Results 1 - 10
of
69
Compressive data gathering for large-scale wireless sensor networks
- in Proc. ACM Mobicom’09
, 2009
"... This paper presents the first complete design to apply compressive sampling theory to sensor data gathering for largescale wireless sensor networks. The successful scheme developed in this research is expected to offer fresh frame of mind for research in both compressive sampling applications and la ..."
Abstract
-
Cited by 76 (4 self)
- Add to MetaCart
(Show Context)
This paper presents the first complete design to apply compressive sampling theory to sensor data gathering for largescale wireless sensor networks. The successful scheme developed in this research is expected to offer fresh frame of mind for research in both compressive sampling applications and large-scale wireless sensor networks. We consider the scenario in which a large number of sensor nodes are densely deployed and sensor readings are spatially correlated. The proposed compressive data gathering is able to reduce global scale communication cost without introducing intensive computation or complicated transmission control. The load balancing characteristic is capable of extending the lifetime of the entire sensor network as well as individual sensors. Furthermore, the proposed scheme can cope with abnormal sensor readings gracefully. We also carry out the analysis of the network capacity of the proposed compressive data gathering and validate the analysis through ns-2 simulations. More importantly, this novel compressive data gathering has been tested on real sensor data and the results show the efficiency and robustness of the proposed scheme.
Opportunistic flooding in low-duty-cycle wireless sensor networks with unreliable links
- in Proc. ACM Int. Conf. Mobile Comput. Netw., 2009
"... ABSTRACT Intended for network-wide dissemination of commands, configurations and code binaries, flooding has been investigated extensively in wireless networks. However, little work has yet been done on low-duty-cycle wireless sensor networks in which nodes stay asleep most of time and wake up asyn ..."
Abstract
-
Cited by 42 (6 self)
- Add to MetaCart
(Show Context)
ABSTRACT Intended for network-wide dissemination of commands, configurations and code binaries, flooding has been investigated extensively in wireless networks. However, little work has yet been done on low-duty-cycle wireless sensor networks in which nodes stay asleep most of time and wake up asynchronously. In this type of network, a broadcasting packet is rarely received by multiple nodes simultaneously, a unique constraining feature that makes existing solutions unsuitable. Combined with unreliable links, flooding in low-duty-cycle networks is a new challenging issue. In this paper, we introduce Opportunistic Flooding, a novel design tailored for low-duty-cycle networks with unreliable wireless links and predetermined working schedules. The key idea is to make probabilistic forwarding decisions at a sender based on the delay distribution of next-hop nodes. Only opportunistically early packets are forwarded using links outside the energy optimal tree to reduce the flooding delay and redundancy in transmission. To improve performance further, we propose a forwarder selection method to alleviate the hidden terminal problem and a link-qualitybased backoff method to resolve simultaneous forwarding operations. We evaluate Opportunistic Flooding with extensive simulation and a test-bed implementation consisting of 30 MicaZ nodes. Evaluation shows our design is close to the optimal performance achievable by oracle flooding designs. Compared with improved traditional flooding, our design achieves significantly shorter flooding delay while consuming only 20% ∼ 60% of the transmission energy in various low-duty-cycle network settings.
The Clustered AGgregation (CAG) technique leveraging spatial and temporal correlations in wireless sensor networks
- ACM Trans. Sens. Netw
, 2007
"... Sensed data in Wireless Sensor Networks (WSN) reflect the spatial and temporal correlations of physical attributes existing intrinsically in the environment. In this article, we present the Clustered AGgregation (CAG) algorithm that forms clusters of nodes sensing similar values within a given thres ..."
