Confirmed traffic in LoRaWAN: Pitfalls and countermeasures

This paper derives fundamental bounds on privacy achievable in future human-centric cyber-physical systems, where time-series sensor data are shared among individuals to compute aggregate information of mutual interest. For example, individual GPS-trajectories may be shared to compute average traffic speed at different locations. An optimal trade-off is explored between individual user privacy, achieved by perturbing data prior to sharing, and the corresponding accuracy of computed aggregate information. The work is motivated by an emergent category of cyber-physical applications that involves large-scale interaction between humans, networked engineered artifacts, and the physical world. These applications are brought about by the proliferation of personal sensing devices of everyday use, leading to unprecedented opportunities for sensory data collection and sharing. The collection of sensory data from large numbers of participants offers privacy as a major new cyber-physical system challenge. In this paper, we propose a novel privacy measure, based on mutual information, and derive a perturbation algorithm, to apply prior to data sharing, that guarantees a least upper bound on the privacy measure. The new algorithm effectively hides individual user data by optimally perturbing the time-series using knowledge of only the mean and the covariance of the original data. We evaluate it using both synthetic data and collected real application data. The results show that the method significantly improves the trade-off between privacy and the accuracy of reconstruction of aggregate information from shared perturbed data.

Wireless body area networks (WBANs) connected with wearable internet of things (WIoT) offer useful features including sensory information collection, analysis, and transmission for continuous behavior monitoring of autism spectrum disorder (ASD) patients. Due to users’ mobility and time-driven sensed data, data collection becomes very difficult. The current approach employs cluster-based multi-objective path-optimized data collection mechanisms that have experienced hotspot issues leading to loss of energy and coverage problems near the base stations. This work presents the high energy and reliable sensory and behavior data collection (HERSBDC) mechanism to address the research difficulties. To ensure network coverage, the HERSBDC initially provides a new uneven clustering mechanism. Next, multi-objective-based cluster head (CH) selection metrics are proposed. The final step is the creation of a multi-objective routing path to gather vital ASD data more reliably and energy-efficiently. Comparing the proposed HERSBDC algorithm to the low energy adaptive cluster-hierarchy (LEACH)-based, and distributed energy-efficient clustering and routing (DECR) methods, the simulation results demonstrate that the HERSBDC mechanism achieves a much better lifetime by 62.28% and 11.89%, the delivery ratio by 15.04% and 9.51%, with minimal delay by 52.65%, and 9.65%, and routing overhead by 32.05%, and 42.65%, respectively.

Mobile crowdsensing enables convenient sensory data collection from a large number of mobile devices and has found various applications. In the real practice, however, the sensory data collected from various mobile devices are usually unreliable. To extract truthful information from the unreliable sensory data in mobile crowdsensing, the topic of truth discovery has received wide attention recently, which essentially operates by estimating user reliability degrees and performing reliability-aware truthful aggregation. Despite the effectiveness, applying truth discovery in mobile crowdsensing faces several privacy and security challenges. First, the sensory data and reliability degrees of users may reveal privacy-sensitive information and, thus, demand strong protection. Second, the requester that initiates a crowdsensing application usually needs to have monetary investment, so the inferred truths can be the requester's proprietary information and should be protected as well. In this paper, we propose a new system architecture enabling encrypted truth discovery in mobile crowdsensing. We focus on general and realistic mobile crowdsensing scenarios with varying levels of user participation, and our security design is built on the confidence-aware truth discovery (CATD) approach for its state-of-the-art accuracy in such scenarios. In our system architecture, users send encrypted sensory data to the cloud, where CATD is then conducted in the encrypted domain. The final encrypted inferred truths are sent to the requester for decryption. Along the whole workflow, the sensory data and reliability degrees of users, as well as the inferred truths of the requester, are kept private. Extensive experiments over real-world mobile crowdsensing dataset show that our design achieves practical performance on mobile devices.

