Department of Electrical and Electronics Engineering

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1925

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Subject: search.filters.subject.Edge computing

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Now showing 1 - 10 of 12
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    Federated Learning-Based Task Offloading in a UAV-Aided Cloud Computing Mobile Network
    (IEEE, 2024-05) Joshi, Sandeep
    Unmanned aerial vehicles (UAVs), due to their flexibility in deployment, offer various advantages in the next-generation wireless networks. In this work, we study a UAV-assisted mobile edge computing network where a UAV is equipped with computing resources. The user equipment (UE) is capable of transferring a part of the computational workload to the UAV. We aim to reduce the maximum processing delay while adhering to the energy consumption limitation. This is accomplished by optimizing user scheduling, task offloading ratio, UAV's flight angle, and flight speed. We propose a computation offloading approach using federated learning based on decentralized federated averaging, taking into account the fact that this optimization problem is not convex, the state space is high-dimensional, and the action space is continuous. We determine the best strategy for offloading computing in an environment whose dynamics are challenging to regulate. We perform extensive simulation testing, and our findings indicate that the proposed method converges faster to the optimal solution. Furthermore, the proposed algorithm significantly improves the processing delay by about 20% at smaller task sizes and even higher for larger task sizes as compared to the baseline algorithms.
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    A Survey on Edge Enabled Metaverse: Applications, Technological Innovations, and Prospective Trajectories Within the Industry
    (IEEE, 2024-08) Chamola, Vinay; Mishra, Rajesh P.
    As the Metaverse promises to revolutionize human interaction and digital life, the role of edge computing emerges as a critical enabler. This paper presents a comprehensive survey of edge-enabled Metaverse applications, technological innovations, challenges, solutions, and future trajectories within the industry. We dissect the landscape of Metaverse applications across various sectors, analyzing how edge computing empowers real-time, immersive experiences. We delve into the cutting-edge advancements in decentralized computing infrastructure, edge networking, and artificial intelligence shaping the Metaverse, highlighting their potential to overcome latency, bandwidth, and privacy challenges. Additionally, we explore enabling technologies such as 5G and IoT, which facilitate seamless connectivity and data processing. We also address significant challenges, including the need for scalable and resilient infrastructure, data security concerns, and the integration of diverse technologies, proposing viable solutions like enhanced edge AI algorithms and robust cybersecurity frameworks. Finally, we chart prospective trajectories for edge-enabled Metaverse development, identifying key trends and potential disruptive forces that will shape the industry’s future. Our survey aims to serve as a definitive resource for researchers, developers, and industry leaders by providing a holistic understanding of edge computing’s pivotal role in realizing the boundless potential of the Metaverse.
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    MERGE: Meta Reinforcement Learning for Tunable RL Agents at the Edge
    (IEEE, 2023-12) Tripathi, Sharda
    The efficient allocation of radio resources is an essential trait of 5G/6G radio access networks (RANs), as they are called to meet diverse QoS requirements of highly demanding applications. To equip RANs with such an ability and, at the same time, meet their function split constraints, we envision a distributed learning approach for radio resource allocation that makes the most out of the Central Unit (CU) and Distributed Unit (DU) components by effectively exploiting their synergy. On the one hand, our solution, named MERGE, leverages the knowledge of the radio connectivity dynamics that each DU can acquire through the local use of a deep reinforcement learning radio agent. On the other hand, it lets the CU collect such agents in a crowdsourcing fashion, and, then, thanks to a meta-learning policy, properly select and aggregate them to create up-to-date radio agents of the right size (hence, complexity level) to fit the computing constraints of the individual DUs. Our results show that MERGE can match the performance of the highest-complexity radio model in [1] with 25% less computational requirements, and, for a given computational resource, it outperforms a single pruned model with a 19% increase in QoS.
