Distributed Algorithms for Maximizing the Lifetime of Wireless Sensor Networks

Wireless sensor networks (WSNs) are emerging as a key enabling technology for applications domains such as military, homeland security, and environment. However, a major constraint of these sensors is their limited battery. In this dissertation we examine the problem of maximizing the duration of time for which the network meets its coverage objective. Since these networks are very dense, only a subset of sensors need to be in sense or on mode at any given time to meet the coverage objective, while others can go into a power conserving sleep mode. This active set of sensors is known as a cover. The lifetime of the network can be extended by shuffling the cover set over time. In this dissertation, we introduce the concept of a local lifetime dependency graph consisting of the cover sets as nodes with any two nodes connected if the corresponding covers intersect, to capture the interdependencies among the covers. We present heuristics based on some simple properties of this graph and show how they improve over existing algorithms. We also present heuristics based on other properties of this graph, new models for dealing with the solution space and a generalization of our approach to other graph problems

Real-Time Cross-Layer Routing Protocol for Ad Hoc Wireless Sensor Networks

Reliable and energy efficient routing is a critical issue in Wireless Sensor Networks (WSNs) deployments. Many approaches have been proposed for WSN routing, but sensor field implementations, compared to computer simulations and fully-controlled testbeds, tend to be lacking in the literature and not fully documented. Typically, WSNs provide the ability to gather information cheaply, accurately and reliably over both small and vast physical regions. Unlike other large data network forms, where the ultimate input/output interface is a human being, WSNs are about collecting data from unattended physical environments. Although WSNs are being studied on a global scale, the major current research is still focusing on simulations experiments. In particular for sensor networks, which have to deal with very stringent resource limitations and that are exposed to severe physical conditions, real experiments with real applications are essential. In addition, the effectiveness of simulation studies is severely limited in terms of the difficulty in modeling the complexities of the radio environment, power consumption on sensor devices, and the interactions between the physical, network and application layers. The routing problem in ad hoc WSNs is nontrivial issue because of sensor node failures due to restricted recourses. Thus, the routing protocols of WSNs encounter two conflicting issue: on the one hand, in order to optimise routes, frequent topology updates are required, while on the other hand, frequent topology updates result in imbalanced energy dissipation and higher message overhead. In the literature, such as in (Rahul et al., 2002), (Woo et al., 2003), (TinyOS, 2004), (Gnawali et al., 2009) and (Burri et al., 2007) several authors have presented routing algorithms for WSNs that consider purely one or two metrics at most in attempting to optimise routes while attempting to keep small message overhead and balanced energy dissipation. Recent studies on energy efficient routing in multihop WSNs have shown a great reliance on radio link quality in the path selection process. If sensor nodes along the routing path and closer to the base station advertise a high quality link to forwarding upstream packets, these sensor nodes will experience a faster depletion rate in their residual energy. This results in a topological routing hole or network partitioning as stated and resolved in and (Daabaj 2010). This chapter presents an empirical study on how to improve energy efficiency for reliable multihop communication by developing a real-time cross-layer lifetime-oriented routing protocol and integrating useful routing information from different layers to examine their joint benefit on the lifetime of individual sensor nodes and the entire sensor network. The proposed approach aims to balance the workload and energy usage among relay nodes to achieve balanced energy dissipation, thereby maximizing the functional network lifetime. The obtained experimental results are presented from prototype real-network experiments based on Crossbow’s sensor motes (Crossbow, 2010), i.e., Mica2 low-power wireless sensor platforms (Crossbow, 2010). The distributed real-time routing protocol which is proposed In this chapter aims to face the dynamics of the real world sensor networks and also to discover multiple paths between the base station and source sensor nodes. The proposed routing protocol is compared experimentally with a reliability-oriented collection-tree protocol, i.e., the TinyOS MintRoute protocol (Woo et al., 2003). The experimental results show that our proposed protocol has a higher node energy efficiency, lower control overhead, and fair average delay

Cost benefit analysis of space communications technology. Volume 2: Final report

For abstract, see preceding accession

Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

Energy-Efficient Querying of Wireless Sensor Networks

Due to the distributed nature of information collection in wireless sensor networks and the inherent limitations of the component devices, the ability to store, locate, and retrieve data and services with minimum energy expenditure is a critical network function. Additionally, effective search protocols must scale efficiently and consume a minimum of network energy and memory reserves. A novel search protocol, the Trajectory-based Selective Broadcast Query protocol, is proposed. An analytical model of the protocol is derived, and an optimization model is formulated. Based on the results of analysis and simulation, the protocol is shown to reduce the expected total network energy expenditure by 45.5 percent to 75 percent compared to current methods. This research also derives an enhanced analytical node model of random walk search protocols for networks with limited-lifetime resources and time-constrained queries. An optimization program is developed to minimize the expected total energy expenditure while simultaneously ensuring the proportion of failed queries does not exceed a specified threshold. Finally, the ability of the analytical node model to predict the performance of random walk search protocols in large-population networks is established through extensive simulation experiments. It is shown that the model provides a reliable estimate of optimum search algorithm parameters