We present development, testing, and deployment of a next generation advanced EMI sensor from a cart-based sensing platform to combine the mapping capability of previous digital geophysical survey instruments with the high-resolution discrimination capability of advanced characterization arrays. The intended end users of the system are non-technical field personnel. Therefore, system development, including sensor operation, data acquisition, and post-processing methods were customized for ease-of-use and robust automated discrimination. The EMI sensor employs a multi-axis receiver configuration to produce data sufficient for anomaly discrimination. Platform development and design focused on navigation and EMI sensor integration onto a custom, low-noise, metal-free platform. Data acquisition is via an Android application with emphasis on ease-of-use and real-time quality control (QC) of collected data. Post-processing methods emphasize QC, inversion-based anomaly location estimation, and automated or supervised polarizability-based discrimination methods to produce a prioritized dig list. Streamlining of the detection, clutter rejection and QC methods into the post-processing software module reduces analysis complexity and the time required between sensor data collection and generation of a prioritized dig list. System concept of operations (CONOPs), data collection, QC, and data processing procedures, will be discussed. System performance versus standard digital geophysical mapping sensors in production OCONUS UXO remediation operations will also be discussed.

Exact observing and forecasting tool conditions fundamentally affect cutting execution, bringing further developed workpiece machining accuracy and lower machining costs. Because of the unpredictability and time-differing nature of the cutting system, existing methodologies cannot achieve ideal oversight progressively. A technique dependent on Digital Twins (DT) is proposed to accomplish extraordinary accuracy in checking and anticipating tool conditions. This technique builds up a balanced virtual instrument framework that matches entirely with the physical system. Collecting data from the physical system (Milling Machine) is initialized, and sensory data collection is carried out. The National Instruments data acquisition system captures vibration data through a uni-axial accelerometer, and a USB-based microphone sensor acquires the sound signals. The data are trained with different Machine Learning (ML) classification-based algorithms. The prediction accuracy is calculated with the help of a confusion matrix with the highest accuracy of 91% through a Probabilistic Neural Network (PNN). This result has been mapped by extracting the statistical features of the vibrational data. Testing has been performed with the trained model to validate the model’s accuracy. Later, the modeling of the DT is initiated using MATLAB-Simulink. This model has been created under the data-driven approach. The physical–virtual balance of the DT model is acknowledged utilizing the advances, taking into consideration the detailed planning of the constant state of the tool’s condition. The tool condition monitoring system through the DT model is deployed through the machine learning technique. The DT model can predict the different tool conditions based on sensory data.

Massive Machine Type Communications is a wireless paradigm which focuses on traffic that is transmitted by a huge number of low cost, low power, infrequently transmitting devices. LoRaWAN, a Low Power Wide Area Network technology, is particularly suited to contribute to coverage of this form of traffic. In this work, we introduce a novel, lightweight timeslot scheduling scheme that supports the requirements for massive Machine Type Communications on LoRaWAN networks (based on traffic periodicity, and the multiple channels and quasiorthogonal data rates in LoRaWAN). Our novel approach does not require extended downlink transmissions from the gateways, and does not require time synchronisation between the devices and the LoRaWAN Network Server. We implement our scheme in a publicly available LoRaWAN ns-3 module. Our results show that the scheme doubles the number of frequently transmitting mMTC devices that can be handled by a single LoRaWAN gateway while providing the same level of performance in terms of successful packet deliveries, and maintaining a reasonable delay for mMTC use cases, without impacting the ability of the network to send downlink frames or acknowledge high priority packets. Our contribution is a novel approach to TDMA that is suited for mMTC networks.