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    Design of In-Memory Computing Enabled SRAM Macro
    (IEEE, 2022) Chaturvedi, Nitin
    The era of nanoscale devices has resulted in tremendously fast and compact modern processing systems. The von-neumann architecture is still one of the most widely adopted architectures in these computing systems comprising separate memory and processing units. However, the growing computational requirements of emerging applications with large data set are posing a great challenge to these conventional computing systems due to constant data transfer between the two physically separate memory and computing block. The heavy data transportation between the processing core and memory results in large power consumption, especially for big-data applications. Addressing this challenge, we propose to bring processing closer to the memory. Therefore, in this work, we design an In-Memory Computing enabled SRAM macro (IMC-SRAM) which is capable of performing logical computations within memory in addition to normal memory operations. We utilize differential 9T bitcell and modified peripheral circuitry to realize boolean logic operation such as AND/NAND and OR/NOR within the memory array. The proposed design has been validated using SPICE simulations with operating frequency of 1GHz across all process corners using NCSU 45nm technology.
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    Edge Computing and Deep Learning Enabled Secure Multitier Network for Internet of Vehicles
    (IEEE, 2021-04) Chamola, Vinay; Singh, Dheerendra
    Internet of Vehicles (IoVs) are fast becoming the norm in our society, but such a trend also comes with its own set of challenges (e.g., new security and privacy risks due to the expanded attack vectors). In this work, we propose an edge-computing-based secure, efficient, and intelligent multitier heterogeneous IoVs network. We first discuss the functionality and objectives of such an architecture. Then, we demonstrate how unsupervised deep learning techniques can facilitate the identification of suspicious vehicle behavior and ensure the security of such an architecture. The findings from our evaluations demonstrate the learning spatiotemporal information and parameter efficiency of the proposed stacked long short-term memory (LSTM) model over single LSTMs.
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    An optimal delay aware task assignment scheme for wireless SDN networked edge cloudlets
    (Elsevier, 2020-01) Chalapathi, G.S.S.; Chamola, Vinay
    Over the past decade, there has been an increasing demand for mobile devices to perform computationally intensive tasks. However, the computational capability of these devices is limited due to memory, power and portability constraints. One of the feasible and attractive ways to enhance the performance of the resource-limited mobile devices is to offload their computationally intensive tasks on to the cloud servers when internet connectivity is available. However, when cloud servers are involved in processing, the latency and cost of computation increases. To mitigate these problems, devices with high computational resources, called cloudlets, can be deployed in the locations close to the mobile users/devices. The mobile devices can then offload their computationally intensive tasks on to them. Due to easier access and nearness of the cloudlets, the cost and latency in processing the tasks decreases. In this work, we focus on task assignment problem in a multi-cloudlet network connected via a wireless SDN network, which services the task offload requests from mobile devices in a given locality. The aim of the proposed solution is to minimize latency and thus enhance the quality of service for mobile devices. We prove the optimality of the proposed solution mathematically and employ an admission control policy to maintain this optimality even in heavily loaded networks. We also perform numerical simulations for two scenarios of small and large networks and evaluate the performance for varying traffic and network parameters. The results demonstrate that the proposed task assignment method offers reduced latency compared to state-of-the-art task assignment approaches and hence improves the quality of service offered to mobile devices.
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    MbRE IDS: An AI and Edge Computing Empowered Framework for Securing Intelligent Transportation Systems
    (IEEE, 2022-05) Chamola, Vinay
    Recent years have seen a widespread growth of research in the Internet of Things (IoT). While mobility networks such as the Intelligent Transportation Systems (ITS) are being increasingly studied for their application in smart cities, there are numerous cyber threats that may disrupt the security and safety of the users of such networks. This study proposes an intelligent, statistical Intrusion Detection System (IDS) called Multi-branch Reconstruction Error (MbRE) for the long term security of ITS against known and unknown threats. The proposed IDS learns only from normal behavior, detects deviation of vehicular from it, and classifies it into eight generalized buckets based on the aspects of the data found to be malicious, i.e. frequency, identity and motion (speed and position). The results obtained show the success of the proposed IDS in detecting different threats with recall and accuracy scores between 97.5% to 100% without the need to train on them.
