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As one of the most widely used applications of the Internet of Things (IoT), intelligent transportation system (ITS) is of great significance for urban traffic planning, traffic control and traffic guidance. However, widespread traffic congestion occurs with the increased number of vehicles. Traffic flow prediction is a good idea for traffic congestion. Therefore, many schemes have been proposed for accurate and real-time traffic flow prediction, but there still exist many issues including low accuracy, weak adaptability and inferior real-time. Meanwhile, the complex spatial and temporal dependencies in traffic flow are still challenging. To address the above issues, we propose a novel spatial-temporal Chebyshev graph neural network model (ST-ChebNet) for traffic flow prediction to capture the spatial-temporal features, which can ensure accurate traffic flow prediction. Concretely, we firstly add a fully connected layer to fuse the features of traffic data into a new feature to generate a matrix, and then the long short-term memory (LSTM) model is adopted to learn traffic state changes for capturing the temporal dependencies. Then, we use the Chebyshev graph neural network (ChebNet) to learn the complex topological structures in the traffic network for capturing the spatial dependencies. Eventually, the spatial features and the temporal features are fused to guarantee the traffic flow prediction. The experiments show that ST-ChebNet can make accurate and real-time traffic flow prediction compared with other 8 baseline methods on real-world traffic datasets PeMS.

One of the best ways to deal with the massive data generated by the Internet of Things (IoT) is storing them in the cloud. However, outsourced storage raises some security and privacy issues, such as data leaking and illegal access. The attribute-based signcryption (ABSC) is one of the most promising approaches which can ensure the confidentiality and authenticity of data simultaneously. Nonetheless, it not only inherits the fine-grained access control but also the heavy computational cost which is intolerable for most resource-limited IoT devices. In this article, we propose lightweight hybrid-policy ABSC (LH-ABSC), a lightweight ABSC scheme which adopts ciphertext-policy encryption (CPABE) and key-policy attribute-based signature (KPABS). Ciphertext-policy attribute-based signature leads the decision making that who can decrypt to the data owners directly. Meanwhile, the signature is related with data owners’ attribute set which can be used to testify the authenticity of data. In particular, LH-ABSC has constant signature size and satisfies public verification which is deeply important for IoT devices. Moreover, LH-ABSC outsources most computing overhead to fog nodes, including signature, verify, and decryption. Comprehensive theoretical analyses, such as confidentiality, unforgeability, and verifiability, are provided. Also, the selective chosen ciphertext security, the selective chosen message security, and signers anonymity are achieved.

The Internet of Things (IoT) changed our lives with huge amounts of data production. Due to source-limited IoT devices, one of the best ways to process the data is cloud storage. However, a series of security and privacy issues arise, such as illegal data access, data tampering, and privacy leak. Though symmetric encryption can guarantee data confidentiality, it cannot realize fine-grained data sharing and searching. The keyword-based searchable attribute-based encryption (KSABE) can achieve data confidentiality and fine-grained access control. More importantly, it realizes a keyword-based search for data users. However, the heavy decryption computation burden and the management of massive user keys appear when implementing attribute-based encryption schemes to IoT. Therefore, this article proposes a blockchain-aided searchable attribute-based encryption (BC-SABE) with efficient revocation and decryption, where the traditional centralized server is replaced with a decentralized blockchain system being in charge of the threshold parameter generation, key management, and user revocation. All revocation tasks are done by the blockchain and it is on longer necessary for ciphertext reencryption and key update. Moreover, users utilize the coalition blockchain to generate partial tokens. Besides, the cloud server contained in our scheme not only stores the massive encrypted data but also performs search and predecryption for users who only require one exponentiation in the group ${\mathbb {G}}$ to decrypt fully. Security analyses prove that our scheme realizes the security under the chosen plaintext attack and the chosen keyword attack. Simulations show that the decryption and token generation cost of our scheme are preferable.

