Design and analysis of an MST-based topology control algorithm
09 Jul 2003-Vol. 3, pp 1702-1712
TL;DR: This paper analytically prove several important properties of LMST: 1) the topology derived under LMST preserves the network connectivity; 2) the node degree of any node in the resulting topology is bounded by 6; and 3) the bottomology can be transformed into one with bidirectional links after removal of all unidirectional Links.
Abstract: In this paper, we present a minimum spanning tree (MST) based topology control algorithm, called local minimum spanning tree (LMST), for wireless multi-hop networks. In this algorithm, each node builds its local minimum spanning tree independently and only keeps on-tree nodes that are one-hop away as its neighbors in the final topology. We analytically prove several important properties of LMST: (1) the topology derived under LMST preserves the network connectivity; (2) the node degree of any node in the resulting topology is bounded by 6; and (3) the topology can be transformed into one with bidirectional links (without impairing the network connectivity) after removal of all uni-directional links. These results are corroborated in the simulation study.
Figures (10)
Fig. 1. A scenario that demonstrates that links in the topology derived under LMST may be uni-directional. 
TABLE I THE MAXIMUM, MINIMUM, AND AVERAGE DEGREES IN THE NETWORK TOPOLOGIES DERIVED USING THE MAXIMUM TRANSMISSION POWER, R&M(TWO-RAY GROUND MODEL), CBTC(5/6π), LMST, AND LMST WITH UNI-DIRECTIONAL LINKS REMOVED. 
Fig. 4. Proof of Theorem 3. 
Fig. 9. Performance comparisons (w.r.t. power consumption) between LMST (with uni-directional link removal) and R&M. 
Fig. 5. Calculation of the probabilities that a new neighbor moves into the transmission range of a node and that an existing neighbor moves out of the transmission range, within a time interval of t. 
Fig. 8. Performance comparisons (w.r.t. degree, radius, and average length of links) among different algorithms. 
Fig. 6. The information update period versus the maximum speed with respect to different values of pth. 
Fig. 7. Network topologies derived under different algorithms. 
Fig. 3. The degree of any node in G0 is bounded by 6. 
Fig. 2. The definition of cone(u, α, v).
Citations
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12 Aug 2005
TL;DR: In this article, the authors state several problems related to topology control in wireless ad hoc and sensor networks, and survey state-of-the-art solutions which have been proposed to tackle them.
Abstract: Topology Control (TC) is one of the most important techniques used in wireless ad hoc and sensor networks to reduce energy consumption (which is essential to extend the network operational time) and radio interference (with a positive effect on the network traffic carrying capacity). The goal of this technique is to control the topology of the graph representing the communication links between network nodes with the purpose of maintaining some global graph property (e.g., connectivity), while reducing energy consumption and/or interference that are strictly related to the nodes' transmitting range. In this article, we state several problems related to topology control in wireless ad hoc and sensor networks, and we survey state-of-the-art solutions which have been proposed to tackle them. We also outline several directions for further research which we hope will motivate researchers to undertake additional studies in this field.
1,367 citations
24 May 2004
TL;DR: This paper provides a concise and intuitive definition of interference and shows that most currently proposed topology control algorithms do not effectively constrain interference and proposes connectivity-preserving an spanner constructions that are interference-minimal.
Abstract: Topology control in ad-hoc networks tries to lower node energy consumption by reducing transmission power and by confining interference, collisions and consequently retransmissions. Commonly low interference is claimed to be a consequence to sparseness of the resulting topology. In this paper we disprove this implication. In contrast to most of the related work claiming to solve the interference issue by graph sparseness without providing clear argumentation or proofs, we provide a concise and intuitive definition of interference. Based on this definition we show that most currently proposed topology control algorithms do not effectively constrain interference. Furthermore we propose connectivity-preserving an spanner constructions that are interference-minimal.
569 citations
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TL;DR: A definition and framework for a novel type of adaptive data network: the cognitive network, in which the collection of elements that make up the network observes network conditions and then, using prior knowledge gained from previous interactions with the network, plans, decides and acts on this information.
