Convergecast scheduling problem in case of given aggregation tree: The complexity status and some special cases

Erzin, Adil; Pyatkin, A. V.

doi:10.1109/csndsp.2016.7574007

Cited by 15 publications

(9 citation statements)

References 15 publications

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“…They used only a single channel, unlike our proposed algorithms of multiple RF channels. Similar to [11], Erzin and Pyatkin [12] have also proposed in-network aggregation scheduling using a single channel. However, the authors have considered only the special cases of rectangular, triangular, and hexagonal grid networks.…”

Section: Related Workmentioning

confidence: 99%

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Energy Efficient Scheduling in Wireless Sensor Networks for Periodic Data Gathering

Kumar

Kim

2019

IEEE Access

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Nodes in the wireless sensor networks are battery operated, and thus, the efficient use of the node's energy during wireless communication is pivotal for the long battery life. This paper addresses the challenges in energy-efficient data aggregation and transmission scheduling for each node by using time division multiple access (TDMA). We introduce multi-channel TDMA scheduling algorithms with the objective of minimizing the total energy consumption in the network. The proposed algorithms utilize multiple radio channels to bestow efficient scheduling while eliminating collisions and overhearing. First, we formulate an integer linear programming (ILP) algorithm that finds the minimum bound for the network's energy consumption. Then, we propose a near-optimal heuristic algorithm based on backtracking, which uses memoization to spurn the suboptimal schedules. Subsequently, we propose a computationally efficient heuristic algorithm by using Langford subset generation. The algorithm reduces the energy consumption in each timeslot while avoiding revisiting the same timeslot. We conducted extensive simulations to evaluate three proposed algorithms. The simulation results demonstrate that the proposed heuristic algorithms provide performance comparable to the optimum results of the ILP algorithm and reduce the magnitude of computation time. INDEX TERMS Wireless sensor network, Internet of Things, meter reading, scheduling algorithm.

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Section: Related Workmentioning

confidence: 99%

“…To satisfies the data aggregation and routing constraints, Eq. (8) - (12), each parent domino is placed above all its children node's dominos. The minimum height can be obtained, when the box is completely packed by the square dominos.…”

Section: C: Maximum Rf Channels Constraintmentioning

confidence: 99%

Energy Efficient Scheduling in Wireless Sensor Networks for Periodic Data Gathering

Kumar

Kim

2019

IEEE Access

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“…Evaluating the actual time to collect data through a subtree, with respect to the collision model mentioned in Section 2, is costly. This is because determining a delay optimal schedule even on a given tree is NP-hard [30]. Another way is to use approximation solutions, i.e., heuristic scheduling algorithms [14], but such algorithms are also time-consuming.…”

Section: Delay-aware Reverse Approach For Schedulingmentioning

confidence: 99%

Delay-Aware Reverse Approach for Data Aggregation Scheduling in Wireless Sensor Networks

Nguyen

Kim

et al. 2019

Sensors

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Many time-sensitive applications require data to be aggregated from wireless sensor networks with minimum latency. However, the minimum latency aggregation scheduling problem has not been optimally solved due to its NP-hardness. Most existing ideas rely on local information (e.g., node degree, number of children) to organize the schedule order, hence results in solutions that might be far from optimal. In this work, we propose RADAS: a delay-aware Reverse Approach for Data Aggregation Scheduling that determines the transmissions sequence of sensors in a reverse order. Specifically, RADAS iteratively finds the transmissions starting from the last time slot, in which the last sender delivers data to the sink, down to the first time slot, when the data aggregation begins. In each time slot, RADAS intends to maximize the number of concurrent transmissions, while giving higher priority to the sender with potentially higher aggregation delay. Scheduling such high-priority sender first would benefit the maximum selections in subsequent time slots and eventually shorten the schedule length. Simulation results show that our proposed algorithm dominates the existing state-of-the-art schemes, especially in large and dense networks, and offers up to 30% delay reduction.

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“…On one hand, there is no result describing the structure of an optimal aggregation tree for a given graph. On the other hand, even on a given aggregation tree, optimal time slot allocation is still NP-hard [9].…”

Section: Introductionmentioning

confidence: 99%

GLS and VNS based heuristics for conflict-free minimum-latency aggregation scheduling in WSN

Plotnikov

Erzin

Zalyubovskiy

2020

Optimization Methods and Software

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We consider a conflict-free minimum latency data aggregation problem that occurs in different wireless networks. Given a network that is presented as an undirected graph with one selected vertex (a sink), the goal is to find a spanning aggregation tree rooted in the sink and to define a conflict-free aggregation minimum length schedule along the arcs of the tree directed to the sink. Herewith, at the same time slot, each element of the network can either send or receive at most one message. Only one message should be sent by each network element during the whole aggregation session, and the conflicts caused by signal interference should be excluded. This problem is NP-hard and remains NP-hard even in the case when the aggregation tree is given. Therefore, the development of efficient approximate algorithms is very essential for this problem. In this paper, we present new heuristic algorithms based on the genetic local search and the variable neighborhood search metaheuristics. We conducted an extensive simulation that demonstrates the superiority of our algorithms compared with the best of the previous approaches.

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Convergecast scheduling problem in case of given aggregation tree: The complexity status and some special cases

Cited by 15 publications

References 15 publications

Energy Efficient Scheduling in Wireless Sensor Networks for Periodic Data Gathering

Energy Efficient Scheduling in Wireless Sensor Networks for Periodic Data Gathering

Delay-Aware Reverse Approach for Data Aggregation Scheduling in Wireless Sensor Networks

GLS and VNS based heuristics for conflict-free minimum-latency aggregation scheduling in WSN

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