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24
Energy-efficient capture of stochastic events by global- and local-periodic network coverage
- In Proceedings of ACM MobiHoc
, 2009
"... Abstract—We consider a high density of sensors randomly placed in a geographical area for event monitoring. The monitoring regions of the sensors may have significant overlap, and a subset of the sensors can be turned off to conserve energy, thereby increasing the lifetime of the monitoring network. ..."
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Abstract—We consider a high density of sensors randomly placed in a geographical area for event monitoring. The monitoring regions of the sensors may have significant overlap, and a subset of the sensors can be turned off to conserve energy, thereby increasing the lifetime of the monitoring network. Prior work in this area does not consider the event dynamics. In this paper, we show that knowledge about the event dynamics can be exploited for significant energy savings, by putting the sensors on a periodic on/off schedule. We discuss energy-aware optimization of the periodic schedule for the cases of a synchronous and an asynchronous network. To reduce the overhead of global synchronization, we further consider a spectrum of regionally synchronous networks where the size of the synchronization region is specifiable. Under the periodic scheduling, coordinated sleep by the sensors can be applied orthogonally to minimize the redundancy of coverage and further improve the energy efficiency. We consider the interactions between the periodic scheduling and coordinated sleep. We show that the asynchronous network exceeds any regionally synchronous network in the coverage intensity, thereby increasing the effectiveness of the event capture, though the opportunities for coordinated sleep decreases as the synchronization region gets smaller. When the sensor density is high, the asynchronous network with coordinated sleep can achieve extremely good event capture performance while being highly energy-efficient.
Obstacle-Resistant Deployment Algorithms for Wireless Sensor Networks
"... Abstract—Node deployment is an important issue in wireless sensor networks (WSNs). Sensor nodes should be efficiently deployed in a predetermined region in a low-cost and highcoverage-quality manner. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and ..."
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Abstract—Node deployment is an important issue in wireless sensor networks (WSNs). Sensor nodes should be efficiently deployed in a predetermined region in a low-cost and highcoverage-quality manner. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and, therefore, increase hardware costs and create coverage holes. This paper presents the efficient obstacle-resistant robot deployment (ORRD) algorithm, which involves the design of a node placement policy, a serpentine movement policy, obstacle-handling rules, and boundary rules. By applying the proposed ORRD, the robot rapidly deploys a near-minimal number of sensor nodes to achieve full sensing coverage, even though there exist unpredicted obstacles with regular or irregular shapes. Performance results reveal that ORRD outperforms the existing robot deployment mechanism in terms of power conservation and obstacle resistance and, therefore, achieves better deployment performance. Index Terms—Deployment, obstacles, wireless sensor network (WSNs). I.
Novel Algorithms for the Network Lifetime Problem in Wireless Settings
"... Abstract. A wireless ad-hoc network is a collection of transceivers positioned in the plane. Each transceiver is equipped with a limited, nonreplenishable battery charge. The battery charge is then reduced after each transmission, depending on the transmission distance. One of the major problems in ..."
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Abstract. A wireless ad-hoc network is a collection of transceivers positioned in the plane. Each transceiver is equipped with a limited, nonreplenishable battery charge. The battery charge is then reduced after each transmission, depending on the transmission distance. One of the major problems in wireless network design is to route network traffic efficiently so as to maximize the network lifetime, i.e., the number of successful transmissions. This problem is known to be NP-Hard for a variety of network operations. In this paper we are interested in two fundamental types of transmissions, broadcast and data gathering. We provide polynomial time approximation algorithms, with guaranteed performance bounds, for the maximum lifetime problem under two communication models, omnidirectional and unidirectional antennas. We also consider an extended variant of the maximum lifetime problem, which simultaneously satisfies additional constraints, such as bounded hop-diameter and degree of the routing tree, and minimizing the total energy used in a single transmission. 1
LAD: A Routing Algorithm to Prolong the Lifetime of Wireless Sensor Networks
"... Abstract — As nodes in wireless sensor networks are usually supplied by a simple non rechargeable battery, the energy available in these nodes is very limited. Moreover, this limited energy is mostly consumed in transmission and reception of data. As transmission and reception is highly affected by ..."