Abstract: This paper presents a definition and framework for a novel type of adaptive data network: the cognitive network. In a cognitive network, the collection of elements that make up the network observes network conditions and then, using prior knowledge gained from previous interactions with the network, plans, decides and acts on this information. Cognitive networks are different from other "intelligent" communication technologies because these actions are taken with respect to the end-to-end goals of a data flow. In addition to the cognitive aspects of the network, a specification language is needed to translate the user's end-to-end goals into a form understandable by the cognitive process. The cognitive network also depends on a software adaptable network that has both an external interface accessible to the cognitive network and network status sensors. These devices are used to provide control and feedback. The paper concludes by presenting a simple case study to illustrate a cognitive network and its framework
544 citations
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28 Jul 2003
TL;DR: The state of the art with respect to general research challenges is discussed, then more specific research challenges that appear in the networking, operating system, and middleware layers are focused on.
Abstract: Sensor networks can be considered distributed computing platforms with many severe constraints, including limited CPU speed, memory size, power, and bandwidth. Individual nodes in sensor networks are typically unreliable and the network topology dynamically changes, possibly frequently. Sensor networks also differ because of their tight interaction with the physical environment via sensors and actuators. Because of this interaction, we find that sensor networks are very data-centric. Due to all of these differences, many solutions developed for general distributed computing platforms and for ad-hoc networks cannot be applied to sensor networks. After discussing several motivating applications, this paper first discusses the state of the art with respect to general research challenges, then focuses on more specific research challenges that appear in the networking, operating system, and middleware layers. For some of the research challenges, initial solutions or approaches are identified.
483 citations
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References
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TL;DR: When n identical randomly located nodes, each capable of transmitting at W bits per second and using a fixed range, form a wireless network, the throughput /spl lambda/(n) obtainable by each node for a randomly chosen destination is /spl Theta/(W//spl radic/(nlogn)) bits persecond under a noninterference protocol.
Abstract: When n identical randomly located nodes, each capable of transmitting at W bits per second and using a fixed range, form a wireless network, the throughput /spl lambda/(n) obtainable by each node for a randomly chosen destination is /spl Theta/(W//spl radic/(nlogn)) bits per second under a noninterference protocol. If the nodes are optimally placed in a disk of unit area, traffic patterns are optimally assigned, and each transmission's range is optimally chosen, the bit-distance product that can be transported by the network per second is /spl Theta/(W/spl radic/An) bit-meters per second. Thus even under optimal circumstances, the throughput is only /spl Theta/(W//spl radic/n) bits per second for each node for a destination nonvanishingly far away. Similar results also hold under an alternate physical model where a required signal-to-interference ratio is specified for successful receptions. Fundamentally, it is the need for every node all over the domain to share whatever portion of the channel it is utilizing with nodes in its local neighborhood that is the reason for the constriction in capacity. Splitting the channel into several subchannels does not change any of the results. Some implications may be worth considering by designers. Since the throughput furnished to each user diminishes to zero as the number of users is increased, perhaps networks connecting smaller numbers of users, or featuring connections mostly with nearby neighbors, may be more likely to be find acceptance.
9,008 citations
"Design and analysis of an MST-based..." refers background in this paper
...For one, as shown in [4], it affects network spatial reuse and hence the traffic carrying capacity....
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25 Oct 1998
TL;DR: The results of a derailed packet-levelsimulationcomparing fourmulti-hopwirelessad hoc networkroutingprotocols, which cover a range of designchoices: DSDV,TORA, DSR and AODV are presented.
