Self Organizing Networks

Elinor Ostrom's contributions to the understanding self-organization in collective action processes are discussed from a complexity theory perspective. It is argued that complexity researchers can learn from Ostrom's theory building process, as well her conceptualization of the conditions of self-organization in the management of common-pool resources. Her focus on self-organization helps rectify the problems with the assumption in the mainstream policy analysis that policy processes can be explained with external causes. The conceptual problems in her utility maximizing rational actor assumption and the potential for conceptual advancements in her recognition of complexity concepts are discussed. It is argued that Ostrom's conceptual framework is sophisticated, but it lacks a dynamic understanding of the micro–macro relationships in complex governance systems, and that complexity theory offers the conceptual tools to remedy this problem. Complexity theory concepts like self-organization can help us better understand gov-ernance processes. Self-organization is not original to complexity theory; the concept has a long history and it has been used by other theorists as well. It is important to trace the lin-eage of the concept and acknowledge the contributions of others, so that the contributions of complexity theory can be distinguished. In this paper, I focus on the works of the late Elinor Ostrom, not only to recognize her contributions, which are substantial, and show how complexity theorists can learn from them, but also to highlight the shortcomings in her conceptualizations and point to the areas in which complexity theorists can offer better conceptualizations. Ostrom's primary contribution is her demonstration that self-organization happens under specific conditions and within specific domains in collective action processes. She and her colleagues conceptualized and empirically verified the conditions of self-organization particularly in the management of common-pool resources (Ostrom, 1990, * I want to express my appreciation for Stefan Verweij's insightful and incisive comments on an earlier version of this paper and the two anonymous reviewers' helpful comments and guidance on the submitted version of the paper.

The International Journal of Wireless & Mobile Networks (IJWMN) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Wireless & Mobile Networks. The journal focuses on all technical and practical aspects of Wireless & Mobile Networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced wireless & mobile networking concepts and establishing new collaborations in these areas.

Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in Wireless and Mobile Networks.

Network elements and their parameters in mobile wireless networks, are largely manually configured. This has been somewhat sufficient; but with the growing data traffic compensated by new and emerging technologies with corresponding larger networks, there is an obvious need to redefine the network operations to achieve optimum performance. A manual configuration approach requires specialized expertise for device deployments, configurations, re-setting network parameters and general management of the network. This process is cost-intensive, time-consuming and prone to errors. Adoption of this approach in the evolved wireless technologies results in poor network performance. Therefore, the introduction of advanced mobile wireless networks has highlighted the need and essence for automation within the network. Self Orga- nizing Networks (SON) developed by 3GPP, using automation, ensures operational efficiency and next generation simplified network management for a mobile wireless network. The introduction of SON in LTE therefore brings about optimum network performance and higher end user Quality of Expe- rience. This paper highlights the SON techniques relevant within an LTE network, a brief description of SON architecture alternatives and then some in- formation on the evolution of SON activities as LTE evolves towards LTE-A.

Fog computing is a novel computing paradigm which enables the execution of applications on compute nodes which reside both in the cloud and at the edge of the network. Various performance benefits, such as low communication latency and high network bandwidth, have turned this paradigm into a well-accepted extension of cloud computing. So far, many fog computing systems have been proposed, consisting of distributed compute nodes which are often organized hierarchically in layers. Such systems commonly rely on the assumption that the nodes of adjacent layers reside close to each other, thereby achieving low latency computations. However, this assumption may not hold in fog computing systems that span over large geographical areas, due to the wide distribution of the nodes. In addition, most proposed fog computing systems route the data on a path which starts at the data source, and goes through various edge and cloud nodes. Each node on this path may accept the data if there are available resources to process this data locally. Otherwise, the data is forwarded to the next node on path. Notably, when the data is forwarded (rather than accepted), the communication latency increases by the delay to reach the next node.

This thesis aims at tackling these problems by proposing distributed algorithms whereby the compute nodes measure the network proximity to each other, and self-organize accordingly. These algorithms are implemented on geographically distributed compute nodes, considering image processing and smart city use cases, and are thoroughly evaluated showing significant latency- and bandwidth-related performance benefits. Furthermore, we analyze the communication latency of sending data to distributed edge and cloud compute nodes, and we propose two novel routing approaches: i) A context-aware routing mechanism which maintains a history of previous transmissions, and uses this history to find nearby nodes with available resources. ii) edgeRouting, which leverages the high bandwidth between nodes of cloud providers in order to select network paths with low communication latency. Both of these mechanisms are evaluated under real-world settings, and are shown to be able to lower the communication latency of fog computing systems significantly, compared to alternative methods.

