With the boom in artificial intelligence (AI) and Internet-of-Things (IoT), thousands of smart devices are interconnected with each other and deeply applied into human society. This prosperity has significantly improved public service and management, which were traditionally based on manual work. As a notable scenario, librarianship has embraced an era of “Smart Libraries” enabled by AI and IoT. Unlike existing surveys, our work comprehensively overviews the AI- and IoT-based technologies in three fundamental aspects: smart service, smart sustainability, and smart security. We then further highlight the trend towards future smart libraries.

As the location-based service (LBS) plays an increasingly important role in real life, the topic of positioning attracts more and more attention. Under different environments and principles, researchers have proposed a series of positioning schemes and implemented many positioning systems. With widely deployed networks and massive devices, wireless fidelity (Wi-Fi) technology is promising in the field of indoor positioning. In this paper, we survey the authoritative or latest positioning schemes for Wi-Fi-assisted indoor positioning. To this end, we describe the problem and corresponding applications, as well as an overview of the alternative methods. Then, we classify and analyze Wi-Fi-assisted indoor positioning schemes in detail, as well as review related work. Furthermore, we point out open challenges and forecast promising directions for future work.

By orchestrating resources of edge and core network, the delays of edge-assisted computing can decrease. Offloading scheduling is challenging though, especially in the presence of many edge devices with randomly varying link and computing conditions. This paper presents a new online learning-based approach to the offloading scheduling, where multi-agent multi-armed bandit (MA-MAB) learning is designed to exploit the randomly varying conditions and asymptotically minimize the computing delay. We first propose a combinatorial bandit upper confidence bound (CB-UCB) algorithm, where users collectively feed back the observed delays of all edge devices and links. The optimistic bound of the delay is derived to facilitate centralized offloading scheduling for all users. In addition, we put forth a distributed bandit upper confidence bound (DB-UCB) algorithm, where users take random turns to make conflict-free, distributed selections of edge devices. The optimistic confidence bound of each user is developed to allow the user’s selection only based on its own observations and decisions. Furthermore, we establish the asymptotic optimality of the proposed algorithms by proving the sublinearity of their regrets, and that the random turns the users take to make decisions do not compromise the asymptotic optimality of the DB-UCB algorithm, as corroborated by numerical simulations.

The exponential growth of data traffic from mobile devices leads to a need of heterogeneous networks (HetNets) which integrate multiple radio access technologies (multi-RATs) to allocate task-offloading with quick coordination. In this paper, we present a novel mobile edge computing (MEC) architecture for multi-RAT HetNets, and propose an MEC-centric offloading decision mechanism. By formulating the intended task as a multi-armed bandit (MAB) problem, we develop a fronthaul-aware upper confidence bound (FA-UCB) algorithm that is able to deal with uncertainty and asymmetry of network state information. It is rigorously established that the proposed FA-UCB algorithm has a sublinear regret bound against the optimal benchmark with full a-priori knowledge, given that the backhaul delays are independently and identically distributed over time. Furthermore, under a restless martingale (RM) bandit condition, we put forth a generalized RM-FA-UCB algorithm that can achieve a sublinear regret bound even under non-stationary network dynamics. Numerical results demonstrate the merits of the proposed schemes and algorithms.

Multi-cloud storage is recently a viable approach to solve the vendor lock-in, reliability, and security issues in cloud storage systems. As a key concern, data placement influences the cost and performance of storage services. Yet, in practice it remains challenging to address the huge solution space. Previous studies typically focus on constructing efficient data placement schemes based on the predicted pattern of workloads or assuming fully a-priori known network conditions. They cannot be easily applied in multi-cloud storage scenarios, which typically involve dynamic network conditions and time-varying workloads. To this end, we formulate the data placement optimization in a combinatorial multi-arm bandit (CMAB) perspective and solve it by learning placement strategy online. In contrast to a stationary setting where reward distributions are unknown but identical over time, we consider a realistic multi-cloud environment with non-stationary conditions, i.e. , reward distributions change over time. To swiftly accommodate this, we propose an adaptive window combinatorial upper confidence bound based data placement (AW-CUCB-DP) scheme to reduce latency and cost. In AW-CUCB-DP, a simple and efficient change detector, i.e., Page-Hinkley test with forgetting mechanism (FM-PHT), is employed to enable variable-size sliding windows to handle both gradual and abrupt variations in network conditions or workloads. We establish that AW-CUCB-DP is asymptotically optimal in the non-stationary multi-cloud environment. Trace-driven experiments further verify that our scheme outperforms alternatives, especially in highly dynamic environments.