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100
Power management in energy harvesting sensor networks
- Networked and Embedded Systems Laboratory, UCLA
, 2006
"... Power management is an important concern in sensor networks, because a tethered energy in-frastructure is usually not available and an obvious concern is to use the available battery energy efficiently. However, in some of the sensor networking applications, an additional facility is available to am ..."
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Cited by 232 (3 self)
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Power management is an important concern in sensor networks, because a tethered energy in-frastructure is usually not available and an obvious concern is to use the available battery energy efficiently. However, in some of the sensor networking applications, an additional facility is available to ameliorate the energy problem: harvesting energy from the environment. Certain considerations in using an energy harvesting source are fundamentally different from that in using a battery, be-cause, rather than a limit on the maximum energy, it has a limit on the maximum rate at which the energy can be used. Further, the harvested energy availability typically varies with time in a nondeterministic manner. While a deterministic metric, such as residual battery, suffices to charac-terize the energy availability in the case of batteries, a more sophisticated characterization may be required for a harvesting source. Another issue that becomes important in networked systems with multiple harvesting nodes is that different nodes may have different harvesting opportunity. In a distributed application, the same end-user performance may be achieved using different workload allocations, and resultant energy consumptions at multiple nodes. In this case, it is important to align the workload allocation with the energy availability at the harvesting nodes. We consider the above issues in power management for energy-harvesting sensor networks. We develop abstractions
SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks
, 2004
"... We study the impact of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of th ..."
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Cited by 159 (0 self)
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We study the impact of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of the population of sensor nodes is equipped with additional energy resources---this is a source of heterogeneity which may result from the initial setting or as the operation of the network evolves. We also assume that the sensors are randomly (uniformly) distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. We show that the behavior of such sensor networks becomes very unstable once the first node dies, especially in the presence of node heterogeneity. Classical clustering protocols assume that all the nodes are equipped with the same amount of energy and as a result, they can not take full advantage of the presence of node heterogeneity. We propose SEP, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (we refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable. SEP is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. We show by simulation that SEP always prolongs the stability period compared to (and that the average throughput is greater than) the one obtained using current clustering protocols. We conclude by studying the sensitivity of our SEP protocol to heterogeneity parameters capturing energy imbalance in the network. We found that SEP yields longer stability region for higher values of extra energy brought by more powerful nodes.
Data aggregation techniques in sensor networks: A survey
- Comm. Surveys & Tutorials, IEEE
, 2006
"... Wireless sensor networks consist of sensor nodes with sensing and communication capabilities. We focus on data aggregation problems in energy constrained sensor networks. The main goal of data aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifeti ..."
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Cited by 138 (0 self)
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Wireless sensor networks consist of sensor nodes with sensing and communication capabilities. We focus on data aggregation problems in energy constrained sensor networks. The main goal of data aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. In this paper, we present a survey of data aggregation algorithms in wireless sensor networks. We compare and contrast different algorithms on the basis of performance measures such as lifetime, latency and data accuracy. We conclude with possible future research directions. 1.
An environmental energy harvesting framework for sensor networks
- In International Symposium on Low Power Electronics and Design, ACM
, 2003
"... Energy constrained systems such as sensor networks can increase their usable lifetimes by extracting energy from their environment. However, environmental energy will typically not be spread homogeneously over the spread of the network. We argue that significant improvements in usable system lifetim ..."
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Cited by 97 (7 self)
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Energy constrained systems such as sensor networks can increase their usable lifetimes by extracting energy from their environment. However, environmental energy will typically not be spread homogeneously over the spread of the network. We argue that significant improvements in usable system lifetime can be achieved if the task allocation is aligned with the spatio-temporal characteristics of energy availability. To the best of our knowledge, this problem has not been addressed before. We present a distributed framework for the sensor network to adaptively learn its energy environment and give localized algorithms to use this information for task sharing among nodes. Our framework allows the system to exploit its energy resources more efficiently, thus increasing its lifetime. These gains are in addition to those from utilizing sleep modes and residual energy based scheduling mechanisms. Performance studies for an experimental energy environment show up to 200 % improvement in lifetime.
Information fusion for wireless sensor networks: methods, models, and classifications,”
- Article ID 1267073,
, 2007
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Maximizing the Functional Lifetime of Sensor Networks
- In ACM Syposium on Information Processing in Sensor Networks (IPSN
, 2005
"... Abstract — The functional lifetime of a sensor network is defined as the maximum number of times a certain data collection function or task can be carried out without any node running out of energy. The specific task considered in this paper is that of communicating a specified quantity of informati ..."
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Cited by 50 (0 self)
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Abstract — The functional lifetime of a sensor network is defined as the maximum number of times a certain data collection function or task can be carried out without any node running out of energy. The specific task considered in this paper is that of communicating a specified quantity of information from each sensor to a collector node. The problem of finding the communication scheme which maximizes functional lifetime can be formulated as a linear program, under “fluid-like ” assumptions on information bits. This paper focuses on analytically solving the linear program for some simple regular network topologies. The two topologies considered are a regular linear array, and a regular two-dimensional network. In the linear case, an upper bound on functional lifetime is derived, as a function of the initial energies and quantities of data held by the sensors. Under some assumptions on the relative amounts of the energies and data, this upper bound is shown to be achievable, and the exact form of the optimal communication strategy is derived. For the regular planar network, upper and lower bounds on functional lifetime, differing only by a constant factor, are obtained. Finally, it is shown that the simple collection scheme of transmitting only to nearest neighbors, yields a nearly optimal lifetime in a scaling sense. I.