In this letter, a preliminary study of habituation in self-organizing networks is reported. The habituation model implemented allows us to obtain a faster learning process and better clustering performances. The habituable neuron is a generalization of the typical neuron and can be used in many self-organizing network models. The habituation mechanism is implemented in a SOM and the clustering performances of the network are compared to the conscience learning mechanism that follows roughly the same principle but is less sophisticated.

En los últimos años se ha observado una evolución en las estrategias de acción de los movimientos sociales. Cada vez más, organizaciones de la sociedad civil y grupos más o menos estructurados, tanto a nivel local como global, invitan a los ciudadanos a reflexionar sobre el significado político de sus consumos, pidiéndoles hacer uso del “poder de la bolsa de la compra” para favorecer prácticas económicas atentas a
los derechos humanos y del medioambiente

Despite the many research efforts addressing the integration of mobile nodes into grids, only a few of them have considered the establishment of mobile grids over wireless ad hoc networks (hereafter, mobile ad hoc grids). Clearly, such grids need specialized resource discovery and scheduling mechanisms. To the best of our knowledge, though, the research on these mechanisms for mobile ad hoc grids is still preliminary. Besides, and more importantly, it has approached discovery and scheduling as separate mechanisms, which, we argue, is not suitable for mobile ad hoc grids. In this paper, we propose the integration of resource discovery and scheduling for mobile ad hoc grids into a single protocol called DICHOTOMY (DIscovery and sCHeduling prOTOcol for MobilitY). This protocol allows computational tasks to be distributed appropriately in a mobile ad hoc grid, while mitigating the overhead of discovery messages exchanged among the nodes. Our experiments show that the protocol: (i) does proper scheduling, allowing an efficient load balancing among the nodes and helping with lowering the average completion time of tasks; (ii) keeps the discovery efficiency at acceptable levels in mobility scenarios; and (iii) scales very well with respect to an increasing number of nodes, both in the total amount of energy savings due to packet transmissions and the distribution of such savings among the nodes.

Despite the many research efforts addressing the integration of mobile nodes into grids, only a few of them have considered the establishment of mobile grids over wireless ad hoc networks (hereafter, mobile ad hoc grids). Clearly, such grids need specialized resource discovery and scheduling mechanisms. To the best of our knowledge, though, the research on these mechanisms for mobile ad hoc grids is still preliminary. Besides, and more importantly, it has approached discovery and scheduling as separate mechanisms, which, we argue, is not suitable for mobile ad hoc grids. In this paper, we propose the integration of resource discovery and scheduling for mobile ad hoc grids into a single protocol called DICHOTOMY (DIscovery and sCHeduling prOTOcol for MobilitY). This protocol allows computational tasks to be distributed appropriately in a mobile ad hoc grid, while mitigating the overhead of discovery messages exchanged among the nodes. Our experiments show that the protocol: (i) does proper scheduling, allowing an efficient load balancing among the nodes and helping with lowering the average completion time of tasks; (ii) keeps the discovery efficiency at acceptable levels in mobility scenarios; and (iii) scales very well with respect to an increasing number of nodes, both in the total amount of energy savings due to packet transmissions and the distribution of such savings among the nodes.

Purpose -The purpose of this paper is to assist an organization to restructure as a bi-modal organization in order to achieve sustainability in today's highly complex business world. Design/methodology/approach -The paper is conceptual and is based on relevant literature and the authors' research and practice. Findings -Although fluid self-organizing networks are the natural state for humankind, in most organizations "organizing" entails the process of autopoiesis. This process does not produce the open fluid organization that is required for success in today's business world. While autopoiesis is taking place, informal socialization is taking place across the organization's interpersonal networks. Under supportive conditions, this leads to the development of a bi-modal organization where one or more open systems may emerge and co-exist concurrently with the autopoietic system; these open systems include fluid networks and complex adaptive system. The bi-modal organization achieves sustainability by balancing a certain amount of organization versus a certain amount of instability, leading to predictability with disorder, and planned long-term strategy achieved through many concurrent short-term actions. Research limitations/implications -Future research will involve an empirical study that will further examine the bi-modal organization, its development, and its properties. Practical implications -The systems that surround a business organization now and for the foreseeable future are highly dynamic, competitive, and socially individualized, and demand a new organizational form and competencies that may only be exhibited by a bi-modal organization based on an open system. The paper describes how an organization can restructure to become a bi-modal organization. Social implications -The paper should help improve quality of work-life and organizational structure. Originality/value -The paper describes a new organizational form designed to flourish in today's complex business contexts.

