Hedonic Coalition Formation for Distributed Task Allocation among Wireless Agents

Autonomous wireless agents such as unmanned aerial vehicles or mobile base stations present a great potential for deployment in next-generation wireless networks. While current literature has been mainly focused on the use of agents within robotics or software applications, we propose a novel usage model for self-organizing agents suited to wireless networks. In the proposed model, a number of agents are required to collect data from several arbitrarily located tasks. Each task represents a queue of packets that require collection and subsequent wireless transmission by the agents to a central receiver. The problem is modeled as a hedonic coalition formation game between the agents and the tasks that interact in order to form disjoint coalitions. Each formed coalition is modeled as a polling system consisting of a number of agents which move between the different tasks present in the coalition, collect and transmit the packets. Within each coalition, some agents can also take the role of a relay for improving the packet success rate of the transmission. The proposed algorithm allows the tasks and the agents to take distributed decisions to join or leave a coalition, based on the achieved benefit in terms of effective throughput, and the cost in terms of delay. As a result of these decisions, the agents and tasks structure themselves into independent disjoint coalitions which constitute a Nash-stable network partition. Moreover, the proposed algorithm allows the agents and tasks to adapt the topology to environmental changes such as the arrival/removal of tasks or the mobility of the tasks. Simulation results show how the proposed algorithm improves the performance, in terms of average player (agent or task) payoff, of at least 30.26% (for a network of 5 agents with up to 25 tasks) relatively to a scheme that allocates nearby tasks equally among agents.Comment: to appear, IEEE Transactions on Mobile Computin

EN-BIRTH Data Collector Training - Handbook and Manual

The EN-BIRTH study aims to validate selected newborn and maternal indicators for routine facility-based tracking of coverage and quality of care for use at district, national and global levels. The item contains the EN-BIRTH_Trainer's Manual (14 June 2017) and EN-BIRTH_Training Handbook (23 May 2017)

Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence

Collective privacy loss becomes a colossal problem, an emergency for personal freedoms and democracy. But, are we prepared to handle personal data as scarce resource and collectively share data under the doctrine: as little as possible, as much as necessary? We hypothesize a significant privacy recovery if a population of individuals, the data collective, coordinates to share minimum data for running online services with the required quality. Here we show how to automate and scale-up complex collective arrangements for privacy recovery using decentralized artificial intelligence. For this, we compare for first time attitudinal, intrinsic, rewarded and coordinated data sharing in a rigorous living-lab experiment of high realism involving >27,000 real data disclosures. Using causal inference and cluster analysis, we differentiate criteria predicting privacy and five key data-sharing behaviors. Strikingly, data-sharing coordination proves to be a win-win for all: remarkable privacy recovery for people with evident costs reduction for service providers.Comment: Contains Supplementary Informatio

LOWERING THE BARRIER TO DEVELOPMENT AND ADOPTION OF PARTICIPATORY SENSING APPLICATIONS

Participatory sensing has the potential to support human-driven sensing and data collection at an unprecedented scale. In this emerging class of software systems, participants use an application on their mobile phone to collect digital samples of the surrounding world using on-board sensors (e.g., camera, GPS, accelerometer). Such an approach can supplement data from special-purpose sensors, or even replace their use, providing data from a fine-grained, human perspective and potentially reducing the costs of large-scale data collection efforts. While many potential participatory sensing campaign organizers have extensive domain knowledge that drives the need for large-scale data collection and analysis, they do not necessarily have the skills required to develop robust software for partic- ipatory sensing. To address this challenge, I present Mobile Campaign Designer, a toolkit which lowers the barrier for the development of participatory sensing applica- tions. Using Mobile Campaign Designer, a campaign organizer can provide a simple, descriptive specification of the requirements of their participatory sensing campaign, and the toolkit generates the source code and an executable for a tailored mobile application that embodies the current best practices in participatory sensing. Since participatory sensing applications typically are used to study physical phenomena, the toolkit includes an algorithm that considers spatiotemporal requirements for the crowdsourced data set and recruits volunteers that can help to satisfy those requirements. Furthermore, this work lowers the barrier for the creation of participatory sens- ing applications for a diverse group through the Mobile Application Development for Science program, an outreach and educational initiative aimed at engaging middle school students with science and technology and increasing their interest in careers in science and technology. Using the Mobile Campaign Designer toolkit, along with other mobile application development tools, students will design and conduct a participatory sensing data collection campaign. The students define their campaign, create their mobile application, collect samples, and analyze the results of their data. In addition to lowering the barrier for participatory sensing application development, the program is intended to serve as an intervention that will impact attitudes and perceptions towards science and computing, thus broadening participation of under- represented groups in science and technology

