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29
ZMAC: a Hybrid MAC for Wireless Sensor Networks
, 2005
"... ZMAC is a hybrid MAC protocol for wireless sensor networks. It combines the strengths of TDMA and CSMA while offsetting their weaknesses. Nodes are assigned time slots using a distributed implementation of RAND. Unlike TDMA where a node is allowed to transmit only during its own assigned slots, a n ..."
Abstract

Cited by 169 (6 self)
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ZMAC is a hybrid MAC protocol for wireless sensor networks. It combines the strengths of TDMA and CSMA while offsetting their weaknesses. Nodes are assigned time slots using a distributed implementation of RAND. Unlike TDMA where a node is allowed to transmit only during its own assigned slots, a node can transmit in both its own time slots and slots assigned to other nodes. Owners of the current time slot always have priority in accessing the channel over nonowners. Therefore, under low contention where not all owners have data to send, nonowners can “steal ” time slots from owners. This has the effect of switching between CSMA and TDMA depending on contention. ZMAC is robust to topology changes and clock synchronization errors; in the worst case its performance falls back to that of CSMA. We implemented ZMAC in TinyOS and evaluated its channel utilization, energy, latency and fairness over singlehop, twohop and multihop sensor network topologies constructed using Mica2. The result shows that ZMAC has remarkably better data throughput than existing sensor MAC protocols while consuming comparable energy (over three times better throughput under high contention).
QoS routing for mobile ad hoc networks
, 2002
"... A QualityofService (QoS) routing protocol is developed for mobile ad hoc networks. It can establish QoS routes with reserved bandwidth on a per flow basis in a network employing TDMA. An efficient algorithm for calculating the endtoend bandwidth on a path is developed and used together with the ..."
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Cited by 90 (0 self)
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A QualityofService (QoS) routing protocol is developed for mobile ad hoc networks. It can establish QoS routes with reserved bandwidth on a per flow basis in a network employing TDMA. An efficient algorithm for calculating the endtoend bandwidth on a path is developed and used together with the route discovery mechanism of AODV to setup QoS routes. In our simulations the QoS routing protocol produces higher throughput and lower delay than its besteffort counterpart.
Models and Approximation Algorithms for Channel Assignment in Radio Networks
, 2000
"... We consider the frequency assignment (broadcast scheduling) problem for packet radio networks. Such networks are naturally modeled by graphs with a certain geometric structure. The problem of broadcast scheduling can be cast as a variant of the vertex coloring problem (called the distance2 coloring ..."
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Cited by 72 (3 self)
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We consider the frequency assignment (broadcast scheduling) problem for packet radio networks. Such networks are naturally modeled by graphs with a certain geometric structure. The problem of broadcast scheduling can be cast as a variant of the vertex coloring problem (called the distance2 coloring problem) on the graph that models a given packet radio network. We present efficient approximation algorithms for the distance2 coloring problem for various geometric graphs including those that naturally model a large class of packet radio networks. The class of graphs considered include (r, s)civilized graphs, planar graphs, graphs with bounded genus, etc.
DRAND: Distributed randomized TDMA scheduling for wireless ad hoc networks
 in MobiHoc
, 2006
"... This paper presents a distributed implementation of RAND, a randomized time slot scheduling algorithm, called DRAND. DRAND runs in O(δ) time and message complexity where δ is the maximum size of a twohop neighborhood in a wireless network while message complexity remains O(δ), assuming that message ..."
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Cited by 49 (1 self)
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This paper presents a distributed implementation of RAND, a randomized time slot scheduling algorithm, called DRAND. DRAND runs in O(δ) time and message complexity where δ is the maximum size of a twohop neighborhood in a wireless network while message complexity remains O(δ), assuming that message delays can be bounded by an unknown constant. DRAND is the first fully distributed version of RAND. The algorithm is suitable for a wireless network where most nodes do not move, such as wireless mesh networks and wireless sensor networks. We implement the algorithm in TinyOS and demonstrate its performance in a real testbed of Mica2 nodes. The algorithm does not require any time synchronization and is shown to be effective in adapting to local topology changes without incurring global overhead in the scheduling. Because of these features, it can also be used even for other scheduling problems such as frequency or code scheduling (for FDMA or CDMA) or local identifier assignment for wireless networks where time synchronization is not enforced.
