Minimum-Latency Broadcast Scheduling in Wireless Ad Hoc Networks

Huang, Scott C.-H.; Wan, Peng‐Jun; Jia, Xiaohua; Du, Hongwei; Shang, Weiping

doi:10.1109/infcom.2007.91

Cited by 110 publications

(103 citation statements)

References 18 publications

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“…If u has only one child of rank r, then rank(u) is set to r; otherwise rank(u) is set to r + 1. As observed in [9,16], each node has rank no more than its parent in T , and for each node v ∈ V , we have rank(v) = O(log |V |).…”

Section: Routing Treementioning

confidence: 90%

“…We will associate the tree T with ranks of the nodes by using the method in [9,16]. We proceed ranking layer-by-layer in the bottom-up manner.…”

Section: Routing Treementioning

confidence: 99%

“…Let W 0 be the set of parents of nodes in V ij with rank j. We apply the algorithm ILBS in [9] to generate a (…”

Section: Broadcast Schedulingmentioning

confidence: 99%

“…In the second phase, we schedule the transmissions by the dominators as follows: partition the set V of dominators into 2-independent sets {V i : 1 ≤ i ≤ 12} [9]. Here 2-independent set means that the pairwise distance of nodes in this set are all greater than 2.…”

Section: Broadcast Schedulingmentioning

confidence: 99%

“…The correctness proof involves two arguments: (1) any pair of concurrent transmitting nodes will not conflict with each other, (2) any node will receive the data before it transmits. Both arguments are similar to [9]. In the first phase, each node in the CDS needs |P | to transmit its message to all the children, thus this phase costs at most (R + 51r)|P |.…”

Section: Theorem 1 the Proposed Algorithm For Broadcast Scheduling Pmentioning

confidence: 99%

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Fast Group Communication Scheduling in Duty-Cycled Multihop Wireless Sensor Networks

Cao

Wan

2012

Wireless Algorithms, Systems, and Applications

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Abstract. We study group communication scheduling in duty-cycled multi-hop wireless sensor networks. Assume that time is divided into time-slots and we group multiple consecutive time-slots into periods. Each node can transmit data at any time-slot while it only wakes up at its active time-slot of every period and thus be allowed to receive data. Under the protocol interference model, we investigate four group communication patterns, i.e., broadcast, data aggregation, data gathering, and gossiping. For each pattern, we develop a delay efficient scheduling algorithm which greatly improve the current state-of-the-art algorithm. Additionally, we propose a novel and efficient design to coherently couple the wireless interference requirement and duty cycle requirement.

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Section: Routing Treementioning

confidence: 90%

“…We will associate the tree T with ranks of the nodes by using the method in [9,16]. We proceed ranking layer-by-layer in the bottom-up manner.…”

Section: Routing Treementioning

confidence: 99%

“…Let W 0 be the set of parents of nodes in V ij with rank j. We apply the algorithm ILBS in [9] to generate a (…”

Section: Broadcast Schedulingmentioning

confidence: 99%

Section: Broadcast Schedulingmentioning

confidence: 99%

Section: Theorem 1 the Proposed Algorithm For Broadcast Scheduling Pmentioning

confidence: 99%

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Fast Group Communication Scheduling in Duty-Cycled Multihop Wireless Sensor Networks

Cao

Wan

2012

Wireless Algorithms, Systems, and Applications

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Distributed low‐latency broadcast scheduling for multi‐channel duty‐cycled wireless IoT networks

Long,

Wu,

Chen

et al. 2024

Concurrency and Computation

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SummaryData broadcast is a fundamental communication pattern in wireless IoT networks, in which the messages are disseminated from a source node to the entire network. The problem of minimum latency broadcast scheduling (MLBS) which is aimed to generate a quick and conflict‐free broadcast schedule has not been extensively explored in duty‐cycled networks. The existing works either work in a centralized scheme or rely on a fixed tree for broadcasting. Additionally, they all employ a strict premise that each node can only utilize one channel for both transmitting and receiving messages. Thus, to address the issues mentioned above, we examine the first distributed broadcasting algorithm in multi‐channel duty‐cycled wireless IoT networks, without relying on a predetermined tree. First, the MLBS problem in such networks is defined and proved to be NP‐hard. Then, in order to avoid transmission conflicts between different links locally, two efficient data structures are designed to help compute the earliest time and channel of receiving messages without conflicts. Based on the above data structures, we introduce an efficient distributed broadcasting algorithm, which can generate a latency‐sensitive broadcast tree while calculating a collision‐free broadcast schedule, simultaneously. Finally, the theoretical analysis and simulations demonstrate the efficiency of the proposed algorithm.

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On the minimum latency transmission scheduling in wireless networks with power control under SINR constraints

Charalambous

Klerides

Wiesemann

et al. 2013

Trans Emerging Tel Tech

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In order to alleviate interference and contention in a wireless network, we may exploit the existence of multiple orthogonal channels or time slots, thus achieving a substantial improvement in performance. In this paper, we study a joint transmission scheduling and power control problem that arises in wireless networks. The goal is to assign channels (or time slots) and transmitting powers to communication links such that all communication requests are processed correctly, specified quality-of-service requirements are met, and the number of required time slots is minimised. First, we formulate the problem as a mixed-integer linear programming. Then, we show that the problem considered is non-deterministic polynomial-time hard, and subsequently, we propose non-trivial bounding techniques to solve it. Optimisation methods are also discussed, including a column generation approach, specifically designed to find bounds for the transmission scheduling problem. Moreover, we develop optimisation techniques in which the bounding techniques are integrated in order to derive the optimal solution to the problem faster. We close with an extensive computational study, which shows that despite the complexity of the problem, the proposed methodology scales to problems of non-trivial size. Our algorithms can therefore be used for static wireless networks where propagation conditions are almost constant and a centralised agent is available (e.g. cellular networks where the base station can act as a centralised agent or wireless mesh networks), and they can also serve as a benchmark for the performance evaluation of heuristic, approximation or distributed algorithms that aim to find near-optimal solutions without information about the whole network.

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Minimum-Latency Broadcast Scheduling in Wireless Ad Hoc Networks

Cited by 110 publications

References 18 publications

Fast Group Communication Scheduling in Duty-Cycled Multihop Wireless Sensor Networks

Fast Group Communication Scheduling in Duty-Cycled Multihop Wireless Sensor Networks

Distributed low‐latency broadcast scheduling for multi‐channel duty‐cycled wireless IoT networks

On the minimum latency transmission scheduling in wireless networks with power control under SINR constraints

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