Smart meters have been installed to report users' real-time electricity consumption data to the utility supplier periodically, which enables fine-grained energy supply, as the utility supplier can adjust its supplement based on users' consumptions. However, these real-time electricity data can also reveal the behaviors of the inhabitants; for example, the real-time electricity consumption data can reveal if the inhabitant is at home, if the television is working, and so on. People are reluctant to disclose these kinds of personal information. In this paper, we come up with a smart meter data aggregation scheme based on the Paillier homomorphic cryptosystem, this aggregation scheme enables a utility supplier to get the total consumption of all the smart meters, while the utility supplier is unable to get the consumption data of a single smart meter. In addition, the proposed scheme enables the smart meter to report multiple types of data in one reporting message, which makes it possible for the supplier to conduct the variance analysis and the one-way analysis of variance on the data. The formal security analysis shows that the proposed scheme is semantically secure. The experiment results show that the proposed scheme can reduce the computation cost both on the smart meter side and on the aggregator side.

The smart meters in electricity grids enable fine-grained consumption monitoring. Thus, suppliers could adjust their tariffs. However, as smart meters are deployed within the smart grid field, authentication and key establishment between smart grid parties (smart meters, aggregators, and servers) become an urgency. Besides, as privacy is becoming a big concern for smart meters, smart grid parties are reluctant to leak their real identities during the authentication phase. In this paper, we analyze the recent authentication schemes in smart grids and other applied fields, and propose an anonymous authentication and key establishment scheme between smart grid parties: FAuth. The proposed scheme is based on bilinear maps and the computational Diffie–Hellman problem. We changed the way the smart meter parties registered at Key Generation Center, making the proposed scheme robust against various potential attacks that could be launched by the Key Generation Center, as the scheme could avoid the private key of the smart meter parties from leaking to the Key Generation Center. Besides, the proposed scheme reduced the computational load, both at the smart meter side and at the aggregator side, which make it perfectly suitable for computation-constrained devices. Security proof results show the proposed scheme is secure under the BAN logic and random oracle model.

The main focus of this paper is on extracting features with SOund Navigation And Ranging (SONAR) sensing for further underwater landmark-based Simultaneous Localization and Mapping (SLAM). According to the characteristics of sonar images, in this paper, an improved Otsu threshold segmentation method (TSM) has been developed for feature detection. In combination with a contour detection algorithm, the foreground objects, although presenting different feature shapes, are separated much faster and more precisely than by other segmentation methods. Tests have been made with side-scan sonar (SSS) and forward-looking sonar (FLS) images in comparison with other four TSMs, namely the traditional Otsu method, the local TSM, the iterative TSM and the maximum entropy TSM. For all the sonar images presented in this work, the computational time of the improved Otsu TSM is much lower than that of the maximum entropy TSM, which achieves the highest segmentation precision among the four above mentioned TSMs. As a result of the segmentations, the centroids of the main extracted regions have been computed to represent point landmarks which can be used for navigation, e.g., with the help of an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-SLAM approach is a recursive and iterative estimation-update process, which besides a prediction and an update stage (as in classical Extended Kalman Filter (EKF)), includes an augmentation stage. During navigation, the robot localizes the centroids of different segments of features in sonar images, which are detected by our improved Otsu TSM, as point landmarks. Using them with the AEKF achieves more accurate and robust estimations of the robot pose and the landmark positions, than with those detected by the maximum entropy TSM. Together with the landmarks identified by the proposed segmentation algorithm, the AEKF-SLAM has achieved reliable detection of cycles in the map and consistent map update on loop closure, which is shown in simulated experiments.

Wireless Sensor Networks (WSNs) are spearheading the efforts taken to build and deploy systems aiming to accomplish the ultimate objectives of the Internet of Things. Due to the sensors WSNs nodes are provided with, and to their ubiquity and pervasive capabilities, these networks become extremely suitable for many applications that so-called conventional cabled or wireless networks are unable to handle. One of these still underdeveloped applications is monitoring physical parameters on a person. This is an especially interesting application regarding their age or activity, for any detected hazardous parameter can be notified not only to the monitored person as a warning, but also to any third party that may be helpful under critical circumstances, such as relatives or healthcare centers. We propose a system built to monitor a sportsman/woman during a workout session or performing a sport-related indoor activity. Sensors have been deployed by means of several nodes acting as the nodes of a WSN, along with a semantic middleware development used for hardware complexity abstraction purposes. The data extracted from the environment, combined with the information obtained from the user, will compose the basis of the services that can be obtained.

Wireless sensor networks (WSNs) may be deployed in failure-prone environments, and WSNs nodes easily fail due to unreliable wireless connections, malicious attacks and resource-constrained features. Nevertheless, if WSNs can tolerate at most losing k - 1 nodes while the rest of nodes remain connected, the network is called k - connected. k is one of the most important indicators for WSNs’ self-healing capability. Following a WSN design flow, this paper surveys resilience issues from the topology control and multi-path routing point of view. This paper provides a discussion on transmission and failure models, which have an important impact on research results. Afterwards, this paper reviews theoretical results and representative topology control approaches to guarantee WSNs to be k - connected at three different network deployment stages: pre-deployment, post-deployment and re-deployment. Multi-path routing protocols are discussed, and many NP-complete or NP-hard problems regarding topology control are identified. The challenging open issues are discussed at the end. This paper can serve as a guideline to design resilient WSNs.