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Anwesha Mukherjee

Bio: Anwesha Mukherjee is an academic researcher from Mahishadal Raj College. The author has contributed to research in topics: Cloud computing & Cellular network. The author has an hindex of 15, co-authored 52 publications receiving 709 citations. Previous affiliations of Anwesha Mukherjee include University of Engineering & Management & Indian Institute of Technology Kharagpur.


Papers
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Journal ArticleDOI
TL;DR: A power and latency aware optimum cloudlet selection strategy for multi-cloudlet environment with the introduction of a proxy server is proposed and results demonstrate that the proposed approach reduces the power consumption and the system response time.
Abstract: Fast interactive response in mobile cloud computing is an emerging area of interest. Execution of applications inside the remote cloud increases the delay and affects the service quality. To avoid this difficulty cloudlet is introduced. Cloudlet provides the same service to the device as cloud at low latency but at high bandwidth. But selection of a cloudlet for offloading computation at low power is a major challenge if more than one cloudlet is available nearby. In this paper we have proposed a power and latency aware optimum cloudlet selection strategy for multi-cloudlet environment with the introduction of a proxy server. Theoretical analysis show that using the proposed approach the power and the latency consumption are reduced by approximately 29-32 and 33-36 percent respectively than offloading to the remote cloud. An experimental analysis of the proposed cloudlet selection scheme is performed using cloudlets and cloud servers located at our university laboratory. Theoretical and experimental results demonstrate that using the proposed strategy power and latency aware cloudlet selection can be performed. The proposed approach is compared with the existing methods on multi-cloudlet scenario to demonstrate that the proposed approach reduces the power consumption and the system response time.

109 citations

Journal ArticleDOI
TL;DR: A mobility-driven cloud-fog-edge collaborative real-time framework, Mobi-IoST, has been proposed, which has IoT, Edge, Fog and Cloud layers and exploits the mobility dynamics of the moving agent.
Abstract: The design of mobility-aware framework for edge/fog computing for IoT systems with back-end cloud is gaining research interest. In this paper, a mobility-driven cloud-fog-edge collaborative real-time framework, Mobi-IoST, has been proposed, which has IoT, Edge, Fog and Cloud layers and exploits the mobility dynamics of the moving agent. The IoT and edge devices are considered to be the moving agents in a 2-D space, typically over the road-network. The framework analyses the spatio-temporal mobility data (GPS logs) along with the other contextual information and employs machine learning algorithm to predict the location of the moving agents (IoT and Edge devices) in real-time. The accumulated spatio-temporal traces from the moving agents are modelled using probabilistic graphical model. The major features of the proposed framework are: (i) hierarchical processing of the information using IoT-Edge-Fog-Cloud architecture to provide better QoS in real-time applications, (ii) uses mobility information for predicting next location of the agents to deliver processed information, and (iii) efficiently handles delay and power consumption. The performance evaluations yield that the proposed Mobi-IoST framework has approximately 93% accuracy and reduced the delay and power by approximately 23–26% and 37–41% respectively than the existing mobility-aware task delegation system.

94 citations

Journal ArticleDOI
TL;DR: An application-aware cloudlet selection strategy for multi-cloudlet scenario that can balance the load of the system by distributing the processes to be offloaded in various cloudlets, and the mathematical models of total power consumption and delay for the proposed strategy are developed.
Abstract: Latency- and power-aware offloading is a promising issue in the field of mobile cloud computing today. To provide latency-aware offloading, the concept of cloudlet has evolved. However, offloading an application to the most appropriate cloudlet is still a major challenge. This paper has proposed an application-aware cloudlet selection strategy for multi-cloudlet scenario. Different cloudlets are able to process different types of applications. When a request comes from a mobile device for offloading a task, the application type is verified first. According to the application type, the most suitable cloudlet is selected among multiple cloudlets present near the mobile device. By offloading computation using the proposed strategy, the energy consumption of mobile terminals can be reduced as well as latency in application execution can be decreased. Moreover, the proposed strategy can balance the load of the system by distributing the processes to be offloaded in various cloudlets. Consequently, the probability of putting all loads on a single cloudlet can be dealt for load balancing. The proposed algorithm is implemented in the mobile cloud computing laboratory of our university. In the experimental analyses, the sorting and searching processes, numerical operations, game and web service are considered as the tasks to be offloaded to the cloudlets based on the application type. The delays involved in offloading various applications to the cloudlets located at the university laboratory, using proposed algorithm are presented. The mathematical models of total power consumption and delay for the proposed strategy are also developed in this paper.

83 citations

Journal ArticleDOI
TL;DR: Five case studies are presented where macrocells, microcells, picocells and femtocells are deployed based on the number of mobile subscribers present in a region, mobile user traffic in that region and the area of the region where cellular coverage has to be provided.

