Location information for events, assets, and individuals, mostly focusing on two dimensions so far, has triggered a multitude of applications across different verticals, such as consumer, networking, ...industrial, health care, public safety, and emergency response use cases. To fully exploit the potential of location awareness and enable new advanced location-based services, localization algorithms need to be combined with complementary technologies including accurate height estimation, i.e., three dimensional location, reliable user mobility classification, and efficient indoor mapping solutions. This survey provides a comprehensive review of such enabling technologies. In particular, we present cellular localization systems including recent results on 5G localization, and solutions based on wireless local area networks, highlighting those that are capable of computing 3D location in multi-floor indoor environments. We overview range-free localization schemes, which have been traditionally explored in wireless sensor networks and are nowadays gaining attention for several envisioned Internet of Things applications. We also present user mobility estimation techniques, particularly those applicable in cellular networks, that can improve localization and tracking accuracy. Regarding the mapping of physical space inside buildings for aiding tracking and navigation applications, we study recent advances and focus on smartphone-based indoor simultaneous localization and mapping approaches. The survey concludes with service availability and system scalability considerations, as well as security and privacy concerns in location architectures, discusses the technology roadmap, and identifies future research directions.
In last two decades, various monitoring systems have been designed and deployed in urban environments, toward the realization of the so called smart cities. Such systems are based on both dedicated ...sensor nodes, and ubiquitous but not dedicated devices such as smart phones and vehicles' sensors. When we design sensor network monitoring systems for smart cities, we have two essential problems: node deployment and sensing management. These design problems are challenging, due to large urban areas to monitor, constrained locations for deployments, and heterogeneous type of sensing devices. There is a vast body of literature from different disciplines that have addressed these challenges. However, we do not have yet a comprehensive understanding and sound design guidelines. This paper addresses such a research gap and provides an overview of the theoretical problems we face, and what possible approaches we may use to solve these problems. Specifically, this paper focuses on the problems on both the deployment of the devices (which is the system design/configuration part) and the sensing management of the devices (which is the system running part). We also discuss how to choose the existing algorithms in different type of monitoring applications in smart cities, such as structural health monitoring, water pipeline networks, traffic monitoring. We finally discuss future research opportunities and open challenges for smart city monitoring.
Distributed optimization increasingly plays a central role in economical and sustainable operation of cyber-physical systems. Nevertheless, the complete potential of the technology has not yet been ...fully exploited in practice due to communication limitations posed by the real-world infrastructures. This work investigates fundamental properties of distributed optimization based on gradient methods, where gradient information is communicated using a limited number of bits. In particular, a general class of quantized gradient methods are studied, where the gradient direction is approximated by a finite quantization set. Sufficient and necessary conditions are provided on such a quantization set to guarantee that the methods minimize any convex objective function with Lipschitz continuous gradient and a nonempty and bounded set of optimizers. A lower bound on the cardinality of the quantization set is provided, along with specific examples of minimal quantizations. Convergence rate results are established that connect the fineness of the quantization and the number of iterations needed to reach a predefined solution accuracy. Generalizations of the results to a relevant class of constrained problems using projections are considered. Finally, the results are illustrated by simulations of practical systems.
The Internet of Musical Things (IoMusT) is an emerging research field positioned at the intersection of Internet of Things, new interfaces for musical expression, ubiquitous music, human-computer ...interaction, artificial intelligence, and participatory art. From a computer science perspective, IoMusT refers to the networks of computing devices embedded in physical objects (musical things) dedicated to the production and/or reception of musical content. Musical things, such as smart musical instruments or wearables, are connected by an infrastructure that enables multidirectional communication, both locally and remotely. We present a vision in which the IoMusT enables the connection of digital and physical domains by means of appropriate information and communication technologies, fostering novel musical applications and services. The ecosystems associated with the IoMusT include interoperable devices and services that connect musicians and audiences to support musician-musician, audience-musicians, and audience-audience interactions. In this paper, we first propose a vision for the IoMusT and its motivations. We then discuss five scenarios illustrating how the IoMusT could support: 1) augmented and immersive concert experiences; 2) audience participation; 3) remote rehearsals; 4) music e-learning; and 5) smart studio production. We identify key capabilities missing from today's systems and discuss the research needed to develop these capabilities across a set of interdisciplinary challenges. These encompass network communication (e.g., ultra-low latency and security), music information research (e.g., artificial intelligence for real-time audio content description and multimodal sensing), music interaction (e.g., distributed performance and music e-learning), as well as legal and responsible innovation aspects to ensure that future IoMusT services are socially desirable and undertaken in the public interest.
Distributed processing through ad hoc and sensor networks is having a major impact on scale and applications of computing. The creation of new cyber-physical services based on wireless sensor devices ...relies heavily on how well communication protocols can be adapted and optimized to meet quality constraints under limited energy resources. The IEEE 802.15.4 medium access control protocol for wireless sensor networks can support energy efficient, reliable, and timely packet transmission by a parallel and distributed tuning of the medium access control parameters. Such a tuning is difficult, because simple and accurate models of the influence of these parameters on the probability of successful packet transmission, packet delay, and energy consumption are not available. Moreover, it is not clear how to adapt the parameters to the changes of the network and traffic regimes by algorithms that can run on resource-constrained devices. In this paper, a Markov chain is proposed to model these relations by simple expressions without giving up the accuracy. In contrast to previous work, the presence of limited number of retransmissions, acknowledgments, unsaturated traffic, packet size, and packet copying delay due to hardware limitations is accounted for. The model is then used to derive a distributed adaptive algorithm for minimizing the power consumption while guaranteeing a given successful packet reception probability and delay constraints in the packet transmission. The algorithm does not require any modification of the IEEE 802.15.4 medium access control and can be easily implemented on network devices. The algorithm has been experimentally implemented and evaluated on a testbed with off-the-shelf wireless sensor devices. Experimental results show that the analysis is accurate, that the proposed algorithm satisfies reliability and delay constraints, and that the approach reduces the energy consumption of the network under both stationary and transient conditions. Specifically, even if the number of devices and traffic configuration change sharply, the proposed parallel and distributed algorithm allows the system to operate close to its optimal state by estimating the busy channel and channel access probabilities. Furthermore, results indicate that the protocol reacts promptly to errors in the estimation of the number of devices and in the traffic load that can appear due to device mobility. It is also shown that the effect of imperfect channel and carrier sensing on system performance heavily depends on the traffic load and limited range of the protocol parameters.
