A Novel Protocol For Barrier K-Coverage In Wireless Sensor Networks

One of major problems in the wireless sensor networks is the barrier coverage problem. This problem deals with the ability to minimizing the probability of undetected penetration through the barrier (sensor network). The reliability and fault tolerance problems are very important for long strip barrier coverage sensor networks. Also, another design challenge in sensor networks is to save limited energy resources to prolong the lifetime of wireless sensor network. In this paper we propose the fault tolerant k-barrier coverage protocol, called APBC. The proposed protocol maintains a good balance in using nodes energy, in order to prolong the network lifetime. The proposed protocol presents a proper way to provide the k-barrier coverage at nodes fails without reexecuting the algorithm. The simulation results show that this method prolongs the lifetime of the network in comparison with RIS method

Opportunities for technological innovations in current construction practices

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2001.Includes bibliographical references (p. 43-44).by Mauricio J. Ortiz.M.Eng

Fuzzy A* for optimum Path Planning in a Large Maze

 Traditional A* path planning, while guaranteeing the shortest path with an admissible heuristic, often employs conservative heuristic functions that neglect potential obstacles and map inaccuracies. This can lead to inefficient searches and increased memory usage in complex environments. To address this, machine learning methods have been explored to predict cost functions, reducing memory load while maintaining optimal solutions. However, these require extensive data collection and struggle in novel, intricate environments. We propose the Fuzzy A* algorithm, an enhancement of the classic A* method, incorporating a new determinant variable to adjust heuristic cost calculations. This adjustment modulates the scope of scanned vertices during searches, optimizing memory usage and computational efficiency. In our approach, unlike traditional A* heuristics that overlook environmental complexities, the Fuzzy A* employs a dynamic heuristic function. This function, leveraging fuzzy logic principles, adapts to varying levels of environmental complexity, allowing a more nuanced estimation of the path cost that considers potential obstructions and route feasibility. This adaptability contrasts with standard machine learning-based solutions, which, while effective in known environments, often falter in unfamiliar or highly complex settings due to their reliance on pre-existing datasets. Our experimental framework involved 100 maze-solving trials in diverse maze configurations, ranging from simple to highly intricate layouts, to evaluate the effectiveness of Fuzzy A*. We employed specific metrics such as path length, computational time, and memory usage for a comprehensive assessment. The results showcased that Fuzzy A* consistently found the shortest paths (99.96% success rate) and significantly reduced memory usage by 67% and 59% compared to Breadth-First-Search (BFS) and traditional A*, respectively. These findings underline the effectiveness of our modified heuristic approach in diverse and challenging environments, highlighting its potential for real-world pathfinding applications

Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems

Majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability. The existing neuro-evolution of augmenting topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process. However, most NEAT implementations allow the consideration of only a single objective. There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios. To address these gaps, this paper develops a multi-objective neuro-evolution algorithm. While adopting the basic elements of NEAT, important modifications are made to the selection, speciation, and mutation processes. With the backdrop of small-robot path-planning applications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system. This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance maximization alone. The effectiveness of the single-objective (optimizing performance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios

Project OASIS: Optimizing Aquaponic Systems to Improve Sustainability

Started in Fall 2015, Project OASIS (Optimizing Aquaponic Systems to Improve Sustainability) is an interdisciplinary capstone project with the goal of designing a sustainable and affordable small-scale aquaponic system for use in developing nations to tackle the problems of malnutrition and food insecurity. Aquaponics is a symbiotic relationship between fish and vegetables growing together in a recirculating system. The project’s goals were to minimize energy consumption and construction costs while using universally available materials. The computational fluid dynamics (CFD) software OpenFOAM was used to create transient and steady-state models of fish tanks to visualize velocity profiles, streamlines, and particle movement. CFD and small scale experiments showed vertical manifolds were more efficient than horizontal inlets. The components’ layout was analyzed to minimize head losses and airlifts were used instead of traditional water pumps. Full-scale research and traditional systems were constructed for side-by-side comparison of biological and energy factors. Flow improvements and use of air-lift pumps dropped energy consumption 40% when compared to a traditional system of the same size. Using local and recycled materials where possible decreased the cost of the UNH pilot system by 27%. The team also partnered with Forjando Alas, a non-profit in Uvita, Costa Rica. During a January 2016 assessment trip, four members spent a week gathering data and building relationships with the community to develop a user-centered design. Project OASIS also successfully competed in two entrepreneurship competitions this year

Spatial and Temporal Changes of Tidal Inlet Using Object-Based Image Analysis of Multibeam Echosounder Measurements: A Case from the Lagoon of Venice, Italy

Scientific exploration of seabed substrata has significantly progressed in the last few years. Hydroacoustic methods of seafloor investigation, including multibeam echosounder measurements, allow us to map large areas of the seabed with unprecedented precision. Through time-series of hydroacoustic measurements, it was possible to determine areas with distinct characteristics in the inlets of the Lagoon of Venice, Italy. Their temporal variability was investigated. Monitoring the changes was particularly relevant, considering the presence at the channel inlets of mobile barriers of the Experimental Electromechanical Module (MoSE) project installed to protect the historical city of Venice from flooding. The detection of temporal and spatial changes was performed by comparing seafloor maps created using object-based image analysis and supervised classifiers. The analysis included extraction of 25 multibeam echosounder bathymetry and backscatter features. Their importance was estimated using an objective approach with two feature selection methods. Moreover, the study investigated how the accuracy of classification could be affected by the scale of object-based segmentation. The application of the classification method at the proper scale allowed us to observe habitat changes in the tidal inlet of the Venice Lagoon, showing that the sediment substrates located in the Chioggia inlet were subjected to very dynamic changes. In general, during the study period, the area was enriched in mixed and muddy sediments and was depleted in sandy deposits. This study presents a unique methodological approach to predictive seabed sediment composition mapping and change detection in a very shallow marine environment. A consistent, repeatable, logical site-specific workflow was designed, whose main assumptions could be applied to other seabed mapping case studies in both shallow and deep marine environments, all over the world

Fast Marching based Rendezvous Path Planning for a Team of Heterogeneous Vehicle

A formulation is developed for deterministically calculating the optimized paths for a multi-agent system consisting of heterogeneous vehicles. The essence of this formulation is the calculation of the shortest time for each agent to reach every grid point from its known initial position. Such arrival time map can be readily assessed using the Fast Marching Method (FMM), a computational algorithm originally designed for solving boundary value problems of the Eikonal equation. Leveraging the FMM method, we demonstrate that the minimal time rendezvous point and paths for all member vehicles can be uniquely determined with minimal computational concerns. To showcase the potential of our method, we use an example of a virtual rendezvous scenario that entails the coordination of a ship, an underwater vehicle, an aerial vehicle, and a ground vehicle to converge at the optimal location within the Tampa Bay area in minimal time. It illustrates the value of the developed framework in efficiently constructing continuous path planning, while accommodating different operational constraints of heterogeneous member vehicles