Using Hybrid Angle/Distance Information for Distributed Topology Control in Vehicular Sensor Networks

In a vehicular sensor network (VSN), the key design issue is how to organize vehicles effectively, such that the local network topology can be stabilized quickly. In this work, each vehicle with on-board sensors can be considered as a local controller associated with a group of communication members. In order to balance the load among the nodes and govern the local topology change, a group formation scheme using localized criteria is implemented. The proposed distributed topology control method focuses on reducing the rate of group member change and avoiding the unnecessary information exchange. Two major phases are sequentially applied to choose the group members of each vehicle using hybrid angle/distance information. The operation of Phase I is based on the concept of the cone-based method, which can select the desired vehicles quickly. Afterwards, the proposed time-slot method is further applied to stabilize the network topology. Given the network structure in Phase I, a routing scheme is presented in Phase II. The network behaviors are explored through simulation and analysis in a variety of scenarios. The results show that the proposed mechanism is a scalable and effective control framework for VSNs

Radio Frequency-Based Indoor Localization in Ad-Hoc Networks

The increasing importance of location‐aware computing and context‐dependent information has led to a growing interest in low‐cost indoor positioning with submeter accuracy. Localization algorithms can be classified into range‐based and range‐free techniques. Additionally, localization algorithms are heavily influenced by the technology and network architecture utilized. Availability, cost, reliability and accuracy of localization are the most important parameters when selecting a localization method. In this chapter, we introduce basic localization techniques, discuss how they are implemented with radio frequency devices and then characterize the localization techniques based on the network architecture, utilized technologies and application of localization. We then investigate and address localization in indoor environments where the absence of global positioning system (GPS) and the presence of unique radio propagation properties make this problem one of the most challenging topics of localization in wireless networks. In particular, we study and review the previous work for indoor localization based on radio frequency (RF) signaling (like Bluetooth‐based localization) to illustrate localization challenges and how some of them can be overcome

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: vehicular ad-hoc networks, security and caching, TCP in ad-hoc networks and emerging applications. It is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

Information Fusion Methodology for Enhancing Situation Awareness in Connected Cars Environment

This dissertation introduces novel approaches to develop a comprehensive model to address situation awareness in the Internet of Cars, called Attention Assist Framework (AAF). The proposed framework utilizes both Low-Level Data Fusion (LLDF), and High-Level Information Fusion (HLIF) to implement traffic entity, situation, and impact assessment, as well as decision making. The Internet of Cars is the convergence of the Internet of Things and Vehicular Ad-hoc Networks (VANETs). In fact, VANETs are the communication platforms that make possible the implementation of the Internet of Cars, and has become an integral part of this research field due to its major role to improve vehicle and road safety, traffic efficiency, and convenience as well as comfort to both drivers and passengers. Significant amount of VANETs research work has been focused on specific areas such as safety, routing, broadcasting, Quality of Service (QoS), and security. Among them, road safety issues are deemed one of the most challenging problems of VANETs. Specifically, lack of proper situational awareness of drivers has been shown to be the main cause of road accidents which makes it a major factor in road safety. The traffic entity assessment relies on a LLDF framework that is able to incorporate various multi-sensor data fusion approaches with means of communication links in VANETs. This is used to implement a cooperative localization approach through fusing common data fusion methods, such as Extended Kalman Filter (EKF) and Unscented Transform (UT), and vehicle-to-vehicle communication in VANETs. Furthermore, traffic situation assessment is based on a fuzzy extension to the Multi-Entity Bayesian Networks (MEBNs), which exploit the expressiveness of first-order logic for semantic relations, and the strength of the Fuzzy Bayesian Networks in handling uncertainty, while tackling the inherent vagueness in the soft data created by human entities. Finally, the impact assessment and decision making is realized through incorporating notions of game theory into Fuzzy-MEBNs, and introducing Active Fuzzy-MEBN (ATFY-MEBN), which is capable in hypothesizing future situations by assessing the impact of the current situation upon taking the actions indicated by an optimal strategy. In fact, such strategies are achieved through solving the games that are generated through a novel situation-specific normal form games generation algorithm that aims to create games based on the given context. In general, ATFY-MEBN presents the concepts of players and actions, and includes new game components, along with a 2-tier architecture, to efficiently model impact assessment and decision making. To demonstrate the capabilities of the proposed framework, a collision warning system simulator is developed, which evaluates the likelihood of a vehicle being in a near-collision situation using a wide variety of both local and global information sources available in the VANETs environment, and suggests an optimal action by assessing the impact of the current situation through generating and solving situation-specific games. Accordingly, first, the entities that highly influence the safety aspect, as well as both their casual and semantic relationships are identified. Next, an ATFY-MEBN-based model is presented, which allows for modeling these entities along with their relationships in specific contexts, assessing the current states of the situations of interest, predicting their future states, and finally suggesting optimal decision. Therefore, if the likelihood of being in a near-collision situation is determined to be high, and if the relevant situation-specific game is generated, then the impact of deciding on different combinations of actions that the game players take are calculated through a pre-fixed payoff function. Finally, the completed game is solved by finding its dominant strategy, that subsequently, results in proposing the optimal action to the driver. Our experimental results are divided into three main sections, through which we evaluate the capabilities of the traffic entity, situation, and impact assessment methods. Accordingly, the performance of the proposed cooperative localization approach is assessed by comparing its results with the ground truth solution and that of the other localization methods in various driving test cases. Moreover, two distinct single-vehicle and multi-vehicles categories of driving scenarios, as well as a novel hybrid MEBN inference, demonstrate the capabilities of the proposed traffic assessment model to efficiently achieve situation and threat assessment on the road. Finally, the impact assessment and decision making models are evaluated through two different scenarios of driving in highway and intersection that are formed with various number of player vehicles, and their actions

