Data Mining Practical Machine Learning Tools and Techniques

This Paper focuses on two distinct facets of globalization: the decrease in the trade costs of goods and the decline of communication costs between headquarters and production facilities within firms. When the unskilled have about the same wage in the two regions, the decrease of these costs fosters the gradual agglomeration of plants in the core region accommodating the headquarters. By contrast, when the wage gap is significant, the process of integration eventually triggers the relocation of plants into the periphery. In particular, when falling communication costs drives the process of relocation, the welfare of all workers living in the core goes down whereas the welfare of those who reside in the periphery rises.

Globalization and the evolution of the supply chain: who gains and who loses ?

Wireless sensor and actuator networks (WSANs) are heterogeneous networks composed of many different nodes that can cooperatively sense the environment, determine an appropriate action to take, then change the environment’s state after acting on it. As a natural extension of wireless sensor networks (WSNs), WSANs inherit from them a variety of research challenges and bring forth many new ones. These challenges are related to dealing with imprecise and vague information, solving complicated optimization problems or collecting and processing data from multiple sources. Computational intelligence (CI) is an overarching term denoting a conglomerate of biologically and linguistically inspired techniques that provide robust solutions to NP-hard problems, reason in imprecise terms and yield high-quality yet computationally tractable approximate solutions to real-world problems. Many researchers have consequently turned to CI in hope of finding answers to a plethora of WSAN-related challenges. This paper reviews the application of several methodologies under the CI umbrella to the WSAN field. We describe and categorize existing works leaning on fuzzy systems , neural networks , evolutionary computation , swarm intelligence , learning systems , and their hybridizations to well-known or emerging WSAN problems along five major axes: 1) actuation; 2) communication; 3) sink mobility; 4) topology control; and 5) localization. The survey offers informative discussions to help reason through all the studies under consideration. Finally, we point to future research avenues by: 1) suggesting suitable CI techniques to specific problems; 2) borrowing concepts from WSNs that have yet to be applied to WSANs; or 3) describing the shortcomings of current methods in order to spark interest on the development of more refined models.

A Review of Computational Intelligence Techniques in Wireless Sensor and Actuator Networks

Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

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A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Effective design of air-conditioner (AC) management system has the potential to reduce the cost of electricity consumption and help users to participate in demand response (DR) program as interruptible loads. However, optimizing the operation of AC is complex and, as a potential solution, computational intelligence (CI) techniques based model predictive algorithms are being explored in the literature. This article aims to provide an overview of the CI techniques that are established in addressing relevant and timely open problems of AC management for residential buildings. To do so, first, we provide a brief background on different DR mechanisms and AC management systems. Second, a review of recent advances in CI-based model prediction and optimal control techniques of AC systems for DR management is presented. The discussion reveals that the interest in CI techniques with adaptive learning algorithms is increasing due to their ability to adjust in varying conditions. Then, we provide a brief description of a testbed, which is used for testing various newly developed CI-based AC management techniques in a residential setting. Finally, key issues related to the coordination of a large number of AC systems, modeling accuracy, and computational tractability are highlighted along with their challenges and future research directions.

A Survey of Computational Intelligence Techniques for Air-Conditioners Energy Management

The world of today is increasingly dependant on a functional, globalized economy. The defence and security establishments’ reliance on supplies and logistics is not new. First responders rely on many tools and systems that are critical to their endeavours. Somewhat disjoint at first glance, these domains share a common need for complex physical or logistical infrastructures such as power plants, ports, supply chains, to name a few examples.All of these are potentially vulnerable to attacks, disruptions, breakdowns, or other activities that disable the infrastructure and consequently cause important physical or economic damage. An obligation exists to protect these critical infrastructures and a decision support system that is able to detect, identify, and mitigate the risk of unwanted events would be invaluable in preventing the disastrous consequences of compromised infrastructure.This thesis explores the design and application of such a system. It starts with a pre-existing, actively researched risk management framework and proposes a methodology to apply it in new contexts, as well as contributions to provide the framework with the ability to solve new problems. Relevant case studies in critical infrastructure protection are presented, as well as applications of the developed methodology with the proposed modifications when suitable. Simulations, results, and insightful discussions are provided for each of the case studies. Finally, research trends, future work, and a conclusion are given, completing this thesis.

