Sensor Deployment for Network-like Environments

This paper considers the problem of optimally deploying omnidirectional sensors, with potentially limited sensing radius, in a network-like environment. This model provides a compact and effective description of complex environments as well as a proper representation of road or river networks. We present a two-step procedure based on a discrete-time gradient ascent algorithm to find a local optimum for this problem. The first step performs a coarse optimization where sensors are allowed to move in the plane, to vary their sensing radius and to make use of a reduced model of the environment called collapsed network. It is made up of a finite discrete set of points, barycenters, produced by collapsing network edges. Sensors can be also clustered to reduce the complexity of this phase. The sensors' positions found in the first step are then projected on the network and used in the second finer optimization, where sensors are constrained to move only on the network. The second step can be performed on-line, in a distributed fashion, by sensors moving in the real environment, and can make use of the full network as well as of the collapsed one. The adoption of a less constrained initial optimization has the merit of reducing the negative impact of the presence of a large number of local optima. The effectiveness of the presented procedure is illustrated by a simulated deployment problem in an airport environment

Discrete phase-space approach to mutually orthogonal Latin squares

We show there is a natural connection between Latin squares and commutative sets of monomials defining geometric structures in finite phase-space of prime power dimensions. A complete set of such monomials defines a mutually unbiased basis (MUB) and may be associated with a complete set of mutually orthogonal Latin squares (MOLS). We translate some possible operations on the monomial sets into isomorphisms of Latin squares, and find a general form of permutations that map between Latin squares corresponding to unitarily equivalent mutually unbiased sets. We extend this result to a conjecture: MOLS associated to unitarily equivalent MUBs will always be isomorphic, and MOLS associated to unitarily inequivalent MUBs will be non-isomorphic

A Consensus-based Approach for Team Allocations: the Case of Logistics in Campania Region

This work proposes a competence-based approach, enriched by consensus, for deciding on team compositions, in particular, time- and quality-dependent contexts, where teams have to perform some assigned activities. This problem is very relevant for the dynamics of logistic networks whose nodes are warehouses, distribution centers, and small family businesses that deal with work orders that have to be satisfied. The approach consists of models that focus on workers' competences by using the Knowledge, Skills, and Attitudes model for workers' knowledge representation, and competence models to describe the activities to be performed. Some consensus strategies among workers are then used to obtain the correct choice of teams to assign to the various work orders. According to the concept of value co-creation, this paper proposes an original and hybrid approach, based on competences and enriched by consensus, in order to obtain and select the most "suitable" teams for the activities to be performed. This approach is tested, carefully and in depth, on the real case of a small family business inside a sophisticated logistics area, consisting of a fleet of trucks that, by transporting goods from one point to another, underpins the logistics chain inside the Campania region (Italy). These areas of logistics consider small family businesses, which manage the maintenance and repair or trucks, as highly critical nodes of the system. We show that our approach produces results similar to the decisions made by the leader of such a small family business

Multisignal 1D-compression by F-transform for wireless sensor networks applications

In wireless sensor networks a large amount of data is collected for each node. The challenge of trans-ferring these data to a sink, because of energy constraints, requires suitable techniques such as datacompression. Transform-based compression, e.g. Discrete Wavelet Transform (DWT), are very popularin this field. These methods behave well enough if there is a correlation in data. However, especiallyfor environmental measurements, data may not be correlated. In this work, we propose two approachesbased on F-transform, a recent fuzzy approximation technique. We evaluate our approaches with Dis-crete Wavelet Transform on publicly available real-world data sets. The comparative study shows thecapabilities of our approaches, which allow a higher data compression rate with a lower distortion, evenif data are not correlated

An AmI-Based Software Architecture Enabling Evolutionary Computation in Blended Commerce: The Shopping Plan Application

This work describes an approach to synergistically exploit ambient intelligence technologies, mobile devices, and evolutionary computation in order to support blended commerce or ubiquitous commerce scenarios. The work proposes a software architecture consisting of three main components: linked data for e-commerce, cloud-based services, and mobile apps. The three components implement a scenario where a shopping mall is presented as an intelligent environment in which customers use NFC capabilities of their smartphones in order to handle e-coupons produced, suggested, and consumed by the abovesaid environment. The main function of the intelligent environment is to help customers define shopping plans, which minimize the overall shopping cost by looking for best prices, discounts, and coupons. The paper proposes a genetic algorithm to find suboptimal solutions for the shopping plan problem in a highly dynamic context, where the final cost of a product for an individual customer is dependent on his previous purchases. In particular, the work provides details on the Shopping Plan software prototype and some experimentation results showing the overall performance of the genetic algorithm