth Generation (5G) cellular capacity and connectivity demands, while maintaining a greener, healthier and prosperous environment. Energy consumption is a demanding consideration in the 5G era to combat several challenges such as reactive mode of operation, high latency wake up times, incorrect user association with the cells, multiple cross-functional operation of Self-Organising Networks (SON), etc. To address this challenge, we propose a novel Mobility Management-Based Autonomous Energy-Aware Framework for analysing bus passengers ridership through statistical Machine Learning (ML) and proactive energy savings coupled with CO2 emissions in Heterogeneous Network (HetNet) architecture using Reinforcement Learning (RL). Furthermore, we compare and report various ML algorithms using bus passengers ridership obtained from London Overground (LO) dataset. Extensive spatiotemporal simulations show that our proposed framework can achieve up to 98.82% prediction accuracy and CO2 reduction gains of up to 31.83%.

ACS Style

Syed Muhammad Asad; Shuja Ansari; Metin Ozturk; Rao Naveed Bin Rais; Kia Dashtipour; Sajjad Hussain; Qammer H. Abbasi; Muhammad Ali Imran. Mobility Management-Based Autonomous Energy-Aware Framework Using Machine Learning Approach in Dense Mobile Networks. Signals 2020, 1, 170 -187.

AMA Style

Syed Muhammad Asad, Shuja Ansari, Metin Ozturk, Rao Naveed Bin Rais, Kia Dashtipour, Sajjad Hussain, Qammer H. Abbasi, Muhammad Ali Imran. Mobility Management-Based Autonomous Energy-Aware Framework Using Machine Learning Approach in Dense Mobile Networks. Signals. 2020; 1 (2):170-187.

Chicago/Turabian Style

Syed Muhammad Asad; Shuja Ansari; Metin Ozturk; Rao Naveed Bin Rais; Kia Dashtipour; Sajjad Hussain; Qammer H. Abbasi; Muhammad Ali Imran. 2020. "Mobility Management-Based Autonomous Energy-Aware Framework Using Machine Learning Approach in Dense Mobile Networks." Signals 1, no. 2: 170-187.

Review
Published: 03 October 2020 in Sensors
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COVID-19, caused by SARS-CoV-2, has resulted in a global pandemic recently. With no approved vaccination or treatment, governments around the world have issued guidance to their citizens to remain at home in efforts to control the spread of the disease. The goal of controlling the spread of the virus is to prevent strain on hospitals. In this paper, we focus on how non-invasive methods are being used to detect COVID-19 and assist healthcare workers in caring for COVID-19 patients. Early detection of COVID-19 can allow for early isolation to prevent further spread. This study outlines the advantages and disadvantages and a breakdown of the methods applied in the current state-of-the-art approaches. In addition, the paper highlights some future research directions, which need to be explored further to produce innovative technologies to control this pandemic.

ACS Style

William Taylor; Qammer H. Abbasi; Kia Dashtipour; Shuja Ansari; Syed Aziz Shah; Arslan Khalid; Muhammad Ali Imran. A Review of the State of the Art in Non-Contact Sensing for COVID-19. Sensors 2020, 20, 5665 .

AMA Style

William Taylor, Qammer H. Abbasi, Kia Dashtipour, Shuja Ansari, Syed Aziz Shah, Arslan Khalid, Muhammad Ali Imran. A Review of the State of the Art in Non-Contact Sensing for COVID-19. Sensors. 2020; 20 (19):5665.

Chicago/Turabian Style

William Taylor; Qammer H. Abbasi; Kia Dashtipour; Shuja Ansari; Syed Aziz Shah; Arslan Khalid; Muhammad Ali Imran. 2020. "A Review of the State of the Art in Non-Contact Sensing for COVID-19." Sensors 20, no. 19: 5665.