Nodes in wireless sensor networks (WSNs) are usually deployed in an unattended even hostile environment. What is worse, these nodes are equipped with limited battery, storage, computation, and communication resources. Therefore, it is challenging to ensure the security of a WSN without decreasing its network performance. Data aggregation (DA) combined with a security mechanism can provide a good scheme for solving the aforementioned problems. This article presents a comprehensive review of secure DA (SDA) in WSNs, including its security goals together with the existing problems. The traditional network topologies as well as new emerging ones are discussed and compared in order to indicate the application scenes and security levels of different topologies. Meanwhile, the contrastive analyses of security strategies are presented which divides SDA protocols into five categories according to different security mechanisms, security goals, and network topologies. Besides, the discussion points out some open issues which may be the valuable topics of SDA in the future.

Health-related Internet of Things (IoT) devices are becoming more popular in recent years. On the one hand, users can access information of their health conditions more conveniently; on the other hand, they are exposed to new security risks. In this paper, we presented, to the best of our knowledge, the first in-depth security analysis on home-use electroencephalography (EEG) IoT devices. Our key contributions are twofold. First, we reverse-engineered the home-use EEG system framework via which we identified the design and implementation flaws. By exploiting these flaws, we developed two sets of novel easy-to-exploit PoC attacks, which consist of four remote attacks and one proximate attack. In a remote attack, an attacker can steal a user’s brain wave data through a carefully crafted program while in the proximate attack, the attacker can steal a victim’s brain wave data over-the-air without accessing the victim’s device on any sense when he is close to the victim. As a result, all the 156 brain–computer interface (BCI) apps in the NeuroSky App store are vulnerable to the proximate attack. We also discovered that all the 31 free apps in the NeuroSky App store are vulnerable to at least one remote attack. Second, we proposed a novel deep learning model of a joint recurrent convolutional neural network (RCNN) to infer a user’s activities based on the reduced-featured EEG data stolen from the home-use EEG IoT devices, and our evaluation over the real-world EEG data indicates that the inference accuracy of the proposed RCNN is can reach 70.55%.

We define the following problem termed ${n}\boldsymbol \times $ 1-out-of- ${n}$ oblivious transfer ( ${n}\boldsymbol \times $ 1-out-of- ${n}$ OT): in a system with one server and ${n}$ clients, how to securely and efficiently assign ${n}$ secrets to ${n}$ clients by the server, with each client getting a unique secret from the server, and the server and clients remain unknown of how the secrets are distributed? This is a novel problem that is fundamentally different than 1-out-of- ${n}$ OT repeated ${n}$ times, and is different than ${k}$ -out-of- ${n}$ OT as well. Nevertheless, the proposed OT has many practical applications such as privacy-preserving data aggregation in smart grids. It can also be employed to design crypto protocols for anonymous communications and group signatures. In this paper, we propose the first algorithm to efficiently and effectively implement the ${n}\boldsymbol \times $ 1-out-of- ${n}$ OT. We construct hidden permutation circuits to obliviously assign ${n}$ secrets to ${n}$ clients by the server within ${O}$ (lg( ${n}$ )) time. A rigorous theoretical analysis is also carried out to investigate the security strength and performance of the protocol.

The Internet of Things (IoT) is changing human lives by connecting everyday objects together. For example, in a grocery store, all items can be connected with each other, forming a smart shopping system. In such an IoT system, an inexpensive radio frequency identification (RFID) tag can be attached to each product which, when placed into a smart shopping cart, can be automatically read by a cart equipped with an RFID reader. As a result, billing can be conducted from the shopping cart itself, preventing customers from waiting in a long queue at checkout. Additionally, smart shelving can be added into this system, equipped with RFID readers, and can monitor stock, perhaps also updating a central server. Another benefit of this kind of system is that inventory management becomes much easier, as all items can be automatically read by an RFID reader instead of manually scanned by a laborer. To validate the feasibility of such a system, in this paper we identify the design requirements of a smart shopping system, build a prototype system to test functionality, and design a secure communication protocol to make the system practical. To the best of our knowledge, this is the first time a smart shopping system is proposed with security under consideration.

