Trajectory Privacy Preservation in Mobile Wireless Sensor Networks

In recent years, there has been an enormous growth of location-aware devices, such as GPS embedded cell phones, mobile sensors and radio-frequency identification tags. The age of combining sensing, processing and communication in one device, gives rise to a vast number of applications leading to endless possibilities and a realization of mobile Wireless Sensor Network (mWSN) applications. As computing, sensing and communication become more ubiquitous, trajectory privacy becomes a critical piece of information and an important factor for commercial success. While on the move, sensor nodes continuously transmit data streams of sensed values and spatiotemporal information, known as ``trajectory information". If adversaries can intercept this information, they can monitor the trajectory path and capture the location of the source node. This research stems from the recognition that the wide applicability of mWSNs will remain elusive unless a trajectory privacy preservation mechanism is developed. The outcome seeks to lay a firm foundation in the field of trajectory privacy preservation in mWSNs against external and internal trajectory privacy attacks. First, to prevent external attacks, we particularly investigated a context-based trajectory privacy-aware routing protocol to prevent the eavesdropping attack. Traditional shortest-path oriented routing algorithms give adversaries the possibility to locate the target node in a certain area. We designed the novel privacy-aware routing phase and utilized the trajectory dissimilarity between mobile nodes to mislead adversaries about the location where the message started its journey. Second, to detect internal attacks, we developed a software-based attestation solution to detect compromised nodes. We created the dynamic attestation node chain among neighboring nodes to examine the memory checksum of suspicious nodes. The computation time for memory traversal had been improved compared to the previous work. Finally, we revisited the trust issue in trajectory privacy preservation mechanism designs. We used Bayesian game theory to model and analyze cooperative, selfish and malicious nodes' behaviors in trajectory privacy preservation activities.

Cities and emerging networks of learning communities

In the 21st century the majority of people live in urban settings and studies show a trend to the increase of this phenomenon. Globalisation and the concentration of multinational and clusters of firms in certain places are attracting people who seek employment and a better living. Many of those agglomerations are situated in developing countries, representing serious challenges both for public and private sectors. Programmes and initiatives in different countries are taking place and best practices are being exchanged globally. The objective is to transform these urban places into sustainable learning cities/regions where citizens can live with quality. The complexity of urban places, sometimes megacities, opened a new field of research. This paper argues that in order to understand the dynamics of such a complex phenomenon, a multidisciplinary, systemic approach is needed and the creation of learning cities and regions calls for the contribution of a multitude of fields of knowledge, ranging from economy to urbanism, educational science, sociology, environmental psychology and others.

Hierarchical Reinforcement Learning in Behavior and the Brain

Dissertation presented to obtain the Ph.D degree in Biology, Neuroscience

A piezoelectric based energy harvester interface for a CMOS wireless sensor IC

In this thesis a piezoelectric energy harvesting system, responsible for regulating the power output of a piezoelectric transducer subjected to ambient vibration, is designed to power an RF receiver with a 6 mW power consump-tion. The electrical characterisation of the chosen piezoelectric transducer is the starting point of the design, which subsequently presents a full-bridge cross-coupled rectifier that rectifies the AC output of the transducer and a low-dropout regulator responsible for delivering a constant voltage system output of 0.6 V, with low voltage ripple, which represents the receiver’s required sup-ply voltage. The circuit is designed using CMOS 130 nm UMC technology, and the system presents an inductorless architecture, with reduced area and cost. The electrical simulations run for the complete circuit lead to the conclusion that the proposed piezoelectric energy harvesting system is a plausible solution to power the RF receiver, provided that the chosen transducer is subjected to moderate levels of vibration.

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Reinforcement learning utilizes proxemics: an avatar learns to manipulate the position of people in immersive virtual reality

A reinforcement learning (RL) method was used to train a virtual character to move participants to a specified location. The virtual environment depicted an alleyway displayed through a wide field-of-view head-tracked stereo head-mounted display. Based on proxemics theory, we predicted that when the character approached within a personal or intimate distance to the participants, they would be inclined to move backwards out of the way. We carried out a between-groups experiment with 30 female participants, with 10 assigned arbitrarily to each of the following three groups: In the Intimate condition the character could approach within 0.38m and in the Social condition no nearer than 1.2m. In the Random condition the actions of the virtual character were chosen randomly from among the same set as in the RL method, and the virtual character could approach within 0.38m. The experiment continued in each case until the participant either reached the target or 7 minutes had elapsed. The distributions of the times taken to reach the target showed significant differences between the three groups, with 9 out of 10 in the Intimate condition reaching the target significantly faster than the 6 out of 10 who reached the target in the Social condition. Only 1 out of 10 in the Random condition reached the target. The experiment is an example of applied presence theory: we rely on the many findings that people tend to respond realistically in immersive virtual environments, and use this to get people to achieve a task of which they had been unaware. This method opens up the door for many such applications where the virtual environment adapts to the responses of the human participants with the aim of achieving particular goals.

