Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Digital processing of speech signals

Wireless sensor network (WSN) has emerged as one of the most promising technologies for the future. This has been enabled by advances in technology and availability of small, inexpensive, and smart sensors resulting in cost effective and easily deployable WSNs. However, researchers must address a variety of challenges to facilitate the widespread deployment of WSN technology in real-world domains. In this survey, we give an overview of wireless sensor networks and their application domains including the challenges that should be addressed in order to push the technology further. Then we review the recent technologies and testbeds for WSNs. Finally, we identify several open research issues that need to be investigated in future.

Our survey is different from existing surveys in that we focus on recent developments in wireless sensor network technologies. We review the leading research projects, standards and technologies, and platforms. Moreover, we highlight a recent phenomenon in WSN research that is to explore synergy between sensor networks and other technologies and explain how this can help sensor networks achieve their full potential. This paper intends to help new researchers entering the domain of WSNs by providing a comprehensive survey on recent developments.

Wireless sensor networks: a survey on recent developments and potential synergies

Space-air-ground integrated network (SAGIN), as an integration of satellite systems, aerial networks, and terrestrial communications, has been becoming an emerging architecture and attracted intensive research interest during the past years. Besides bringing significant benefits for various practical services and applications, SAGIN is also facing many unprecedented challenges due to its specific characteristics, such as heterogeneity, self-organization, and time-variability. Compared to traditional ground or satellite networks, SAGIN is affected by the limited and unbalanced network resources in all three network segments, so that it is difficult to obtain the best performances for traffic delivery. Therefore, the system integration, protocol optimization, resource management, and allocation in SAGIN is of great significance. To the best of our knowledge, we are the first to present the state-of-the-art of the SAGIN since existing survey papers focused on either only one single network segment in space or air, or the integration of space-ground, neglecting the integration of all the three network segments. In light of this, we present in this paper a comprehensive review of recent research works concerning SAGIN from network design and resource allocation to performance analysis and optimization. After discussing several existing network architectures, we also point out some technology challenges and future directions.

Space-Air-Ground Integrated Network: A Survey

Consider a discrete source producing a sequence of message letters from a finite alphabet. A single-letter distortion measure is given by a non-negative matrix (d ij ). The entryd ij measures the ?cost? or ?distortion? if letteriis reproduced at the receiver as letterj. The average distortion of a communications system (source-coder-noisy channel-decoder) is taken to bed= ? i.j P ij d ij whereP ij is the probability ofibeing reproduced asj. It is shown that there is a functionR(d) that measures the ?equivalent rate? of the source for a given level of distortion. For coding purposes where a leveldof distortion can be tolerated, the source acts like one with information rateR(d). Methods are given for calculatingR(d), and various properties discussed. Finally, generalizations to ergodic sources, to continuous sources, and to distortion measures involving blocks of letters are developed. In this paper a study is made of the problem of coding a discrete source of information, given afidelity criterionor ameasure of the distortionof the final recovered message at the receiving point relative to the actual transmitted message. In a particular case there might be a certain tolerable level of distortion as determined by this measure. It is desired to so encode the information that the maximum possible signaling rate is obtained without exceeding the tolerable distortion level. This work is an expansion and detailed elaboration of ideas presented earlier [1], with particular reference to the discrete case. We shall show that for a wide class of distortion measures and discrete sources of information there exists a functionR(d) (depending on the particular distortion measure and source) which measures, in a sense, the equivalent rateRof the source (in bits per letter produced) whendis the allowed distortion level. Methods will be given for evaluatingR(d) explicitly in certain simple cases and for evaluatingR(d) by a limiting process in more complex cases. The basic results are roughly that it is impossible to signal at a rate faster thanC/R(d) (source letters per second) over a memoryless channel of capacityC(bits per second) with a distortion measure less than or equal tod. On the other hand, by sufficiently long block codes it is possible to approach as closely as desired the rateC/R(d) with distortion leveld. Finally, some particular examples, using error probability per letter of message and other simple distortion measures, are worked out in detail.

Coding Theorems for a Discrete Source With a Fidelity CriterionInstitute of Radio Engineers, International Convention Record, vol. 7, 1959.