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    Energy and latency aware mobile task assignment for green cloudlets
    (Elsevier, 2022-07) Chalapathi, G.S.S.; Chamola, Vinay
    Edge computing places cloudlets with high computational capabilities near mobile devices to reduce the latency and network congestion encountered in cloud server-based task offloading. However, many cloudlets are required in such an edge computing network, leading to a tremendous increase in carbon emissions of computing networks globally. This increase in carbon emission envisages the need to employ green energy resources to power these cloudlets. This need has led to the concept of Green Cloudlet Networks (GCNs). But GCNs must deal with the problem of the unpredictability of green energy available to them while optimizing the performance (in terms of latency) delivered to the mobile user. This paper proposes a novel task-assignment called Green Energy and Latency Aware Task Assignment (Ge-LATA) for GCNs to address this issue. The primary aim of Ge-LATA is to optimize the latency and the green energy consumed in processing the offloaded tasks from the mobile devices. In this GCN, the cloudlets are connected in a network to process the incoming tasks cooperatively to ensure load-balancing at the cloudlets. Ge-LATA considers various factors like the current load, available green energy, service rate offered by cloudlets, and the distance from the mobile user, leading to optimal decisions in terms of latency and green energy consumed. Simulations are performed using the actual solar insolation data taken from the NREL database. Ge-LATA is tested with other offloading schemes for latency in processing the offloaded tasks and green-energy consumed under different solar insolation scenarios in these simulations. Simulation results show that Ge-LATA achieves up to 31.87% of reduction in the latency while ensuring up to 50.15% of reduction in the energy consumption than other comparable task-assignment schemes.Thus, Ge-LATA suggests that it leads to an optimal task assignment by considering the various factors mentioned above during the task assignment process. Thus, Ge-LATA considers the above-mentioned extensive set of parameters during the task allotment process. It also proposes an efficient green energy allotment scheme that adapts itself to actual weather and network conditions, leading to optimal task assignment decisions in GCNs.
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    A Blockchain and Edge-Computing-Based Secure Framework for Government Tender Allocation
    (IEEE, 2021-02) Chamola, Vinay
    Governments and public sector entities around the world are actively exploring new ways to keep up with technological advancements to achieve smart governance, work efficiency, and cost optimization. Blockchain technology is an example of such technology that has been attracting the attention of Governments across the globe in recent years. Enhanced security, improved traceability, and lowest cost infrastructure empower the blockchain to penetrate various domains. Generally, governments release tenders to some third-party organizations for different projects. During this process, different competitors try to eavesdrop the tender values of others to win the tender. The corrupt government officials also charge high bribe to pass the tender in favor of some particular third party. In this article, we presented a secure and transparent framework for government tenders using blockchain. Blockchain is used as a secure and immutable data structure to store the government records that are highly susceptible to tampering. This work aims to create a transparent and secure edge computing infrastructure for the workflow in government tenders to implement government schemes and policies by limiting human supervision to the minimal.
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    Latency aware mobile task assignment and load balancing for edge cloudlets
    (IEEE, 2017) Chamola, Vinay; Chalapathi, G.S.S.
    With the various technological advances, mobile devices are not just being used as a means to make voice calls; but are being used to accomplish a variety of tasks. Mobile devices are being envisioned to practically accomplish any task which could be done on a computer. This is hurdled by the limited computational resources available with the mobile devices due to their portable size. With the mobile devices being connected to the Internet, leveraging cloud services is being seen as a promising solution to overcome this hurdle. Computationally intensive tasks can be offloaded to the Cloud servers. However, owing to the latency and cost associated with using cloud services, edge devices (termed cloudlets) stationed near the mobile devices are being seen as a prospective alternative to replace/assist the Cloud services. The mobile devices have an easier access to the cloudlets being situated in their vicinity and can offload their task requests to them to be served at a lower cost. This paper considers a network of such connected cloudlets which provide service to the mobile devices in a given area. We address the issue of task assignment in such a scenario (i.e. which cloudlet serves which mobile device) aimed towards improving the quality of service experienced by the mobile devices in terms of minimizing the latency. Through numerical simulations we demonstrate the performance gains of the proposed task assignment scheme showing lower latency as compared to the traditional scheme for task assignment.