An adaptive neuro-fuzzy propagation model for LoRaWAN

This article proposes an adaptive-network-based fuzzy inference system (ANFIS) model for accurate estimation of signal propagation using LoRaWAN. By using ANFIS, the basic knowledge of propagation is embedded into the proposed model. This reduces the training complexity of artificial neural network (ANN)-based models. Therefore, the size of the training dataset is reduced by 70% compared to an ANN model. The proposed model consists of an efficient clustering method to identify the optimum number of the fuzzy nodes to avoid overfitting, and a hybrid training algorithm to train and optimize the ANFIS parameters. Finally, the proposed model is benchmarked with extensive practical data, where superior accuracy is achieved compared to deterministic models, and better generalization is attained compared to ANN models. The proposed model outperforms the nondeterministic models in terms of accuracy, has the flexibility to account for new modeling parameters, is easier to use as it does not require a model for propagation environment, is resistant to data collection inaccuracies and uncertain environmental information, has excellent generalization capability, and features a knowledge-based implementation that alleviates the training process. This work will facilitate network planning and propagation prediction in complex scenarios

Genetic algorithms with elitism-based immigrants for dynamic load balanced clustering problem in mobile ad hoc networks

This article is posted here with permission of IEEE - Copyright @ 2011 IEEEIn recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile networks [mobile ad hoc networks (MANETs)], wireless sensor networks, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, i.e., the network topology changes over time due to energy conservation or node mobility. Therefore, the SP routing problem in MANETs turns out to be a dynamic optimization problem. In this paper, we propose to use GAs with immigrants and memory schemes to solve the dynamic SP routing problem in MANETs. We consider MANETs as target systems because they represent new-generation wireless networks. The experimental results show that these immigrants and memory-based GAs can quickly adapt to environmental changes (i.e., the network topology changes) and produce high-quality solutions after each change.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1 and Grant EP/E060722/2

Are cost models useful for telecoms regulators in developing countries?

Worldwide privatization of the telecommunications industry, and the introduction of competition in the sector, together with the ever-increasing rate of technological advance in telecommunications, raise new and critical challenges for regulation. Fo matters of pricing, universal service obligations, and the like, one question to be answered is this: What is the efficient cost of providing the service to a certain area or type of customer? As developing countries build up their capacity to regulate their privatized infrastructure monopolies, cost models are likely to prove increasingly important in answering this question. Cost models deliver a number of benefits to a regulator willing to apply them, but they also ask for something in advance: information. Without information, the question cannot be answered. The authors introduce cost models and establish their applicability when different degrees of information are available to the regulator. They do no by running a cost model with different sets of actual data form Argentina's second largest city, and comparing results. Reliable, detailed information is generally scarce in developing countries. The authors establish the minimum information requirements for a regulator implementing a cost proxy model approach, showing that this data constraint need not be that binding.ICT Policy and Strategies,Decentralization,Environmental Economics&Policies,Economic Theory&Research,Business Environment,ICT Policy and Strategies,Environmental Economics&Policies,Geographical Information Systems,Economic Theory&Research,Educational Technology and Distance Education

Routing efficiency in wireless sensor-actor networks considering semi-automated architecture

Wireless networks have become increasingly popular and advances in wireless communications and electronics have enabled the development of different kind of networks such as Mobile Ad-hoc Networks (MANETs), Wireless Sensor Networks (WSNs) and Wireless Sensor-Actor Networks (WSANs). These networks have different kind of characteristics, therefore new protocols that fit their features should be developed. We have developed a simulation system to test MANETs, WSNs and WSANs. In this paper, we consider the performance behavior of two protocols: AODV and DSR using TwoRayGround model and Shadowing model for lattice and random topologies. We study the routing efficiency and compare the performance of two protocols for different scenarios. By computer simulations, we found that for large number of nodes when we used TwoRayGround model and random topology, the DSR protocol has a better performance. However, when the transmission rate is higher, the routing efficiency parameter is unstable.Peer ReviewedPostprint (published version

Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning

With the advent of the Internet of Things (IoT), an increasing number of energy harvesting methods are being used to supplement or supplant battery based sensors. Energy harvesting sensors need to be configured according to the application, hardware, and environmental conditions to maximize their usefulness. As of today, the configuration of sensors is either manual or heuristics based, requiring valuable domain expertise. Reinforcement learning (RL) is a promising approach to automate configuration and efficiently scale IoT deployments, but it is not yet adopted in practice. We propose solutions to bridge this gap: reduce the training phase of RL so that nodes are operational within a short time after deployment and reduce the computational requirements to scale to large deployments. We focus on configuration of the sampling rate of indoor solar panel based energy harvesting sensors. We created a simulator based on 3 months of data collected from 5 sensor nodes subject to different lighting conditions. Our simulation results show that RL can effectively learn energy availability patterns and configure the sampling rate of the sensor nodes to maximize the sensing data while ensuring that energy storage is not depleted. The nodes can be operational within the first day by using our methods. We show that it is possible to reduce the number of RL policies by using a single policy for nodes that share similar lighting conditions.Comment: 7 pages, 5 figure

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

De-centralized Learning for Radio Network Key Performance Indicator Prediction

A recent machine learning technique called federated learning (Konecny, McMahan, et. al., 2016) offers a new paradigm for distributed learning. It consists of performing machine learning on multiple edge devices and simultaneously optimizing a global model for all of them, without transmitting user data. The goal for this thesis was to prove the benefits of applying federated learning to forecasting telecom key performance indicator (KPI) values from radio network cells. After performing experiments with different data sources' aggregations and comparing against a centralized learning model, the results revealed that a federated model can shorten the training time for modelling new radio cells. Moreover, the amount of transferred data to a central server is minimized drastically while keeping equivalent performance to a traditional centralized model. These experiments were performed with multi-layer perceptron as model architecture after comparing its performance against LSTM. Both, input and output data were sequences of KPI values

Bottom-up radio: creating a new media format using living lab research

This study resulted in the creation of a new media format for urban youth, adopting a living lab-approach, as current studies have shown that this group is currently not reached with the contemporary media offer. Living lab research is a state-of-the art methodology that aims at involving end-users in the innovation process over a longer time span, combining both quantitative and qualitative research techniques and tools. In a first phase, a panel of urban youngsters was created using an intake survey (N=290). These data were analyzed resulting in three distinct types of urban youngsters. In a second phase, a qualitative research trajectory was organized in order to refine the three profiles and get an insight in their media use, digital skills, media preferences and needs with regards to the current media offer. Research methods during this phase included diary studies, participatory observation during workshops and probe research. In a third phase, co-creation sessions were organized with youngsters from the urban panel in order to get feedback on a concept that was iteratively developed during the first two phases of the project. Results show that mobile devices and social media are important for these urban youngsters and that most of these youngsters have quite some creative skills. Radio seems to be a less popular medium, although they spend a significant amount of time listening to music. Further, results show that these youngsters are in need of a platform which stimulates community building and offers a space to express their creativity. A third requirement for the development of a new media format that would meet the needs of these youngers is a format that provides space for local elements and niche markets. This all resulted in the launch of Chase, an urban, crowdsourced radio station