The technological evolution of the Internet of Things (IoT) and of the related devices, and their increasing diffusion, give mobile network providers the opportunity to come up with more advanced and innovative services. Among these are the so-called context-aware services: highly customizable services tailored to the user’s preferences and needs, which rely on real-time knowledge of the user’s surroundings, without requiring complex configuration on the user’s part. Examples of context-aware services are user profile changes that result from context changes, user proximity-based advertising, or media content tagging, etc. Applications based on the information acquired, processed and distributed by the IoT can answer the following questions about the objects’ surroundings: what, who, where, when, why, and how.

Guest Editorial “Things” as Intelligent Sensors and Actuators in the Users’ Context: Processing and Communications Issues

Brain stroke treatments are strongly time-dependent. Indeed, the literature stresses the need for quick diagnosis to guarantee the most effective therapy with the famous motto “time is brain” [1]. Numerically speaking, a crucial goal for strokes is trying to achieve a door-to-needle time shorter than 60 minutes, called The Golden Hour [2], within which patients have the highest probability of a full recovery. This article presents a prototype of a Smart Helmet, a wearable device originally introduced in [3], [4], thought to be employed on site, to efficiently recognize and detect a brain stroke in a patient when the first aid medical team arrives. This work presents two different but complementary approaches: signal-processing-based and electromagnetic (EM). The former aims at outputting a two-level diagnosis: the presence (or not) of a stroke and (if it is present) of which type (ischemic or hemorrhagic), while the latter provides an accurate imaging of the brain that must be interpreted by medical staff. Both approaches are analyzed and compared, showing their strengths and highlighting their weaknesses.

Two Ways for Early Detection of a Stroke Through a Wearable Smart Helmet: Signal Processing vs. Electromagnetism

The exponential increase in the number and types of mobile devices, along with their ever-growing sets of capabilities, have enabled the development of new architectures that aim to harness such heterogeneity. Transient Clouds (TCs) are examples of mobile clouds which are created on-the-fly by the devices present in an environment to share their physical resources (e.g., CPU, memory, network) and would disappear as the nodes leave the network. They enable a device to go beyond its own physical limitations through utilizing the capabilities offered by nearby devices over an ad-hoc network. In this paper we present a Transient Context-Aware Cloud (TCAC) in which the nodes of the network care more about providing/learning higher level functionalities rather than lower level capabilities. We make the case for such an architecture in scenarios where it is not feasible for all the nodes to compute the context due to privacy, energy, and delay constraints rather than an unreachable network.We present a prototype implementation of our architecture over Android smartphones connected via WiFi along with the performance metrics (power/energy consumption and accuracy)to show the benefits of context awareness in TCs.

Context Awareness over Transient Clouds

This paper presents the Heuristic Application Layer Joint Coding (Heuristic-ALJC) scheme for video transmissions aimed at adaptively and jointly varying both applied video compression and source encoding at the application layer used to protect video streams. Heuristic-ALJC includes also a simple acknowledgement based adaptation of the transmission rate and acts on the basis of feedback information about the overall network status estimated in terms of maximum allowable network throughput and link quality (lossiness). Heuristic-ALJC is implemented through two smartphone Apps (transmitter and receiver) and is suitable to be employed to transmit video streams over networks based on time varying and possibly lossy channels. A performance investigation, carried out through a real implementation of the Apps over Android smartphones, compares Heuristic-ALJC with static schemes.

Smartphones Apps Implementing a Heuristic Joint Coding for Video Transmissions Over Mobile Networks

As current Internet frontiers are rapidly extending towards space, the scientific community's interest is increasingly addressed to next-generation network architectures suited to enable data communications over interplanetary networks. In this light, given the networking and communication challenges posed by such environments, the design of complex telecommunication infrastructures deserves particular attention, especially with regard to routing and congestion control strategies. To this end, this paper proposes a congestion-aware routing paradigm that applies multi attribute decision making (MADM) concepts for next-hop selection, by formulating an optimisation problem and proposing some possible resolution criteria. Effectiveness of the proposed solutions is assessed through a preliminary performance analysis that shows promising results.

Igor Bisio

Papers

Guest Editorial “Things” as Intelligent Sensors and Actuators in the Users’ Context: Processing and Communications Issues

Two Ways for Early Detection of a Stroke Through a Wearable Smart Helmet: Signal Processing vs. Electromagnetism

Context Awareness over Transient Clouds

Smartphones Apps Implementing a Heuristic Joint Coding for Video Transmissions Over Mobile Networks

Multi-Attribute Decision Making Routing Strategy for Interplanetary Communications