Yogesh Sankarasubramaniam

Wireless sensor networks: a survey

This paper describes the concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are also discussed.

A survey on sensor networks

The advancement in wireless communications and electronics has enabled the development of low-cost sensor networks. The sensor networks can be used for various application areas (e.g., health, military, home). For different application areas, there are different technical issues that researchers are currently resolving. The current state of the art of sensor networks is captured in this article, where solutions are discussed under their related protocol stack layer sections. This article also points out the open research issues and intends to spark new interests and developments in this field.

ESRT: event-to-sink reliable transport in wireless sensor networks

Wireless sensor networks (WSN) are event based systems that rely on the collective effort of several microsensor nodes. Reliable event detection at the sink is based on collective information provided by source nodes and not on any individual report. Hence, conventional end-to-end reliability definitions and solutions are inapplicable in the WSN regime and would only lead to a waste of scarce sensor resources. However, the absence of reliable transport altogether can seriously impair event detection. Hence, the WSN paradigm necessitates a collective phevent-to-sink reliability notion rather than the traditional end-to-end notion. To the best of our knowledge, reliable transport in WSN has not been studied from this perspective before.In order to address this need, a new reliable transport scheme for WSN, the event-to-sink reliable transport (ESRT) protocol, is presented in this paper. ESRT is a novel transport solution developed to achieve reliable event detection in WSN with minimum energy expenditure. It includes a congestion control component that serves the dual purpose of achieving reliability and conserving energy. Importantly, the algorithms of ESRT mainly run on the sink, with minimal functionality required at resource constrained sensor nodes. ESRT protocol operation is determined by the current network state based on the reliability achieved and congestion condition in the network. If the event-to-sink reliability is lower than required, ESRT adjusts the reporting frequency of source nodes aggressively in order to reach the target reliability level as soon as possible. If the reliability is higher than required, then ESRT reduces the reporting frequency conservatively in order to conserve energy while still maintaining reliability. This self-configuring nature of ESRT makes it robust to random, dynamic topology in WSN. Analytical performance evaluation and simulation results show that ESRT converges to the desired reliability with minimum energy expenditure, starting from any initial network state.

Energy efficiency based packet size optimization in wireless sensor networks

This paper addresses the question of optimal packet size for data communication in energy constrained wireless sensor networks. Unlike previous work on packet length optimization in other wired and wireless networks, energy efficiency is chosen as the optimization metric. The use of fixed size packets is proposed in light of the limited resources and management costs in sensor networks. The optimal fixed packet size is then determined for a set of radio and channel parameters by maximizing the energy efficiency metric. Further, the effect of error control on packet size optimization and energy efficiency is examined. While retransmission schemes are found to be energy inefficient, it is shown that forward error correction can improve the energy efficiency eventhough it introduces additional parity bits and encoding/decoding energy consumptions. In this regard, binary BCH codes are found to be 15% more energy efficient than the best performing convolutional codes, which have thus far been considered for error control in sensor networks.

Random-access scheduling with service differentiation in wireless networks

Recent years have seen tremendous growth in the deployment of wireless local area networks (WLANs). An important design issue in such networks is that of distributed scheduling. The lack of centralized control leads to multiple users competing for channel access. This leads to significant throughput degradation. Existing approaches, such as the slotted Aloha protocol and IEEE 802.11 DCF, also fail to provide differentiated service to users. The upcoming IEEE 802.11e enhanced DCF incorporates additional mechanisms to provide support for service differentiation. However, the level of differentiation achieved with these mechanisms is difficult to quantify. In this paper, we propose a class of distributed scheduling algorithms, regulated contention medium access control (RCMAC), which provides dynamic prioritized access to users for service differentiation in a quantifiable manner. Furthermore, by regulating multi-user contention, RCMAC achieves higher throughput when traffic is bursty, as is typically the case. In addition to WLANs, the basic concepts of RCMAC have applications in ad hoc networks and emerging sensor networks.