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Abstract — As nodes in wireless sensor networks are usually supplied by a simple non rechargeable battery, the energy available in these nodes is very limited. Moreover, this limited energy is mostly consumed in transmission and reception of data. As transmission and reception is highly affected by routing algorithms, designing a proper routing algorithm will prolong the network lifetime. In this paper we proposed a novel routing algorithm, called Locality-Aware Diffusion (LAD) that leverages spatial locality of sensed data in sensor networks to reduce energy consumption and prolong the network lifetime. The proposed algorithm is an extension to Shortest Path Tree which is a common approach in routing algorithms. Simulation results show the validity and effectiveness of the proposed algorithm. I.
Improved Approximation Algorithms for Maximum Lifetime Problems in Wireless Networks
"... A wireless ad-hoc network is a collection of transceivers positioned in the plane. Each transceiver is equipped with a limited battery charge. The battery charge is then reduced after each transmission, depending on the transmission distance. One of the major problems in wireless network design is t ..."
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A wireless ad-hoc network is a collection of transceivers positioned in the plane. Each transceiver is equipped with a limited battery charge. The battery charge is then reduced after each transmission, depending on the transmission distance. One of the major problems in wireless network design is to route network traffic efficiently so as to maximize the network lifetime, i.e., the number of successful transmissions. In this paper we consider Rooted Maximum Lifetime Broadcast/Convergecast problems in wireless settings. The instance consists of a directed graph G = (V, E) with edge-weights {w(e) : e ∈ E}, node capacities {b(v) : v ∈ V}, and a root r. The goal is to find a maximum size collection {T1,..., Tk} of Broadcast/Convergecast trees rooted at r so that ∑k i=1 w(δTi(v)) ≤ b(v), where δT (v) is the set of edges leaving v in T. In the Single Topology version all the Broadcast/Convergecast trees Ti are identical. We present a number of polynomial time algorithms giving constant ratio approximation for various broadcast and convergecast problems, improving previously known result of Ω(⌊1 / log n⌋)-approximation by [6]. We also consider a generalized Rooted Maximum Lifetime Mixedcast problem, where we are also given an integer γ ≥ 0, and the goal is to find the maximum integer k so that k Broadcast and γk Convergecast rounds can be performed. 1
Stream Data Gathering in Wireless Sensor Networks Within Expected Lifetime
- the Proceedings of MobiMedia’07, Nafpaktos Greece
"... Sensor networks aim at collecting important sensor data for environment monitoring, e-health or hazardous conditions. Some applications do not need sensor networks with a long lifetime, such as monitoring an erupting volcano or monitoring hazardous conditions. These applications generally expect tha ..."
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Sensor networks aim at collecting important sensor data for environment monitoring, e-health or hazardous conditions. Some applications do not need sensor networks with a long lifetime, such as monitoring an erupting volcano or monitoring hazardous conditions. These applications generally expect that sensor networks have reliable performance and provide continuous data streams during a short expected lifetime. In this work, we investigate the stream data gathering problem in sensor networks within an expected lifetime. Two important problems for stream data gathering are: 1) maximizing stream data gathering in wireless sensor networks within an expected lifetime; 2) minimizing transmission delay for stream data gathering in wireless sensor networks within an expected lifetime. The Maximum Stream Data Gathering (MSDG) algorithm and the Minimum Transmission Delay (MTD) algorithm are proposed to solve these two problems. Simulation results show that our algorithms can essentially solve the identified problems.
An Online Prediction Framework for Sensor Networks
"... Abstract: This paper presents a novel approach to online prediction in sensor networks based on temporal correlation of sensed data. The proposed approach greatly reduces the amount of data transmitted by sensor nodes and thus increasing network lifetime. Current prediction frameworks in sensor netw ..."
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Abstract: This paper presents a novel approach to online prediction in sensor networks based on temporal correlation of sensed data. The proposed approach greatly reduces the amount of data transmitted by sensor nodes and thus increasing network lifetime. Current prediction frameworks in sensor networks use an offline created model to predict the sensed value. In contrast, our approach creates and uses a prediction model in an online manner, no extra buffering or model creation delay is needed. Moreover, the amount of error caused by using our framework is bounded and often negligible.