This paper explores the possibility of having confirmed traffic in LoRaWAN networks under channel-oblivious jamming. Our results show that a LoRaWAN cell can handle up to 500 end-devices with a relatively good message success probability (∼ 0.8) if the network is strongly jammed (60% of the time) by using a maximum of 16 re-transmissions. We have also proved that, using a channel for downlink transmissions operating in the lowest SF is a major weakness in the LoRaWAN specification. Indeed, our results suggest that for a LoRaWAN cell with 600 end-devices the network goodput can be decreased by ∼ 53.06% when ACK transmissions on the second receive window are allowed. This was done by using an open-source network simulator that allows to evaluate many scenarios that can help LoRaWAN operators to better scale their networks in order to be more resilient against jamming attacks before actual deployments.

By means of compressive sensing (CS) technique, this paper considers the collection of sensor data with spatiotemporal correlations in wireless sensor networks (WSNs). In energy-constrained WSNs, one-dimensional CS methods need a lot of data transmissions since they are less applicable in fully exploiting the spatiotemporal correlations, while the Kronecker CS (KCS) methods suffer performance degradations when the signal dimension increases. In this paper, an appropriate sensing matrix as well as an efficient sensing method is proposed to further reduce the data transmissions without the loss of the recovery performance. Different matrices for the temporal signal of each sensor node are separately designed. The corresponding energy-efficient data gathering method is presented, which only transmitting a subset of sensor readings to recover data of the entire WSN. Theoretical analysis indicates that the sensing structure could have the relatively small mutual coherence according to the selection of matrix. Compared with the existing spatiotemporal CS (CS-ST) method, the simulation results show that the proposed efficient measurement method could reduce data transmissions by about 25% with the similar recovery performance. In addition, compared with the conventional KCS method, for 95% successful recovery, the proposed sensing structure could improve the recovery performance by about 20%.

The Internet of Things (IoT) has given rise to new types of data, emerging for instance from the collection of sensor data and the control of actuators. The explosion of devices that have automated and perhaps improved the lives of all of us has generated a huge mass of information that will continue to grow exponentially. For this reason the need to store, manage, and treat the ever increasing amounts of data that comes via the Internet of Things has become urgent. In this context, Big Data becomes immensely important, making possible to turn into this amount of data in information, knowledge, and, ultimately, wisdom. This paper aims at analyze Big Data challenges and requirements coming from different Smart City applications providing an original solution that uses Big Data technologies for redesigning an IoT context aware application for the exploitation of pervasive environment addressing problems and discussing the important aspects of the selected solution.

In this article, learning is studied in in situ applications that involve sensors. The main questions are how to conceptualize experiential learning involving sensors and what kinds of learning applications using sensors already exist or could be designed. It is claimed that experiential learning, context information and sensor data supports twenty first century learning. The concepts of context, technology-mediated experiences, shared felt experiences and experiential learning theory will be used to describe a framework for sensor-based mobile learning environments. Several scenarios and case examples using sensors and sensor data will be presented, and they will be analyzed using the framework. Finally, the article contributes to the discussion concerning the role of technology-mediated learning experiences and collective sensor data in developing twenty first century learning by characterizing what kinds of skills and competences are supported in learning situations that involve sensors.

Security for cooperative intelligent transport systems (C-ITS), which are expected to contribute to realizing a safe and efficient transportation environment, will become more important in the near future. Among various proposed C-ITS services, this study examines security measures against cyber attacks for navigation services that collect sensor data from vehicles to estimate real-time traffic and distribute trip planning and route selection. In such services, a threat is assumed in which a malicious attacker prevents the traffic condition from being correctly recognized by injecting false data. As a result of such attacks, the services may fail to decide appropriate routes, and there is a concern that this cyber attack will likely disrupt traffic in the real world. By using a simulator on the basis of actual traffic data, we experimentally clarify that false data injection attacks against C-ITS services cause artificial congestion. Due to the potential for serious consequences of traffic disruption, security countermeasure technology is vital. To respond to cyber attacks that use false sensor data, technology to analyze sensor data and detect abnormalities is required. For this reason, we propose an analysis technique that efficiently analyzes sensor data transmitted from a huge number of vehicles to identify an attacker. The idea of the proposed method is to narrow down the sensor data for detailed analysis by screening on the basis of simple statistical analysis. The evaluation results show that the proposed method can efficiently detect false data injection attacks against C-ITS navigation services.