To cite:
Wilkins, A. 2011. School choice, consumerism and the ethical strand in talk. Discourse: Studies in the Cultural Politics of Education, 32 (3), 357-370

Research on school choice highlights the extent to which a communitarian impulse informs the way some parents engage with their role as chooser. This suggests that the responsibilities of parents as consumers are often negotiated in collective as well as individualising terms. Drawing on data from a group of mothers of diverse social class and racial backgrounds, this paper builds on some of these perspectives through deploying elements of a critical discursive analytic approach. Its aim is to explore how some mothers engage with the meaning and practice of school choice. Focusing on the emotional labouring that often underpins mothers’ rationalisations of choice, this paper examines the discursive role of emotion in these contexts as a form of social action geared towards achieving certain ends. In turn I discuss the implications of this for thinking through choice as a framing, function and discourse inhabited and performed by mothers.

How does the Blockchain system work? Explain it to me as if you were 5 years old. It is a consequence of the fact that everything is connected and it is not possible to imitate "reality": on the net they instantly return to everything and you can immediately see if there is someone who pretends to be god ... conceptually it is analogous to decoherence as interference or measure that collapses the system ... if you want it is an entropy of the curious that ghe brusa ...

Abstract: This article pretends to provide an easy-to-understand overview of the requirements and the technologies envisioned to support 5G cellular systems. Some meaningful statistics regarding the way traffic behaved during the year 2015 and a forecast up to year 2020 are presented. This allows a better understanding of the requirements for 5G that are currently being outlined and the applications that will be enabled. Given the orientation of this paper, two general aspects have been considered: a) How to increase and reach the required capacity of 5G systems by combining more spectrum, more cells and enhancing spectral efficiency by dealing with interference; and, b) The main aspects of the overall architecture of 5G systems, including discussions of the access network, backhaul and core. For each case several technologies that are under enhancements and optimization and others that are just being researched and still are in their initial steps are introduced and discussed.

Resumen: Este artículo pretende dar una visión fácil de entender sobre los requisitos y las tecnologías que se prevé brindarán soporte a los sistemas celulares de 5G. Se presentan algunas estadísticas de importancia relacionadas a la forma en la que el tráfico se comportó durante el año 2015 y el pronóstico hasta el año 2020. Esto permite una mejor comprensión de los requisitos para sistemas 5G que se están delineando actualmente y las aplicaciones que se podrán especificar. Dada la orientación de este trabajo, se han considerado dos aspectos generales: a) Cómo aumentar y alcanzar la capacidad de los sistemas 5G combinando más espectro, más celdas y mejorando la eficiencia espectral disminuyendo la interferencia; y, b) Los aspectos principales de la arquitectura de los sistemas 5G, incluyendo discusiones sobre la red de acceso, backhaul y el core de la red. Para cada caso, se presentan y discuten algunas tecnologías que están siendo mejoradas y optimizadas y otras que están siendo investigadas y que aún están en sus etapas iniciales.

Signal processing of present era is becoming more and more complex day by day. To meet the demand of modern signal processing, emphasis has been given to develop systems with minimum hardware. As a matter of fact, reduction in hardware complexity of digital filter has emerged as one of the upcoming research areas in present time. This paper highlights the design of multiplier-less finite impulse response (FIR) filter with the aid of an evolutionary optimization technique, namely Self-organizing Random Immigrants Genetic Algorithm (SORIGA). For this purpose, the coefficients of the filter have been encoded by binary and Canonic Signed Digit (CSD) number systems and subsequently optimized by means of SORIGA. Performance of the proposed filter has been analyzed in terms of its frequency and impulse response. Subsequently, hardware cost of the designed filter has been measured by means of a number of performance parameters and compared with few such existing filters from the literature in order to substantiate the efficiency of the proposed design. KEYWORDS Differential Evolution (DE), Finite impulse response (FIR) filter, hardware cost, multiplier-less filter, Self-organizing Random Immigrants Genetic Algorithm (SORIGA).

