Rafael Falcon

The one-commodity traveling salesman problem with selective pickup and delivery: An ant colony approach

We introduce a novel combinatorial optimization problem: the one-commodity traveling salesman problem with selective pickup and delivery (1-TSP-SELPD), characterized by the fact that the demand of any delivery customer can be met by a relatively large number of pickup customers. While all delivery spots are to be visited, only profitable pickup locations will be included in the tour so as to minimize its cost. The motivation for 1-TSP-SELPD stems from the carrier-based coverage repair problem in wireless sensor and robot networks, wherein a mobile robot replaces damaged sensors with spare ones. The ant colony optimization (ACO) meta-heuristic elegantly solves this problem within reasonable time and space constraints. Six ACO heuristic functions are put forward and a recently proposed exploration strategy is exploited to accelerate convergence in dense networks. Results gathered from extensive simulations confirm that our ACO-based model outperforms existing competitive approaches.

Fault identification with binary adaptive fireflies in parallel and distributed systems

The efficient identification of hardware and software faults in parallel and distributed systems still remains a serious challenge in todays most prolific decentralized environments. System-level fault diagnosis is concerned with the detection of all faulty nodes in a set of interconnected units. This is accomplished by thoroughly examining the collection of outcomes of all tests carried out by the nodes under a particular test model. Such task has non-polynomial complexity and can be posed as a combinatorial optimization problem, whose optimal solution has been sought through bio-inspired methods like genetic algorithms, ant colonies and artificial immune systems. In this paper, we employ a swarm of artificial fireflies to quickly and reliably navigate across the search space of all feasible sets of faulty units under the invalidation and comparison test models. Our approach uses a binary encoding of the potential solutions (fireflies), an adaptive light absorption coefficient to accelerate the search and problem-specific knowledge to handle infeasible solutions. The empirical analysis confirms that the proposed algorithm outperforms existing techniques in terms of convergence speed and memory requirements, thus becoming a viable approach for real-time fault diagnosis in large-size systems.

An evolving risk management framework for wireless sensor networks

Individual units in a wireless sensor network (WSN) are exposed to multiple risks, either during or after their deployment. The identification of the risk sources and their watchful monitoring in dynamic, unpredictable environments is pivotal to ensure a smooth, long-term functioning of the WSN. We introduce an evolving risk management framework for WSNs that captures multiple risk features and provides both a visual depiction of the corporate network threats at any time and a numerical assessment of any sensors overall risk. The visualization module is embodied through an evolving clustering architecture which heavily relies on shadowed sets. The risk assessment module embraces fuzzy and shadowed evaluations of the risk sources and incorporates a simple adaptive learning process that weights the risk sources proportionally to their observed impact on failed sensors. A distinctive trait of the proposed framework is its highly automated yet still human-centric nature. Experiments utilizing different sensor models and deployment scenarios confirm the feasibility of the risk management platform under consideration.

Carrier-Based Focused Coverage Formation in Wireless Sensor and Robot Networks

Carrier-based sensor placement involves mobile robots carrying and dropping (static) sensors for optimal coverage formation. Existing solutions target the traditional area coverage problem and unrealistically assume that robots carry sensors all together (ignoring the physical dimension of sensors and the finite robot capacity). In this paper, we consider a more realistic scenario in which robots have to repeatedly reload sensors and address the FOCUSED coverage (F-coverage) problem in an unknown 2-D environment. In F-coverage, sensors are required to surround a point of interest (POI) as far as possible, thus maximizing the coverage radius. We propose a Carrier-Based Coverage Augmentation protocol (CBCA) that seamlessly tolerates node failures. Robots enter the environment from fixed locations, called base points, and move toward the POI. As soon as they get in touch with already deployed sensors, they search (by communication) along the network border for best sensor placement spots (to improve F-coverage) and move to drop sensors at the discovered locations. Border nodes store the coordinates of failed sensors (if any exists) inside the network as well as of adjacent available deployment positions outside the network, and recommend them to robots during the search process. Robots return to base points for reloading after deploying their current payload and immediately re-enter the environment to augment existing F-coverage. An optimization technique was introduced to reduce augmentation delay and save robot energy. Extensive simulations were conducted to assess CBCAs energy expenditures and deployment latency.