Multi-agent robotic system (MARS) for UAV-UGV path planning and automatic sensory data collection in cluttered environments

A cumulative descriptor enhanced ensemble deep neural networks method for remaining useful life prediction of cutting tools

The increasing popularity of smartphones with their embedded capability and the availability of new application distribution channels, such as, the Apple AppStore and the Google Android Market, is giving researchers a unique opportunity to deploy mobile applications at unprecedented scale and collect sensor data way beyond the boundaries of traditional small-scale research laboratory deployments. This thesis makes a number of contributions to smartphone by introducing new models, algorithms, applications, and systems. First, we propose CenceMe, the first large-scale personal and social application for smartphones, which allows users to share their real-time sensing presence (i.e., activity and context) with friends using the phone, web, and social network sites (i.e., Facebook, Myspace, Twitter). CenceMe exploits the smartphone's onboard sensors (viz. accelerometer, microphone, GPS, Bluetooth, WiFi, camera) and lightweight, efficient machine learning algorithms on the phone and backend servers to automatically infer people's activity and social context (e.g., having a conversation, in a meeting, at a party). The development, deployment, and evaluation of CenceMe opened up new problems also studied in this dissertation. Sensing with smartphones presents several technical challenges that need to be surmounted; for example, the smartphone's context (i.e., the position of the phone relative to the event being sensed varies over time) and limited computational resources present important challenges that limit the inference accuracy using phones. To address these challenges, we propose an evolve-pool-collaborate model that allows smartphones to automatically adapt to new environments and conduct collaborative among co-located phones resulting in increased robustness and classification accuracy of smartphone in the wild. We call this system, Darwin Phones. The final contribution of this dissertation explores a new mobile application called VibN, which continuously runs on smartphones allowing users to view live feeds associated with hotspots in a city; that is, what is going on at different locations, the number of people and demographics, and the context of a particular place. VibN addresses a number of critical problems to the success of smartphone sensing, such as, running continuous algorithms on resource limited smartphones, resolving privacy issues, and developing a sensor data validation methodology for applications released via the app stores (i.e., validating sensor data and identifying patterns without any notion of ground truth evidence). Such a methodology is crucial to the large-scale adoption of smartphone in the future. Smartphone is an emerging field that requires significant advances in mobile computing, machine learning, and systems design. It is an exciting area of research that is cross-disciplinary and likely to touch on many application areas and scientific domains moving forward. The work presented in this dissertation identifies new problems and solutions that help advance our understanding in what is now a fast-moving area of research.

Detecting the existence of anomaly and localizing the faulty sensors in the Internet-of-Things (IoT) systems are extremely critical, since the incorrect data could lead to catastrophic consequences in many vertical industry applications. The difficulties of such problems come from deriving an explicit error function for each sensor in IoT, and the data continuity in the temporal domain would also be seriously challenged. To overcome these difficulties, the irregular spatial information of the IoT sensors is utilized by constructing an adjacency matrix using the distances among different sensors, and the nonlinear polynomial graph filter (NPGF) is employed to characterize the relationships among the collected sensor data. The NPGF provides a more accurate model for reconstructing the sensor data by taking the data nonlinear relationships into account. The error functions at each sensor for newly detection data are theoretically derived, and it is demonstrated that the error at the anomalous sensor performs differently from that of the normal sensors if the adjacency matrix is designed appropriately. The proposed NPGF-based algorithm is illustrated and validated with a real-world data set for temperature monitoring. The simulation results demonstrate the superior performance of our scheme in both anomaly detection and faulty sensor localization when compared with existing algorithms, such as the graph frequency algorithm and oversampling PCA (OS-PCA) method, especially for the case of small sensor data deviations.