The Distance2 Matching Problem and its Relationship to the MACLayer Capacity of Ad Hoc Wireless Networks
 IEEE Journal on Selected Areas in Communications
, 2004
"... Abstract—We consider the problem of determining the maximum capacity of the media access (MAC) layer in wireless ad hoc networks. Due to spatial contention for the shared wireless medium, not all nodes can concurrently transmit packets to each other in these networks. The maximum number of possible ..."
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Cited by 42 (5 self)
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Abstract—We consider the problem of determining the maximum capacity of the media access (MAC) layer in wireless ad hoc networks. Due to spatial contention for the shared wireless medium, not all nodes can concurrently transmit packets to each other in these networks. The maximum number of possible concurrent transmissions is, therefore, an estimate of the maximum network capacity, and depends on the MAC protocol being used. We show that for a large class of MAC protocols based on virtual carrier sensing using RTS/CTS messages, which includes the popular IEEE 802.11 standard, this problem may be modeled as a maximum Distance2 matching (D2EMIS) in the underlying wireless network: Given a graph @ A, find a set of edges such that no two edges in are connected by another edge in. D2EMIS is NPcomplete. Our primary goal is to show that it
Bounds for the capacity of wireless multihop networks imposed by topology and demand
 in Proc. ACM MobiHoc
, 2007
"... Existing work on the capacity of wireless networks predominantly considers homogeneous random networks with random work load. The most relevant bounds on the network capacity, e.g., take into account only the number of nodes and the area of the network. However, these bounds can significantly overes ..."
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Cited by 27 (0 self)
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Existing work on the capacity of wireless networks predominantly considers homogeneous random networks with random work load. The most relevant bounds on the network capacity, e.g., take into account only the number of nodes and the area of the network. However, these bounds can significantly overestimate the achievable capacity in real world situations where network topology or traffic patterns often deviate from these simplistic assumptions. To provide analytically tractable yet asymptotically tight approximations of network capacity we propose a novel spacebased approach. At the heart of our methodology lie simple functions which indicate the presence of active transmissions near any given location in the network and which constitute a tool well suited to untangle the interactions of simultaneous transmissions. We are able to provide capacity bounds which are tighter than the traditional ones and which involve topology and traffic patterns explicitly, e.g., through the length of Euclidean Minimum Spanning Tree, or through traffic demands between clusters of nodes. As an additional novelty our results cover unicast, multicast and broadcast and are asymptotically tight. Notably, our capacity bounds are simple enough to require only knowledge of node location, and there is no need for solving or optimizing multivariable equations in our approach.
Distributed onDemand Address Assignment in Wireless Sensor Networks
 IEEE SIGNAL PROCESSING
, 2002
"... Sensor networks consist of autonomous wireless sensor nodes that are networked together in an adhoc fashion. The tiny nodes are equipped with substantial processing capabilities, enabling them to combine and compress their sensor data. The aim is to limit the amount of network traffic, and as such ..."
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Cited by 25 (0 self)
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Sensor networks consist of autonomous wireless sensor nodes that are networked together in an adhoc fashion. The tiny nodes are equipped with substantial processing capabilities, enabling them to combine and compress their sensor data. The aim is to limit the amount of network traffic, and as such conserve the nodes' limited battery energy. However, due to the small packet payload, the MAC header is a significant, and energycostly, overhead. To remedy this, we propose a novel scheme for MAC address assignment. The two key features which make our approach unique are the exploitation of spatial address reuse and an encoded representation of the addresses in data packets. To assign the addresses, we develop a purely distributed algorithm that relies solely on local messsage exchanges. Other salient features of our approach are the ability to handle unidirectional links and the excellent scalability of both the assignment algorithm and address representation. In typical scenarios, the MAC overhead is reduced by a factor of three compared to existing approaches.
