A Fast Hierarchical Physical Topology Update Scheme for Edge-Cloud Collaborative IoT Systems

Abstract : The awareness of physical network topology in a large-scale Internet of Things (IoT) system is critical to enable location-based service provisioning and performance optimization. However, due to the dynamics and complexity of IoT networks, it is usually very difficult to discover and update the physical topology of the large-scale IoT systems in real-time. Considering the stringent latency requirements in IoT systems, while the initial processing time for topology discovery can be tolerated, latency due to real-time topology update constitutes an even higher level of challenge. In this paper, a novel fast hierarchical topology update scheme is proposed for the large-scale IoT systems enabled by using the edge-cloud collaborative architecture. Specifically, an event-driven neighbor update algorithm, termed as TriggerOn, is firstly developed to update the local neighbor table of the end devices when device association or disassociation occurs. Based on the updated neighbor tables, the physical topology update of the subnet is conducted at the coordinated edge device, where a hybrid multidimensional scaling (MDS) based 3D localization algorithm is developed to locate the newly associated devices. Simulation results have indicated that as compared to the benchmark methods, the neighbor discovery latency has been reduced dramatically, and the 3D localization accuracy has been improved. Furthermore, the overall latency incurred by the proposed hierarchical physical topology update scheme is significantly lower than the distributed consensus-based update scheme, especially for the large-scale IoT subnets.

 EXISTING SYSTEM :

 ? These µDCs are mainly designed to accommodate computing power and telecommunications equipment in unprepared premises, such as offices, warehouses, utility rooms or production facilities in an enterprise intranet. ? Fog computing platform deals with heterogeneous devices and with different type of data. ? MEC cloud platform is located on the cellular base station servers providing a delay between the user’s mobile device and Mobile Core Network no more than 1 ms, making location awareness applications. ? A mobile device represents a thin-client which can move all the computational tasks via wireless connectivity to the cloudlet resources placed on one hop from it.

 DISADVANTAGE :

 ? This requires the need of adapting the existing algorithms and investigating new optimization techniques for big data analytics problems. ? The main problems are vendorspecific interfaces and software associated with hardware, complex and expensive network operation, and the tight coupling of data and control planes. ? One of the promising approaches to tackle the issues of the big data could be to enable synergies among communications, computing and caching components of future wireless IoT networks. ? To address the aforementioned issues, some ad-hoc solutions have been recently proposed by the research community.

 PROPOSED SYSTEM :

 • Several approaches have been proposed to the organization of ICT infrastructure for applications that are critical to response time: Fog computing, Cloudlets, MEC, MDC. • Fog computing was proposed by Cisco and represent the approach to ICT organization where small virtualized cloud platform brought as closer as possible to the enduser. • Mobile Edge Computing was proposed within ETSI 5G mobile network specification in 2014 as a cloud computing to provide virtualized network services. • Even traditions cloud platform and fog platform were purposed to support the same set of services (computing, storing, networking virtualization) there are some differences.

 ADVANTAGE :

 ? Various performance tradeoffs such as offloading gain versus energy, and cost of cloud resources versus service execution time under the proposed framework are interesting aspects to be investigated in future works. ? In the considered framework, several performance tradeoffs such as caching gain and memory size/cache content size, computing and communication delays can be investigated. ? The proposed framework can exploit the networkwide knowledge and historical information available at the cloud center to guide edge computing units towards satisfying various performance requirements of heterogeneous wireless IoT networks. ? By properly extracting and effectively utilizing these features, the performance of various wireless networks could be significantly enhanced.