Map-aided fingerprint-based indoor positioning

The objective of this work is to investigate potential accuracy improvements in the fingerprint-based indoor positioning processes, by imposing map-constraints into the positioning algorithms in the form of a-priori knowledge. In our approach, we propose the introduction of a Route Probability Factor (RPF), which reflects the possibility of a user, to be located on one position instead of all others. The RPF does not only affect the probabilities of the points along the pre-defined frequent routes, but also influences all the neighbouring points that lie at the proximity of each frequent route. The outcome of the evaluation process, indicates the validity of the RPF approach, demonstrated by the significant reduction of the positioning error

Self-Learning Power Control in Wireless Sensor Networks

Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for new mechanisms to improve spectrum management and energy efficiency, such as transmission power control. Existing protocols are based on simplistic heuristics that often approach interference problems (i.e., packet loss, delay and energy waste) by increasing power, leading to detrimental results. The scope of this work is to investigate how machine learning may be used to bring wireless nodes to the lowest possible transmission power level and, in turn, to respect the quality requirements of the overall network. Lowering transmission power has benefits in terms of both energy consumption and interference. We propose a protocol of transmission power control through a reinforcement learning process that we have set in a multi-agent system. The agents are independent learners using the same exploration strategy and reward structure, leading to an overall cooperative network. The simulation results show that the system converges to an equilibrium where each node transmits at the minimum power while respecting high packet reception ratio constraints. Consequently, the system benefits from low energy consumption and packet delay

Statistical Assessment of IP Multimedia Subsystem in a Softwarized Environment: a Queueing Networks Approach

The Next Generation 5G Networks can greatly benefit from the synergy between virtualization paradigms, such as the Network Function Virtualization (NFV), and service provisioning platforms such as the IP Multimedia Subsystem (IMS). The NFV concept is evolving towards a lightweight solution based on containers that, by contrast to classic virtual machines, do not carry a whole operating system and result in more efficient and scalable deployments. On the other hand, IMS has become an integral part of the 5G core network, for instance, to provide advanced services like Voice over LTE (VoLTE). In this paper we combine these virtualization and service provisioning concepts, deriving a containerized IMS infrastructure, dubbed cIMS, providing its assessment through statistical characterization and experimental measurements. Specifically, we: i) model cIMS through the queueing networks methodology to characterize the utilization of virtual resources under constrained conditions; ii) draw an extended version of the Pollaczek-Khinchin formula, which is useful to deal with bulk arrivals; iii) afford an optimization problem focused at maximizing the whole cIMS performance in the presence of capacity constraints, thus providing new means for the service provider to manage service level agreements (SLAs); iv) evaluate a range of cIMS scenarios, considering different queuing disciplines including also multiple job classes. An experimental testbed based on the open source platform Clearwater has been deployed to derive some realistic values of key parameters (e.g. arrival and service times)

The Multifaceted Origin of Taurine Cattle Reflected by the Mitochondrial Genome

A Neolithic domestication of taurine cattle in the Fertile Crescent from local aurochsen (Bos primigenius) is generally accepted, but a genetic contribution from European aurochsen has been proposed. Here we performed a survey of a large number of taurine cattle mitochondrial DNA (mtDNA) control regions from numerous European breeds confirming the overall clustering within haplogroups (T1, T2 and T3) of Near Eastern ancestry, but also identifying eight mtDNAs (1.3%) that did not fit in haplogroup T. Sequencing of the entire mitochondrial genome showed that four mtDNAs formed a novel branch (haplogroup R) which, after the deep bifurcation that gave rise to the taurine and zebuine lineages, constitutes the earliest known split in the mtDNA phylogeny of B. primigenius. The remaining four mtDNAs were members of the recently discovered haplogroup Q. Phylogeographic data indicate that R mtDNAs were derived from female European aurochsen, possibly in the Italian Peninsula, and sporadically included in domestic herds. In contrast, the available data suggest that Q mtDNAs and T subclades were involved in the same Neolithic event of domestication in the Near East. Thus, the existence of novel (and rare) taurine haplogroups highlights a multifaceted genetic legacy from distinct B. primigenius populations. Taking into account that the maternally transmitted mtDNA tends to underestimate the extent of gene flow from European aurochsen, the detection of the R mtDNAs in autochthonous breeds, some of which are endangered, identifies an unexpected reservoir of genetic variation that should be carefully preserved

Decentralized dynamic understanding of hidden relations in complex networks

Almost all the natural or human made systems can be understood and controlled using complex networks. This is a difficult problem due to the very large number of elements in such networks, on the order of billions and higher, which makes it impossible to use conventional network analysis methods. Herein, we employ artificial intelligence (specifically swarm computing), to compute centrality metrics in a completely decentralized fashion. More exactly, we show that by overlaying a homogeneous artificial system (inspired by swarm intelligence) over a complex network (which is a heterogeneous system), and playing a game in the fused system, the changes in the homogeneous system will reflect perfectly the complex network properties. Our method, dubbed Game of Thieves (GOT), computes the importance of all network elements (both nodes and edges) in polylogarithmic time with respect to the total number of nodes. Contrary, the state-of-the-art methods need at least a quadratic time. Moreover, the excellent capabilities of our proposed approach, it terms of speed, accuracy, and functionality, open the path for better ways of understanding and controlling complex networks