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Internet of Things (IoT) has become a vital part of our infrastructure to enrich lives and make processes easier. In these people-centric IoT applications, massive personal data are collected in real-time and stored by the resource constrained sensor network. Because any misusage of these personal data might result in the leakage of privacy and economic losses, it is expected that the data requesters can only access to the data what they have purchased, or are entitled to use. How to accurately control data access permissions is one of the prerequisites for IoT data protection. Based on a revised one-way hash chain technique, we proposed a novel Time-based Access Control (TAC) scheme for multi-attribute data in Internet of Things. All the data are partitioned into 2-D subspaces representing generation time and data attribute. Data in each subspace is encrypted with the corresponding sub-key before its transmission to the base station to achieve data privacy and access control. Anyone who wants to read or use data with specified attribute at a particular time must get the corresponding sub-key from the data source node or the owner. TAC can generate a sub-key for data in each subspace in an efficient manner in terms of less sub-key generation time and low memory space usage. We proposed three improved schemes to further reduce sub-key computation time according to different application scenarios. The experimental results show that TAC can be applied to the resource limited WSNs efficiently. [ABSTRACT FROM AUTHOR]

With the development of the drying rack system, users with limited time tend to use the fully functional drying rack system to realize various intelligent control functions. However, the existing control methods for drying rack system has some defects such as, low intelligence and system delay, which are unsuitable for most users. In this paper, an efficient intelligent control algorithm based on the back‐propagation (BP) neural network is proposed. In this system, STM32F103 is first utilized as the central controller and multiple sensors are used to collect environmental information. Then, the remote control, voice keywords, and buttons can be used to achieve intelligent control. Subsequently, a motor drive intelligence control algorithm based on the BP neural network (MCBP) is proposed to improve the accuracy of the intelligent control. Next, an Application (APP) that can display environmental data such as wind speed, temperature, and humidity is developed. The APP can realize various intelligent control functions such as lifting, panning, rotating, and harvesting. Finally, MCBP is compared with normal control and programmable logic controller control. The accuracy of the MCBP is higher than other two control methods. The final extensive experiments confirm the accuracy and efficiency of the proposed intelligent control algorithm. [ABSTRACT FROM AUTHOR]

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes' diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services. [ABSTRACT FROM AUTHOR]

This paper considers the distributed access and control problem of massive wireless sensor networks' data access center for the Internet of Things, which is an extension of wireless sensor networks and an element of its topology structure. In the context of the arrival of massive service access requests at a virtual data center, this paper designs a massive sensing data access and control mechanism to improve the access efficiency of service requests and makes full use of the available resources at the data access center for the Internet of things. Firstly, this paper proposes a synergistically distributed buffer access model, which separates the information of resource and location. Secondly, the paper divides the service access requests into multiple virtual groups based on their characteristics and locations using an optimized self-organizing feature map neural network. Furthermore, this paper designs an optimal scheduling algorithm of group migration based on the combination scheme between the artificial bee colony algorithm and chaos searching theory. Finally, the experimental results demonstrate that this mechanism outperforms the existing schemes in terms of enhancing the accessibility of service requests effectively, reducing network delay, and has higher load balancing capacity and higher resource utility rate. [ABSTRACT FROM AUTHOR]

Network-accessible task offloading is for low latency; offline task offloading is for low cost. Offline devices cannot directly access service nodes due to lack of resources. Accordingly, the latter involves more steps and optimization variables such as: where to offload tasks, how to allocate computation resources, how to adjust offloading ratio and transmit power, and such optimization variables and hybrid combination features are highly coupled with each other. In this paper, we first formulate a Mixed Integer Nonlinear Programming Problem (MINLP) for such task offloading under energy and delay constraints. Furthermore, we decompose it into two subproblems so as to efficiently solve the formulated MINLP, and design a low-cost and low-complexity Joint Optimization for Energy Consumption and Task Processing Delay (JOET) algorithm to optimize selection decisions, resource allocation, offloading ratio and transmit power adjustment. We carry out extensive simulation experiments to validate JOET. Simulation results demonstrate that JOET outperforms many representative existing schemes in quickly converge and effective reduction of energy consumption and delay. Specifically, the average energy consumption and the average delay have been reduced by 15.93% and 13.70%, respectively, and the load balancing efficiency has increased by 10.20%. [ABSTRACT FROM AUTHOR]