Outdoor location tracking of mobile devices in cellular networks

This paper presents a technique and experimental validation for anonymous outdoor location tracking of all users residing on a mobile cellular network. The proposed technique does not require any intervention or cooperation on the mobile side but runs completely on the network side, which is useful to automatically monitor traffic, estimate population movements, or detect criminal activity. The proposed technique exploits the topology of a mobile cellular network, enriched open map data, mode of transportation, and advanced route filtering. Current tracking algorithms for cellular networks are validated in optimal or controlled environments on a small dataset or are merely validated by simulations. In this work, validation data consisting of millions of parallel location estimations from over a million users are collected and processed in real time, in cooperation with a major network operator in Belgium. Experiments are conducted in urban and rural environments near Ghent and Antwerp, with trajectories on foot, by bike, and by car, in the months May and September 2017. It is shown that the mode of transportation, smartphone usage, and environment impact the accuracy and that the proposed AMT location tracking algorithm is more robust and outperforms existing techniques with relative improvements up to 88%. Best performances were obtained in urban environments with median accuracies up to 112 m

Enabling Cyber Physical Systems with Wireless Sensor Networking Technologies

[[abstract]]Over the last few years, we have witnessed a growing interest in Cyber Physical Systems (CPSs) that rely on a strong synergy between computational and physical components. CPSs are expected to have a tremendous impact on many critical sectors (such as energy, manufacturing, healthcare, transportation, aerospace, etc) of the economy. CPSs have the ability to transform the way human-to-human, human-toobject, and object-to-object interactions take place in the physical and virtual worlds. The increasing pervasiveness of Wireless Sensor Networking (WSN) technologies in many applications make them an important component of emerging CPS designs. We present some of the most important design requirements of CPS architectures. We discuss key sensor network characteristics that can be leveraged in CPS designs. In addition, we also review a few well-known CPS application domains that depend on WSNs in their design architectures and implementations. Finally, we present some of the challenges that still need to be addressed to enable seamless integration of WSN with CPS designs.[[incitationindex]]SCI[[booktype]]紙

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

Improving Security for the Internet of Things: Applications of Blockchain, Machine Learning and Inter-Pulse Interval

The Internet of Things (IoT) is a concept where physical objects of various sizes can seamlessly connect and communicate with each other without human intervention. The concept covers various applications, including healthcare, utility services, automotive/vehicular transportation, smart agriculture and smart city. The number of interconnected IoT devices has recently grown rapidly as a result of technological advancement in communications and computational systems. Consequently, this trend also highlights the need to address issues associated with IoT, the biggest risk of which is commonly known to be security. This thesis focuses on three selected security challenges from the IoT application areas of connected and autonomous vehicles (CAVs), Internet of Flying Things (IoFT), and human body interface and control systems (HBICS). For each of these challenges, a novel and innovative solution is proposed to address the identified problems. The research contributions of this thesis to the literature can be summarised as follows: • A blockchain-based conditionally anonymised pseudonym management scheme for CAVs, supporting multi-jurisdictional road networks. • A Sybil attack detection scheme for IoFT using machine learning carried out on intrinsically generated physical layer data of radio signals. • A potential approach of using inter-pulse interval (IPI) biometrics for frequency hopping to mitigate jamming attacks on HBICS devices

Device discovery in D2D communication: A survey

Device to Device (D2D) communication was first considered in out-band to manage energy issues in the wireless sensor networks. The primary target was to secure information about system topology for successive communication. Now the D2D communication has been legitimated in in-band by the 3rd Generation Partnership Project (3GPP). To initiate D2D communication, Device Discovery (DD) is a primary task and every D2D application benefits from DD as an end to end link maintenance and data relay when the direct path is obstructed. The DD is facing new difficulties because of the mobility of the devices over static systems, and the mobility makes it more challenging for D2D communication. For in-band D2D, DD in a single cell and multi-cell, and dense area is not legitimated properly, causing latency, inaccuracy, and energy consumption. Among extensive studies on limiting energy consumption and latency, DD is one of the essential parts concentrating on access and communication. In this paper, a comprehensive survey on DD challenges, for example single cell/multi-cell and dense area DD, energy consumption during discovery, discovery delay, and discovery security, etc., has been presented to accomplish an effective paradigm of D2D networks. In order to undertake the device (user) needs, an architecture has been projected, which promises to overwhelm the various implementation challenges of DD. The paper mainly focuses on DD taxonomy and classification with an emphasis on discovery procedures and algorithms, a summary of advances and issues, and ways for potential enhancements. For ensuring a secure DD and D2D, auspicious research directions have been proposed, based on taxonomy