—In this paper, we propose a distributed, self-organizing, adaptive and semi-static Soft Frequency Reuse based downlink Inter-Cell Interference Coordination scheme for an LTE system to increase the throughput of cell-edge users. Cells are able to self-organize the allocation of frequency sub-bands for cell-edge users using a distributed algorithm without requiring extensive pre-planning needed for static schemes. The proposed scheme is adaptive to changes in the number and location of users in the cell and to changes in cell-edge frequency sub-band allocations of neighbouring cells. The changes in frequency sub-band allocations is semi-static and occurs periodically (eg. every 200 ms). We also explain how the algorithm can be implemented in an LTE system by using the existing specification and not requiring proprietary extensions to standards. Results from extensive simulation of the algorithm using the open source Network Simulator 3 show that the scheme outperforms the static Soft Frequency Reuse scheme.

The constant evolution and growth of multimedia applications supported by today's mobile devices leads to an increase on the demand for video based services which impose new  requirements in terms of bitrate and delay. In order to face these challenges, LTE appears as a technology capable of dealing with the video traffic load on mobile networks. However, given the inherent nature of mobile networks the devices located at the cell's edge face some difficulties when a certain SINR must be achieved. To overcome this issue, LTE Advanced comes into the picture as it makes use of Relay Nodes which were included as part of the network architecture. Based on simulations, the present work evaluates the performance of real time video applications on devices located at the cell's edge. The results show the advantage of using Relay Nodes in LTE in aspects such as the reduction of lost packets leading to an improvement of the video quality at the receiver's side.

http://goo.gl/2fi6Xq

A novel approach to handover management for Long-Term Evolution (LTE) femtocells is presented. Within LTE, the use of self-organizing networks (SONs) is included as standard, and handover management is one of its use cases. Base stations can autonomously decide whether handover should take place and assign the values of relevant parameters. Due to the limited range of femtocells, handover requires more delicate attention in an indoor scenario to allow for efficient and seamless handover from indoor femtocells to outdoor macrocells. As a result of the complexity of the indoor radio environment, frequent ping-pong handovers between the femtocell and macrocell layers can occur. A novel approach requiring a small amount of additional processing using neural networks is presented. A modified self-organizing map (SOM) is used to allow a femtocell to learn the locations of the indoor environment from where handover requests have occurred and, based on previous experience, decide whether to permit or prohibit these handovers. Once the regions that coincide with unnecessary handovers have been detected, the algorithm can reduce the total number of handovers that occur by up to 70% while still permitting any necessary handover requests to proceed. By reducing the number of handovers, the overall efficiency of the system will improve as the consequence of a reduction in associated but unnecessary signaling. Using machine learning for this task complies with the plug-and-play functionality required from SONs in LTE systems. Index Terms-Handover, long-term evolution (LTE), neural networks, self-organizing feature maps, self-organizing networks (SON). I. INTRODUCTION T HE increasing demand by mobile subscribers for high data rates is a driver for two key aspects of the Long-Term Evolution (LTE) of cellular systems. First, the highly flexible air interface based on orthogonal frequency-division multiple access, which is combined with multiple-input-multiple-output (MIMO) antenna technology, increases bandwidth efficiency compared with that of existing third-generation (3G) systems. Second, LTE-based systems are expected to utilize additional femtocell base stations to meet the demand for high-data-rate

Lifelong learning is fundamental in autonomous robotics for the acquisition and fine-tuning of knowledge through experience. However, conventional deep neural models for action recognition from videos do not account for lifelong learning but rather learn a batch of training data with a predefined number of action classes and samples. Thus, there is the need to develop learning systems with the ability to incrementally process available perceptual cues and to adapt their responses over time. We propose a self-organizing neural architecture for incrementally learning to classify human actions from video sequences. The architecture comprises growing self-organizing networks equipped with recurrent neurons for processing time-varying patterns. We use a set of hierarchically arranged recurrent networks for the unsupervised learning of action representations with increasingly large spatiotemporal receptive fields. Lifelong learning is achieved in terms of prediction-driven neural dynamics in which the growth and the adaptation of the recurrent networks are driven by their capability to reconstruct temporally ordered input sequences. Experimental results on a classification task using two action benchmark datasets show that our model is competitive with state-of-the-art methods for batch learning also when a significant number of sample labels are missing or corrupted during training sessions. Additional experiments show the ability of our model to adapt to non-stationary input avoiding catastrophic interference.