Epilepsy is a common neurological disease worldwide, characterized by recurrent seizures. There is currently no cure for epilepsy. However, seizures can be controlled by drugs and surgeries in about 70% of epileptic patients. A timely and accurate prediction of seizures can prevent injuries during seizures and improve the patients' quality of life. In this paper, we proposed an intelligent epileptic prediction system based on Synchrosqueezed Wavelet Transform (SWT) and Multi-Level Feature Convolutional Neural Network (MLF-CNN) for smart healthcare IoT network. In this system, we used SWT to map EEG signals to the frequency domain, which was able to measure the energy changes in EEG signals caused by seizures within a well-defined Time-Frequency (TF) plane. MLF-CNN was then applied to extract multi-level features from the processed EEG signals and classify the different seizure segments. The performance of our proposed system was evaluated with the publicly available CHB-MIT dataset and our private ZJU4H dataset. The system achieved an accuracy of 96.99% and 94.25%, a sensitivity of 96.48% and 97.76%, a specificity of 97.46% and 94.07% and a false prediction rate (FPR/h) of 0.031 and 0.049 FPR/h on the CHB-MIT dataset and the ZJU4H dataset, respectively. [ABSTRACT FROM AUTHOR]

• Our algorithm is more efficient, has less communication overhead, and has greater throughput than the original PBFT algorithm. • We propose a Vehicle - Service Provider - Roadside Unit architecture to ensure the orderliness and safety of vehicles. • We propose an identity authentication scheme based on blockchain. • Experimental results demonstrate that our solution is better than PBFT, G-PBFT and CPBFT. As an application of Internet of Things (IoT) technology, the Internet of Vehicles (IoV) faces two main security issues: (1) the central server of the IoV may not be powerful enough to support the centralized authentication of the rapidly increasing connected vehicles, (2) the IoV itself may not be robust enough to single-node attacks. To address these issues, in this paper, we propose SG-PBFT (Score Grouping-PBFT), a secure and efficient distributed consensus algorithm for blockchain applications in the IoV. The distributed structure can reduce the pressure on the central server and decrease the risk of single-node attacks. The SG-PBFT consensus algorithm improves the traditional practical Byzantine fault tolerance (PBFT) consensus algorithm by optimizing the PBFT consensus process and using a score grouping mechanism to achieve a higher consensus efficiency. The experimental results show that the method can greatly improve the consistency efficiency and effectively prevent single-node attacks. Specifically, when the number of consensus nodes reaches 1000, the consensus time of our algorithm is only about 27% of what is required for the state-of-the-art PBFT consensus algorithm. Our proposed SG-PBFT algorithm is versatile and can be used in other application scenarios which require high consensus efficiency. [ABSTRACT FROM AUTHOR]

Cryptography chips are often used in some applications, such as smart grids and Internet of Things (IoT) to ensure their security. Cryptographic chips must be strictly tested to guarantee the correctness of the encryption and decryption. Scan-based design-for-testability (DFT) provides high test quality. However, it can also be misused to steal the cipher key of cryptographic chips by hackers. In this article, we present a new scan design methodology that can resist scan-based side-channel attacks by the dynamical obfuscation of scan input data and scan output data. The scan test is managed by a test password, which consists of load password and scan password. When the chip enters into the test mode, it is required to apply the test password via some external input ports. Once the correct load password is delivered, the scan password can be loaded into a special shift register. If the scan password is also correct, the chip testing can proceed normally. In case the load password or the scan password is wrong, the data in scan chains cannot be propagated correctly. Specifically, some elusory bits are sneaked into scan chains dynamically. The advantage of the proposed method is that it has no negative impact on design performance and test flow when powerfully protecting cryptographic chips. The area penalty is also acceptably low compared with other schemes. [ABSTRACT FROM AUTHOR]

