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IoT service recommendation techniques can help a user select appropriate IoT services efficiently. Aiming at improving the recommendation efficiency and preserving the data privacy, the locality-sensitive hashing (LSH) technique is adopted in service recommendation. However, existing LSH-based service recommendation methods ignore the intrinsic temporal feature of IoT services. In light of this challenge, we integrate the temporal feature into the conventional LSH-based method and present a time-aware approach with the capability of privacy preservation for IoT service recommendation across multiple platforms. Experiments on a real-world dataset are conducted to validate the advantage of our proposed approach in terms of accuracy and efficiency in recommendation.

With the rapid development of the Internet of Things (IoT), large quantities of data have been generated. Due to the limitation of the network bandwidth, the time and energy consumption of data transmission are increased. Data feature information can be extracted in real-time by the deployment of a data processing center. In this article, a novel dimension reduction approach is proposed in edge computing. First, a four-layer data processing framework is designed for data acquisition. A task assignment algorithm (TAA) is used for the condition when the edge node stops working due to an accident. Second, a threshold strategy is proposed to filter the data and reduce the dimension. Finally, the dimension reduction algorithm based on adaptive maximum linear neighborhood selection (AMLNS) is proposed. The harmonic geodesic distance is introduced to avoid the deformation of the manifold structure in AMLNS algorithm. Particularly, multiple weights are used to construct linear structure, which has a better embedding effect than single weight. The maximum linear neighborhood error weight is used to calculate the data coordinates. Experimental results show that the TAA improves the task completion rate about 15% and 36% over the random assignment method in mobile layer and edge layer, respectively. Compared with the local linear embedding (LLE), the points distribution of AMLNS is more uniform and regular, the execution time of AMLNS is reduced by about 17%. Furthermore, the embedding errors are less than those of LLE.

A growing number of web APIs published on the Internet allows mashup developers to discover appropriate web APIs for polishing mashups. Developers often have to manually pick and choose several web APIs from extremely massive candidates, which is a laborious and cumbersome task. Fortunately, recommender system comes into existence. Some approaches perform recommendations in cloud platforms by utilizing historical records of Mashup-API interactions stored in edge nodes. However, many of these methods often pay more attention to recommendation accuracy while ignoring recommendation diversity, i.e., there are usually popular web APIs in recommendation list while most of the other novel web APIs are absent. The poor recommendation diversity may limit the usefulness of the recommendation results due to the lack of novelty. In order to implement an accurate and diversified web API recommendation, a novel MF-based recommendation approach named Div_PreAPI is put forward in this paper. Div_PreAPI integrates a weighting mechanism and neighborhood information into matrix factorization (MF) to implement diversified and personalized APIs recommendations. Finally, we conduct a series of experiments on a real-world dataset. Experimental results show the effectiveness of our proposal.

The cyber-physical cloud systems (CPCSs) release powerful capability in provisioning the complicated industrial services. Due to the advances of machine learning (ML) in attack detection, a wide range of ML applications are involved in industrial CPCSs. However, how to ensure the implementation efficiency of these applications, and meanwhile avoid the privacy disclosure of the datasets due to data acquisition by different operators, remain challenging for the design of the CPCSs. To fill this gap, in this article a privacy-aware deployment method (PDM), named PDM, is devised for hosting the ML applications in the industrial CPCSs. In PDM, the ML applications are partitioned as multiple computing tasks with certain execution order, like workflows. Specifically, the deployment problem is formulated as a multiobjective problem for improving the implementation performance and resource utility. Then, the most balanced and optimal strategy is selected by leveraging an improved differential evolution technique. Finally, through comprehensive experiments and comparison analysis, PDM is fully evaluated.