This paper presents the findings of a study aimed at decomposing a flow hydrograph into different segments based on physical concepts in a catchment, and modelling different segments using different technique viz. conceptual and artificial neural networks (ANNs). An integrated modelling framework is proposed capable of modelling infiltration, base flow, evapotranspiration, soil moisture accounting, and certain segments of the decomposed flow hydrograph using conceptual techniques and the complex, non-linear, and dynamic rainfall-runoff process using ANN technique. Specifically, five different multi-layer perceptron (MLP) and two self-organizing map (SOM) models have been developed. The rainfall and streamflow data derived from the Kentucky River catchment were employed to test the proposed methodology and develop all the models. The performance of all the models was evaluated using seven different standard statistical measures. The results obtained in this study indicate that (a) the rainfall-runoff relationship in a large catchment consists of at least three or four different mappings corresponding to different dynamics of the underlying physical processes, (b) an integrated approach that models the different segments of the decomposed flow hydrograph using different techniques is better than a single ANN in modelling the complex, dynamic, non-linear, and fragmented rainfall runoff process, (c) a simple model based on the concept of flow recession is better than an ANN to model the falling limb of a flow hydrograph, and (d) decomposing a flow hydrograph into the different segments corresponding to the different dynamics based on the physical concepts is better than using the soft decomposition employed using SOM. q

The typical load profiles are used in formulating the electricity distribution tariffs. Usually, clustering
algorithms are used in identification of typical load profiles from a sample with load curves
measurements. These methods group together load curves having similar shapes, from the which are
extracted the typical profiles. The various clustering algorithms can be applied to identify load profiles
but only the Self-Organising Map also known as Kohonen Map, a kind of neural network, can
organize a map where the clusters with similar load diagrams appears together in the map. The map
shows simultaneously all clusters and allows the visual identification of the load profiles. This paper
shows some results of a case study performed with Kohonen map applied on a sample with 125 load
curves.

The electric power industry is in transition as it moves towards a competitive and deregulated environment. In this emerging market, traditional electric utilities as well as energy traders, power pools and independent system operators (ISOs) need the capability to predict as precisely as possible how much energy their customers will use in the near future. This paper presents a medium-term energy demand forecasting system that helps utilities identify and forecast energy demand for each of the end-use consumption sector of the energy system, representing residential, industrial, commercial, non-industrial, entertainment and public lighting load. The demand forecasting system is organized and implemented in a modular fashion using high accuracy forecast models. These models are developed for each sector to account for the growth trends and seasonal effects. A comparative evaluation of various traditional and neural network-based methods for obtaining the forecast of monthly energy demand was carried out. Among the models tested, artificial neural network (ANN)-based models were determined to produce better results. In particular, group method of data handling (GMDH) neural network, composed of self-organizing active neurons, was proven very effective in producing forecasts that were significantly more accurate and less labor-intensive than traditional time-series and regression-based models.

We propose a new principle that adds temporal plasticity to self-organizing networks. The algorithm uses activity diffusion to couple space and time into a single set of dynamics that can help disambiguate the static spatial information with temporal information. The approach has been successfully applied to the Neural Gas algorithm. We present a simple temporal example which illustrates the fundamentals of the network as well as comparing the results of our approach vs. the Neural Gas algorithm as applied to time-series prediction of a chaotic signal. Table 1-Average prediction length for the Mackey Glass time series Model Ave. Prediction Length Neural Gas 33 SOTPAR2 β=0.05 40 SOTPAR2 β=0.10 43 SOTPAR2 β=0.15 39 SOTPAR2 β=0.20 33

Signal processing of present era is becoming more and more complex day by day. To meet the demand of modern signal processing, emphasis has been given to develop systems with minimum hardware. As a matter of fact, reduction in hardware complexity of digital filter has emerged as one of the upcoming research areas in present time. This paper highlights the design of multiplier-less finite impulse response (FIR) filter with the aid of an evolutionary optimization technique, namely Self-organizing Random Immigrants Genetic Algorithm (SORIGA). For this purpose, the coefficients of the filter have been encoded by binary and Canonic Signed Digit (CSD) number systems and subsequently optimized by means of SORIGA. Performance of the proposed filter has been analyzed in terms of its frequency and impulse response. Subsequently, hardware cost of the designed filter has been measured by means of a number of performance parameters and compared with few such existing filters from the literature in order to substantiate the efficiency of the proposed design.