In recent years, Industrial Internet of Things (IIoT) has gradually attracted the attention of the industry owing to its accurate time synchronization, communication accuracy, and high adaptability. As an important data structure, graphs are widely used in IIoT applications, where entities and their relationships can be expressed in the form of graphs. With the widespread adoption of IIoT and cloud computing, an increasing number of individuals or organizations are outsourcing their IIoT graph data to cloud servers to enjoy the unlimited storage space and fast computing service. To protect the privacy of graph data, graphs are usually encrypted before being outsourced. In this article, we propose a search pattern privacy system for approximate shortest distance query of encrypted graphs in IIoT. To realize search pattern privacy, we adopt two noncolluded cloud servers to accomplish different tasks. We leverage the first server to store the encrypted data and perform query operations, and use the second one to rerandomize the contents and shuffle the locations of the queried records. Before queries, we generate the trapdoors by using different random numbers. After queries, we ask the second server to rerandomize the contents of the records that the first server touched. In addition, we shuffle the physical locations of original records by inserting some fake records. In this way, all contents and physical locations of the touched records change, so that the first server cannot distinguish whether two queries are the same or not. To enhance the efficiency on the user side, we further improve this system by moving some heavy workloads from the user to the cloud. The security analysis and the performance evaluation show that our work is secure and efficient. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) realizes the interconnection of heterogeneous devices by the technology of wireless and mobile communication. The data of target regions are collected by widely distributed sensing devices and transmitted to the processing center for aggregation and analysis as the basis of IoT. The quality of IoT services usually depends on the accuracy and integrity of data. However, due to the adverse environment or device defects, the collected data will be anomalous. Therefore, the effective method of anomaly detection is the crucial issue for guaranteeing service quality. Deep learning is one of the most concerned technology in recent years which realizes automatic feature extraction from raw data. In this article, the integrated model of the convolutional neural network (CNN) and recurrent autoencoder is proposed for anomaly detection. Simple combination of CNN and autoencoder cannot improve classification performance, especially, for time series. Therefore, we utilize the two-stage sliding window in data preprocessing to learn better representations. Based on the characteristics of the Yahoo Webscope S5 dataset, raw time series with anomalous points are extended to fixed-length sequences with normal or anomaly label via the first-stage sliding window. Then, each sequence is transformed into continuous time-dependent subsequences by another smaller sliding window. The preprocessing of the two-stage sliding window can be considered as low-level temporal feature extraction, and we empirically prove that the preprocessing of the two-stage sliding window will be useful for high-level feature extraction in the integrated model. After data preprocessing, spatial and temporal features are extracted in CNN and recurrent autoencoder for the classification in fully connected networks. Empiric results show that the proposed model has better performances on multiple classification metrics and achieves preferable effect on anomaly detection. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) is one of the main application scenarios of 5th generation mobile networks (5G). Along with the rapid development of 5G, IoT terminal devices will create big data. Generally, IoT terminal devices are lightweight user equipments, for example, wearable devices. In order to take use of these lightweight terminal devices, it is a feasible way to outsource these created big data to the public cloud. When these data are out of the client's control, it is of crucial importance to ensure the remote data integrity. To solve the weaknesses of the existing remote data integrity checking schemes, we propose the concept of blockchain-based remote data integrity checking (RDIC) scheme for big data. The new concept makes use of blockchain technique which greatly improves the efficiency and security of RDIC. First, the system model and security definition are given for the proposed RDIC scheme by using blockchain. Then, by using efficient modular arithmetic, RSA digital signature, blockchain, etc , we construct a lightweight blockchain-based RDIC scheme. Finally, we analyze its security and performance. The detailed analysis shows that our scheme is provably secure and lightweight. • The concept of blockchain-based RDIC for IoT in 5G Networks. • The first concrete scheme for the model. • Detailed performance analysis and experimental results. [ABSTRACT FROM AUTHOR]

With the emergence of new smart technologies, including the Internet of Things, wireless media are playing an important role to connect numerous devices to fulfill the requirements of newly developed communication systems. The massive connectivity, therefore, made the wireless spectrum too crowded and gave several challenges to resisting against potential wireless jammers. Note that, the two main challenges that have always been a part of any communication system, especially in the case of wireless communication, are information security and information jamming. Carefully considering the given challenges, this study uses a new advanced anti-jamming approach, a modulation technique based on the frequency-hopping spread spectrum, which has notably high resistance accounted against various potential jammers. The objective of this study is two-fold. First, the physical channel properties are considered, and the random bits are transmitted, employing a cryptographic secured hoping-spread pattern, having a set of carrier frequencies, known at both sides of the transmission. Second, the hashing code is computed only for the key, and transmitted along the original hopset, but with distinct frequencies set. The deployed practical anti-jamming approach, therefore, computed a high efficiency to examine the information secrecy well and primarily the connection availability even in the presence of the jammers. Moreover, this study considered and modeled a communication system and evaluated the proposed system's performance, applying the theories of Shannon's entropy and Wyner's entropy (i.e. Wyner's wiretap channel), to anticipate the system's perfect secrecy, even in the worst case when jammer has unlimited computational capabilities. [ABSTRACT FROM AUTHOR]