Special issue article
Published: 27 April 2020 in Transactions on Emerging Telecommunications Technologies
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There is a high demand for secure and reliable communications for Connected Autonomous Vehicles (CAVs) in the automotive industry. Privacy and security are key issues in CAVs, where network attacks can result in fatal accidents. The computational time, cost, and robustness of encryption algorithms are important factors in low latency 5G‐enabled secure CAV networks. The presented chaotic Tangent‐Delay Ellipse Reflecting Cavity‐Map system and PieceWise Linear Chaotic Map‐based encryption on short messages exchanged in a CAV network provide both robustness and high speed encryption. In this work, we propose a 5G radio network architecture, which leverages multiple radio access technologies and utilizes Cloud Radio Access Network functionalities for privacy preserved and secure CAV networks. The proposed Vehicular Safety Message identifier algorithm meets transmission requirements with a high probability of 85% for low round trip delay of ≤50 milliseconds. The proposed chaos‐based encryption algorithm exhibits faster speeds with a computational time of 2 to 3 milliseconds, showcasing its lightweight properties ideal for time critical applications.

ACS Style

Shuja Ansari; Jawad Ahmad; Syed Aziz Shah; Ali Kashif Bashir; Tuleen Boutaleb; Sinan Sinanovic. Chaos‐based privacy preserving vehicle safety protocol for 5G Connected Autonomous Vehicle networks. Transactions on Emerging Telecommunications Technologies 2020, 31, 1 .

AMA Style

Shuja Ansari, Jawad Ahmad, Syed Aziz Shah, Ali Kashif Bashir, Tuleen Boutaleb, Sinan Sinanovic. Chaos‐based privacy preserving vehicle safety protocol for 5G Connected Autonomous Vehicle networks. Transactions on Emerging Telecommunications Technologies. 2020; 31 (5):1.

Chicago/Turabian Style

Shuja Ansari; Jawad Ahmad; Syed Aziz Shah; Ali Kashif Bashir; Tuleen Boutaleb; Sinan Sinanovic. 2020. "Chaos‐based privacy preserving vehicle safety protocol for 5G Connected Autonomous Vehicle networks." Transactions on Emerging Telecommunications Technologies 31, no. 5: 1.

Research article
Published: 27 February 2018 in Wireless Communications and Mobile Computing
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Vehicular safety applications have much significance in preventing road accidents and fatalities. Among others, cellular networks have been under investigation for the procurement of these applications subject to stringent requirements for latency, transmission parameters, and successful delivery of messages. Earlier contributions have studied utilization of Long-Term Evolution (LTE) under single cell, Friis radio, or simplified higher layer. In this paper, we study the utilization of LTE under multicell and multipath fading environment and introduce the use of adaptive awareness range. Then, we propose an algorithm that uses the concept of quality of service (QoS) class identifiers (QCIs) along with dynamic adaptive awareness range. Furthermore, we investigate the impact of background traffic on the proposed algorithm. Finally, we utilize medium access control (MAC) layer elements in order to fulfill vehicular application requirements through extensive system-level simulations. The results show that, by using an awareness range of up to 250 m, the LTE system is capable of fulfilling the safety application requirements for up to 10 beacons/s with 150 vehicles in an area of 2 × 2 km2. The urban vehicular radio environment has a significant impact and decreases the probability for end-to-end delay to be ≤100 ms from 93%–97% to 76%–78% compared to the Friis radio environment. The proposed algorithm reduces the amount of vehicular application traffic from 21 Mbps to 13 Mbps, while improving the probability of end-to-end delay being ≤100 ms by 20%. Lastly, use of MAC layer control elements brings the processing of messages towards the edge of network increasing capacity of the system by about 50%.

ACS Style

Shuja Ansari; Marvin Sanchez; Tuleen Boutaleb; Sinan Sinanovic; Carlos Gamio; Ioannis Krikidis. SAI: Safety Application Identifier Algorithm at MAC Layer for Vehicular Safety Message Dissemination Over LTE VANET Networks. Wireless Communications and Mobile Computing 2018, 2018, 1 -17.

