Blockchain for the Internet of Things: Present and Future

One of the key challenges to the IoT's success is how to secure and anonymize billions of IoT transactions and devices per day, an issue that still lingers despite significant research efforts over the last few years. On the other hand, technologies based on blockchain algorithms are disrupting today's cryptocurrency markets and showing tremendous potential, since they provide a distributed transaction ledger that cannot be tampered with or controlled by a single entity. Although the blockchain may present itself as a cure-all for the IoT's security and privacy challenges, significant research efforts still need to be put forth to adapt the computation-intensive blockchain algorithms to the stringent energy and processing constraints of today's IoT devices. In this paper, we provide an overview of existing literature on the topic of blockchain for IoT, and present a roadmap of research challenges that will need to be addressed to enable the usage of blockchain technologies in the IoT

All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey

With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.Comment: 48 pages, 7 tables, 11 figures, 450 references. The data (categories and features/objectives of the papers) of this survey are now available publicly. Accepted by Elsevier Journal of Systems Architectur

BlendMAS: A BLockchain-ENabled Decentralized Microservices Architecture for Smart Public Safety

Thanks to rapid technological advances in the Internet of Things (IoT), a smart public safety (SPS) system has become feasible by integrating heterogeneous computing devices to collaboratively provide public protection services. While a service oriented architecture (SOA) has been adopted by IoT and cyber-physical systems (CPS), it is difficult for a monolithic architecture to provide scalable and extensible services for a distributed IoT based SPS system. Furthermore, traditional security solutions rely on a centralized authority, which can be a performance bottleneck or single point failure. Inspired by microservices architecture and blockchain technology, this paper proposes a BLockchain-ENabled Decentralized Microservices Architecture for Smart public safety (BlendMAS). Within a permissioned blockchain network, a microservices based security mechanism is introduced to secure data access control in an SPS system. The functionality of security services are decoupled into separate containerized microservices that are built using a smart contract, and deployed on edge and fog computing nodes. An extensive experimental study verified that the proposed BlendMAS is able to offer a decentralized, scalable and secured data sharing and access control to distributed IoT based SPS system.Comment: Submitted to the 2019 IEEE International Conference on Blockchain (Blockchain-2019

Internet of Cloud: Security and Privacy issues

The synergy between the cloud and the IoT has emerged largely due to the cloud having attributes which directly benefit the IoT and enable its continued growth. IoT adopting Cloud services has brought new security challenges. In this book chapter, we pursue two main goals: 1) to analyse the different components of Cloud computing and the IoT and 2) to present security and privacy problems that these systems face. We thoroughly investigate current security and privacy preservation solutions that exist in this area, with an eye on the Industrial Internet of Things, discuss open issues and propose future directionsComment: 27 pages, 4 figure

Dependability in Edge Computing

Edge computing is the practice of placing computing resources at the edges of the Internet in close proximity to devices and information sources. This, much like a cache on a CPU, increases bandwidth and reduces latency for applications but at a potential cost of dependability and capacity. This is because these edge devices are often not as well maintained, dependable, powerful, or robust as centralized server-class cloud resources. This article explores dependability and deployment challenges in the field of edge computing, what aspects are solvable with today's technology, and what aspects call for new solutions. The first issue addressed is failures, both hard (crash, hang, etc.) and soft (performance-related), and real-time constraint violation. In this domain, edge computing bolsters real-time system capacity through reduced end-to-end latency. However, much like cache misses, overloaded or malfunctioning edge computers can drive latency beyond tolerable limits. Second, decentralized management and device tampering can lead to chain of trust and security or privacy violations. Authentication, access control, and distributed intrusion detection techniques have to be extended from current cloud deployments and need to be customized for the edge ecosystem. The third issue deals with handling multi-tenancy in the typically resource-constrained edge devices and the need for standardization to allow for interoperability across vendor products. We explore the key challenges in each of these three broad issues as they relate to dependability of edge computing and then hypothesize about promising avenues of work in this area

Internet of Things: An Overview

As technology proceeds and the number of smart devices continues to grow substantially, need for ubiquitous context-aware platforms that support interconnected, heterogeneous, and distributed network of devices has given rise to what is referred today as Internet-of-Things. However, paving the path for achieving aforementioned objectives and making the IoT paradigm more tangible requires integration and convergence of different knowledge and research domains, covering aspects from identification and communication to resource discovery and service integration. Through this chapter, we aim to highlight researches in topics including proposed architectures, security and privacy, network communication means and protocols, and eventually conclude by providing future directions and open challenges facing the IoT development.Comment: Keywords: Internet of Things; IoT; Web of Things; Cloud of Thing

