FireFly: a cross-layer platform for real-time embedded wireless networks

We propose a cross-layer approach with tightly-coupled time synchronization for real-time support and predictable lifetime in battery-operated sensor networks. Our design spans a sensor hardware platform with hardware-based global time synchronization, a TDMA link layer protocol with collision-free multi-hop support and node scheduling algorithms for maximum concurrency and streaming. Our dual-radio sensor platform, FireFly, features an IEEE 802.15.4 transceiver and supports global time synchronization indoors by using an AM radio carrier-current method and an atomic clock receiver for outdoors. A TDMA-based link protocol, RT-Link, leverages the hardware for fixed and mobile nodes with a near-optimal and predictable node lifetime of over 2 years. It outperforms comparable sensor network link protocols such as B-MAC and S-MAC in terms of end-to-end latency and throughput and node lifetime across all duty cycle ratios. Operating over RT-Link is MAX, a scheduling framework which offers optimal transmission concurrency and bandwidth management for networks with regular structure. Through analysis and experiments we show that global time sync is a robust, economical and scalable alternative to in-band software-based techniques. To illustrate the capabilities and flexibility of our platform, we describe our experiences with two-way voice streaming over multiple hops. We have deployed a 42-node network with sub-100 μs synchronization accuracy in the NIOSH experimental coal mine for people-tracking and voice communication.

The concert queueing game: to wait or to be late

We introduce the concert (or cafeteria) queueing problem: A finite but large number of customers arrive into a queueing system that starts service at a specified opening time. Each customer is free to choose her arrival time (before or after opening time), and is interested in early service completion with minimal wait. These goals are captured by a cost function which is additive and linear in the waiting time and service completion time, with coefficients that may be class dependent. We consider a fluid model of this system, which is motivated as the fluid-scale limit of the stochastic system. In the fluid setting, we explicitly identify the unique Nash-equilibrium arrival profile for each class of customers. Our structural results imply that, in equilibrium, the arrival rate is increasing up until the closing time where all customers are served. Furthermore, the waiting queue is maximal at the opening time, and monotonically decreases thereafter. In the simple single class setting, we show that the price of anarchy (PoA, the efficiency loss relative to the socially optimal solution) is exactly two, while in the multi-class setting we develop tight upper and lower bounds on the PoA. In addition, we consider several mechanisms that may be used to reduce the PoA. The proposed model may explain queueing phenomena in diverse settings that involve a pre-assigned opening time.     

Majorisations for the eigenvectors of graph-adjacency matrices

We develop majorisation results that characterise changes in eigenvector components of a graph's adjacency matrix when its topology is changed. Specifically, for general (weighted, directed) graphs, we characterise changes in dominant eigenvector components for single- and multi-row incrementations. We also show that topology changes can be tailored to set ratios between the components of the dominant eigenvector. For more limited graph classes (specifically, undirected, and reversibly-structured ones), majorisations for components of the subdominant and other eigenvectors upon graph modifications are also obtained.     

On the sizing of a solar thermal electricity plant for multiple objectives using evolutionary optimization

Design, implementation and operation of solar thermal electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks.     

Fuzzy Fruit Fly Optimized Node Quality-Based Clustering Algorithm for Network Load Balancing

Recently, the fundamental problem with Hybrid Mobile Ad-hoc Networks (H-MANETs) is to find a suitable and secure way of balancing the load through Internet gateways. Moreover, the selection of the gateway and overload of the network results in packet loss and Delay (DL). For optimal performance, it is important to load balance between different gateways. As a result, a stable load balancing procedure is implemented, which selects gateways based on Fuzzy Logic (FL) and increases the efficiency of the network. In this case, since gateways are selected based on the number of nodes, the Energy Consumption (EC) was high. This paper presents a novel Node Quality-based Clustering Algorithm (NQCA) based on Fuzzy-Genetic for Cluster Head and Gateway Selection (FGCHGS). This algorithm combines NQCA with the Improved Weighted Clustering Algorithm (IWCA). The NQCA algorithm divides the network into clusters based upon node priority, transmission range, and neighbour fidelity. In addition, the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC, packet loss rate (PLR), etc.     