Distributed Caching in Small Cell Networks

The dense deployment of small cells in indoor and outdoor areas contributes mainly in increasing the capacity of cellular networks. On the other hand, the high number of deployed base stations coupled with the increasing growth of data traffic have prompted the apparition of base stations fi tted with storage capacity to avoid network saturation. The storage devices are used as caching units to overcome the limited backhaul capacity in small cells networks (SCNs). Extending the concept of storage to SCNs, gives rise to many new challenges related to the specific characteristics of these networks such as the heterogeneity of the base stations. Formulating the caching problem while taking into account all these specific characteristics with the aim to satisfy the users expectations result in combinatorial optimization problems. However, classical optimization tools do not ensure the optimality of the provided solutions or often the proposed algorithms have an exponential complexity. While most of the existing works are based on the classical optimization tools, in this thesis, we explore another approach to provide a practical solution for the caching problem. In particular, we focus on matching theory which is a game theoretic approach that provides mathematical tools to formulate, analyze and understand scenarios between sets of players. We model the caching problem as a one-to-one matching game between a set of files and a set of base stations and then, we propose an iterative extension of the deferred acceptance algorithm that needs a stable and optimal matching between the two sets. The experimental results show that the proposed algorithm reduces the backhaul load by 10-15 % compared to a random caching algorithm

Copycats vs. Original NFTs Detection: A Design Science Approach

The Non-Fungible Token (NFT) makes trading digitalized artworks online possible, which creates great opportunities in the artwork markets. Besides the extraordinary wealth it has created, the NFT trading market also brings many issues, such as intellectual property protection. Although there are a large number of transactions happening every day in the NFT market, there is no platform mechanism built to avoid copycat behaviors happening blatantly. In this paper, we propose a copycat detection and investigation framework. Besides, we propose to examine the effect of copycats on the price of original NFTs. The proposed project contributes to the literature on NFT management and NFT copyright, and also helps the NFT developers to protect their rights and benefits and helps NFT platforms to avoid potential legal issues

e-Reverse logistics for remanufacture-to-order : an online auction-based and multi- agent system supported solution

Due to the rapid obsolescent nature of consumer products, the remanufacture-to-stock strategy, in which remanufacturers tend to collect certain amount of end-of-life products, remanufacturing them as many as they can and keep these remanufactured products in stock waiting for customers come to buy, is not always an optimal solution. Under this circumstance, remanufacture-to-order policy, as an effective complement, provides a good trade-off for remanufacturers between meeting consumers’ demand and, in the meantime, keeping the inventory cost at a lower level. To remanufacture the used items, the manufacturer must retrieve them from the market where they are dispersed among consumers. This is accomplished by means of a reverse logistics chain that is comparable to the new product distribution system in reverse. However, the current reverse logistics do not respond to remanufacture-to-order at an efficient level. Therefore it is a necessity to develop a novel infrastructure, which can deal with these issues. This paper presents a framework called e-reverse logistics that aims at filling this gap. The major features and architecture of the proposed e-reverse logistics are detailed in this work

Machine learning applications in operations management and digital marketing

In this dissertation, I study how machine learning can be used to solve prominent problems in operations management and digital marketing. The primary motivation is to show that the application of machine learning can solve problems in ways that existing approaches cannot. In its entirety, this dissertation is a study of four problems—two in operations management and two in digital marketing—and develops solutions to these problems via data-driven approaches by leveraging machine learning. These four problems are distinct, and are presented in the form of individual self-containing essays. Each essay is the result of collaborations with industry partners and is of academic and practical importance. In some cases, the solutions presented in this dissertation outperform existing state-of-the-art methods, and in other cases, it presents a solution when no reasonable alternatives are available. The problems are: consumer debt collection (Chapter 3), contact center staffing and scheduling (Chapter 4), digital marketing attribution (Chapter 5), and probabilistic device matching (Chapters 6 and 7). An introduction of the thesis is presented in Chapter 1 and some basic machine learning concepts are described in Chapter 2