Cognitive Edge Computing based resource allocation framework for Internet of Things

Abstract

Due to the inherent property of the processing resource request from mobile active or passive devices as part of internet of things (IoT), processing capacity as well as latency become major optimization criteria. To achieve overall optimized uses of cloud resources -having dynamic tracking, monitoring as well as orchestration framework is one of the key challenges to overcome. In the same context, enhanced uses of computing devices at distributed location is predicted to facilitate the success of IoT; subsequently the success of fifth generation (5G) of Wireless technologies. This opens enormous potential to integrate the unused resources of such distributed computed devices within the conventional cloudlet or cloud federation. However, this requires an efficient micro-level distributed computing resource tracking, monitoring and orchestration; where resources are distributed in geo-location as well as the availability of unused resources are time variant in nature. In this paper, we have proposed a cognitive edge-computing based framework solution for these requirements in order to achieve an efficient use of these distributed resources. This provides the end-user with a dynamic soft extension of computing facilities of cloudlet and cloud federation, as well as a revenue generation avenue to enduser. The simulation results show that such extension can be an exponential function of the number of local processing platforms agreed to participate in the proposed cognitive resource sharing.

Artificial intelligent system for multimedia services in smart home environments

“…Authors as Wu et al [25] discover the relations between the trend of the big data era, and that of the new generation green revolution, through a comprehensive and panoramic literature survey in big data technologies toward various green objectives and a discussion on relevant challenges and future directions. Amjad et al [26] propose a cognitive edge-computingbased framework solution, to integrate the advancement of edge computing resource requirement schemes as well as the resource allocation schemes found in the literature for enterprise cloud; to attain a universal resource allocation framework for IoT. Others Authors as Jararweh et al [27] present a novel experimental framework for IoT-based environmental monitoring applications, using concepts from Data Fusion (DF) and software defined systems (SDS).…”

Research challenges in nextgen service orchestration

“…Fog/Edge computing dynamic coalitions of edge devices and cloudlets [145,159,48,166,122,19,41,40,78] Dynamism, Churn, Scalability, Locality-awareness going beyond shadow devices for reliability [7,48,166,122] Churn dynamic end-to-end service availability [101,48] Multi-tenant, Multi-domain smaller execution units [86,166,19,99] Larger scale, Finer grain diversity [131,116,79,69] Heterogeneity M2M confidentiality, wireless-based attacks [130], trust management [30] Security AAA [118] [151], privacy-leakage [103]. privacy ensure quality-of-service on a variety of infrastructure elements [106,78] Heterogeneity, Multi-domain…”

Distributed Learning for Low Latency Machine Type Communication in a Massive Internet of Things

“…In [13], the authors introduce an edge-cloud based label-less learning to offload only valuable data to the cloud and to reduce the traffic. The authors in [14] propose a framework based on cognitive edge computing to optimize the use of distributed cloud resources for the IoT. In [15], the authors integrate software-defined networking to machine-tomachine communication to enable a smart energy management for various environments.…”