Dynamic state-based routing for load-balancing and efficient data-gathering in Wireless Sensor Networks
"... Data gathering is a fundamental operation in wireless sensor networks. For the online data gathering problem, we consider the key issues of balancing the load on the nodes to achieve longer network lifetime, and that of balancing the load on the network links to achieve greater reliability in the ne ..."
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Data gathering is a fundamental operation in wireless sensor networks. For the online data gathering problem, we consider the key issues of balancing the load on the nodes to achieve longer network lifetime, and that of balancing the load on the network links to achieve greater reliability in the network. We model the given network as a shortest-distance DAG D, which defines a set of parent nodes for each node that determine the minimum-hops paths from the node to a sink. Data gathering in D is accomplished using a dynamic routing approach, where each node selects a parent using a parent selection function σ to forward the sensed data, which generates a dynamic forest (D, σ) in the network. We investigate a dynamic state-based routing approach where σ is defined using the current state of the network. Based on our earlier work [1], we propose two dynamic state-based routing algorithms — MPE Routing and WPE Routing, that aim to load-balance the nodes as well as the edges of D in order to achieve longer network lifetime as well as greater disjointness. We evaluate the performance of our algorithms with respect to the three goodness measures — network lifetime, nodal load-balancing and disjointness, and compare it with two benchmark algorithms as well as existing data gathering schemes. Our simulation results show that our algorithms perform consistently better with respect to our goodness measures than the benchmark algorithms and other approaches. 1
A Pull Based Energy Efficient Data Aggregation Scheme for Wireless Sensor Networks
"... In case of wireless routing in sensor networks, data aggregation has been proposed as a predominantly constructive prototype. Most of the routing algorithms for traditional networks are address centric, and the ad hoc nature of wireless sensor network makes them unsuitable for practical applications ..."
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In case of wireless routing in sensor networks, data aggregation has been proposed as a predominantly constructive prototype. Most of the routing algorithms for traditional networks are address centric, and the ad hoc nature of wireless sensor network makes them unsuitable for practical applications. Data-centric technologies that carry out in-network aggregation of data to capitulate energy-efficient dissemination are essential. In this paper, we propose a Pull based Energy Efficient Data Aggregation (PEEDA) approach, to effectively deliver the data to the sink. In this approach, the sink will broadcast an interest message containing its required data model, to all the nodes. We form an cost effective aggregation tree towards the sink based on the ToD structure. When the aggregator receives the data from the sources, it aggregates the data depending on the interest message using spatial and temporal convergence. To achieve energy efficient aggregation, the MAC protocol uses the partially overlapped channels. By simulation results, we show that the proposed scheme consumes less energy and reduces the overhead and delay.
EUCLIDEAN STEINER SHALLOW-LIGHT TREES
- JOURNAL OF COMPUTATIONAL GEOMETRY
, 2015
"... A spanning tree that simultaneously approximates a shortest-path tree and a minimum spanning tree is called a shallow-light tree (shortly, SLT). More specifically, an (α, β)-SLT of a weighted undirected graph G = (V,E,w) with respect to a designated vertex rt ∈ V is a spanning tree of G with: (1) r ..."
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A spanning tree that simultaneously approximates a shortest-path tree and a minimum spanning tree is called a shallow-light tree (shortly, SLT). More specifically, an (α, β)-SLT of a weighted undirected graph G = (V,E,w) with respect to a designated vertex rt ∈ V is a spanning tree of G with: (1) root-stretch α – it preserves all distances between rt and the other vertices up to a factor of α, and (2) lightness β – it has weight at most β times the weight of a minimum spanning tree MST (G) of G. Tight tradeoffs between the parameters of SLTs were established by Awerbuch et al. in PODC’90 and by Khuller et al. in SODA’93. They showed that for any > 0, any graph admits a (1 + , O(1 ))-SLT with respect to any root vertex, and complemented this result with a matching lower bound. Khuller et al. asked if the upper bound β = O(1 ) on the lightness of SLTs can be improved in Euclidean spaces. In FOCS’11 Elkin and this author gave a negative answer to this question, showing a lower bound of β = Ω(1 ) that applies to 2-dimensional Euclidean spaces. In this paper we show that Steiner points lead to a quadratic improvement in Eu-clidean SLTs, by presenting a construction of Euclidean Steiner (1 + , O( 1