LASeR: Lightweight Authentication and Secured Routing for NDN IoT in Smart Cities

Recent literature suggests that the Internet of Things (IoT) scales much better in an Information-Centric Networking (ICN) model instead of the current host-centric Internet Protocol (IP) model. In particular, the Named Data Networking (NDN) project (one of the ICN architecture flavors) offers features exploitable by IoT applications, such as stateful forwarding, in- network caching, and built-in assurance of data provenance. Though NDN-based IoT frameworks have been proposed, none have adequately and holistically addressed concerns related to secure onboarding and routing. Additionally, emerging IoT applications such as smart cities require high scalability and thus pose new challenges to NDN routing. Therefore, in this work, we propose and evaluate a novel, scalable framework for lightweight authentication and hierarchical routing in the NDN IoT (ND- NoT). Our ns-3 based simulation analyses demonstrate that our framework is scalable and efficient. It supports deployment densities as high as 40,000 nodes/km2 with an average onboarding convergence time of around 250 seconds and overhead of less than 20 KiB per node. This demonstrates its efficacy for emerging large-scale IoT applications such as smart cities.Comment: 9 pages, 8 figures, journal pape

Decentralized Smart Surveillance through Microservices Platform

Connected societies require reliable measures to assure the safety, privacy, and security of members. Public safety technology has made fundamental improvements since the first generation of surveillance cameras were introduced, which aims to reduce the role of observer agents so that no abnormality goes unnoticed. While the edge computing paradigm promises solutions to address the shortcomings of cloud computing, e.g., the extra communication delay and network security issues, it also introduces new challenges. One of the main concerns is the limited computing power at the edge to meet the on-site dynamic data processing. In this paper, a Lightweight IoT (Internet of Things) based Smart Public Safety (LISPS) framework is proposed on top of microservices architecture. As a computing hierarchy at the edge, the LISPS system possesses high flexibility in the design process, loose coupling to add new services or update existing functions without interrupting the normal operations, and efficient power balancing. A real-world public safety monitoring scenario is selected to verify the effectiveness of LISPS, which detects, tracks human objects and identify suspicious activities. The experimental results demonstrate the feasibility of the approach.Comment: 2019 SPIE Defense + Commercial Sensin

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

Along with the rapid developments in communication technologies and the surge in the use of mobile devices, a brand-new computation paradigm, Edge Computing, is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications are thriving with the breakthroughs in deep learning and the many improvements in hardware architectures. Billions of data bytes, generated at the network edge, put massive demands on data processing and structural optimization. Thus, there exists a strong demand to integrate Edge Computing and AI, which gives birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial Intelligence on Edge). The former focuses on providing more optimal solutions to key problems in Edge Computing with the help of popular and effective AI technologies while the latter studies how to carry out the entire process of building AI models, i.e., model training and inference, on the edge. This paper provides insights into this new inter-disciplinary field from a broader perspective. It discusses the core concepts and the research road-map, which should provide the necessary background for potential future research initiatives in Edge Intelligence.Comment: 13 pages, 3 figure

Scalable and Secure Architecture for Distributed IoT Systems

Internet-of-things (IoT) is perpetually revolutionizing our daily life and rapidly transforming physical objects into an ubiquitous connected ecosystem. Due to their massive deployment and moderate security levels, those devices face a lot of security, management, and control challenges. Their classical centralized architecture is still cloaking vulnerabilities and anomalies that can be exploited by hackers for spying, eavesdropping, and taking control of the network. In this paper, we propose to improve the IoT architecture with additional security features using Artificial Intelligence (AI) and blockchain technology. We propose a novel architecture based on permissioned blockchain technology in order to build a scalable and decentralized end-to-end secure IoT system. Furthermore, we enhance the IoT system security with an AI-component at the gateway level to detect and classify suspected activities, malware, and cyber-attacks using machine learning techniques. Simulations and practical implementation show that the proposed architecture delivers high performance against cyber-attacks.Comment: This paper is accepted for publication in IEEE Technology & Engineering Management Conference (TEMSCON'20), Detroit, USA, jun, 202