Reliability analysis of the internet of things using Space Fault Network

The Internet of Things (IoT) is a network topology structure based on the interconnection of many nodes. It realizes the basic functions of IoT through the transmission of information, data, and energy between the nodes. To study the reliability of Internet of Things Network Topology (IoTNT) structure, we must abstract IoT as network topology and study the reliability of the network itself from the topology structure. This paper attempts to apply the Space Fault Network (SFN) to the study the reliability of IoTNT. To achieve this goal, the nodes and edges of IoTNT are equivalent to events and connections of SFN respectively. A structure analysis method based on SFN is proposed and used to study the reliability of IoTNT. At the same time, the influence of possible logical relationship between nodes on the reliability of IoTNT is studied. According to the SFN structure representation methods (SFNSRMs), considering different network structures and induced modes, the analysis methods and calculation methods of the evolution process of target event are given. An example is given to illustrate the analysis and calculation process. The research provides the new methods for the reliability study of IoT and the development of SFN

FiFo: Fishbone Forwarding in Massive IoT Networks

Massive Internet of Things (IoT) networks have a wide range of applications, including but not limited to the rapid delivery of emergency and disaster messages. Although various benchmark algorithms have been developed to date for message delivery in such applications, they pose several practical challenges such as insufficient network coverage and/or highly redundant transmissions to expand the coverage area, resulting in considerable energy consumption for each IoT device. To overcome this problem, we first characterize a new performance metric, forwarding efficiency, which is defined as the ratio of the coverage probability to the average number of transmissions per device, to evaluate the data dissemination performance more appropriately. Then, we propose a novel and effective forwarding method, fishbone forwarding (FiFo), which aims to improve the forwarding efficiency with acceptable computational complexity. Our FiFo method completes two tasks: 1) it clusters devices based on the unweighted pair group method with the arithmetic average; and 2) it creates the main axis and sub axes of each cluster using both the expectation-maximization algorithm for the Gaussian mixture model and principal component analysis. We demonstrate the superiority of FiFo by using a real-world dataset. Through intensive and comprehensive simulations, we show that the proposed FiFo method outperforms benchmark algorithms in terms of the forwarding efficiency.Comment: 13 pages, 16 figures, 5 tables; to appear in the IEEE Internet of Things Journal (Please cite our journal version that will appear in an upcoming issue.

SCALABLE MULTI-HOP DATA DISSEMINATION IN VEHICULAR AD HOC NETWORKS

Vehicular Ad hoc Networks (VANETs) aim at improving road safety and travel comfort, by providing self-organizing environments to disseminate traffic data, without requiring fixed infrastructure or centralized administration. Since traffic data is of public interest and usually benefit a group of users rather than a specific individual, it is more appropriate to rely on broadcasting for data dissemination in VANETs. However, broadcasting under dense networks suffers from high percentage of data redundancy that wastes the limited radio channel bandwidth. Moreover, packet collisions may lead to the broadcast storm problem when large number of vehicles in the same vicinity rebroadcast nearly simultaneously. The broadcast storm problem is still challenging in the context of VANET, due to the rapid changes in the network topology, which are difficult to predict and manage. Existing solutions either do not scale well under high density scenarios, or require extra communication overhead to estimate traffic density, so as to manage data dissemination accordingly. In this dissertation, we specifically aim at providing an efficient solution for the broadcast storm problem in VANETs, in order to support different types of applications. A novel approach is developed to provide scalable broadcast without extra communication overhead, by relying on traffic regime estimation using speed data. We theoretically validate the utilization of speed instead of the density to estimate traffic flow. The results of simulating our approach under different density scenarios show its efficiency in providing scalable multi-hop data dissemination for VANETs

Tractable reliable communication in compromised networks

Reliable communication is a fundamental primitive in distributed systems prone to Byzantine (i.e. arbitrary, and possibly malicious) failures to guarantee the integrity, delivery, and authorship of the messages exchanged between processes. Its practical adoption strongly depends on the system assumptions. Several solutions have been proposed so far in the literature implementing such a primitive, but some lack in scalability and/or demand topological network conditions computationally hard to be verified. This thesis aims to investigate and address some of the open problems and challenges implementing such a communication primitive. Specifically, we analyze how a reliable communication primitive can be implemented in 1) a static distributed system where a subset of processes is compromised, 2) a dynamic distributed system where part of the processes is Byzantine faulty, and 3) a static distributed system where every process can be compromised and recover. We define several more efficient protocols and we characterize alternative network conditions guaranteeing their correctness

