Baljeet Malhotra

Aggregation convergecast scheduling in wireless sensor networks

We consider the problem of scheduling in wireless sensor networks for the purposes of aggregation convergecast. We observe that existing schemes adopt essentially a two phase approach, consisting of, first, a tree construction and, second, a scheduling phase. Following a similar approach, we propose two new improvements, one to each of the two phases. Starting with a new lower bound on the schedule length, we make use of it in the tree construction phase. The tree construction phase consists of solutions to instances of bipartite graph semi-matchings. The scheduling phase is a weight-based priority scheme that obeys dependency (tree) and interference constraints. Our extensive experiments show that, overall, our proposed solution not only outperforms all previously proposed solutions in terms of schedule length, but it also significantly extends the network’s lifetime.

Exact Top-K Queries in Wireless Sensor Networks

In this paper, we consider the exact top-k query problem in wireless sensor networks, i.e., where one seeks to find the k highest reported values as well as the complete set of nodes that reported them. Our primary contribution in this context is EXTOK, a provably correct and topology-independent new filtering-based algorithm for processing exact top-k queries. As a secondary contribution we confirm a previous result of ours by showing that the efficiency of top-k query processing algorithms, including EXTOK, can be further improved by simply choosing a proper underlying logical tree topology. We examine EXTOKs performance with respect to a number of parameters and different logical tree topologies while using both synthetic and real data sets. Our simulation reveal that EXTOK consistently outperforms the current state-of-the-art algorithm by a very significant margin and regardless of the underlying logical tree topology.

Distributed classification of acoustic targets in wireless audio-sensor networks

Target tracking is an important application for wireless sensor networks. One important aspect of tracking is target classification. Classification helps in selecting particular target(s) of interest. In this paper, we address the problem of classification of moving ground vehicles. The basis of classification are the audible signals produced by these vehicles. We present a distributed framework to classify vehicles based on features extracted from acoustic signals of vehicles. The main features used in our study are based on FFT (fast Fourier transform) and PSD (power spectral density). We propose three distributed algorithms for classification that are based on the k-nearest neighbor (k-NN) classification method. An experimental study has been conducted using real acoustic signals of different vehicles recorded in the city of Edmonton. We compare our proposed algorithms with a naive distributed implementation of the k-NN algorithm. Performance results reveal that our proposed algorithms are energy efficient, and thus suitable for sensor network deployment.

Distributed and efficient classifiers for wireless audio-sensor networks

This paper presents schemes to generate effective feature vectors of low dimension, and also presents a cluster-based algorithm, where sensors form clusters on-demand for the sake of running a classification task based on the produced feature vectors. The features generated through our proposed schemes are evaluated using k-nearest neighbor (k-NN) and maximum likelihood (ML) classifiers. The proposed schemes are effective in terms of classification accuracy, and can even outperform previously proposed approaches, but, in addition, they are also efficient in terms of communication overhead.

Better tree - better fruits: using dominating set trees for MAX queries

Finding an aggregation of observed values, in particular the maximum value, is an important type of query in wireless sensor networks. Previous proposals to find the maximum value, the so-called MAX query, relied on a given underlying logical tree topology for data aggregation/forwarding, but did not pay due attention to the role of such topology. Focusing on the MAX queries we first argue that the underlying tree topology plays a very important role in the query processing cost. We then propose the use of a particular tree topology, based on Dominating Sets that is well suited to explore the networks physical topology for processing MAX queries efficiently. Experimental results obtained using real and synthetic datasets confirm that by simply replacing the tree topologies used in previous proposals with the Dominating Set-based Tree (DST) one can reduce the transmission cost of MAX queries by up to 70% and overall energy consumption by up to 53%.

Path-Adaptive On-Site Tracking in Wireless Sensor Networks

Wireless sensor networks present a promising opportunity for realizing many practical applications. Tracking is one of the important applications of these networks. Many approaches have been proposed in the literature to deal with the tracking problem. Recently, a particular type of tracking problem called on-site tracking has been introduced [15], [16]. On-site tracking has been characterized as the tracking in which the sink is eventually required to be present in the vicinity of the target, possibly to perform further actions. In this paper, first we propose two efficient on-site tracking algorithms. Then, we derive theoretical upper bounds for the tracking time and the number of messages generated by the sensor nodes during the tracking for our algorithms. Finally, we present a simulation study that we conducted to evaluate the performance of our algorithms. The results show that our algorithms are efficient as compared to the other existing methods that can solve the on-site tracking problem. In particular, the path adaptive nature of the sink in our algorithms allows the network to conserve the energy and the sink to reduce the tracking time.

Mining maritime schedules for analysing global shipping networks

Shipping plays a vital role as trade facilitator in providing cost-efficient transportation. The International Maritime Organisation (IMO) reports that over 90% of the world trade volume is carried by merchant ships. The analysis of shipping networks therefore can create invaluable insight into global trade. In this paper we study the appropriateness of various graph centrality measures to rate, compare and rank ports from various perspectives of global shipping networks. In particular, we illustrate the potential of such analysis on the example of shipping networks constructed from the schedules, readily available on the World Wide Web, of six shipping companies that transport 35-40% of the total volume traded (in TEUs) worldwide.

Biased shortest path trees in wireless networks

Broadcasting is an elementary problem in wireless networks. Energy — efficient broadcasting is important, e.g., to coordinate the distributed computing operations by sending periodic messages in a network of Automatic Identification System installed on energy constrained maritime lighthouses. To that end logical tree topologies that are based on Connected Dominating Sets have been proposed vigorously in the literature. In this paper we present Biased Shortest Path Tree (BISPT), a new logical tree topology for efficient broadcasting in wireless networks. In simulations we find that BISPT outperforms state-of-the-art solutions.

Monitoring Exact Top-K Values in Wireless Sensor Networks Using Dominating Set Trees

Top-k queries form an important class of aggregation queries in wireless sensor networks. Unlike previous proposals we consider the exact top-k values query problem, i.e., where one seeks to find exactly all k highest unique values in the network regardless of how many nodes report it. Previous proposals also did not pay due attention to the underlying logical tree topology used for data aggregation and forwarding. In this context, this paper presents two main contributions: (1) we propose the use of a particular tree topology, based on Dominating Sets, which is well suited to explore the network’s physical topology for processing top-k queries efficiently; and (2) we propose EXTOK, a filtering-based algorithm for processing the exact top-k values query, we also prove its correctness and investigate its performance with respect to a number of parameters, including network link failures. In all examined cases, EXTOK performs consistently well while effectively exploiting the proposed logical tree topology and it is also resilient to link failures.

Modelling and analysis of shipping networks from online maritime schedules

90% of the world trade is reportedly carried by sea. The analysis of shipping networks therefore can create invaluable insight into global trade. In this paper we study the appropriateness of various graph centrality measures to rate, compare and rank ports from various perspectives of a shipping network. In particular, we illustrate the potential of such analysis on the example of a shipping network constructed from the schedules, readily available on the World Wide Web, of one arbitrarily chosen shipping company.