The explosive advancement of Artificial Intelligence (AI) and Edge Computing (EC) is accelerating the development of the future Internet of Things (IoT), which spans various application domains and has become an integral part of modern life. However, certain limitations of IoT introduce security and privacy challenges for users and enterprises, hindering its broader adoption. Identity and Access Management (IAM), capable of handling the massive scale of digital identities and permissions while ensuring that only authenticated entities can access authorized resources and interact securely, has been widely adopted as an entry point for securing IoT services. However, traditional IAM systems, primarily designed for cloud-based web services, face several challenges, such as scalability and privacy, when integrated into edge-native IoT architectures. This paper provides a systematic review of IAM solutions in IoT environments. It identifies four key requirements for future IoT services, analyzes IAM architecture and the state of the art of its six primary functionalities, namely storage, identity management, authentication, authorization, audit, and federation, and studies ten more comprehensive IAM solutions. A detailed evaluation of aspects such as functionality completeness, security, privacy, and scalability is carried out. Finally, based on the evaluation results, the missing points in the existing literature, as well as upcoming challenges and development trends, are pointed out. The study aims to inspire further research and development efforts towards building a comprehensive IAM solution for edge-enabled IoT services.

The Internet of Things (IoT) landscape is hindered by a critical challenge: the lack of semantic interoperability among diverse data models. Existing IoT solutions often function as isolated data silos, impeding the seamless integration of heterogeneous data sources crucial for informed decision-making and streamlined processes. This research addresses this issue by introducing a pioneering solution: the Semantic Interoperability Support System (SISS). SISS is an innovative tool designed to bridge the semantic divide between disparate data models within a common application domain. To address the lack of interoperability between current IoT platforms, devices, and solutions that use native data models, SISS facilitates integration by enabling the generation of gateways or translator components. These components establish mappings between the semantic properties of source and target data models, leveraging advanced semantic analysis and inference techniques. The core principle underpinning SISS is its ability to discern and map the semantic content of data models. Through a meticulous analysis of the temporal and spatial dimensions inherent in the data, SISS establishes meaningful connections. This innovative approach fosters interoperability and enables a deeper understanding of the underlying information, enhancing the potential for data-driven insights. This paper delves into the pervasive issue of semantic interoperability in the current IoT paradigm and presents SISS as a transformative solution. By emphasizing its ability to transcend the limitations of existing solutions and its methodology to generate mappings between disparate data models, this research contributes to the achievement of global semantic interoperability in IoT.

Abstract The sketch is one of the typical and widely used data structures for estimating the frequencies of items in data streams. However, since the counter sizes in traditional rectangular sketch (r-sketch) are the same, it is hard to achieve small space usage, high capacity (i.e. the maximum frequency can be recorded) and high estimated accuracy simultaneously. Moreover, when considering the high skewness of data streams, this problem will become even worse. Consequently, we propose the trapezoidal sketch (t-sketch) in this paper. In the t-sketch, different from the r-sketch, the counter sizes in different layers are different. Therefore, the low space usage and high capacity can be achieved simultaneously in the t-sketch. Moreover, based on the basic t-sketch, we propose the space-saving t-sketch and the capacity-improvement t-sketch and analyze the properties of these two t-sketches. Finally, for improving the estimation accuracy of the t-sketch further, we propose the probabilistic-based estimation error-reducing algorithm. Compared with the CM sketch, CU sketch, C sketch and A sketch, the simulation results show that the performances on space usage, capacity and estimation accuracy are improved successfully by the space-saving t-sketch and the capacity-improvement t-sketch.

The sketch is one of the typical and widely-used data structures for estimating the frequencies of items in data streams. However, since the counter sizes in traditional rectangular sketch (r-sketch) are the same, it is hard to achieve small space usage, high capacity (i.e., the maximum frequency can be recorded), and high estimated accuracy simultaneously. Moreover, when considering the high skewness of data streams, this problem will become even worse. Consequently, we propose the trapezoidal sketch (t-sketch) in this paper. In the t-sketch, different from the r-sketch, the counter sizes in different layers are different. Therefore, the low space usage and high capacity can be achieved simultaneously in the t-sketch. Moreover, based on the basic t-sketch, we propose the space-saving t-sketch and the capacity-improvement t-sketch, and analyze the properties of these two t-sketches. Compared with the CM sketch, CU sketch, C sketch, and A sketch, the simulation results show that the performances on space usage, capacity, and estimation accuracy are improved successfully by the space-saving t-sketch and the capacity-improvement t-sketch.

Edge computing is a reality for the current IoT systems that need fast processing and quick response time to make real-time decisions and IoT systems without permanent connectivity to the cloud (e.g., car manufacturing, precision agriculture, or cattle raising). Additionally, these industries are facing the need for rapid and continuous innovation by accelerating the delivery of over-the-air (OTA) software updates in edge devices. DevOps promotes collaboration between development and operation teams and automation at all steps of software construction to achieve continuous delivery (CD) of business value. Although DevOps has demonstrated numerous successful cases in the Web domain, in the IoT domain and, more specifically, at the edge, there are few reported cases. This work presents a success case of CD of customized software as a service software as a service (SaaS) updates at the IoT Edge. This may enable new business models at the IoT Edge. This article presents an architectural model of a highly distributed (cloud and edge) IoT system and a CD process flow for customized SaaS applications in edge nodes. Both the architectural model and the CD process flow are instantiated in a case study for precision agriculture.

Due to the different transmission media, the underwater environment poses a more severe situation for routing algorithm design than that of the terrestrial environment. In this paper, we propose a weight based fuzzy logic (WBFL) relay node selection algorithm for the underwater Ad-hoc network, which can get the most balanced result than the previous algorithms without increasing the computation complexity. In this algorithm, the parameter scatters instead of the parameter values are inputted into the fuzzy logic inference system. By this innovation, the algorithm can take as much cross-layer parameters into account as possible during the relay node selection. Moreover, taking the node mobility into account, we propose a geographic based link lifetime prediction algorithm for underwater Ad-hoc network. The simulation results show that the WBFL algorithm can improve the network throughput at most 50% compare with the ExOR algorithm; moreover, the WBFL is effective and accurate on selecting relay nodes and the computation complexity is less than the previous algorithms.