Efficient interference-aware TDMA link scheduling for static wireless networks

Wang, Weizhao; Wang, Weichao; Li, Xiangyang; Song, Wei; Frieder, Ophir

doi:10.1145/1161089.1161119

Cited by 221 publications

(219 citation statements)

References 27 publications

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“…Phase 1 uses a weighted graph-coloring heuristic algorithm [13] to compute the transmission cycle of a given multicast tree. Phase 2 formulates a simple linear program (LP) to compute the time interval needed between each time unit of a cycle.…”

Section: A Generating a Random Multicast Tree Solutionmentioning

confidence: 99%

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Multi-objective multicast routing optimization in Cognitive Radio Networks

Kamal

2014

2014 IEEE Wireless Communications and Networking Conference (WCNC)

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Abstract-In this paper, we study the multicast routing problem in Cognitive Radio Networks (CRNs). We propose a new network modeling method, where we model CRNs using a Multi-rate Multilayer Hyper-Graph (MMHG). Given a multicast session of the MMHG, our goal is to find the multicast routing trees that minimize the worst case end-to-end delay (delay), maximize the multicast rate (rate) and minimize the number of transmission links (numOfLinks) used in the multicast tree. We apply two metaheuristic algorithms (Multi-Objective Ant Colony System optimization algorithm (MOACS) [1] and A Simulated Annealing-Based Multi-objective Optimization Algorithm (AMOSA) [2]) in solving the problem. We also study the scheduling problem of multicast routing trees obtained using the MMHG model. Our simulation results show that within a few seconds, MOACS can find more than 60% of the approximated Pareto Front (APF) in small CRNs, and AMOSA can find approximately 45%. Moreover, the solutions found by MOACS and AMOSA that are not in the APF are within 10% relative distances to solutions in the APF.

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Section: A Generating a Random Multicast Tree Solutionmentioning

confidence: 99%

“…With the directed conflict graph F g and weights for all Csupernodes, we apply the weighted graph-coloring algorithm proposed in reference [13] to compute the transmission cycle.…”

Section: A Generating a Random Multicast Tree Solutionmentioning

confidence: 99%

Multi-objective multicast routing optimization in Cognitive Radio Networks

Kamal

2014

2014 IEEE Wireless Communications and Networking Conference (WCNC)

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“…The node-based scheduling algorithm has been adapted from classical multi-hop scheduling algorithm developed for general ad hoc networks with the idea of scheduling as many non-conflicting set of nodes as possible in each time slot [12,19,20]. The algorithm has two parts.…”

Section: Node-based Scheduling Algorithmmentioning

confidence: 99%

“…2. The interferers of the nodes are determined according to the fixed power protocol interference model [12]: Each node has its own fixed transmission power and each node has an interference range r m such that any node v j will be interfered by the signal from v k if the distance between them is less than r m and node v k is sending signal to some node other than node v j . Therefore, C = (V,I) contain the nodes that are inside a larger range r m , r m C r s , of each node other than its parent and children in the routing tree G. In the coloring part of node and level-based scheduling algorithms, the nodes are ordered in non-increasing order of degree since high degree vertices have more color constraints and so are more likely to require an additional color if inserted late.…”

Section: Simulationmentioning

confidence: 99%

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TDMA scheduling algorithms for wireless sensor networks

Coleri¹,

2009

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Algorithms for scheduling TDMA transmissions in multi-hop networks usually determine the smallest length conflict-free assignment of slots in which each link or node is activated at least once. This is based on the assumption that there are many independent point-to-point flows in the network. In sensor networks however often data are transferred from the sensor nodes to a few central data collectors. The scheduling problem is therefore to determine the smallest length conflict-free assignment of slots during which the packets generated at each node reach their destination. The conflicting node transmissions are determined based on an interference graph, which may be different from connectivity graph due to the broadcast nature of wireless transmissions. We show that this problem is NP-complete. We first propose two centralized heuristic algorithms: one based on direct scheduling of the nodes or node-based scheduling, which is adapted from classical multi-hop scheduling algorithms for general ad hoc networks, and the other based on scheduling the levels in the routing tree before scheduling the nodes or levelbased scheduling, which is a novel scheduling algorithm for many-to-one communication in sensor networks. The performance of these algorithms depends on the distribution of the nodes across the levels. We then propose a distributed algorithm based on the distributed coloring of the nodes, that increases the delay by a factor of 10-70 over centralized algorithms for 1000 nodes. We also obtain upper bound for these schedules as a function of the total number of packets generated in the network.

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A backoff differentiation scheme for contention resolution in wireless converge‐cast networks

Wang

Zhou

Qin

et al. 2012

Concurrency and Computation

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Wireless converge-cast networks (WCNs), such as data collection-based wireless sensor networks, exhibit certain phenomena called funneling effect, where the region close to the sink node is heavily overloaded. In this paper, we identify that the funneling effect occurs not only close to the sink but also within the network region where nodes have collision and induce heavy traffic to relay; we name it hot-spot funneling effect. This paper aims to improve the throughput and fairness of WCNs by mitigating the micro funneling effect. We propose a new mechanism, the backoff differentiation for contention resolution (BDCR), which is targeted to a system-wide high throughput on the basis of the contention resolution mechanism. To achieve high spatial reuses, BDCR divides the network into several regions and does backoff differentiation within each region. Within each backoff differentiation region, the backoff window range is adjusted according to the traffic rate, and at the same time, the backoff values are set with the awareness of the traffic intensity level. All regions share the same algorithm, which uses Kelly's rate control theory and method to allow each sensor to locally adjust its backoff value. One of the key advantages of BDCR is that it is extremely easy to implement. With extensive simulations and testbed experiments, BDCR is proved to achieve much higher throughput over the traditional carrier sense multiple access and some recent media access control protocols in literature, particularly when the network suffers intensive congestions.

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Efficient interference-aware TDMA link scheduling for static wireless networks

Cited by 221 publications

References 27 publications

Multi-objective multicast routing optimization in Cognitive Radio Networks

Multi-objective multicast routing optimization in Cognitive Radio Networks

TDMA scheduling algorithms for wireless sensor networks

A backoff differentiation scheme for contention resolution in wireless converge‐cast networks

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