Load-Aware Offloading Strategy in Two-Tier Heterogeneous Network

Although small cell offloading technology can alleviate the congestion in macrocell, aggressively offloading data traffic from macrocell to small cell can also degrade the performance of small cell due to the heavy load. Because of collision and backoff, the degradation is significant especially in network with contention-based channel access, and finally decreases throughput of the whole network. To find an optimal fraction of traffic to be offloaded in heterogeneous network, we combine Markov chain with the Poisson point process model to analyze contention-based throughput in irregularly deployment networks. Then we derive the close-form solution of the throughput and find that it is a function of the transmit power and density of base stations.Based on this, we propose the load-aware offloading strategies via power control and base station density adjustment. The numerical results verify our analysis and show a great performance gain compared with non-load-aware offloading.

Path Computing Scheme with Low-Latency and Low-Power in Hybrid Cloud-Fog Network for IIoT

With the rapid development of the Industrial Internet of Things(IIoT),the traditional centralized cloud processing model has encountered the challenges of high communication latency and high energy consumption in handling industrial big data tasks.This paper aims to propose a low-latency and lowenergy path computing scheme for the above problems.This scheme is based on the cloud-fog network architecture.The computing resources of fog network devices in the fog computing layer are used to complete task processing step by step during the data interaction from industrial field devices to the cloud center.A collaborative scheduling strategy based on the particle diversity discrete binary particle swarm optimization(PDBPSO)algorithm is proposed to deploy manufacturing tasks to the fog computing layer reasonably.The task in the form of a directed acyclic graph(DAG)is mapped to a factory fog network in the form of an undirected graph(UG)to find the appropriate computing path for the task,significantly reducing the task processing latency under energy consumption constraints.Simulation experiments show that this scheme’s latency performance outperforms the strategy that tasks are wholly offloaded to the cloud and the strategy that tasks are entirely offloaded to the edge equipment.