This paper presents a simulation-based framework to model the potential benefits from dynamic vehicle on-line routing in a self-organizing, distributed traffic information system based upon a vehicle-to-vehicle information-sharing architecture. Within this framework, certain vehicles with specific inter-vehicle communication equipment in the traffic network capable of autonomous traffic surveillance and peer-to-peer information sharing, independently optimize their routes based on real-time and historical traffic information, forming a self-organizing traffic information overlay to the existing vehicular roadway network. In-trip re-routing decisions arising from drivers' accessing the self-organizing information system are modeled according both to a rational-boundary model and to a binary-logit model under the assumption that each driver is a rational entity. A path-based microscopic traffic simulation model is developed to study on-line vehicle navigation in the proposed distributed traffic information system, testing non-recurrent congestion cases on two different large-scale networks representing typical roadway scenarios in daily commuting. Based on simulation study results, potential benefits both for travelers with access to the proposed traffic information system as well as for the traffic system as a whole are demonstrated.

Wireless mesh networks are emerging as a promising solution for ubiquitous Internet access with mobility support. In such networks, user mobility may lead to Internet gateway changes and consequently, impact the performance of continuous media applications. In this article, we investigate the impact of user mobility on the performance of peer-to-peer video applications over wireless mesh networks. Peer-to-peer video streaming applications rely on the collaborative behavior of peers to assist the source in delivering multimedia content, reduce costs, and increase the scalability of video distribution. We identify practical issues related to mobility for P2P video streaming implementation in WMNs, such as addressing and forwarding strategies. Furthermore, we evaluate the performance of different P2P streaming applications as the user walks in our WMN testbed. Results indicate mobile users benefit from the frequent shortlived connections established in modern P2P video sessions.

This paper presents an enhanced approach to predictive modeling for determining tool-wear in endmilling operations based on enhanced-group method of data handling (e-GMDH). Using milling input parameters (speed, feed, and depth-of-cut) and response (tool wear), the data for the model is partitioned into training and testing datasets, and the training dataset is used to realize a predictive model that is a function of the input parameters and the coefficients determined. In our approach, we first present a methodology for modeling, and then develop predictive model(s) of the problem being solved in the form of second-order equations based on the input data and coefficients realized. This approach leads to some generalization because it becomes possible to predict not only the test data obtained during experimentation, but other test data outside the experimental results can also be used. Moreover, this approach makes it easy to present the realized solution in a form that can be further optimized for the input parameters using some optimization techniques. The results realized using our e-GMDH method are promising, and the comparative study presented shows that the e-GMDH outperforms polynomial neural network (PNN); moreover, it is more flexible than the conventional GMDH, which tends to produce nonlinear solutions even for simple problems. In the investigation, the extended particle swarm optimization (PSO) technique was applied to obtain the optimal parameters. Consequently, the modeling approach is extremely useful in realizing a computer-aided process-planning system in an advanced manufacturing environment.

Wireless mesh networks (WMNs) are emerging as a promising solution for ubiquitous Internet access with mobility support. In such networks, user mobility may lead to Internet gateway changes and, consequently, impact the performance of continuous media applications. In this article, we investigate the impact of user mobility on the performance of peer-to-peer (P2P) video applications over wireless mesh networks. Peer-to-peer video streaming applications rely on the collaborative behavior of peers to assist the source in delivering multimedia content, reducing costs and increasing the scalability of video distribution. We identify practical issues related to mobility for P2P video streaming implementation in wireless mesh networks, such as addressing and forwarding strategies. Furthermore, we evaluate the performance of different P2P streaming applications as the user walks in our WMN testbed. Results indicate mobile users benefit from the frequent short-lived connections established in modern P2P video sessions.