Abstract
-
Cited by 34 (1 self)
- Add to MetaCart
Sensed data in Wireless Sensor Networks (WSN) reflect the spatial and temporal correlations of physical attributes existing intrinsically in the environment. In this article, we present the Clustered AGgregation (CAG) algorithm that forms clusters of nodes sensing similar values within a given threshold (spatial correlation), and these clusters remain unchanged as long as the sensor values stay within a threshold over time (temporal correlation). With CAG, only one sensor reading per cluster is transmitted whereas with Tiny AGgregation (TAG) all the nodes in the network transmit the sensor readings. Thus, CAG provides energy efficient and approximate aggregation results with small and often negligible and bounded error. In this article we extend our initial work in CAG in five directions: First, we investigate the effectiveness of CAG that exploits the temporal as well as spatial correlations using both the measured and modeled data. Second, we design CAG for two modes of operation (interactive and streaming) to enable CAG to be used in different environments and for different purposes. Interactive mode provides mechanisms for one-shot queries, whereas the streaming mode provides those for continuous queries. Third, we propose a fixed range clustering method, which makes the performance of our system independent of the magnitude of the sensor readings and the network topology. Fourth,
Energy efficient information collection in wireless sensor networks using adaptive compressive sensing
- in IEEE 34th Conference on Local Computer Networks (LCN), Oct 2009
"... Abstract—We consider the problem of using Wireless Sensor Networks (WSNs) to measure the temporal-spatial field of some scalar physical quantities. Our goal is to obtain a sufficiently accurate approximation of the temporal-spatial field with as little energy as possible. We propose an adaptive algo ..."
Abstract
-
Cited by 21 (5 self)
- Add to MetaCart
(Show Context)
Abstract—We consider the problem of using Wireless Sensor Networks (WSNs) to measure the temporal-spatial field of some scalar physical quantities. Our goal is to obtain a sufficiently accurate approximation of the temporal-spatial field with as little energy as possible. We propose an adaptive algorithm, based on the recently developed theory of adaptive compressive sensing, to collect information from WSNs in an energy efficient manner. The key idea of the algorithm is to perform “projections” iteratively to maximise the amount of information gain per energy expenditure. We prove that this maximisation problem is NPhard and propose a number of heuristics to solve this problem. We evaluate the performance of our proposed algorithms using data from both simulation and an outdoor WSN testbed. The results show that our proposed algorithms are able to give a more accurate approximation of the temporal-spatial field for a given energy expenditure. I.
Does compressed sensing improve the throughput of wireless sensor networks
- in ICC 2010. IEEE
"... Abstract—Although compressed sensing (CS) has been envi-sioned as a useful technique to improve the performance of wireless sensor networks (WSNs), it is still not very clear how exactly it will be applied and how big the improvements will be. In this paper, we propose two different ways (plain-CS a ..."
Abstract
-
Cited by 17 (1 self)
- Add to MetaCart
(Show Context)
Abstract—Although compressed sensing (CS) has been envi-sioned as a useful technique to improve the performance of wireless sensor networks (WSNs), it is still not very clear how exactly it will be applied and how big the improvements will be. In this paper, we propose two different ways (plain-CS and hybrid-CS) of applying CS to WSNs at the networking layer, in the form of a particular data aggregation mechanism. We formulate three flow-based optimization problems to compute the throughput of the non-CS, plain-CS, and hybrid-CS schemes. We provide the exact solution to the first problem corresponding to the non-CS case and lower bounds for the cases with CS. Our preliminary numerical results are only for a low-power regime. They illustrate two crucial insights: first, applying CS naively may not bring any improvement, and secondly, our hybrid-CS can achieve significant improvement in throughput. Index Terms—Wireless sensor networks, compressed sensing, data aggregation, routing, scheduling. I.
Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering
, 2010
"... We proposed compressive data gathering (CDG) that leverages compressive sampling (CS) principle to efficiently reduce communication cost and prolong network lifetime for large scale monitoring sensor networks. The network capacity has been proven to increase proportionally to the sparsity of sensor ..."
Abstract
-
Cited by 17 (0 self)
- Add to MetaCart
(Show Context)
We proposed compressive data gathering (CDG) that leverages compressive sampling (CS) principle to efficiently reduce communication cost and prolong network lifetime for large scale monitoring sensor networks. The network capacity has been proven to increase proportionally to the sparsity of sensor readings. In this paper, we further address two key problems in the CDG framework. First, we investigate how to generate RIP (restricted isometry property) preserving measurements of sensor readings by taking multi-hop communication cost into account. Excitingly, we discover that a simple form of measurement matrix [
Data gathering in wireless sensor networks through intelligent compressive sensing
- In Proceedings of the 31th IEEE International Conference on Computer Communications (INFOCOM
, 2012
"... Abstract—The recently emerged compressive sensing (CS) theory provides a whole new avenue for data gathering in wireless sensor networks with benefits of universal sampling and decentralized encoding. However, existing compressive sens-ing based data gathering approaches assume the sensed data has a ..."