Healthcare uses state-of-the-art technologies (such as wearable devices, blood glucose meters, electrocardiographs), which results in the generation of large amounts of data. Healthcare data is essential in patient management and plays a critical role in transforming healthcare services, medical scheme design, and scientific research. Missing data is a challenging problem in healthcare due to system failure and untimely filing, resulting in inaccurate diagnosis treatment anomalies. Therefore, there is a need to accurately predict and impute missing data as only complete data could provide a scientific and comprehensive basis for patients, doctors, and researchers. However, traditional approaches in this paradigm often neglect the effect of the time factor on forecasting results. This paper proposes a time-aware missing healthcare data prediction approach based on the autoregressive integrated moving average (ARIMA) model. We combine a truncated singular value decomposition (SVD) with the ARIMA model to improve the prediction efficiency of the ARIMA model and remove data redundancy and noise. Through the improved ARIMA model, our proposed approach (named MHDP

With the development of the Internet of Things (IoT), the number of mobile terminal devices is increasing rapidly. Due to high transmission delay and bandwidth limitation, computing power requirements for IoT devices are getting higher and higher. Recently, edge computing is an effective way to reduce system delay, and blockchain solves the security problem of edge computing. In this paper, a three-layer network model, named blockchain-based mobile edge computing system (BMEC), is proposed for clone block identification. Specifically, a neural network based clone block identification (NCBI) method is proposed to prevent clone block attacks. After that, the Prim algorithm is applied to BMEC to generate a weighted undirected graph minimum spanning tree that is composed of edge blocks. This can divide a main chain into several side chains to improve the transaction speed of blockchain. Finally, the blockchain is constructed based on the time slicing round-robin scheduling algorithm to control resources from edge servers and regulate edge devices’ activities based on the predefined rules of priority, application type, and past behavior. Experimental results show that our clone block identification method can achieve block validation effectively in BMEC, and our construction of blockchain delay is lower than conventional edge computing methods.

A least squares support vector machine (LS-SVM) offers performance comparable to that of SVMs for classification and regression. The main limitation of LS-SVM is that it lacks sparsity compared with SVMs, making LS-SVM unsuitable for handling large-scale data due to computation and memory costs. To obtain sparse LS-SVM, several pruning methods based on an iterative strategy were recently proposed but did not consider the quantity constraint on the number of reserved support vectors, as widely used in real-life applications. In this article, a noniterative algorithm is proposed based on the selection of globally representative points (global-representation-based sparse least squares support vector machine, GRS-LSSVM) to improve the performance of sparse LS-SVM. For the first time, we present a model of sparse LS-SVM with a quantity constraint. In solving the optimal solution of the model, we find that using globally representative points to construct the reserved support vector set produces a better solution than other methods. We design an indicator based on point density and point dispersion to evaluate the global representation of points in feature space. Using the indicator, the top globally representative points are selected in one step from all points to construct the reserved support vector set of sparse LS-SVM. After obtaining the set, the decision hyperplane of sparse LS-SVM is directly computed using an algebraic formula. This algorithm only consumes O(N2) in computational complexity and O(N) in memory cost which makes it suitable for large-scale data sets. The experimental results show that the proposed algorithm has higher sparsity, greater stability, and lower computational complexity than the traditional iterative algorithms.

In this paper, we focus on the design of optimal relay and jammer selection strategy in relay-aided wireless networks. Different from previous works, assuming that the channel state information (CSI) of illegitimate nodes was available and only an eavesdropper existed, we first analyze disadvantages of joint relay and jammer selection (JRJS), average optimal relay selection (AORS), traditional maximum relay selection (TMRS) schemes. Then, we design an optimal relay and jammer selection strategy where the ratio of received SNRs at the destination generated by any two relays is maximized. By applying proposed strategy, computation complexity can be reduced. Moreover, we derive the lower and upper bounds of the secrecy outage probability based on the assumptions of existence of only illegitimate node and symmetric case for mathematical convenience. Finally, simulation shows that the proposed strategy operating with no CSI of illegitimate nodes can work efficiently compared with JRJS, TMRS and AORS strategies.

