Papers by Muhammad S U L E M A N Memon
Cluster Computing, 2021
These days, the usage of the internet of Vehicle Things (IVoT) applications such as E-Business, E... more These days, the usage of the internet of Vehicle Things (IVoT) applications such as E-Business, E-Train, E-Ambulance has been growing progressively. These applications require mobility-aware delay-sensitive services to execute their tasks. With this motivation, the study has the following contribution. Initially, the study devises a novel cooperative vehicular fog cloud network (VFCN) based on container microservices which offers cost-efficient and mobility-aware services with rich resources for processing. This study devises the cost-efficient task offloading and scheduling (CEMOTS) algorithm framework, which consists of the mobility aware task offloading phase (MTOP) method, which determines the optimal offloading time to minimize the communication cost of applications. Furthermore, CEMOTS offers Cooperative Task Offloading Scheduling (CTOS), including task sequencing and scheduling. The goal is to reduce the application costs of communication cost and computational costs under a given deadline constraint. Performance evaluation shows the CTOS and MTOP outperform existing task offloading and scheduling methods in the VCFN in terms of costs and the deadline for IoT applications.
ENTERPRISE INFORMATION SYSTEMS, 2021
These days, fog-cloud based healthcare application partitioning
techniques have been growing prog... more These days, fog-cloud based healthcare application partitioning
techniques have been growing progressively. However, existing
static fog-cloud based application partitioning methods are static
and cannot adopt dynamic changes in the dynamic environment (e.
g., where network and computing nodes have resource value variation) during the execution process. This study devises a Deep
Neural Networks Energy Cost-Efficient Partitioning and Task
Scheduling (DNNECTS) algorithm framework which consists of the
following components: application partitioning, task sequencing,
and scheduling. Experimental results show the suggested methods
in terms of energy consumption and the applications' cost in the
dynamic environment.
Mobile wireless sensor networks are made up of resource-constrained sensor nodes. The nodes in Mo... more Mobile wireless sensor networks are made up of resource-constrained sensor nodes. The nodes in Mobile wireless sensor networks tend to move wirelessly from one location to another. In physically unattended and insecure areas, these networks are installed where they are vulnerable to numerous security threats, including physical security threats. Clone node attack is a physical attack in which the adversary physically compromises legitimate nodes and copies their data onto fake nodes to create clones of captured nodes. In static wireless sensor networks, comprehensive work is already done to mitigate and detect the underlying attack. Mitigating and detecting this assault on mobile wireless sensor networks, however, remains a critical issue. To our knowledge, there is no framework-based method for bolstering multiple attacks on mobile wireless sensor networks.Consequently, this article proposes a framework-based approach that mitigates multiple attacks on mobile WSNs. To provide proof-of-concept for the framework, along with clone node attacks solution to jamming attacks is also incorporated in the framework. The implementation of the framework is done with the COOJA simulator and Contiki OS.
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Papers by Muhammad S U L E M A N Memon
techniques have been growing progressively. However, existing
static fog-cloud based application partitioning methods are static
and cannot adopt dynamic changes in the dynamic environment (e.
g., where network and computing nodes have resource value variation) during the execution process. This study devises a Deep
Neural Networks Energy Cost-Efficient Partitioning and Task
Scheduling (DNNECTS) algorithm framework which consists of the
following components: application partitioning, task sequencing,
and scheduling. Experimental results show the suggested methods
in terms of energy consumption and the applications' cost in the
dynamic environment.
techniques have been growing progressively. However, existing
static fog-cloud based application partitioning methods are static
and cannot adopt dynamic changes in the dynamic environment (e.
g., where network and computing nodes have resource value variation) during the execution process. This study devises a Deep
Neural Networks Energy Cost-Efficient Partitioning and Task
Scheduling (DNNECTS) algorithm framework which consists of the
following components: application partitioning, task sequencing,
and scheduling. Experimental results show the suggested methods
in terms of energy consumption and the applications' cost in the
dynamic environment.