Abstract
-
Cited by 17 (0 self)
- Add to MetaCart
Abstract—The recently emerged compressive sensing (CS) theory provides a whole new avenue for data gathering in wireless sensor networks with benefits of universal sampling and decentralized encoding. However, existing compressive sens-ing based data gathering approaches assume the sensed data has a known constant sparsity, ignoring that the sparsity of natural signals vary in temporal and spatial domain. In this paper, we present an adaptive data gathering scheme by com-pressive sensing for wireless sensor networks. By introducing autoregressive (AR) model into the reconstruction of the sensed data, the local correlation in sensed data is exploited and thus local adaptive sparsity is achieved. The recovered data at the sink is evaluated by utilizing successive reconstructions, the relation between error and measurements. Then the number of measurements is adjusted according to the variation of the sensed data. Furthermore, a novel abnormal readings detection and identification mechanism based on combinational sparsity reconstruction is proposed. Internal error and external event are distinguished by their specific features. We perform extensive testing of our scheme on the real data sets and experimental results validate the efficiency and efficacy of the proposed scheme. Up to about 8dB SNR gain can be achieved over conventional CS based method with moderate increase of complexity. I.
Decomposing Data-Centric Storage Query Hot-Spots in Sensor Networks
"... Arising when a large percentage of queries is accessing data stored in few sensor nodes, query hot-spots reduce the Quality of Data (QoD) and the lifetime of the sensor network. All current In-Network Data-Centric Storage (IN-DCS) schemes fail to deal with query hot-spots resulting from skewed query ..."
Abstract
-
Cited by 11 (5 self)
- Add to MetaCart
Arising when a large percentage of queries is accessing data stored in few sensor nodes, query hot-spots reduce the Quality of Data (QoD) and the lifetime of the sensor network. All current In-Network Data-Centric Storage (IN-DCS) schemes fail to deal with query hot-spots resulting from skewed query loads as well as skewed sensor deployments. In this paper, we present two algorithms to locally detect and decompose query hot-spots, namely Zone Partitioning (ZP) and Zone Partial Replication (ZPR). We build both algorithms on top of the DIM scheme, which has been shown to exhibit the best performance among all INDCS schemes. Experimental evaluation illustrates the efficiency of ZP/ZPR in decomposing query hot-spots while increasing QoD as well as energy savings by balancing energy consumption among sensor nodes.
Energy Scaling Laws for Distributed Inference in Random Networks
, 2008
"... The energy scaling laws of multihop data fusion networks for distributed inference are considered. The fusion network consists of randomly located sensors independently distributed according to a general spatial distribution in an expanding region. Among the class of data fusion schemes that enable ..."
Abstract
-
Cited by 11 (6 self)
- Add to MetaCart
The energy scaling laws of multihop data fusion networks for distributed inference are considered. The fusion network consists of randomly located sensors independently distributed according to a general spatial distribution in an expanding region. Among the class of data fusion schemes that enable optimal inference at the fusion center for Markov random field hypotheses, the minimum per-sensor energy cost is bounded below by a minimum spanning tree data fusion and above by a suboptimal scheme referred to as Data Fusion for Markov Random Field (DFMRF). Scaling laws are derived for the optimal and suboptimal fusion policies.
Predictive Modeling-Based Data Collection in Wireless Sensor Networks
, 2008
"... We address the problem of designing practical, energy-efficient protocols for data collection in wireless sensor networks using predictive modeling. Prior work has suggested several approaches to capture and exploit the rich spatio-temporal correlations prevalent in WSNs during data collection. Alt ..."
Abstract
-
Cited by 8 (2 self)
- Add to MetaCart
We address the problem of designing practical, energy-efficient protocols for data collection in wireless sensor networks using predictive modeling. Prior work has suggested several approaches to capture and exploit the rich spatio-temporal correlations prevalent in WSNs during data collection. Although shown to be effective in reducing the data collection cost, those approaches use simplistic corelation models and further, ignore many idiosyncrasies of WSNs, in particular the broadcast nature of communication. Our proposed approach is based on approximating the joint probability distribution over the sensors using undirected graphical models, ideally suited to exploit both the spatial correlations and the broadcast nature of communication. We present algorithms for optimally using such a model for data collection under different communication models, and for identifying an appropriate model to use for a given sensor network. Experiments over synthetic and real-world datasets show that our approach significantly reduces the data collection cost.