Due to the close correlation with individual’s physical features and status, the adoption of cyber-physical social systems (CPSSs) has been inevitably hindered by users’ privacy concerns. Such concerns keep growing as our bile devices have more embedded sensors, while the existing countermeasures only provide incapable and limited privacy preservation for sensitive physical information. Therefore, we propose a novel privacy preservation framework for CPSSs. We formulate both the privacy concerns and user expectations in CPSSs based on real-world knowledge. We also design a corresponding data publishing mechanism for users. It regulates the publishing behaviors to hide sensitive physical profiles. Meanwhile, the published data retain comprehensive social profiles for users. Our analysis demonstrates that the mechanism achieves a local maximized performance on the aspect published data size. The experiment results toward real datasets reveals that the performance is comparable to the global optimal one.

Fog computing (FC) is an emerging research area that targets on providing services and satisfying customers’ needs in the space between “Ground” and “Cloud.” In the current cloud-based Internet of Things (IoT) model, smart devices (such as sensors and smartphones) exchange information through the Internet (routers and/or servers on cloud) to cooperate and provide services to users, which could be citizens, smart home systems, and industrial applications. The cloud-based IoT model describes a uniform, concise, and scalable solution for supporting IoT applications. The deployments of IoT applications on cloud, however, are facing the challenges originated from economic considerations, social concerns, technical limitations, and administrative issues.

Ultraviolet (UV) radiation has a great impact on human health. Nowadays, the public basically gets information about UV radiation through weather forecasts, which can only provide rough and average prediction for a certain large area. Since CMOS sensors in smartphone cameras are very sensitive to UV radiation, smartphones have potential to be the ideal equipment to measure it. At the same time, result optimization can be achieved in real time by taking advantage of fog computing because fog servers are able to aggregate UV radiation data and compute the results at local areas. This paper exhaustively discussed a novel procedure that could measure UV radiation through smartphone cameras, and also briefly covered how to leverage fog computing to improve UV measurement accuracy. To implement the procedure, an Android app called UV meter was developed. Experiments were conducted by utilizing the app to validate and evaluate the correctness and accuracy of the procedure on both smartphones and smart watches. Results showed that the proposed procedure could achieve an average of 95% accuracy of a typical professional digital UV meter, and could be easily implemented on smart devices.

Today, there is widespread use of mobile applications that take advantage of a user’s location. Popular usages of location information include geotagging on social media websites, driver assistance and navigation, and querying nearby locations of interest. However, the average user may not realize the high energy costs of using location services (namely the GPS) or may not make smart decisions regarding when to enable or disable location services—for example, when indoors. As a result, a mechanism that can make these decisions on the user’s behalf can significantly improve a smartphone’s battery life. In this paper, we present an energy consumption analysis of the localization methods available on modern Android smartphones and propose the addition of an indoor localization mechanism that can be triggered depending on whether a user is detected to be indoors or outdoors. Based on our energy analysis and implementation of our proposed system, we provide experimental results—monitoring battery life over time—and show that an indoor localization method triggered by indoor or outdoor context can improve smartphone battery life and, potentially, location accuracy.

With the wide deployment of Internet-of-Things (IoT), blockchain systems have been playing a crucial role to establish a trusted computing environment among potentially mistrusting agents without depending on a centralized server. Different from previous blockchain consensus protocols adopted in IoT, which rely on efficient and stable transmissions, in this paper, we consider how to reach blockchain consensus in wireless networks without reliable network support. Specifically, a realistic SINR model is adopted to depict the unreliable transmissions in wireless channels. Based on the SINR model, a distributed and randomized consensus algorithm is proposed to reach k-times consensus among n devices within O(k+logn) time steps with high probability. Note that the time complexity of our algorithm is asymptotically optimal since Ω(k+logn) is a lower bound to achieve k-times consensus in a distributed environment. We conduct both rigorous theoretical analysis and extensive simulations to validate our method. It is believed that our work can facilitate the implementation of blockchains in many wireless scenarios in which the reliable and fast transmissions cannot be guaranteed.