Approximation Algorithms for Channel Assignment in Radio Networks
 Wireless Networks
, 1998
"... We consider the channel assignment (broadcast scheduling) problem for packet radio networks. Such networks are naturally modeled by graphs with certain geometric structure. The channel assignment problem can be cast as a variant of the vertex coloring problem (called the distance2 coloring problem) ..."
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Cited by 24 (1 self)
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We consider the channel assignment (broadcast scheduling) problem for packet radio networks. Such networks are naturally modeled by graphs with certain geometric structure. The channel assignment problem can be cast as a variant of the vertex coloring problem (called the distance2 coloring problem) on the graph that models a given packet radio network. We present efficient approximation algorithms for the distance2 coloring problem for several classes of graphs including a class of geometric graphs that naturally model a large class of packet radio networks. The classes of graphs considered include (r, s)civilized graphs, planar graphs, graphs with bounded genus, etc. Many of the approximation results presented here are the first such results in the literature.
Approximation Algorithms for Computing Capacity of Wireless Networks with SINR constraints
"... Abstract—A fundamental problem in wireless networks is to estimate its throughput capacity given a set of wireless nodes, and a set of connections, what is the maximum rate at which data can be sent on these connections. Most of the research in this direction has focused on either random distributi ..."
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Cited by 24 (1 self)
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Abstract—A fundamental problem in wireless networks is to estimate its throughput capacity given a set of wireless nodes, and a set of connections, what is the maximum rate at which data can be sent on these connections. Most of the research in this direction has focused on either random distributions of points, or has assumed simple graphbased models for wireless interference. In this paper, we study capacity estimation problem using the more general Signal to Interference Plus Noise Ratio (SINR) model for interference, on arbitrary wireless networks. The problem becomes much harder in this setting, because of the nonlocality of the SINR model. Recent work by Moscibroda et al. [16], [18] has shown that the throughput in this model can differ from graph based models significantly. We develop polynomial time algorithms to provably approximate the total throughput in this setting. I.
CrossLayer Latency Minimization in Wireless Networks with SINR Constraints
 MOBIHOC’07, SEPTEMBER 9–14, 2007, MONTREAL, QUEBEC, CANADA
, 2007
"... Recently, there has been substantial interest in the design of cross
layer protocols for wireless networks. These protocols optimize
certain performance metric(s) of interest (e.g. latency, energy, rate)
by jointly optimizing the performance of multiple layers of the
protocol stack. Algorithm desig ..."
Abstract

Cited by 22 (1 self)
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Recently, there has been substantial interest in the design of cross
layer protocols for wireless networks. These protocols optimize
certain performance metric(s) of interest (e.g. latency, energy, rate)
by jointly optimizing the performance of multiple layers of the
protocol stack. Algorithm designers often use geometricgraph
theoretic models for radio interference to design such crosslayer
protocols. In this paper we study the problem of designing cross
layer protocols for multihop wireless networks using a more real
istic Signal to Interference plus Noise Ratio (SINR) model for radio
interference. The following crosslayer latency minimization prob
lem is studied: Given a set V of transceivers, and a set of source
destination pairs, (i) choose power levels for all the transceivers, (ii)
choose routes for all connections, and (iii) construct an endtoend
schedule such that the SINR constraints are satisfied at each time
step so as to minimize the makespan of the schedule (the time
by which all packets have reached their respective destinations).
We present a polynomialtime algorithm with provable worstcase
performance guarantee for this crosslayer latency minimization
problem. As corollaries of the algorithmic technique we show that
a number of variants of the crosslayer latency minimization prob
lem can also be approximated efficiently in polynomial time. Our
work extends the results of Kumar et al. (Proc. SODA, 2004) and
Moscibroda et al. (Proc. MOBIHOC, 2006). Although our algo
rithm considers multiple layers of the protocol stack, it can natu
rally be viewed as compositions of tasks specific to each layer —
this allows us to improve the overall performance while preserving
the modularity of the layered structure.