Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Reasons and opportunism control in public grants policies for development and innovations of businesses

In this paper we would to analyze the mechanism of public grants on economic growth. In particular this topic has been the subject of scientific interest by economists and, recently, also by business economics scholars. The studies of the economists focused on the motivations of the intervention itself; the business economic studies, on the other hand, have analyzed the impact both on the behavior of entrepreneurs and on the firms themselves by public grants. The studies examined so far highlight two basic conceptual dimensions, different, but also complementary to each other: on the one hand the economic-oriented to investigate the motivations and effectiveness of the public intervention; the second, business-oriented, focused on the firm’s behavior following public grant. Based on these arguments, our research question arises: could the effectiveness of public intervention for funding development and business innovation be influenced by the differences in the various socio-political and institutional contexts in which they are applied? The aim of paper is analyze the motivations of public grants policies and their influence on the behavior of firms. In this way we want to identify asolutions scheme able to recover efficiency and effectiveness of public actions to support development. It is therefore possible to identify some corrective mechanisms on public intervention policies. In particular with reference to the behaviors induced by the grants policies, the idea is to re-design the grants policies in consideration of the different forms of pre and post contractual opportunism. With reference, instead, to the motivations underlying the public grants policies, it is necessary to examine the relationship between the State (Principal) and the beneficiary firms (Agent) in relation to the respective dimensions of the contractual force

Electricity Transmission Pricing and Performance-Based Regulation

Performance-based regulation (PBR) is influenced by the Bayesian and non-Bayesian incentive mechanisms. While Bayesian incentives are impractical, the insights from their properties can be combined with practical non-Bayesian mechanisms for application to transmission pricing. This combination suggests an approach based on the distinction between ultra-short, short and long periods. Ultra-short periods are marked by real-time pricing of point-to-point transmission services. Pricing in short periods involves fixed fees and adjustments via price-cap formulas or profit sharing. Productivity-enhancing incentives have to be tempered by long-term commitment considerations, so that profit sharing may dominate pure price caps. Investment incentives require long-term adjustments based on rate-of-return regulation with a “used and useful” criterion.

Unattended network operations technology assessment study. Technical support for defining advanced satellite systems concepts

The results are summarized of an unattended network operations technology assessment study for the Space Exploration Initiative (SEI). The scope of the work included: (1) identified possible enhancements due to the proposed Mars communications network; (2) identified network operations on Mars; (3) performed a technology assessment of possible supporting technologies based on current and future approaches to network operations; and (4) developed a plan for the testing and development of these technologies. The most important results obtained are as follows: (1) addition of a third Mars Relay Satellite (MRS) and MRS cross link capabilities will enhance the network's fault tolerance capabilities through improved connectivity; (2) network functions can be divided into the six basic ISO network functional groups; (3) distributed artificial intelligence technologies will augment more traditional network management technologies to form the technological infrastructure of a virtually unattended network; and (4) a great effort is required to bring the current network technology levels for manned space communications up to the level needed for an automated fault tolerance Mars communications network

Habitual accountability routines in the boardroom: How boards balance control and collaboration

open3siCorporate accountability is a complex chain of reporting that reaches from external stakeholders into the organization’s management structure. The transition from external to internal accountability mechanisms primarily occurs at the board of directors. Yet outside of incentive mechanisms, we know surprisingly little about how internal actors (management) are held to account by the representatives of external shareholders (the board). This paper explores the process of accountability at this transition point by documenting the routines used by boards to hold the firm’s management to account. In so doing we develop our understanding of the important transition between internal and external firm accountability.embargoed_20190401Nicholson, Gavin; Pugliese, Amedeo; Bezemer, Pieter JanNicholson, Gavin; Pugliese, Amedeo; Bezemer, Pieter Ja

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future