Reinforcement learning utilizes proxemics: an avatar learns to manipulate the position of people in immersive virtual reality

A reinforcement learning (RL) method was used to train a virtual character to move participants to a specified location. The virtual environment depicted an alleyway displayed through a wide field-of-view head-tracked stereo head-mounted display. Based on proxemics theory, we predicted that when the character approached within a personal or intimate distance to the participants, they would be inclined to move backwards out of the way. We carried out a between-groups experiment with 30 female participants, with 10 assigned arbitrarily to each of the following three groups: In the Intimate condition the character could approach within 0.38m and in the Social condition no nearer than 1.2m. In the Random condition the actions of the virtual character were chosen randomly from among the same set as in the RL method, and the virtual character could approach within 0.38m. The experiment continued in each case until the participant either reached the target or 7 minutes had elapsed. The distributions of the times taken to reach the target showed significant differences between the three groups, with 9 out of 10 in the Intimate condition reaching the target significantly faster than the 6 out of 10 who reached the target in the Social condition. Only 1 out of 10 in the Random condition reached the target. The experiment is an example of applied presence theory: we rely on the many findings that people tend to respond realistically in immersive virtual environments, and use this to get people to achieve a task of which they had been unaware. This method opens up the door for many such applications where the virtual environment adapts to the responses of the human participants with the aim of achieving particular goals.

Experimental evaluation of the performance of a wireless sensor network in agricultural environments

The aim of this study was to perform an experimental study to evaluate the proper operation distance between the nodes of a wireless sensor network available on the market for different agricultural crops (maize, physic nut, eucalyptus). The experimental data of the network performance offers to farmers and researchers information that might be useful to the sizing and project of the wireless sensor networks in similar situations to those studied. The evaluation showed that the separation of the nodes depends on the type of culture and it is a critical factor to ensure the feasibility of using WSN. In the configuration used, sending packets every 2 seconds, the battery life was about four days. Therefore, the autonomy may be increased with a longer interval of time between sending packets.

Energy Aware Cluster-based Multihop Routing Protocol for Sensor Networks

Clustering combined with multihop communication is a promising solution to cope with the energy requirements of large scale Wireless Sensor Networks. In this work, a new cluster based routing protocol referred to as Energy Aware Cluster-based Multihop (EACM) Routing Protocol is introduced, with multihop communication between cluster heads for transmitting messages to the base station and direct communication within clusters. We propose EACM with both static and dynamic clustering. The network is partitioned into near optimal load balanced clusters by using a voting technique, which ensures that the suitability of a node to become a cluster head is determined by all its neighbors. Results show that the new protocol performs better than LEACH on network lifetime and energy dissipation

Reinforcement Learning Solution for Unit Commitment Problem Considering Minimum Start Up and Shut Down Times

Unit Commitment Problem (UCP) in power system refers to the problem of determining the on/ off status of generating units that minimize the operating cost during a given time horizon. Since various system and generation constraints are to be satisfied while finding the optimum schedule, UCP turns to be a constrained optimization problem in power system scheduling. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision making task and an efficient Reinforcement Learning solution is formulated considering minimum up time /down time constraints. The correctness and efficiency of the developed solutions are verified for standard test systems

A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks

We propose a nonparametric method for estimating derivative financial asset pricing formulae using learning networks. To demonstrate feasibility, we first simulate Black-Scholes option prices and show that learning networks can recover the Black-Scholes formula from a two-year training set of daily options prices, and that the resulting network formula can be used successfully to both price and delta-hedge options out-of-sample. For comparison, we estimate models using four popular methods: ordinary least squares, radial basis functions, multilayer perceptrons, and projection pursuit. To illustrate practical relevance, we also apply our approach to S&P 500 futures options data from 1987 to 1991.

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

An artificial economy based on reinforcement learning and agent based modeling

In this paper, we employ techniques from artificial intelligence such as reinforcement learning and agent based modeling as building blocks of a computational model for an economy based on conventions. First we model the interaction among firms in the private sector. These firms behave in an information environment based on conventions, meaning that a firm is likely to behave as its neighbors if it observes that their actions lead to a good pay off. On the other hand, we propose the use of reinforcement learning as a computational model for the role of the government in the economy, as the agent that determines the fiscal policy, and whose objective is to maximize the growth of the economy. We present the implementation of a simulator of the proposed model based on SWARM, that employs the SARSA(λ) algorithm combined with a multilayer perceptron as the function approximation for the action value function.

Estimation of electrical machine speed using sensorless technology and neural networks

The use of sensorless technologies is an increasing tendency on industrial drivers for electrical machines. The estimation of electrical and mechanical parameters involved with the electrical machine control is used very frequently in order to avoid measurement of all variables related to this process. The cost reduction may also be considered in industrial drivers, besides the increasing robustness of the system, as an advantage of the use of sensorless technologies. This work proposes the use of a recurrent artificial neural network to estimate the speed of induction motor for sensorless control schemes using one single current sensor. Simulation and experimental results are presented to validate the proposed approach. ©2008 IEEE.