Maximizing Data Extraction in Energy-Limited Sensor Networks
- IEEE INFOCOM
, 2004
"... We examine the problem of maximizing data collection from an energy-limited store-and-extract wireless sensor network, which is analogous to the maximum lifetime problem of interest in continuous data-gathering sensor networks. One significant difference is that this problem requires attention to &q ..."
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Cited by 47 (4 self)
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We examine the problem of maximizing data collection from an energy-limited store-and-extract wireless sensor network, which is analogous to the maximum lifetime problem of interest in continuous data-gathering sensor networks. One significant difference is that this problem requires attention to "data-awareness" in addition to "energy-awareness." We formulate the maximum data extraction problem as a linear program and present a iterative approximation algorithm for it. As a practical distributed implementation we develop a faster greedy heuristic for this problem that uses an exponential metric based on the approximation algorithm. We then show through simulation results that the greedy heuristic incorporating this exponential metric performs nearoptimally (within 1 to 20% of optimal, with low overhead) and significantly better than other shortest-path routing approaches, particularly when nodes are heterogeneous in their energy and data availability.
An adaptive propagation-delaytolerant MAC protocol for underwater acoustic sensor networks
- In Proceedings of MTS/IEEE OCEANS 2007
"... Abstract — Underwater acoustic sensor networks (UASN) can be employed in a vast range of applications, retrieving accurate and up-to-date information from underneath the ocean’s surface. Although widely used by terrestrial sensor networks, radio frequencies (RF) do not propagate well underwater. Aco ..."
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Cited by 34 (0 self)
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Abstract — Underwater acoustic sensor networks (UASN) can be employed in a vast range of applications, retrieving accurate and up-to-date information from underneath the ocean’s surface. Although widely used by terrestrial sensor networks, radio frequencies (RF) do not propagate well underwater. Acoustic channels are therefore employed as an alternative to support longdistance and low-power communication in underwater sensor networks even though acoustic signals suffer from long propagation delay and have very limited bandwidth. In this paper, we introduce an adaptive propagation-delay-tolerant collision-avoidance protocol (APCAP) for the MAC sublayer of UASN. The protocol includes an improved handshaking mechanism that improves efficiency and throughput in UASN where there is a large propagation delay. The mechanism guarantees nodes that can potentially interfere with a forthcoming transmission are properly informed. In addition, it also allows a node to utilise its idle time whilst waiting for messages to propagate, which is otherwise wasted by most existing MAC protocols. The simulation results indicate that where employed by UASN, APCAP exhibits good performance and outperforms the other MAC protocols examined in this paper.
A Distributed Framework for Correlated Data Gathering in Sensor Networks
, 2008
"... We consider the problem of correlated data gathering in sensor networks with multiple sink nodes. The problem has two objectives. First, we would like to find a rate allocation on the correlated sensor nodes such that the data gathered by the sink nodes can reproduce the field of observation. Second ..."
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Cited by 28 (0 self)
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We consider the problem of correlated data gathering in sensor networks with multiple sink nodes. The problem has two objectives. First, we would like to find a rate allocation on the correlated sensor nodes such that the data gathered by the sink nodes can reproduce the field of observation. Second, we would like to find a transmission structure on the network graph such that the total transmission energy consumed by the network is minimized. The existing solutions to this problem are impractical for deployment because they have not considered all of the following factors: 1) distributed implementation; 2) capacity and interference associated with the shared medium; and 3) realistic data correlation model. In this paper, we propose a new distributed framework to achieve minimum energy data gathering while considering these three factors. Based on a localized version of Slepian–Wolf coding, the problem is modeled as an optimization formulation with a distributed solution. The formulation is first relaxed with Lagrangian dualization and then solved with the subgradient algorithm. The algorithm is amenable to fully distributed implementations, which corresponds to the decentralized nature of sensor networks. To evaluate its effectiveness, we have conducted extensive simulations under a variety of network environments. The results indicate that the algorithm supports asynchronous network settings, sink mobility, and duty schedules.
Online Data Gathering for Maximizing Network Lifetime in Sensor Networks
"... Abstract—Energy-constrained sensor networks have been deployed widely for monitoring and surveillance purposes. Data gathering in such networks is often a prevalent operation. Since sensors have significant power constraints (battery life), energy efficient methods must be employed for data gatherin ..."
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Cited by 24 (1 self)
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Abstract—Energy-constrained sensor networks have been deployed widely for monitoring and surveillance purposes. Data gathering in such networks is often a prevalent operation. Since sensors have significant power constraints (battery life), energy efficient methods must be employed for data gathering to prolong network lifetime. We consider an online data gathering problem in sensor networks, which is stated as follows: Assume that there is a sequence of data gathering queries, which arrive one by one. To respond to each query as it arrives, the system builds a routing tree for it. Within the tree, the volume of the data transmitted by each internal node depends on not only the volume of sensed data by the node itself, but also the volume of data received from its children. The objective is to maximize the network lifetime without any knowledge of future query arrivals and generation rates. In other words, the objective is to maximize the number of data gathering queries answered until the first node in the network fails. For the problem of concern, in this paper, we first present a generic cost model of energy consumption for data gathering queries if a routing tree is used for the query evaluation. We then show the problem to be NP-complete and propose several heuristic algorithms for it. We finally conduct experiments by simulation to evaluate the performance of the proposed algorithms in terms of network lifetime delivered. The experimental results show that, among the proposed algorithms, one algorithm that takes into account both the residual energy and the volume of data at each sensor node significantly outperforms the others. Index Terms—Sensor network, data gathering, energy consumption optimization, network lifetime, sensor database, sensornet query optimization. 1