With the emergence and development of the Internet of Vehicles (IoV), quick response time and ultralow delay are required. Cloud computing services are unfavorable for reducing delay and response time. Mobile edge computing (MEC) is a promising solution to address this problem. In this paper, we combined MEC and IoV to propose a specific vehicle edge resource management framework, which consists of fog nodes (FNs), data service agents (DSAs), and cars. A dynamic service area partitioning algorithm is designed to balance the load of DSA and improve the quality of service. A resource allocation framework based on the Stackelberg game model is proposed to analyze the pricing problem of FNs and the data resource strategy of DSA with a distributed iteration algorithm. The simulation results show that the proposed framework can ensure the allocation efficiency of FN resources among the cars. The framework achieves the optimal strategy of the participants and subgame perfect Nash equilibrium.

For the shortest link scheduling (SLS), i.e., scheduling a given set of links with minimum time slots, we consider the distributed algorithm design by using the locality of the protocol model with high fidelity under the Rayleigh fading. Different from most previous works, focusing on distributed algorithm design under the deterministic SINR model, which ignores the fading effects in signal propagation, we first show that a successful link of protocol model is also feasible under the deterministic SINR model, then it can be scheduled successfully with high probability under the Rayleigh fading, by upper-bounding interference outside interference range of protocol model. Then on the basis of this key conclusion, we design LLS-SLS algorithm to solve SLS within (2eΔmaxT + 1)δ log2 ΔmaxT time slots for a constant δ. Specifically, ΔmaxT is the number of a sender's neighbors within some certain range, and can be upper-bounded. Next, based on the concept of random contention, we design CLLS algorithm to schedule all links after costing 4(δ + 1)ΔmaxT ln (ΔmaxT + 1) time slots. In addition, extensive simulations evaluate the performance of two proposed algorithms.

The latency of cloud computing is high for the reason that it is far from terminal users. Edge computing can transfer computing from the center to the network edge. However, the problem of load balancing among different edge nodes still needs to be solved. In this paper, we propose a load balancing strategy by task allocation in edge computing based on intermediary nodes. The intermediary node is used to monitor the global information to obtain the real-time attributes of the edge nodes and complete the classification evaluation. First, edge nodes can be classified to three categories (light-load, normal-load, and heavy-load), according to their inherent attributes and real-time attributes. Then, we propose a task assignment model and allocate new tasks to the relatively lightest load node. Experiments show that our method can balance load among edge nodes and reduce the completion time of tasks.

With the continuous increment of city volume and size, a number of traffic-related urban units (e.g., vehicles, roads, buildings, etc.) are emerging rapidly, which plays a heavy burden on the scientific traffic control of smart cities. In this situation, it is becoming a necessity to utilize the sensor data from massive cameras deployed at city crossings for accurate traffic flow prediction. However, the traffic sensor data are often distributed and stored by different organizations or parties with zero trust, which impedes the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it requires challenging efforts to balance the tradeoff between data sharing and data privacy to enable cross-organization traffic data fusion and prediction. In light of this challenge, we put forward an accurate LSH (locality-sensitive hashing)-based traffic flow prediction approach with the ability to protect privacy. Finally, through a series of experiments deployed on a real-world traffic dataset, we demonstrate the feasibility of our proposal in terms of prediction accuracy and efficiency while guaranteeing sensor data privacy.

Mobile edge computing (MEC) solves the high latency problem of cloud computing by offloading tasks to edge servers. Due to limited resources, it is necessary to improve the efficiency of computation offloading. However, there is a lot of redundant data transmission between MEC servers and users in the existing methods. Additional data transmission increases the task processing delay. To reduce the total delay, a new cache-assisted computation offloading strategy is proposed. In response to a large number of similar requests from users, a new cache management mechanism is designed. This mechanism can select reusable calculation results more accurately in the cache space through an approximate matching method and improve the cache hit ratio. Then, aiming at the problem of offloading efficiency, the delay optimization problem is transformed into an optimal path problem, a cost function is defined to determine the optimal offloading position, and an improved path planning method is used to plan the optimal offloading path. The simulation results indicate that the proposed scheme can improve the cache hit ratio and reduce the total processing delay of tasks compared with other standard schemes.