AMA Style

Shuja Ansari, Marvin Sanchez, Tuleen Boutaleb, Sinan Sinanovic, Carlos Gamio, Ioannis Krikidis. SAI: Safety Application Identifier Algorithm at MAC Layer for Vehicular Safety Message Dissemination Over LTE VANET Networks. Wireless Communications and Mobile Computing. 2018; 2018 ():1-17.

Chicago/Turabian Style

Shuja Ansari; Marvin Sanchez; Tuleen Boutaleb; Sinan Sinanovic; Carlos Gamio; Ioannis Krikidis. 2018. "SAI: Safety Application Identifier Algorithm at MAC Layer for Vehicular Safety Message Dissemination Over LTE VANET Networks." Wireless Communications and Mobile Computing 2018, no. : 1-17.

Journal article
Published: 01 December 2017 in ICT Express
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Enabling cooperation between vehicles form vehicular networks, which provide safety, traffic efficiency and infotainment. The most vital of these applications require reliability and low latency. Considering these requirements, this paper presents a multitier heterogeneous adaptive vehicular (MHAV) network. Comprising of transport operator or authority owned vehicles in high tier and all the other privately owned vehicles in low tier, integrating cellular network with dedicated short range communications. The proposed framework is implemented and evaluated in Glasgow city center model. Simulation results demonstrate that the proposed architecture outperforms previous multitier architectures in terms of latency while offloading traffic from cellular networks

ACS Style

S. Ansari; T. Boutaleb; Sinan Sinanovic; C. Gamio; I. Krikidis. MHAV: Multitier Heterogeneous Adaptive Vehicular Network with LTE and DSRC. ICT Express 2017, 3, 199 -203.

AMA Style

S. Ansari, T. Boutaleb, Sinan Sinanovic, C. Gamio, I. Krikidis. MHAV: Multitier Heterogeneous Adaptive Vehicular Network with LTE and DSRC. ICT Express. 2017; 3 (4):199-203.

Chicago/Turabian Style

S. Ansari; T. Boutaleb; Sinan Sinanovic; C. Gamio; I. Krikidis. 2017. "MHAV: Multitier Heterogeneous Adaptive Vehicular Network with LTE and DSRC." ICT Express 3, no. 4: 199-203.

Proceedings article
Published: 01 October 2016 in 2016 8th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)
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Vehicular communications have been an incentive for driver safety and ultimately autonomous smart vehicles. These vehicular networks have strict requirements with transmission frequency, range, and delay. From previous contributions, Long Term Evolution (LTE) has been found to meet the requirements for vehicular networks. Extensive realistic system level simulations, including multipath and multi cell environments, have been carried out to evaluate the performance of LTE networks for vehicular communications. This paper improves upon previously contributed simulations by introducing Safety Application Identifier along with an algorithm that implements differentiated Quality of Service (QoS) for different safety applications that are handled by the vehicular server located within the LTE core network. Results show that the probability of end-to-end delay below 100 ms increases by 20%, downlink goodput of the system improves reducing the amount of vehicular application traffic, and eventually the number of downlink flows is reduced by 60%; improving network capacity. Moreover, with the implementation of the proposed algorithm, high QoS can be achieved for vehicular safety applications in terms of delay and packet delivery.

ACS Style

Shuja Ansari; Tuleen Boutaleb; Carlos Gamio; Sinan Sinanovic; Ioannis Krikidis; Marvin Sanchez. Vehicular Safety Application Identifier algorithm for LTE VANET server. 2016 8th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) 2016, 37 -42.

AMA Style

Shuja Ansari, Tuleen Boutaleb, Carlos Gamio, Sinan Sinanovic, Ioannis Krikidis, Marvin Sanchez. Vehicular Safety Application Identifier algorithm for LTE VANET server. 2016 8th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). 2016; ():37-42.

Chicago/Turabian Style

Shuja Ansari; Tuleen Boutaleb; Carlos Gamio; Sinan Sinanovic; Ioannis Krikidis; Marvin Sanchez. 2016. "Vehicular Safety Application Identifier algorithm for LTE VANET server." 2016 8th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) , no. : 37-42.