74 citations

Journal ArticleDOI
TL;DR: The experimental analysis of the proposed mobile healthcare framework shows that the proposed mobility prediction model has better precision, recall value and time-efficiency than the existing models.
Abstract: This paper proposes a mobile healthcare framework based on edge-fog-cloud collaborative network. It uses edge and fog devices for parameterized health monitoring, and cloud for further health data analysis in case of abnormal health status. The continuous location change of users is a critical issue, and the connection interruption and delay in delivering health related data may be fatal in case of emergency. In this direction, in the proposed framework, mobility information of the users is considered and the users’ mobility pattern detection is performed inside the cloud for advising the user regarding nearby health centre. From the theoretical analysis, it is observed that the proposed framework reduces the delay and energy consumption of user device by $$\sim 28\%$$ and $$\sim 27\%$$ respectively than the cloud only health care model. The proposed healthcare framework has been implemented in the laboratory and health data of few student volunteers are analyzed to predict their health status. The experimental analysis also shows that the proposed mobility prediction model has better precision, recall value and time-efficiency than the existing models.

46 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper proposes a dynamic energy-aware cloudlet-based mobile cloud computing model (DECM) focusing on solving the additional energy consumptions during the wireless communications by leveraging dynamic cloudlets (DCL)-based model.

453 citations

Journal ArticleDOI
TL;DR: It is shown here that considering the effect of traffic-load-dependent factors on energy consumption may lead to noticeably lower benefit than in models that ignore this effect, and potential future research directions are discussed.
Abstract: Due to global climate change as well as economic concern of network operators, energy consumption of the infrastructure of cellular networks, or “Green Cellular Networking,” has become a popular research topic. While energy saving can be achieved by adopting renewable energy resources or improving design of certain hardware (e.g., power amplifier) to make it more energy-efficient, the cost of purchasing, replacing, and installing new equipment (including manpower, transportation, disruption to normal operation, as well as associated energy and direct cost) is often prohibitive. By comparison, approaches that work on the operating protocols of the system do not require changes to current network architecture, making them far less costly and easier for testing and implementation. In this survey, we first present facts and figures that highlight the importance of green mobile networking and then review existing green cellular networking research with particular focus on techniques that incorporate the concept of the “sleep mode” in base stations. It takes advantage of changing traffic patterns on daily or weekly basis and selectively switches some lightly loaded base stations to low energy consumption modes. As base stations are responsible for the large amount of energy consumed in cellular networks, these approaches have the potential to save a significant amount of energy, as shown in various studies. However, it is noticed that certain simplifying assumptions made in the published papers introduce inaccuracies. This review will discuss these assumptions, particularly, an assumption that ignores the effect of traffic-load-dependent factors on energy consumption. We show here that considering this effect may lead to noticeably lower benefit than in models that ignore this effect. Finally, potential future research directions are discussed.

384 citations

Journal ArticleDOI
TL;DR: A clear collaboration model for the SDN-Edge Computing interaction is put forward through practical architectures and it is shown that SDN related mechanisms can feasibly operate within the Edge Computing infrastructures.
Abstract: A novel paradigm that changes the scene for the modern communication and computation systems is the Edge Computing. It is not a coincidence that terms like Mobile Cloud Computing, Cloudlets, Fog Computing, and Mobile-Edge Computing are gaining popularity both in academia and industry. In this paper, we embrace all these terms under the umbrella concept of “Edge Computing” to name the trend where computational infrastructures hence the services themselves are getting closer to the end user. However, we observe that bringing computational infrastructures to the proximity of the user does not magically solve all technical challenges. Moreover, it creates complexities of its own when not carefully handled. In this paper, these challenges are discussed in depth and categorically analyzed. As a solution direction, we propose that another major trend in networking, namely software-defined networking (SDN), should be taken into account. SDN, which is not proposed specifically for Edge Computing, can in fact serve as an enabler to lower the complexity barriers involved and let the real potential of Edge Computing be achieved. To fully demonstrate our ideas, initially, we put forward a clear collaboration model for the SDN-Edge Computing interaction through practical architectures and show that SDN related mechanisms can feasibly operate within the Edge Computing infrastructures. Then, we provide a detailed survey of the approaches that comprise the Edge Computing domain. A comparative discussion elaborates on where these technologies meet as well as how they differ. Later, we discuss the capabilities of SDN and align them with the technical shortcomings of Edge Computing implementations. We thoroughly investigate the possible modes of operation and interaction between the aforementioned technologies in all directions and technically deduce a set of “Benefit Areas” which is discussed in detail. Lastly, as SDN is an evolving technology, we give the future directions for enhancing the SDN development so that it can take this collaboration to a further level.

331 citations

01 Jun 2016
TL;DR: A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC).
Abstract: A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13]

248 citations

Journal ArticleDOI
TL;DR: A system model and dynamic schedules of data/control-constrained computing tasks are investigated, including the execution time and energy consumption for mobile devices, and NSGA-III (non-dominated sorting genetic algorithm III) is employed to address the multi-objective optimization problem of task offloading in cloud-edge computing.

237 citations