Yazar "Ali, Rashid" seçeneğine göre listele

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    Hybrid Cache Management in IoT-Based Named Data Networking
    (IEEE-Inst Electrical Electronics Engineers Inc, 2022) Naeem, Muhammad Ali; Nguyen, Tu N.; Ali, Rashid; Cengiz, Korhan; Meng, Yahui; Khurshaid, Tahir
    Internet of Things (IoT) and named data network (NDN) are innovative technologies to meet up the future Internet requirements. NDN is considered as an enabling approach to improving data dissemination in IoT scenarios. NDN delivers in-network caching, which is the most prominent feature to provide fast data dissemination as compared to Internet protocol (IP)-based communication. The proper integration of caching placement strategies and replacement policies is the most suitable approach to support IoT networks. It can improve multicast communication which minimizes the delay in responding to IoT-based environments. Besides, these approaches are playing a most significant role in increasing the overall performance of NDN-based IoT networks. To this end, in this article, the challenges of NDN-IoT caching are identified with the aim to develop a new hybrid strategy for efficient data delivery. The proposed strategy is comparatively and extensively studied with NDN-IoT caching strategies through an extensive simulation in terms of average latency, cache hit ratio, and average stretch ratio. From the simulation findings, it is observed that the proposed hybrid strategy outperformed to achieve a higher caching performance of NDN-based IoT scenarios.
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    Reinforcement learning-enabled Intelligent Device-to-Device (I-D2D) communication in Narrowband Internet of Things (NB-IoT)
    (Elsevier, 2021) Nauman, Ali; Jamshed, Muhammad Ali; Ali, Rashid; Cengiz, Korhan; Zulqarnain; Kim, Sung Won
    The 5th Generation (5G) and Beyond 5G (B5G) are expected to be the enabling technologies for Internet-of-Everything (IoE). The quality-of-service (QoS) for IoE in the context of uplink data delivery of the content is of prime importance. The 3rd Generation Partnership Project (3GPP) standardizes the Narrowband Internet-of-Things (NB-IoT) in 5G, which is Low Power Wide Area (LPWA) technology to enhance the coverage and to optimize the power consumption for the IoT devices. Repetitions of control and data signals between NB-IoT User Equipment (UE) and the evolved NodeB/Base Station (eNB/BS), is one of the most prominent characteristics in NB-IoT. These repetitions ensure high reliability in the context of data delivery of time-sensitive applications, e.g., healthcare applications. However, these repetitions degrade the performance of the resource-constrained IoT network in terms of energy consumption. Device-to-Device (D2D) communication standardized in Long Term Evolution-Advanced (LTE-A) offers a key solution for NB-IoT UE to transmit in two hops route instead of direct uplink, which augments the efficiency of the system. In an effort to improve the data packet delivery, this study investigates D2D communication for NB-IoT delay-sensitive applications, such as healthcare-IoT services. This study formulates the selection of D2D communication relay as Multi-Armed Bandit (MAB) problem and incorporates Upper Confidence Bound (UCB) based Reinforcement Learning (RL) to solve MAB problem. The proposed Intelligent-D2D (I-D2D) communication methodology selects the optimum relay with a maximum Packet Delivery Ratio (PDR) with minimum End-to-End Delay (EED), which ultimately augments energy efficiency.