An optimal feedback model to prevent manipulation behaviours in consensus under social network group decision making

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.A novel framework to prevent manipulation behaviour in consensus reaching process under social network group decision making is proposed, which is based on a theoretically sound optimal feedback model. The manipulation behaviour classification is twofold: (1) ‘individual manipulation’ where each expert manipulates his/her own behaviour to achieve higher importance degree (weight); and (2) ‘group manipulation’ where a group of experts force inconsistent experts to adopt specific recommendation advices obtained via the use of fixed feedback parameter. To counteract ‘individual manipulation’, a behavioural weights assignment method modelling sequential attitude ranging from ‘dictatorship’ to ‘democracy’ is developed, and then a reasonable policy for group minimum adjustment cost is established to assign appropriate weights to experts. To prevent ‘group manipulation’, an optimal feedback model with objective function the individual adjustments cost and constraints related to the threshold of group consensus is investigated. This approach allows the inconsistent experts to balance group consensus and adjustment cost, which enhances their willingness to adopt the recommendation advices and consequently the group reaching consensus on the decision making problem at hand. A numerical example is presented to illustrate and verify the proposed optimal feedback model

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

Modelado complejo de información lingüística en problemas de toma de decisión en grupo bajo incertidumbre

El modelado de información lingüística en problemas de Toma de Decisión en Grupo (TDG) con incertidumbre y sus Procesos de Alcance de Consenso (PAC), se ha convertido en una línea de investigación de gran importancia dentro del ámbito de la toma de decisión. La mayoría de propuestas enfocadas al modelado lingüístico, se basan en el enfoque lingüístico difuso y emplean expresiones lingüísticas cercanas a la forma de pensar de los seres humanos para modelar la incertidumbre inherente en los problemas de decisión. Sin embargo, muchas de estas propuestas presentan limitaciones en términos de interpretación y/o precisión. En esta tesis doctoral, se ha propuesto un nuevo marco metodológico para el modelado y tratamiento de incertidumbre para problemas de TDG y PAC mediante expresiones lingüísticas complejas que permite modelar las opiniones de los expertos en este tipo de problemas.The modelling of linguistic information in Group Decision Making (GDM) problems with uncertainty and its Consensus Reaching Processes (CRPs) has become a very important research line in the field of decision making. Most of the proposals focused on linguistic modelling are based on the fuzzy linguistic approach and use linguistic expressions close to the way human beings’ thinking to model the uncertainty inherent in decision problems. However, many of these proposals have limitations in terms of interpretation and/or accuracy. In this doctoral thesis, a new methodological framework has been proposed for the modelling and treatment of uncertainty for GDM and CRPs problems by means of complex linguistic expressions that allow modelling the experts’ opinions in this type of problems.Tesis Univ. Jaén. Departamento de Informática. Leída el 30 de abril de 2021

Multi-UAV network control through dynamic task allocation: Ensuring data-rate and bit-error-rate support

A multi-UAV system relies on communications to operate. Failure to communicate remotely sensed mission data to the base may render the system ineffective, and the inability to exchange command and control messages can lead to system failures. This paper describes a unique method to control communications through distributed task allocation to engage under-utilized UAVs to serve as communication relays and to ensure that the network supports mission tasks. The distributed algorithm uses task assignment information, including task location and proposed execution time, to predict the network topology and plan support using relays. By explicitly coupling task assignment and relay creation processes the team is able to optimize the use of agents to address the needs of dynamic complex missions. The framework is designed to consider realistic network communication dynamics including path loss, stochastic fading, and information routing. The planning strategy is shown to ensure that agents support both datarate and interconnectivity bit-error-rate requirements during task execution. System performance is characterized through experiments both in simulation and in outdoor flight testing with a team of three UAVs.Aurora Flight Sciences Corp. (Fellowship Program

Consensus and Products of Random Stochastic Matrices: Exact Rate for Convergence in Probability

Distributed consensus and other linear systems with system stochastic matrices $W_k$ emerge in various settings, like opinion formation in social networks, rendezvous of robots, and distributed inference in sensor networks. The matrices $W_k$ are often random, due to, e.g., random packet dropouts in wireless sensor networks. Key in analyzing the performance of such systems is studying convergence of matrix products $W_kW_{k-1}... W_1$. In this paper, we find the exact exponential rate $I$ for the convergence in probability of the product of such matrices when time $k$ grows large, under the assumption that the $W_k$'s are symmetric and independent identically distributed in time. Further, for commonly used random models like with gossip and link failure, we show that the rate $I$ is found by solving a min-cut problem and, hence, easily computable. Finally, we apply our results to optimally allocate the sensors' transmission power in consensus+innovations distributed detection