A harmony-seeking firefly swarm to the periodic replacement of damaged sensors by a team of mobile robots

Mobile robots nowadays can assist wireless sensor networks (WSNs) in many jeopardizing scenarios that unexpectedly arise during their operational lifetime. We focus on an emerging kind of cooperative networking system in which a small team of robotic agents lies at a base station. Their mission is to service an already-deployed WSN by periodically replacing all damaged sensors in the field with passive, spare ones so as to preserve the existing network coverage. This novel application scenario is here baptized as “multiple-carrier coverage repair” (MC2R) and modeled as a new generalization of the vehicle routing problem. A hybrid metaheuristic algorithm is put forward to derive nearly-optimal sensor replacement trajectories for the robotic fleet in a short running time. The composite scheme relies on a swarm of artificial fireflies in which each individual follows the exploratory principles featured by Harmony Search. Infeasible candidate solutions are gradually driven into feasibility under the influence of a weak Pareto dominance relationship. A repair heuristic is finally applied to yield a full-blown solution. To the best of our knowledge, our scheme is the first one in literature that tackles MC2R instances. Empirical results indicate that promising solutions can be achieved in a limited time span.

PSO driven collaborative clustering: A clustering algorithm for ubiquitous environments

The goal of this article is to introduce a collaborative clustering approach to the domain of ubiquitous knowledge discovery. This clustering approach is suitable in peer-to-peer networks where different data sites want to cluster their local data as if they consolidated their data sets, but which is prevented by privacy restrictions. Two variants exist, i.e. one for data sites with the same observations but different features and one for data sites with the same features but different observations. The technique contains two parts, i.e. a collaborative fuzzy clustering technique and a particle swarm optimization to optimize the collaboration between data sites. Empirical analysis show how and when this PSO-CFC approach outperforms local fuzzy clustering.

Risk management with hard-soft data fusion in maritime domain awareness

Enhanced situational awareness is integral to risk management and response evaluation. Dynamic systems that incorporate both hard and soft data sources allow for comprehensive situational frameworks which can supplement physical models with conceptual notions of risk. The processing of widely available semi-structured textual data sources can produce soft information that is readily consumable by such a framework. In this paper, we augment the situational awareness capabilities of a recently proposed risk management framework (RMF) with the incorporation of soft data. We illustrate the beneficial role of the hard-soft data fusion in the characterization and evaluation of potential vessels in distress within Maritime Domain Awareness (MDA) scenarios. Risk features pertaining to maritime vessels are defined a priori and then quantified in real time using both hard (e.g., Automatic Identification System, Douglas Sea Scale) as well as soft (e.g., historical records of worldwide maritime incidents) data sources. A risk-aware metric to quantify the effectiveness of the hard-soft fusion process is also proposed. Though illustrated with MDA scenarios, the proposed hard-soft fusion methodology within the RMF can be readily applied to other domains.

A response-aware risk management framework for search-and-rescue operations

Efficient coordination among all assets participating in a response to a search-and-rescue (SAR) incident has long been a focus of many governments and organizations. Finding innovative solutions that guarantee a swift reaction to the distressed entity with a rational use of the available resources is pivotal to the success of the SAR operation. In spite of the plethora of successfully deployed SAR systems, we witness a substantial gap when it comes to the integration of risk-driven analyses into the underlying machinery of any decision support platform that leans upon the in-field SAR assets. This paper extends a recently proposed risk management framework [1] by adding automated modules for risk monitoring and response selection. An evolutionary multi-objective optimization algorithm is used to navigate across the discrete space of all available assets and their set of actions in order to present a limited number of promising responses to a SAR operator, who will ultimately decide what action must be carried out. The proposed methodology was validated in the context of a simulated nautical SAR scenario in the Canadian Atlantic coastline with nine different types of ground, maritime and aerial assets.

Controlled Straight Mobility and Energy-Aware Routing in Robotic Wireless Sensor Networks

Power-aware routing and controlled mobility schemes are two commonly used mechanisms for improving communications in a wireless sensor network. While the former actively consider the transmission costs when selecting the next hop on the route, the latter instruct mobile relay nodes (either sensors or actuators) to pursue more promising locations so as to optimize end-to-end transmission power. Rarely, if ever, the two methodologies are exploited together for achieving relevant energy savings and prolonging network lifetime. In this paper, we introduce a hybrid routing-mobility model for the optimization of network communications. First, we find a multi-hop path between a source and its destination in an energy-efficient fashion and then we move all hop nodes in an uninterrupted, straight manner to some predefined spots with optimal energy-saving properties, fully preserving the path connectivity as they move. Such synergetic approach allowed us to: (1) seamlessly guarantee message delivery regardless of the network density (average number of neighbors per node), (2) easily incorporate any power-related optimization criterion to the routing protocol and (3) even target scenarios where both end nodes are actually disconnected from each other. Results gathered from extensive simulations argue for the introduction of the proposed hybrid framework.

Feature Selection through Dynamic Mesh Optimization

This paper introduces the Dynamic Mesh Optimization meta-heuristic, which falls under the evolutionary computation techniques. Moreover, we outline its application to the feature selection problem. A set of nodes representing subsets of features makes up a mesh which dynamically grows and moves across the search space. The novel methodology is compared with other existing meta-heuristic approaches, thus leading to encouraging empirical results.