In this paper, we investigate a novel computation resource allocation scheme for reconfigurable intelligent surfaces (RIS) backscatter communication (BackCom) enabled integrated sensing, communication and computation (ISCC) systems. We consider the joint design of transmit beamforming at the BS and the reflecting coefficients at the RIS as well as the computation resource allocation of each user. The optimization problem for the max computation efficiency (CE) under the constraints of power consumption, the Cramér-Rao bound (CRB) for angles estimation and communication requirement of each user is formulated. To deal with the intractable optimization problem, the alternative optimization (OA) and the alternating direction method of multipliers (ADMM) algorithm is developed. Furthermore, a more computationally efficient approach is introduced, which utilizes transmit beamforming based on an accelerated primal gradient (APG) method. Furthermore, the approximation principle is proposed to transform non-convex constraints in the optimization of the reflection coefficients at RISs. Simulation results show that introduction of RIS-BackCom can improve the efficiency of computing and maintain the tradeoff between CE and sensing performance.

In future networks, the integrated sensing, communication and computation (ISCC) has gradually become a research hotspot. In this paper, we investigate a novel computation resource allocation scheme for reconfigurable intelligent surfaces (RIS) backscatter communication (BackCom)-aided ISCC system. We consider the joint design of transmit beamforming at BS and the reflecting coefficients at RIS as well as the computation resource allocation of each user. The optimization problem for the max-min computation efficiency (CE) under the constraints of power consumption, the Cramér-Rao bound (CRB) for angles estimation and communication requirement of each user is formulated. To deal with the intractable optimization problem, the block coordinate descent (BCD) algorithm is utilized to tackle the joint optimization problem. We propose the penalty function-based successive convex approximation (SCA) method to optimize the reflecting coefficients and the majorization-minimization (MM) framework to design the transmit beamforming, respectively. In addition, considering the high complexity of the proposed SCA based algorithm, we design a low-complexity beamforming and reflection coefficient scheme for a special case of single target scenario. Simulation results show that the introduction of RIS-BackCom can improve the efficiency of computing and maintain the tradeoff between CE and sensing performance.

The Internet of Things (IoT) has been extensively applied to human-centric smart environments. Services provisioned within these IoT-enabled smart settings can substantially enhance the quality of life, mitigate potential hazards, and thereby offer personalized services for their users. However, there is a notable deficiency in the consideration of human factors necessary for realizing more refined and personalized situation-aware services. Moreover, as learning-based approaches are widely used in providing situation analysis in the current era, the challenge of training a robust learning model is aggravated by the scarcity of locally collected user data. Federated Learning (FL) was proposed to address the issue in a centralized, cloud-edge-based setting. Nonetheless, it falls short of facilitating personalized learning, which is crucial for the provisioning of local situation-aware services. In this paper, we propose a decen-tralized, clustering-based mutual learning approach that enables each edge server to learn a personalized model by iteratively sharing knowledge within clusters formed based on situational similarities. We used connected smart homes as an example to demonstrate the learning approach, and show the effectiveness of achieving personalized situation analysis, which ultimately leads to robust and accurate service in smart environments.

In an enterprise-grade hybrid-multicloud computing environment, capability-providing as-a-service endpoints (or aaS-endpoints) can be deployed across diverse computing platforms, e.g., public clouds and on-prem enterprise private clouds. To ensure a seamless, unified, and enterprise-compliant acquisition of the capabilities by client applications, the presence of a cross-cloud API mediation service is crucial. However, as the number and heterogeneity of aaS-endpoints increase, delivering the API mediation service at scale becomes increasingly costly. This paper presents a robust approach to API service mediation in enterprise-grade hybrid-multicloud computing environments. It tackles the challenges, offering a distributed architecture comprising dynamically composed managed microservices, microservice zones, intelligent endpoint selection, and adaptive statistical learning (aiming to exploit localities in performance history of aaS-endpoint invocations and to facilitate adding or removing active aaS-endpoints). The successful reference implementation and$24\mathrm{x}7\mathrm{x}365$delivery in real-world settings of the approach validate its efficacy as a practical solution for API service mediation.

The emergence of blockchain technologies and the rapid growth of the Internet of Things (IoT) have brought blockchain-premised IoT (BIoT) systems into the focus of recent studies. The decentralized nature of blockchain enables data traceability, transparency, and immutability as complementary security features to the existing IoT systems. It has been applied to prevent malicious control or data leakages in traditional cloud-based, vendor-specific IoT use case scenarios. Nevertheless, as we gradually step towards the situation-aware IoT era, the lack of means to incorporate situation awareness with BIoT systems has limited the full potential of such integration. In this work, we propose a framework, Situ-Oracle, as an attempt to provide situation analysis as a service to BIoT systems. The framework utilizes a Recurrent Neural Network (RNN) based learning model to perform sensory-based situation analysis. We used smart home as an example to demonstrate the feasibility of the integration in bringing situation awareness to smart-contract-enabled IoT systems. Following that, system-wide performance evaluations were conducted over a physically constructed BIoT system, the results show that the proposed system achieves better situation analysis accuracy and network performance compared to a baseline system. Overall, the paper presents a promising approach for improving situation analysis in BIoT systems, with potential applications in various domains such as smart homes, healthcare, and industrial automation.