Facial recognition and resolution technology have extensive application scenarios in the era of big data. It ensures the consistency of personal identity in physical space and cyberspace by establishing correspondence between physical objects and network entities. However, massive data brings huge processing pressure to cloud service, and there are data leakage risks about personal information. To address this problem, we propose a privacy security protection scheme for facial recognition and resolution based on edge computing. Firstly, a facial recognition and resolution framework based on edge computing is established, which improves the communication and storage efficiency through task partition and relieves the pressure of cloud computing. Then, a verifiable deletion scheme based on Hidden CP-ABE is proposed to provide fine-grained access control and ensure the safe deletion of target data in the cloud. Moreover, after applying the verifiable deletion method, the safe deletion of the target data in the cloud can be achieved. Finally, the simulation results show the effectiveness and security of the proposed scheme.

The performance of multiple input multiple output (MIMO) wireless networks is limited mainly by concurrent interference among sensor nodes. Effective link scheduling algorithms with the technology of successive interference cancellation (SIC) can maximize throughput in MIMO wireless networks. Most previous works on link scheduling in MIMO wireless networks did not consider SIC. In this paper, we propose a MIMO-SIC (MSIC) algorithm under the SINR model. First, a mathematical framework is established for the cross-layer optimization of routing and scheduling, with constraints of traffic balance and link capacity. Second, the interference regions are divided to characterize the level of interference between links. Finally, we propose a distributed link scheduling algorithm based on MSIC to eliminate the interference between competing links in the MIMO network. Experimental results show that the MSIC algorithm can increase the end-to-end throughput per unit by approximately 73% on average compared with non-SIC algorithms.

In this paper, we propose a compression sensing ghost imaging algorithm to reduce the computation time with high image quality via compression sensing based on the total variation reconstruction. A small amount of measurements can be used for shortening the sampling time. The total variation is used as criteria during the search process. It makes the ghost image achieving a high image reconstruction quality. The simulation results demonstrate that the proposed method can enhance imaging quality with the reduced computation time.

With the rapid development of the Internet of Things (IoT), the number of mobile terminal devices is increasing. Massive data are generated by mobile terminal devices, resulting in high delay and high energy consumption. In most cases, however, a low delay means high energy consumption. To balance energy consumption and delay, we adopt a tradeoff strategy that can realize optimal energy consumption with a delay threshold in this paper. First, we introduce the role of the delay threshold in reducing delay. Then, we describe the delay and energy consumption of the mobile terminal layer, fog node layer and cloud server layer with queue theory. Nonlinear programming is used to solve the energy optimization problem by calculating the optimal workload of each layer. We design a cloud-fog cooperation scheduling algorithm to reduce energy consumption. A task offloading algorithm is also designed to complete tasks when their nodes leave. The experimental results show that the energy consumption is reduced by approximately 22%, while the delay is 12.5% less than the first come first served (FCFS) approach.

Mobile edge computing is a new cloud computing paradigm that utilizes small-sized edge clouds to provide real-time services to users. These mobile edge clouds (MECs) are located near users, thereby enabling users to seamlessly access applications that are running on MECs and to easily access MECs. Terminal devices can transfer tasks to MEC servers nearby to improve the quality of computing. In this paper, we study multi-user computation offloading problem for mobile-edge computing in a multichannel wireless interference environment. Then, we analyze the overhead of each mobile devices, and we propose strategies for task scheduling and offloading in a multi-user MEC system. For reducing the energy consumption, we propose a server partitioning algorithm that is based on clustering. We formulate the task offloading decision problem as a multi-user game, which always has a Nash equilibrium. The simulation results demonstrate that our scheme outperforms the traditional offloading strategy in terms of energy consumption.