Abstract: An ad hoc networkis a collwtion of wirelessmobilenodes dynamically forminga temporarynetworkwithouttheuseof anyexistingnetworkirrfrastructureor centralizedadministration.Dueto the limitedtransmissionrange of ~vlrelessnenvorkinterfaces,multiplenetwork“hops”maybe neededfor onenodeto exchangedata ivithanotheracrox thenetwork.Inrecentyears, a ttiery of nelvroutingprotocols~geted specificallyat this environment havebeen developed.but little pcrfomrartwinformationon mch protocol and no ralistic performancecomparisonbehvwrrthem ISavailable. ~Is paper presentsthe results of a derailedpacket-levelsimulationcomparing fourmulti-hopwirelessad hoc networkroutingprotocolsthatcovera range of designchoices: DSDV,TORA, DSR and AODV. \Vehave extended the /~r-2networksimulatorto accuratelymodelthe MACandphysical-layer behaviorof the IEEE 802.1I wirelessLANstandard,includinga realistic wtrelesstransmissionchannelmodel, and present the resultsof simulations of net(vorksof 50 mobilenodes.
5,147 citations
"Design and analysis of an MST-based..." refers result in this paper
...This is in contrast to the “traditional” network in which each node transmits using its maximum transmission power and the topology is built implicitly by routing protocols (that update their routing caches as timely as possible) [2] [3] without considering the power issue....
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TL;DR: In this paper, the basic problem of interconnecting a given set of terminals with a shortest possible network of direct links is considered, and a set of simple and practical procedures are given for solving this problem both graphically and computationally.
Abstract: The basic problem considered is that of interconnecting a given set of terminals with a shortest possible network of direct links Simple and practical procedures are given for solving this problem both graphically and computationally It develops that these procedures also provide solutions for a much broader class of problems, containing other examples of practical interest
4,395 citations
"Design and analysis of an MST-based..." refers background in this paper
...Definition 8 (Cone): As shown in Figure 2, a cone(u, α, v) is the region in the plane that lies between OA and OB, where ∠COA = ∠COB = α/2....
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01 Jan 2003
TL;DR: This paper presents APIT, a novel localization algorithm that is range-free, and shows that the APIT scheme performs best when an irregular radio pattern and random node placement are considered, and low communication overhead is desired.
Abstract: Sensor Networks have been proposed for a multitude of location-dependent applications. For such systems, the cost and limitations of the hardware on sensing nodes prevent the use of range-based localization schemes that depend on absolute point- to-point distance estimates. Because coarse accuracy is sufficient for most sensor network applications, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. In this paper, we present APIT, a novel localization algorithm that is range-free. We show that our APIT scheme performs best when an irregular radio pattern and random node placement are considered, and low communication overhead is desired. We compare our work via extensive simulation, with three state-of-the-art range-free localization schemes to identify the preferable system configurations of each. In addition, we study the effect of location error on routing and tracking performance. We show that routing performance and tracking accuracy are not significantly affected by localization error when the error is less than 0.4 times the communication radio radius.
2,515 citations
"Design and analysis of an MST-based..." refers methods in this paper
...Then we present a distributed topology control algorithm called LMST (Local Minimum Spanning Tree)....
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TL;DR: Using F-heaps, a new data structure for implementing heaps that extends the binomial queues proposed by Vuillemin and studied further by Brown, the improved bound for minimum spanning trees is the most striking.
Abstract: In this paper we develop a new data structure for implementing heaps (priority queues). Our structure, Fibonacci heaps (abbreviated F-heaps), extends the binomial queues proposed by Vuillemin and studied further by Brown. F-heaps support arbitrary deletion from an n-item heap in O(log n) amortized time and all other standard heap operations in O(1) amortized time. Using F-heaps we are able to obtain improved running times for several network optimization algorithms. In particular, we obtain the following worst-case bounds, where n is the number of vertices and m the number of edges in the problem graph: O(n log n + m) for the single-source shortest path problem with nonnegative edge lengths, improved from O(mlog(m/n+2)n);O(n2log n + nm) for the all-pairs shortest path problem, improved from O(nm log(m/n+2)n);O(n2log n + nm) for the assignment problem (weighted bipartite matching), improved from O(nmlog(m/n+2)n);O(mβ(m, n)) for the minimum spanning tree problem, improved from O(mlog log(m/n+2)n); where β(m, n) = min {i | log(i)n ≤ m/n}. Note that β(m, n) ≤ log*n if m ≥ n.Of these results, the improved bound for minimum spanning trees is the most striking, although all the results give asymptotic improvements for graphs of appropriate densities.
2,484 citations