As electric power system operators shift from conventional energy to renewable energy sources, power distribution systems will experience increasing fluctuations in supply. These fluctuations present ...the need to not only design online decentralized power allocation algorithms, but also characterize how effective they are given fast-changing consumer demand and generation. In this paper, we present an online decentralized dual descent (OD3) power allocation algorithm and determine (in the worst case) how much of observed social welfare can be explained by fluctuations in generation capacity and consumer demand. Convergence properties and performance guarantees of the OD3 algorithm are analyzed by characterizing the difference between the online decision and the optimal decision. We demonstrate validity and accuracy of the theoretical results in the paper through numerical experiments using real power generation data.
Nonconvex and structured optimization problems arise in many engineering applications that demand scalable and distributed solution methods. The study of the convergence properties of these methods ...is, in general, difficult due to the nonconvexity of the problem. In this paper, two distributed solution methods that combine the fast convergence properties of augmented Lagrangian-based methods with the separability properties of alternating optimization are investigated. The first method is adapted from the classic quadratic penalty function method and is called the alternating direction penalty method (ADPM). Unlike the original quadratic penalty function method, where single-step optimizations are adopted, ADPM uses an alternating optimization which, in turn, makes it scalable. The second method is the well-known alternating direction method of multipliers (ADMM). It is shown that ADPM for nonconvex problems asymptotically converges to a primal feasible point under mild conditions and an additional condition ensuring that it asymptotically reaches the standard first-order necessary conditions for local optimality is introduced. In the case of the ADMM, novel sufficient conditions under which the algorithm asymptotically reaches the standard first-order necessary conditions are established. Based on this, complete convergence of the ADMM for a class of low-dimensional problems is characterized. Finally, the results are illustrated by applying ADPM and ADMM to a nonconvex localization problem in wireless-sensor networks.
The computational detection of musical patterns is widely studied in the field of Music Information Retrieval and has numerous applications. However, pattern detection in real-time has not yet ...received adequate attention. The real-time detection is important in several application domains, especially in the field of the Internet of Musical Things. This study considers a single musical instrument and investigates the detection in real-time of patterns of a monophonic music stream. We present a representation mechanism to denote musical notes as a single column matrix, whose content corresponds to three key attributes of each musical note-pitch, amplitude and duration. The note attributes are obtained from a symbolic MIDI representation. Based on such representation, we compare the most prominent candidate methods based on neural networks and one deterministic method. Numerical results show the accuracy of each method, and allow us to characterize the trade-offs among those methods.
Abstract
This work proposes a reliable leakage detection methodology for water distribution networks (WDNs) using machine-learning strategies. Our solution aims at detecting leakage in WDNs using ...efficient machine-learning strategies. We analyze pressure measurements from pumps in district metered areas (DMAs) in Stockholm, Sweden, where we consider a residential DMA of the water distribution network. Our proposed methodology uses learning strategies from unsupervised learning (K-means and cluster validation techniques), and supervised learning (learning vector quantization algorithms). The learning strategies we propose have low complexity, and the numerical experiments show the potential of using machine-learning strategies in leakage detection for monitored WDNs. Specifically, our experiments show that the proposed learning strategies are able to obtain correct classification rates up to 93.98%.
Millimeter-wave (mmWave) communication systems use a large number of antenna elements that can potentially overcome severe channel attenuation by narrow beamforming. Narrow-beam operation in mmWave ...networks also reduces multiuser interference, introducing the concept of noise-limited wireless networks as opposed to interference-limited ones. The noise-limited or interference-limited regime heavily reflects on the medium access control (MAC) layer throughput and on proper resource allocation and interference management strategies. Yet, these regimes are ignored in current approaches to mmWave MAC layer design, with the potential disastrous consequences on the communication performance. In this paper, we investigate these regimes in terms of collision probability and throughput. We derive tractable closed-form expressions for the collision probability and MAC layer throughput of mmWave ad hoc networks, operating under slotted ALOHA. The new analysis reveals that mmWave networks may exhibit a non-negligible transitional behavior from a noise-limited regime to an interference-limited one, depending on the density of the transmitters, density and size of obstacles, transmission probability, operating beamwidth, and transmission power. Such transitional behavior necessitates a new framework of adaptive hybrid resource allocation procedure, containing both contention-based and contention-free phases with on-demand realization of the contention-free phase. Moreover, the conventional collision avoidance procedure in the contention-based phase should be revisited, due to the transitional behavior of interference, to maximize throughput/delay performance of mmWave networks. We conclude that, unless proper hybrid schemes are investigated, the severity of the transitional behavior may significantly reduce throughput/delay performance of mmWave networks.
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