Intelligent and Low Overhead Network Synchronization over Large-Scale Industrial Internet of Things Systems

With the extensive development of information and communication technologies and vertical industry applications, industrial IoT (IIoT) systems are expected to enable a wide variety of applications, including advanced manufacturing, networked control, and smart supply chain, which all exclusively hinge on the efficient cooperation and coordination among the involved IIoT machines and infrastructures. The ubiquitous connection among IIoT entities and the associated exchange of collaborative information necessitate the achievement of accurate network synchronization, which can guarantee the temporal alignment of the critical information. To enhance the temporal correlation of heterogeneous devices in large-scale IIoT systems, this thesis aims at designing industry-oriented network synchronization protocols in terms of accuracy improvement, resource-saving, and security enhancement with the assistance of learning-based methods. Initially, the real-time timestamps and historical information of each IIoT devices are collected and analyzed to explore the varying rate of the skew (VRS) at each enclosed clock. K-means clustering algorithm is adopted to organize the distributed devices into a few groups, and each of them is assigned with an optimized synchronization frequency to avoid potential resource waste while ensuring synchronization accuracy. Historical VRS values are further utilized as the identification of each clock for providing verification information so that the security against message manipulation attacks during network synchronization can be enhanced. Moreover, a digital twin-enabled clock model is established by comprehensively investigating the characteristics of each clock with diversified operating environments. A cloud-edge-collaborative system architecture is orchestrated to enhance the efficiency of data gathering and processing. With the assistance of the accurate estimation generated by the digital twin model for each clock, the situation-awareness of network synchronization is enhanced in terms of a better understanding of the clock feature and necessary synchronization frequency. Meanwhile, since temporal information generated at each local IIoT devices are efficiently gathered at the edge devices, the effect of packet delay variation is significantly reduced while the synchronization performance under various network conditions can be guaranteed. To further reduce the network resource consumption and improvement the performance under abnormal behaviors during network synchronization, a passive network synchronization protocol based on concurrent observations is proposed, where timestamps are exchanged without occupying dedicated network resources during synchronization. The proposed scheme is established based on the fact that a group of IIoT devices close to each other can observe the same physical phenomena, e.g., electromagnetic signal radiation, almost simultaneously. Moreover, multiple relay nodes are coordinated by the cloud center to disseminate the reference time information throughout the IIoT system in accomplishing global network synchronization. Additionally, a principal component analysis-assisted outlier detection mechanism is designed to tackle untrustworthy timestamps in the network according to the historical observation instants recorded in the cloud center. Simulation results indicate that accurate network synchronization can be achieved with significantly reduced explicit interactions

An IoT enabled system for marine data acquisition and cartography

Traditional marine monitoring systems such as oceanographic and hydrographic re- search vessels use either wireless sensor networks with a limited coverage, or expensive satellite communication that is not suitable for small and mid-sized vessels. This the- sis proposes an Internet of Marine Things data acquisition and cartography system in the marine environment using Very High Frequency (VHF) available on the majority of ships. The proposed system is equipped with sensors such as sea depth, tempera- ture, wind speed and direction, and the collected data is sent through a Ship Ad-hoc Network (SANET) to 5G edge clouds connected to sink/base station nodes on shore. The sensory data is ultimately aggregated at a central cloud on the internet to produce up to date cartography systems. Several observations and challenges unique to the marine environment have been discussed and feed into the solutions presented. We have investigated the application of appropriate data quantization and compression techniques to the marine sensor data collected in order to reduce the size of transmit- ted data and achieve better transmission efficiency. The impact of marine sparsity on the network is examined and a marine Mobile Ad-hoc/Delay Tolerant hybrid routing protocol (MADNET) is proposed to switch automatically between Mobile Ad-hoc Network (MANET) and Delay Tolerant Network (DTN) routing according to the network connectivity. The low rate data transmission offered by VHF radio has been investigated in terms of the network bottlenecks and the data collection rate achiev- able near the sinks. A sensory data management and transmission approach has also been proposed at the 5G network core using Information Centric Networks (ICN) aimed at providing efficient and duplicate less transmission of marine sensory read- ings from the base station/sink nodes towards the central cloud. Therefore, SANETs are realized as part of a 5G infrastructure for marine environment monitoring, paving the way to the Internet of Marine Things (IoMaT)