Text summarization using Wikipedia

Abstract Automatic text summarization has been an active field of research for many years. Several approaches have been proposed, ranging from simple position and word-frequency methods, to learning and graph based algorithms. The advent of human-generated knowledge bases like Wikipedia offer a further possibility in text summarization – they can be used to understand the input text in terms of salient concepts from the knowledge base. In this paper, we study a novel approach that leverages Wikipedia in conjunction with graph-based ranking. Our approach is to first construct a bipartite sentence–concept graph, and then rank the input sentences using iterative updates on this graph. We consider several models for the bipartite graph, and derive convergence properties under each model. Then, we take up personalized and query-focused summarization, where the sentence ranks additionally depend on user interests and queries, respectively. Finally, we present a Wikipedia-based multi-document summarization algorithm. An important feature of the proposed algorithms is that they enable real-time incremental summarization – users can first view an initial summary, and then request additional content if interested. We evaluate the performance of our proposed summarizer using the ROUGE metric, and the results show that leveraging Wikipedia can significantly improve summary quality. We also present results from a user study, which suggests that using incremental summarization can help in better understanding news articles.

Document Summarization using Wikipedia

Although most of the developing world is likely to first access the Internet through mobile phones, mobile devices are constrained by screen space, bandwidth and limited attention span. Single document summarization techniques have the potential to simplify information consumption on mobile phones by presenting only the most relevant information contained in the document. In this paper we present a language independent single-document summarization method. We map document sentences to semantic concepts in Wikipedia and select sentences for the summary based on the frequency of the mapped-to concepts. Our evaluation on English documents using the ROUGE package indicates our summarization method is competitive with the state of the art in single document summarization.

Achieving SK capacity in the source model: When must all terminals talk?

In this paper, we address the problem of characterizing the instances of the multiterminal source model of Csiszár and Narayan in which communication from all terminals is needed for establishing a secret key of maximum rate. We give an information-theoretic sufficient condition for identifying such instances. We believe that our sufficient condition is in fact an exact characterization, but we are only able to prove this in the case of the three-terminal source model.

On the Public Communication Needed to Achieve SK Capacity in the Multiterminal Source Model

The focus of this paper is on the public communication required for generating a maximal-rate secret key (SK) within the multiterminal source model of Csiszár and Narayan. Building on the prior work of Tyagi for the two-terminal scenario, we derive a lower bound on the communication complexity, RSK, defined to be the minimum rate of public communication needed to generate a maximal-rate SK. It is well known that the minimum rate of communication for omniscience, denoted by RCO, is an upper bound on RSK. For the class of pairwise independent network (PIN) models defined on uniform hypergraphs, we show that a certain Type S condition, which is verifiable in polynomial time, guarantees that our lower bound on RSK meets the RCO upper bound. Thus, the PIN models satisfying our condition are RSK-maximal, indicating that the upper bound RSK ≤ RCO holds with equality. This allows us to explicitly evaluate RSK for such PIN models. We also give several examples of PIN models that satisfy our Type S condition. Finally, we prove that for an arbitrary multiterminal source model, a stricter version of our Type S condition implies that communication from all terminals (omnivocality) is needed for establishing an SK of maximum rate. For three-terminal source models, the converse is also true: omnivocality is needed for generating a maximal-rate SK only if the strict Type S condition is satisfied. However, for the source models with four or more terminals, counterexamples exist showing that the converse does not hold in general.

Finite-state wiretap channels: Secrecy under memory constraints

Information-theoretic security offered by the wiretap channel model has been extensively studied for various scenarios recently. One scenario that has not received much attention is secrecy for systems with memory in the form of input constraints or inter-symbol interference (ISI). In this work, we consider finite state wiretap channels (FSWCs), which model the scenario of secrecy with memory. Using results on secrecy capacity for arbitrary wiretap channels, we first arrive at the secrecy capacity of a FSWC. Then, we develop a stochastic algorithm for computing tight lower bounds on the secrecy capacity of a less-noisy FSWC, and illustrate the computation through examples. Our results provide numerical comparisons between secrecy capacities with and without memory, and provide specific targets for code design.