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Risk-Aware Decision Support for Critical Infrastructure Protection using Multi-Objective Optimization

With the rise of more resourceful unmanned aerial vehicles (UAVs), their inclusion into robotic sensor networks (RSNs) is inevitable The highly mobile nature of UAVs allows greater monitoring capabilities, making them most suitable for RSNs Compared to traditional nodes in RSNs, UAVs suffer even more from communication disruptions and energy depletion, must often rapidly determine actions for themselves, and consequently require more autonomy Prior work has been done in wireless sensor network (WSN)/aerial sensor network (ASN) coordination in a few applications such as protecting critical infrastructure, restoring communication between nodes, and healing networks, while other work has been accomplished on using the UAV network for augmenting the monitoring capabilities of WSNs We introduce a novel methodology to integrate UAVs into RSNs for monitoring purposes by formulating the problem in the context of a risk management framework (RMF) This methodology allows a more precise risk feature classification and a more efficient task allocation for the ground network by utilizing the monitoring capabilities of the UAVs to informatively warn the RSN of any incoming events We also present a fictitious but credible maritime smuggling scenario near the Port of Barcelona based on expert knowledge, and apply the methodology to detect and mitigate maritime smuggling The network's behaviour is traced throughout the scenario and is repeated with civilian ships to assure that they are not flagged as smugglers The applied methodology results in a successful classification and mitigation of the smuggling activity

Maritime smuggling detection and mitigation using risk-aware hybrid robotic sensor networks

Optimized task allocation in robotic sensor networks is a crucial yet complex problem. A successful allocation must efficiently and rapidly coordinate agents to resolve a greater objective while being resilient to incomplete contextual information. Prior work introduced a two-step task allocation process which first determines a set of suitable robots via an auction, then optimizes the task allocation for these agents with the use of multiobjective evolutionary algorithms. The first step has had success but often yields suboptimal sets in certain conditions, this limitation can be traced back to the auctioning protocol's simplicity, which then constraints the set of potential coalitions sought by the optimizer. We introduce two new auction protocols which are able to choose optimal agents. These protocols are resilient to communication failures, can rapidly determine optimal sets and can select agents based on multiple factors. A valuation system which renders agents aware of the value of entities is also introduced. Finally, a rigorous experimentation is designed to evaluated the performance of the different protocols in multiple scenarios. In the experiment, both proposed protocols in conjunction with the valuation system proved to be efficient methods to select appropriate agents. In both sparse and dense network scenarios, the proposed protocols performed better than the current implementation in terms of selecting appropriate coalitions, at the cost of time and network utilization.

Improving task allocation in risk-aware robotic sensor networks via auction protocol selection

Supply chains in the current world require extensive use of transportation modes In combination with ever growing supply chain streamlining efforts, supply chains are particularly vulnerable to disruptions that cause profound effects on downstream actors Much information is needed to mitigate disruptions, information that could be gathered via a maritime Internet of Things (mIoT) In this paper, we put forth a methodology to detect potentially disruptive events in a maritime supply chain and generate candidate mitigating responses The proposed framework places risk as the cornerstone of the data-driven analysis and uses a multi-criteria decision approach to propose appropriate actions An application of the system to mitigate a disruption in a maritime segment of a supply chain is studied Solutions are composed of combinations of actions to reduce the consequences of a disruption This system is validated through a scenario in which a weather event causes a disruption in maritime transportation destined to fulfill a supply contract Finally, conclusions on the system are provided

Nicolas Primeau

Papers

A Review of Computational Intelligence Techniques in Wireless Sensor and Actuator Networks

Risk-Aware Decision Support for Critical Infrastructure Protection using Multi-Objective Optimization

Maritime smuggling detection and mitigation using risk-aware hybrid robotic sensor networks

Improving task allocation in risk-aware robotic sensor networks via auction protocol selection

Continuous Risk-Aware Response Generation for Maritime Supply Chain Disruption Mitigation