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 sensing-event-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.

A narrow bandwidth may lead to a large amount of redundant data, which further causes the interruptions of the communication network. In order to address this problem, an Optimized Clustering Communication Protocol Based on Intelligent Computing (CCP-IC) is proposed in this paper. First, we adopt the intelligent algorithm to perform the optimization of the clustering in the sensor network. The adaptation function and the heuristic function are introduced to make a targeted choice on the cluster head (CH) for the next hop of the nodes in the network. Second, the controllable threshold parameter and variation coefficient are employed to optimize the shortest path chosen by the network routing. Therefore, the node energy consumption is lowered when the minimum network delay is guaranteed and the transmission efficiency is improved. Finally, it is verified via the simulation results, and compared with other algorithms; the proposed protocol reduces the network energy consumption by 15.3% and prolongs the network lifetime by 18.72%, which proves the validity and effectiveness of the proposed protocol.

In the Fog Computer (FC), the process of data is prone to problems such as low data similarity and poor data tolerance. This paper proposes a hierarchical data job scheduling strategy Based on Intelligent Sensor-Cloud in Fog Computer (HDJS). HDJS dynamically adjusts the priority of the job to avoid job starvation and maximize the use of resources, uses the key frame to the resource occupied information, distributes the frame sequence to the unit, and then combines the intra frame distribution strategy to balance the load between the nodes. The experimental results show our proposed strategy may be possible to avoid the operation of hunger and resource fragmentation problems, make full use of the advantages of multi-core and multi-thread, improve system resource utilization, and shorten the execution time and response time.

Many companies and institutions currently use centralized or distributed databases to store massive amounts of data. However, the use of untrusted centralized third-party auditors can result in security issues, as these auditors may be malicious and tamper with or delete user data. This poses a significant challenge to ensuring the reliability of data verification results. Although introducing a third-party auditor can help address this issue, this auditor may also be untrustworthy and collude with the database service provider to forge false data verification results. In this paper, we propose a data integrity verification scheme using smart contracts (DIV-SC) to address this challenge in a centralized database environment. Our approach utilizes blockchain technology as a decentralized third-party auditor, ensuring that information stored on the blockchain is immutable and cannot be tampered with maliciously. Additionally, smart contracts deployed on the blockchain can ensure that the verification procedures are correct and are not impacted by any malicious parties. We also leverage game theory to improve the reliability of multiple verifications, reducing verification time and improving overall performance. Our proposed scheme reduces total verification time consumption by up to 53.44% while increasing the number of verifiable times by nearly 3.8 times, compared to conventional data integrity verification schemes.

In order to alleviate the impacts of the rapid network energy exhaustion and the unreliable links on the data gathering in the Internet of Things (IoT), a Mobile Intelligent Computing Based on Compressive Sensing Date Gathering (MIC-CSDG) algorithm is proposed in this work, which could improve the data reconstruction accuracy. We conduct research from the following four links. Firstly, this method employs the mobile intelligent computing to derive the multi-hop function among sensor nodes, which is further utilized to determine the proportional relationship for the sensor nodes. Secondly, based on the sparse matrix, an observation matrix is designed with low correlation to mitigate the influences of the data packet loss on the entire IoT system and improve the data reconstruction accuracy for the sink node. Then, the acknowledge mechanism for the data forwarding strategy is employed to improve the reliability of the data transmission among clusters. Therefore, the reliable data handover is accomplished for the multi-path routing data among different nodes. Results which are about the simulation shows that the loss rate of the packet is equal to 40%, the data reconstruction error of the MIC-CSDG algorithm still remains lower than 5%. Compared with other existing algorithms, the data forwarding time is reduced by 16.36%, while the average network energy consumption is reduced by 23.59%. Therefore, the validity and efficiency of the proposed method is verified.