An Energy-Efficient Cross-Layer-Sensing Clustering Method Based on Intelligent Fog Computing in WSNs

Abstract

The conventional data-based routing protocols are usually vulnerable to a large number of energy voids or hotspots in Wireless Sensor Networks (WSNs). In order to address this problem, we propose Mobile Intelligent Fog Computing: An Energy-efficient Cross-layer-sensing Clustering Method (ECCM). The first, according to the cross-layer projection principle, the proposed algorithm employs the sensingevent-driven mechanism to project the fog nodes onto the sensing layer, and constructs a powerful virtual control node. Then the control procedure of the cluster-based routing protocol in sensor networks is uploaded to the fog layer and the fog computation is employed to achieve the distributed clustering of the event-field nodes. The second, the optimized data aggregation routing is constructed, which centers the projectile fog nodes. The data in the bottom-layer routing of the sensor network is thus replaced, and the network load is balanced and reduced. The third, in the optimization of the routing protocol, we introduce the Particles Swarm Optimization, (PSO) algorithm and elect a group of optimal nodes as the cluster heads, without the cost of any competition overhead, the energy overhead of the network can be effectively reduced and balanced, which curbs the rapid exhaustion of the node energy and prolongs the network lifetime. Finally, it is shown by the simulation results that the construction and the maintenance of the routing structure are small, which could optimize the data aggregation efficiency and improve the network performance.INDEX TERMS Fog computation, wireless sensor network, clustering method, routing protocol, particles swarm optimization.

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“…According to formula (21), if the distance between any node and the sink node is set as d, the received power is:…”

Sensing Cloud Computing in Internet of Things: A Novel Data Scheduling Optimization Algorithm

“…The Source Worker Node Choosing (SWNC) algorithm uses the prefetch load factor to select the source data node: first, query the node location and network load information of the copy of the migration task input data block; then, calculate the prefetch load factor of each replica node of the prefetched data block according to formula (15); finally, select the appropriate prefetch source data node according to the prefetch load factor of each replica node.…”

“…The associate editor coordinating the review of this manuscript and approving it for publication was Lu Liu . located, that is, local execution of the task, thereby avoiding the transmission of task input data across the network and improving System network resource utilization [12]- [15]. The Internet of Things, as a typical example of big data processing systems, is mainly used to perform data-intensive tasks.…”

Distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks: A survey

“…In summary, the merits of fog computing mainly involve (a) quick response to delay-sensitive requirements, (b) data aggregation from heterogeneous devices, (c) data protection and security for sensitive data applications due to avoiding to send data to the cloud, and (d) avoiding unnecessary communication, see [1] for more details. Recently, some preliminary results on fog computing for sensor networks are published in the literature, such as edge node reconfiguration [51,60], the choice of the sensing routing [70], as well as network architecture managements [54,62]. It should be stressed that fog computing, being in its infancy stage, exposes several challenges that need to be further addressed, such as fog-cloud collaboration, service scalability, fog scalability, storage security and communication security, tradeoff between energy consumption and communication efficiency and so forth.…”