The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. Changes to the configuration management (CM) parameters for network elements could be a cause for degraded network performance and stability; hence, the verification of their effects becomes crucial. In this paper, we present SONVer, a tool that performs SON verification, using anomaly detection and diagnosis techniques that operate within a specified spatial scope larger than an individual cell. SONVer automatically classifies the state of the network in the presence of CM changes, indicating the root cause for anomalous conditions. SONVer uses Key Performance Indicators (KPIs) and CM history from real cellular networks to determine the state of the network; visualize anomalies at a large scale; and identify the causes of anomalies and the group of cells that were affected.

Current P2P systems do not fit well into mobile environments where each node have to follow multi-hop path routing and have to face unpredictable mobility. If current P2P systems are deployed on the MANET, application layer and network layer both perform different routing mechanism independent of each other, resulting in multiple layer routing redundancy. Current adhoc network protocols do not fulfill the P2P requirements as basically they all are flooding based protocols. Here, we propose a new controlled-flooding based approach which works at network layer and helps P2P systems to work in mobile environment efficiently.

This paper initiates the study of self-adjusting networks (or distributed data structures) whose topologies dynamically adapt to a communication pattern σ. We present a fully decentralized self-adjusting solution called SplayNet. A SplayNet is a distributed generalization of the classic splay tree concept. It ensures short paths (which can be found using local-greedy routing) between communication partners while minimizing topological rearrangements. We derive an upper bound for the amortized communication cost of a SplayNet based on empirical entropies of σ, and show that SplayNets have several interesting convergence properties. For instance, SplayNets features a provable online optimality under special requests scenarios. We also investigate the optimal static network and prove different lower bounds for the average communication cost based on graph cuts and on the empirical entropy of the communication pattern σ. From these lower bounds it follows, e.g., that SplayNets are optimal in scenarios where the requests follow a product distribution as well. Finally, this paper shows that in contrast to the Minimum Linear Arrangement problem which is generally NP-hard, the optimal static tree network can be computed in polynomial time for any guest graph, despite the exponentially large graph family. We complement our formal analysis with a small simulation study on a Facebook graph.

The unpredictable variations of the characteristics of wireless and ad hoc networks make Quality of Service (QoS) support difficult. Wireless Mesh Networks constitute an alternative to provide QoS in an ad hoc network. This paper proposes a wireless mesh solution that reduces co-channel interference and simplifies both services and resources management. The architecture is based on a topology control or clustering scheme and a channel assignment mechanism. The network is organized hierarchically in order to obtain a seamless stable backbone. The clustering scheme aims to achieve a maximum number of covered nodes and a minimum hop count, while providing a topology minimizing co-channel interference by means of a channel assignment algorithm. Performance evaluation in NS-2 shows that resource management in a cluster can be uncoupled from that of neighboring clusters and that the network achieves improved performance compared to a classical ad hoc network.

The growing self-organizing map (GSOM) possesses effective capability to generate feature maps and visualizing high-dimensional data without predetermining their size. Most of the proposed growing SOM algorithms use an incremental learning strategy. The conventional growing approach of GSOM is based on filling all available position around the candidate neuron which can decrease the topology preservation quality of the map due to the misconfiguration and twisting of the map which could be a consequence of unexpected network growth and improper neuron addition and weight initialization. To overcome this problem, in this paper we introduce a batch learning strategy for growing self-organizing maps called DBGSOM which direct the growing process based on the accumulative error around the candidate boundary neuron. In the proposed growing approach, just one new neuron is added around each candidate boundary neuron. The DBGSOM offers suitable mechanisms to find a proper growing positions and allocating initial weight vectors for the new neurons. The potential of the DBGSOM was investigated with one synthetic dataset and six real-world benchmark datasets in terms of topology preservation and mapping quality. Experimental results showed that the proposed growing strategy provides an enhanced topology preserved map and reduces the susceptibility of twisting compared to GSOM. Furthermore, the proposed method has a better clustering ability than GSOM and SOM. According to the lower number of neurons generated by DBGSOM, it needs less time to learn the manifold of the data points compared to GSOM.

Dynamic Time-division duplex (TDD) can provide efficient and flexible splitting of the common wireless cellular resources between uplink (UL) and downlink (DL) users. In this paper, the UL/DL optimization problem is formulated as a noncooperative game among the small cell base stations (SCBSs) in which each base station aims at minimizing its total UL and DL flow delays. To solve this game, a self-organizing UL/DL resource configuration scheme for TDD-based small cell networks is proposed. Using the proposed scheme, an SCBS is able to estimate and learn the UL and DL loads autonomously while optimizing its UL/DL configuration accordingly. Simulations results show that the proposed algorithm achieves significant gains in terms of packet throughput in case of asymmetric UL and DL traffic loads. This gain increases as the traffic asymmetry increases, reaching up to 97% and 200% gains relative to random and fixed duplexing schemes respectively. Our results also show that the proposed algorithm is well-adapted to dynamic traffic conditions and different network sizes, and operates efficiently in case of severe cross-link interference in which neighboring cells transmit in opposite directions.

