We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Summary
Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM consumption and execution time is verified under different circumstances.

iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments

The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever-growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed.

/pdf/a-survey-on-iot-security-application-areas-security-threats-1gb31j78px.pdf

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

Review Article Tactile sensing for mechatronics—a state of the art survey

We develop Feedback Control real-time Scheduling (FCS) as a unified framework to provide Quality of Service (QoS) guarantees in unpredictable environments (such as e-business servers on the Internet). FCS includes four major components. First, novel scheduling architectures provide performance control to a new category of QoS critical systems that cannot be addressed by traditional open loop scheduling paradigms. Second, we derive dynamic models for computing systems for the purpose of performance control. These models provide a theoretical foundation for adaptive performance control. Third, we apply established control methodology to design scheduling algorithms with proven performance guarantees, which is in contrast with existing heuristics-based solutions relying on laborious design/tuning/testing iterations. Fourth, a set of control-based performance specifications characterizes the efficiency, accuracy, and robustness of QoS guarantees. 
The generality and strength of FCS are demonstrated by its instantiations in three important applications with significantly different characteristics. First, we develop real-time CPU scheduling algorithms that guarantees low deadline miss ratios in systems where task execution times may deviate from estimations at run-time. We solve the saturation problems of real-time CPU scheduling systems with a novel integrated control structure. Second, we develop an adaptive web server architecture to provide relative and absolute delay guarantees to different service classes with unpredictable workloads. The adaptive architecture has been implemented by modifying an Apache web server. Evaluation experiments on a testbed of networked Linux PC's demonstrate that our server provides robust relative/absolute delay guarantees despite of instantaneous changes in the user population. Third, we develop a data migration executor for networked storage systems that migrate data on-line while guaranteeing specified I/O throughput of concurrent applications.

https://www.cse.wustl.edu/~lu/papers/thesis.pdf

Feedback Control Real-Time Scheduling: Framework, Modeling, and Algorithms*

In this paper, we propose IoTChain, a combination of the OSCAR architecture [1] and the ACE authorization framework [2] to provide an E2E solution for the secure authorized access to IoT resources. IoTChain consists of two components, an authorization blockchain based on the ACE framework and the OSCAR object security model, extended with a group key scheme. The blockchain provides a flexible and trustless way to handle authorization while OSCAR uses the public ledger to set up multicast groups for authorized clients. To evaluate the feasibility of our architecture, we have implemented the authorization blockchain on top of a private Ethereum network. We report on several experiments that assess the performance of different architecture components.

/pdf/iotchain-a-blockchain-security-architecture-for-the-internet-5d5jxmnkz9.pdf

IoTChain: A blockchain security architecture for the Internet of Things

Mobile cloud computing (MCC) is an emerging paradigm for transparent elastic augmenta- tion of mobile devices capabilities, exploiting ubiquitous wireless access to cloud storage and computing resources. MCC aims at increasing the range of resource-intensive tasks supported by mobile devices, while preserving and extending their resources. Its main con- cerns regard the augmentation of energy efficiency, storage capabilities, processing power and data safety, to improve the experience of mobile users. The design of MCC systems is a challenging task, because both the mobile device and the Cloud have to find energy-time tradeoffs and the choices on one side affect the performance of the other side. The analysis of the MCC literature points out that all existing models focus on mobile devices, consid- ering the Cloud as a system with unlimited resources. Also, to the best of our knowledge, no MCC-specific simulation tool exists. To fill this gap, in this paper, we propose a modeling and simulation framework for the design and analysis of MCC systems, encompassing all their components. The main pillar of the proposed framework is the autonomic strategy consisting of adaptive loops between every mobile devices and the Cloud. The proposed model of the mobile device takes into account online estimations of the actual Cloud per- formance - not only the nominal values of the performance indicators. At the same time, the model of the Cloud takes into consideration the characteristics of the workload, to adapt its configuration in terms of active virtual machines and task management strategies. Moreover, the developed discrete event simulator is an effective tool for the evaluation of an MCC system as a whole, or single components, considering different classes of parallel jobs.

Simulation Modelling Practice and Theory

The Internet of Things (IoT) refers to the interconnection of billions of IP-enabled devices, denoted as "smart objects", with limited capabilities, in terms of computational power and memory capacity, which typically operate in constrained environments, in an Internet-like structure. Large-scale systems and applications that rely on such a high number of devices, due to their complexity, need careful analysis and test, before being deployed to target environments. Traditional IoT simulators do not focus on the simulation of large scale deployments, as they are intended to evaluate and analyze low-level networking aspects, with groups of smart objects arranged in specific topologies. In this paper, we illustrate an efficient simulation methodology, which is particularly suitable to test IoT systems with a large number of interconnected devices in Urban environments from an application-layer perspective. The main advantages of such an approach are: i) the capability to simulate large-scale systems with thousands of geographically distributed devices; ii) the maximization of code reuse; and iii) the high generality of simulated nodes, which can be characterized by multiple network interfaces and protocols, as well as different mobility, network, and energy consumption models.

A simulation platform for large-scale internet of things scenarios in urban environments

Due to the high number of sensors managed and need to perform complex reasoning activities, real-time control systems of autonomous robots exhibit a high potential for overload, i.e., real-time tasks missing their deadlines. In these systems overload should be regarded as a likely occurrence and hence managed accordingly. In this paper we illustrate a novel scheduling technique for adaptation of soft real-time load to available computational capacity in the context of autonomous robot control architectures. The technique is based on rate modulation of a set of periodic tasks in a range of admissible rates. The technique is shown to be easily computable and several variations in implementation are reviewed within the paper.

Rate modulation of soft real-time tasks in autonomous robot control systems

Currently available discrete event simulation tools exhibit important limitations, either being too specific, or providing only a partial API and possibly not enough scalability. In this paper we introduce our novel general purpose simulator, called DEUS, which aims at becoming one of the reference tools in the field of complex system simulation. Its essential Java API provides basic interfaces and classes for modelling nodes, events and processes characterizing the structure and dynamics of any complex system. High usability, configurability and memory efficiency are among the strengths of DEUS, as exemplified in this paper by means of the simulator of Chord peer-to-peer systems we implemented with minor coding effort.

/pdf/deus-a-discrete-event-universal-simulator-2nxnknor7a.pdf

Francesco Zanichelli

Papers

IoTChain: A blockchain security architecture for the Internet of Things

Simulation Modelling Practice and Theory

A simulation platform for large-scale internet of things scenarios in urban environments

Rate modulation of soft real-time tasks in autonomous robot control systems

DEUS: a discrete event universal simulator