An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

In today’s digital world, millions of individuals are linked to one another via the Internet and social media. This opens up new avenues for information exchange with others. Sentiment analysis (SA) has gotten a lot of attention during the last decade. We analyse the challenges of Sentiment Analysis (SA) in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which we first produced an annotated dataset composed of Marathi text acquired from microblogging websites such as Twitter. We also choose domain experts to manually annotate Marathi microblogging posts with positive, negative, and neutral polarity. In addition, to show the efficient use of the annotated dataset, an ensemble-based model for sentiment analysis was created. In contrast to others machine learning classifier, we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation (cv), outcomes as 97.77%, f-score is 97.89%.     

MULTIROBOT MOTION PLANNING USING HYBRID MNHS AND GENETIC ALGORITHMS

Planning the motion of multiple robots deals with computing the motion of all robots avoiding any collision. This article focuses on the use of hybrid Multi Neuron Heuristic Search (MNHS) and Genetic Algorithm (GA). The MNHS is an advancement over the conventional A* algorithm and is better suited for maze-like conditions where there is a high degree of uncertainty. The MNHS contributes toward optimality of the solution, and the GA gives it an iterative nature and enables the approach to be used on high-resolution maps. MNHS works over the set of points returned by the GA in its fitness function evaluation. A priority-based approach is used, in which the priorities are decided by the GA. Path feasibility is speeded up by using the concept of coarser-to-finer lookup called momentum. Experimental results show that the combined approach is able to easily solve the problem for a variety of scenarios.

[Supplementary materials are available for this article. Go to the publisher's online edition of Applied Artificial Intelligence for the following free supplemental resource(s): Videos 1-4]     

Rapidly exploring random graphs: motion planning of multiple mobile robots

Rapidly exploring random trees (RRT) and probabilistic roadmaps (PRM) are sampling-based techniques being extensively used for robot path planning. In this paper, the tree structure of the RRT is generalized to a graph structure which enables a greater exploration. Exploration takes place simultaneously from multiple points in the map, all explorations fusing at multiple points producing well-connected graph architecture. Initially, in a typical RRT manner, the search algorithm attempts to reach the goal by expansions, and thereafter furtherer areas are explored. With some additional computation cost, as compared to RRT with a single robot, the results can be significantly improved. The so-formed graph is similar to roadmap produced by PRM. However as compared to PRM, the proposed algorithm has a more judicious search strategy and is adaptable to the number of nodes as a parameter. Experimental results are shown with multiple robots planned using prioritization scheme. Results show the betterment of the proposed algorithm as compared to RRT and PRM techniques.     

Fabrication of Integrated Vertical Mirror Surfaces and Transparent Window for Packaging MEMS Devices

A scheme for creating metal-coated vertical mirrors in silicon, along with an integrated transparent package lid for assembling, packaging, and testing microelectromechanical systems (MEMS) devices is presented. Deep reaction ion etching (DRIE) method described here reduces the loading effect and maintains a uniform etch rate resulting in highly vertical structures. A novel self-masking lithography and liftoff process was developed to ensure that the vertical mirrors undergo uniform metallization while leaving a transparent window for optical probing. Front side of a Si wafer was shallow-etched using DRIE to define an eventual optical window. This surface was then anodically bonded to a Pyrex wafer. Backside Si was then patterned to define thin channels around the optical window. These channels were vertically etched using DRIE, after which the unattached portions of the window region were removed. Negative photoresist was spun on the remaining vertical structures and the stack was exposed from the Pyrex side using Si structures as a self-mask. Subsequent metal sputtering and liftoff results in the metallized top and mirror sidewalls while leaving a clear window. These integrated mirrors and lids are then bonded to the active MEMS mirrors. Various processes and results are illustrated with an example of packaged corner cube retroreflectors (CCRs)