This paper presents i-AA1* a constructive, incremental learning algorithm for a special class of weightless, self-organizing networks. In i-AA1*, learning consists of adapting the nodes' functions and the network's overall topology as each new training pattern is presented. Provided the training data is consistent, computational complexity is low and prior factual knowledge may be used to" prime" the network and improve its predictive accuracy. Empirical generalization results on both toy problems and more realistic tasks ...

Despite the many research efforts addressing the integration of mobile nodes into grids, only a few of them have considered the establishment of mobile grids over wireless ad hoc networks (hereafter, mobile ad hoc grids). Clearly, such grids need specialized resource discovery and scheduling mechanisms. To the best of our knowledge, though, the research on these mechanisms for mobile ad hoc grids is still preliminary. Besides, and more importantly, it has approached discovery and scheduling as separate mechanisms, which, we argue, is not suitable for mobile ad hoc grids. In this paper, we propose the integration of resource discovery and scheduling for mobile ad hoc grids into a single protocol called DICHOTOMY (DIscovery and sCHeduling prOTOcol for MobilitY). This protocol allows computational tasks to be distributed appropriately in a mobile ad hoc grid, while mitigating the overhead of discovery messages exchanged among the nodes. Our experiments show that the protocol: (i) does proper scheduling, allowing an efficient load balancing among the nodes and helping with lowering the average completion time of tasks; (ii) keeps the discovery efficiency at acceptable levels in mobility scenarios; and (iii) scales very well with respect to an increasing number of nodes, both in the total amount of energy savings due to packet transmissions and the distribution of such savings among the nodes.

This paper presents a self-optimizing solution for Mobility Load Balancing (MLB). The MLB-SON is performed in two phases. In the first, a MLB controller is designed using Multi-Objective Particle Swarm Optimization (MO-PSO) which incorporates a priori expert knowledge to considerably reduce the search space and optimization time. The dynamicity of the optimization phase is addressed. In the second phase, the controller is pushed into the base stations to implement the MLB SON. The method is applied to dynamically adapt Handover Margin parameters of a large scale LTE network in order to balance traffic of the network eNodeBs. Numerical results illustrate the benefits of the proposed solution.

Network elements and their parameters in mobile wireless networks, are largely manually configured. This has been somewhat sufficient; but with the growing data traffic compensated by new and emerging technologies with corresponding larger networks, there is an obvious need to redefine the network operations to achieve optimum performance. A manual configuration approach requires specialized expertise for device deployments, configurations, re-setting network parameters and general management of the network. This process is cost-intensive, time-consuming and prone to errors. Adoption of this approach in the evolved wireless technologies results in poor network performance. Therefore, the introduction of advanced mobile wireless networks has highlighted the need and essence for automation within the network. Self Organizing Networks (SON) developed by 3GPP, using automation, ensures operational efficiency and next generation simplified network management for a mobile wireless network. The introduction of SON in LTE therefore brings about optimum network performance and higher end user Quality of Experience. This paper highlights the SON techniques relevant within an LTE network, a brief description of SON architecture alternatives and then some information on the evolution of SON activities as LTE evolves towards LTE-A.

Conventional Traffic and Travel Information (TTI) systems are based on a centralized structure: Sensors deployed at the roadside monitor the traffic and transmit the results to a central unit, which performs a situation analysis. TTI is made available to the drivers via broadcast service (e.g. TMC/RDS) or on demand via cellular phone. In contrast to this centralized approach, a decentralized, Self-Organizing Traffic Information System (SOTIS) based on Inter-Vehicle Communication (IVC) has many advantages: It requires no expensive infrastructure, can provide detailed information for the local area with low delay and is available everywhere. In such an IVC based decentralized system, TTI is sensed by each individual vehicle. The information is analyzed, combined with TTI received from other vehicles and the results are distributed using recurring broadcast packets.