In the RFID indoor positioning system, how to realize the positional positioning of the tag to be positioned is an important issue on the basis of ensuring the positioning accuracy. In order to solve this problem, a new search method is proposed in this paper. Firstly, the positioning area to which the tag to be located belongs is determined, the positioning range is narrowed, and the center point of the positioning area is selected as the search starting point, and the target search is performed in a certain step along the six directions in the positioning area until the virtual point RSSI value is searched. When the Euclidean distance between the signal strength value of the tag to be located satisfies the accuracy e, the point coordinate is output, and then improve the positioning accuracy by introducing repeat calibration technology. Finally, the influence of search distance on the improved algorithm is observed by adjusting the size of search step. The simulation results show that the minimum positioning error of the new algorithm is 0.5m and the average positioning accuracy is improved by 40% compared with the original algorithm.

In the practical application of node positioning in the Internet of Things (IOT), the distribution of beacon nodes is generally non-uniform, so there is a certain error in positioning accuracy. In order to make the positioning more accurate, The MMSDV-Hop localization algorithm which improved DV-Hop localization algorithm is proposed in this paper. At first, this algorithm needs to determine the valid beacon nodes in a local range by selecting threshold value, and after using minimization criterion of mean-square error and correcting to obtain the final average hop distance. The dynamic weight is added with the hop number and the number of valid nodes as weights to determine the distance between the unknown nodes and the beacon nodes. A Weighted centroid localization algorithm and a weighted least square method are used to obtain an estimated position separately and the final position is determined by the arithmetic mean value of the two estimated positions. The Matlab simulation experiment shows that compared with the DV-Hop localization algorithm, the improved MMSDV-Hop localization algorithm has increased significantly in the positioning accuracy.

With the development of cloud computing in the Internet of Things and the wide application based on location services, the location trajectory information of mobile users is increasing. The risk of privacy leakage is also increasing. In order to protect the user’s trajectory privacy in the location service, we have designed a trajectory privacy protection method with several properties in the fog-based architecture. Fog computing extends the capabilities of cloud computing to the edge of the network, with local computing and storage capabilities, and a wide geographical mobility. Therefore, this paper uses the fog server instead of the traditional TTP anonymous server in the location-based privacy protection to solve the problem that performance bottleneck and concentrated attack in the TTP server anonymity process, and then completes our design with several properties through the fog server. The TRT algorithm implements k-anonymity of the trajectory. Finally, experimental analysis proves that our method can effectively enhance the user’s trajectory privacy.

Non-small cell lung cancer (NSCLC) is a common malignancy worldwide. MicroRNA (miR)-217 and sirtuin 1 (SIRT1) have been reported to play significant roles in different types of cancer, such as osteosarcoma and prostate cancer; however, the association between miR-217 and SIRT1 in the cell proliferation, apoptosis and invasion of NSCLC remain unknown. Thus, the present study aimed to investigate the roles of miR-217 and SIRT1 in NSCLC. The expression levels of miR-217 and SIRT1 were detected via reverse transcription-quantitative (RT-q)PCR and western blot analyses. The effect of miR-217 on A549 and H1299 cell proliferation, apoptosis and invasion was assessed via the Cell Counting Kit-8, flow cytometry and Transwell assays, respectively. In addition, the association between SIRT1 and miR-217 was predicted using the TargetScan database, and verified via the dual-luciferase reporter assay, and RT-qPCR and western blot analyses. The results demonstrated that miR-217 expression was significantly downregulated, while SIRT1 expression was significantly upregulated in A549 and H1299 cells compared with the human bronchial epithelial cells. Furthermore, transfection with miR-217 mimic significantly inhibited A549 and H1299 cell proliferation and invasion, and induced A549 and H1299 cell apoptosis. The results of the dual-luciferase reporter assay and western blot analysis confirmed that SIRT1 is a target gene of miR-217. In addition, miR-217 inhibited the activation of AMP-activated protein kinase (AMPK) and mTOR signaling. Taken together, the results of the present study suggest that miR-217 inhibits A549 and H1299 cell proliferation and invasion, and induces A549 and H1299 cell apoptosis by targeting SIRT1 and inactivating the SIRT1-mediated AMPK/mTOR signaling pathway. Thus, miR-217 may be used as a potential therapeutic target for the treatment of patients with NSCLC.

