Maintaining peer-to-peer connectivity with low energy overhead is a key requirement for several emerging Internet of Things (IoT) applications. It is also desirable to develop such connectivity solutions for non-static network topologies, so that resilience to device failures can be fully realized. Decentralized clustering has emerged as a promising technique to address this critical challenge. Clustering of nodes around cluster heads (CHs) provides an energy-efficient two-tier framework for peer-to-peer communication. At the same time, decentralization ensures that the framework can quickly adapt to a dynamically changing network topology. Although some decentralized clustering solutions have been proposed in the literature, they either lack guarantees on connectivity or incur significant energy overhead to maintain the clusters. In this paper, we present Decentralized Connected Resilient IoT Clustering (DeCoRIC), an energy-efficient clustering scheme that is self-organizing and resilient to network changes while guaranteeing connectivity. Using experiments implemented on the Contiki simulator, we show that our clustering scheme adapts itself to node faults in a time-bound manner. Our experiments show that DeCoRIC achieves 100% connectivity among all nodes while improving the power efficiency of nodes in the system compared to the state-of-the-art techniques BEEM and LEACH by up to 110% and 70%, respectively. The improved power efficiency also translates to longer lifetime before first node death with a best-case of 109% longer than BEEM and 42% longer than LEACH.
Edge computing has gained attention in recent years due to the adoption of many Internet of Things (IoT) applications in domestic, industrial and wild settings. The resiliency and reliability requirements of these applications vary from non-critical (best delivery efforts) to safety-critical with time-bounded guarantees. The network connectivity of IoT edge devices remains the central critical component that needs to meet the time-bounded Quality of Service (QoS) and fault-tolerance guarantees of the applications. Therefore, in this work, we systematically investigate how to meet IoT applicati…
In Resilient IoT, the revenue of service provider is resilient to uncertain usage-contexts(e.g. emotion, environmental contexts) of Smart-device users. Hence, Autonomic Resilient IoT Management problem is decomposed into two subproblems, namely m-connectivity and k-dominance, such that m-alternations on revenue making process is resilient to users common interests, which might be depicted through k-1 alternations of usage-contexts. In this context, a greedy approximation scheme Bee is proposed, which resolves aforementioned sub-problems with five consecutive models, namely Maverick, Siren, Pig…
The Internet of Things (IoT) has been applied to a large number of heterogeneous devices and is used in the deployment of a variety of applications on the basis of its distributed open architecture. The majority of these IoT devices are battery-powered and are interconnected via a wireless network. IoT devices may be used to carry out critical tasks. Thus, the IoT network requires a resilient architecture that supports semantic search, failure discovery, data recovery, and dynamic and autonomous network maintenance. In this paper, we present a new resilience scheme for IoT networks. We evaluat…
This paper revisits NDN deployment in the IoT with a special focus on the interaction of sensors and actuators. Such scenarios require high responsiveness and limited control state at the constrained nodes. We argue that the NDN request-response pattern which prevents data push is vital for IoT networks. We contribute HoP-and-Pull (HoPP), a robust publish-subscribe scheme for typical IoT scenarios that targets IoT networks consisting of hundreds of resource constrained devices at intermittent connectivity. Our approach limits the FIB tables to a minimum and naturally supports mobility, tempora…
Kyoto University（京都大学）；Tokyo Institute of Technology（东京工业大学）；University of Oulu（奥卢大学）
The distributed inference (DI) framework has gained traction as a technique for real-time applications empowered by cutting-edge deep machine learning (ML) on resource-constrained Internet of things (IoT) devices. In DI, computational tasks are offloaded from the IoT device to the edge server via lossy IoT networks. However, generally, there is a communication system-level trade-off between communication latency and reliability; thus, to provide accurate DI results, a reliable and high-latency communication system is required to be adapted, which results in non-negligible end-to-end latency of…
The Internet of Things (IoT) is a ubiquitous system connecting many different devices - the things - which can be accessed from the distance. The cyber-physical systems (CPS) monitor and control the things from the distance. As a result, the concepts of dependability and security get deeply intertwined. The increasing level of dynamicity, heterogeneity, and complexity adds to the system's vulnerability, and challenges its ability to react to faults. This paper summarizes state-of-the-art of existing work on anomaly detection, fault-tolerance and self-healing, and adds a number of other methods…
With the ever growing Internet of Things (IoT) market, ledger systems are facing new challenges to efficiently store and secure enormous customer records collected by the IoT devices. The authenticity, availability, and integrity of these records are critically important for both business providers and customers. In this paper, we describe DLedger, a lightweight and resilient distributed ledger system. Instead of a single chain of blocks, DLedger builds the ledger over a directed acyclic graph (DAG), so that its operations can tolerate network partition and intermittent connectivity. Instead o…
Internet of Things (IoT) is considered as a key enabler of health informatics. IoT-enabled devices are used for in-hospital and in-home patient monitoring to collect and transfer biomedical data pertaining to blood pressure, electrocardiography (ECG), blood sugar levels, body temperature, etc. Among these devices, wearables have found their presence in a wide range of healthcare applications. These devices generate data in real-time and transmit them to nearby gateways and remote servers for processing and visualization. The data transmitted by these devices are vulnerable to a range of advers…
Enhancing Adversarial Robustness of IoT Intrusion Detection via SHAP-Based Attribution Fingerprinting
School of Information Technology, York University（约克大学信息科技学院）；School of Computer Science, University of Guelph（圭尔夫大学计算机科学学院）；National Research Council of Canada（加拿大国家研究委员会）
Enhancing Resilience for IoE: A Perspective of Networking-Level Safeguard
Department of Electrical Engineering（电子工程系）；Institute of Information Science, Academia Sinica（学术院信息科学研究所）；Institute of Artificial Intelligence Innovation（人工智能创新研究所）；Department of Information Management（信息管理系）