Following the recent advances in the Internet of Things (IoT), it is drawing lots of attention to design distributed algorithms for various network optimization problems under the SINR (Signal-to-Interference-and-Noise-Ratio) interference model, such as spanner construction. Since a spanner can maintain a linear number of links while still preserving efficient routes for any pair of nodes in wireless networks, it is important to design distributed algorithms for spanners. Given a constant t > 1 as the required stretch factor, the problem of our concern is to design an efficient distributed algorithm to construct a t -spanner of the communication graph under SINR such that the delay for the task completion is minimized, where the delay is the time interval between the time slot that the first node commences its operation to the time slot that all the nodes finish their task of constructing the t -spanner. Our main contributions include four aspects. First, we propose a proximity range and proximity independent set (PISet) to increase the number of nodes transmitting successfully at the same time in order to reduce the delay. Second, we develop a distributed randomized algorithm SINR-Spanner to construct a required t -spanner with high probability. Third, the approximation ratio of SINR-Spanner is proven to be a constant. Finally, extensive simulations are carried out to verify the effectiveness and efficiency of our proposed algorithm.

With the popularity of edge-based trajectory applications, application providers have accumulated a large amount of user trajectory data. However, direct use of trajectory data containing rich privacy information has the risk of leaking user privacy. In this paper, we propose an edge trajectory protection approach using the technique of blockchain. This protection mechanism not only takes account of users information protection and identity authentication in block generation, but considers the screening mechanism to ensure the integrity of most authorized nodes. We propose trajectory entropy suppression method that combines it with a cost function evaluation sequence and achieve collaboration between regions by deploying smart contracts. Our experimental results demonstrate the efficiency and effectiveness of our proposed model.

With the booming development of big data technology and health care applications, data in the medical field is characterized by explosive growth, and medical data is valuable, which is the privacy data of patients. However, the characteristics and storage environment of medical big data have brought great challenges to the realization of privacy protection of medical data. In order to ensure the protection of data privacy when sharing medical data, we propose a medical data privacy protection framework based on blockchain (MPBC). In this framework, we protect privacy by adding differential privacy noise into federated learning. In addition, the growing volume of medical data could make blockchain storage problematic. Therefore, a storage mode is proposed to reduce the storage burden of blockchain. The raw data are stored locally and only the hash value calculated by IPFS are stored in blockchain. To enhance the performance, a mechanism is used to validate transactions and aggregate the model. Security analysis shows that our method is a safe and effective way to implement medical data.

Multi-sensor data fusion technique plays a key role in the agricultural services such as data collection and processing. However, the collected data usually is featured by redundancies and errors, which deteriorate the reliability of network. In this paper, based on adaptive trust estimation, we propose a multisensor data fusion algorithm in Trust Neural Network (T-NN), aiming to solve the problem of low accuracy and poor stability of multi-sensor data fusion. In particular, the original data collected by the sensors first are pre-processed by exponential smoothing. Then, trust estimation model is applied to calculate the value of trust among the sensing nodes and optimize the data, and the performance of redundancy and reliability are enhanced. Furthermore, the data optimized is introduced into BP neural network for training and fusion. Extensive simulations show that the algorithm proposed in this paper greatly outperforms adaptive weighted average model and traditional BPNN model, in terms of the accuracy of data fusion.