Minimum Latency Aggregation Scheduling Research Articles

Energy-collision-aware Minimum Latency Aggregation Scheduling for Energy-harvesting Sensor Networks

The emerging energy-harvesting technology enables charging sensor batteries with renewable energy sources, which has been effectively integrated into Wireless Sensor Networks (EH-WSNs). Due to the limited energy-harvesting capacities of tiny sensors, the captured energy remains scarce and differs greatly among nodes, which makes the data aggregation scheduling problem more challenging than that in energy-abundant WSNs. In this article, we investigate the Minimum Latency Aggregation Scheduling (MLAS) problem in EH-WSNs. First, we identify a new kind of collision in EH-WSNs, named as energy-collision, and design several special structures to avoid it during data aggregation. To reduce the latency, we try to choose the parent adaptively according to nodes’ transmission tasks and energy-harvesting ability, under the consideration of collisions avoidance. By considering transmitting time, residual energy, and energy-collision, three scheduling algorithms are proposed under protocol interference model. Under physical interference model, several approximate algorithms are also designed by taking account of the interference from the nodes several hops away. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.

Structure-Free General Data Aggregation Scheduling for Multihop Battery-Free Wireless Networks

With advances in wireless power transfer techniques, battery-free wireless sensor networks (BF-WSNs) which can support long-term applications, has been attracting increasing interests in recent years. Unfortunately, the problem of minimum latency aggregation scheduling (MLAS) is not well studied in BF-WSNs. Existing works always have a rigid assumption that there is only one single query which is targeted at the whole network. Aiming at making the work more practical and general, we investigate the general MLAS problem in BF-WSNs, which is targeted at any subset of nodes in the network and aimed for an arbitrary number of aggregation queries. First, the general MLAS problem when there is one single query is studied. To control the number of nodes participating in the aggregation process, a node selection algorithm is proposed to cover and connect the whole target nodes. Then, a latency and energy aware scheduling algorithm is proposed to integrate the construction of aggregation tree with the chosen nodes, and the computation of a conflict-free schedule simultaneously, relying on non-predetermined structures. Second, the general MLAS problem when there is a group of aggregation queries is studied. Through designing some special structures to avoid collisions between both current and existing aggregation schedules, an algorithm without any waiting time is proposed. Additionally, the algorithm under physical interference model and dynamic energy arrival model are also presented. The theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency and energy efficiency.

Latency-and-Coverage Aware Data Aggregation Scheduling for Multihop Battery-Free Wireless Networks

Battery-Free Wireless Sensor Networks (BF-WSNs) have been attracting increasing interests in the recent years. To reduce the latency in BF-WSNs, the Minimum Latency Aggregation Scheduling (MLAS) problem with coverage requirement $q$ is proposed recently, which tries to choose $q$ percent of nodes for communication and aggregation. In the existing method, the authors try to select nodes adaptively according to their energy status and schedule these nodes to achieve the minimum latency. Unfortunately, it cannot guarantee the distribution of the aggregated nodes and may result in these nodes being squeezed in a small area and a poor aggregation quality. Thus, we re-investigate the $q$ -coverage MLAS problem in this article, which can guarantee that the aggregated nodes are distributed evenly. Firstly, the 1-coverage MLAS problem, in which each node can be covered by at least one aggregated node, is studied. To reduce the latency, we intertwine the selection of aggregated nodes and the computation of a collision-free communication schedule simultaneously. Two algorithms are proposed by scheduling the communication tasks in the bottom-up and top-down manner respectively. Secondly, to satisfy the arbitrary coverage requirement $q$ , three algorithms are proposed to guarantee the aggregated nodes are evenly distributed in the network with a low latency. Additionally, the method to extend the proposed algorithms for the BF-WSNs with multiple channels is also studied. The theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.

Low-Latency Data Aggregation Scheduling for Cognitive Radio Networks With Non-Predetermined Structure

Data aggregation is a fundamental yet popular operation in wireless networks where the sink needs to obtain the combined information of the whole network. However, the problem of minimum latency aggregation scheduling (MLAS) is not well studied in cognitive radio networks. Few studies have addressed this issue and most previous aggregation methods all assume that a fixed-structure based aggregation tree is constructed in advance, which may result in the selection of a node with limited spectrum opportunities as the parent by many nodes and by extension results in a large latency. Thus, the MLAS problem in cognitive radio networks (MLAS-CR) without the above limitation is investigated in this paper. First, the MLAS-CR problem with primary social behaviors where the activity of primary users can be predicted is studied. To make full use of the limited spectrum opportunities, we integrate the construction of the aggregation tree, and the computation of a conflict-free schedule simultaneously, without any predetermined structures. Second, the MLAS-CR problem without the above assumption is also investigated. To reduce the latency, a two-way aggregation scheduling method is proposed to adaptively choose the parent with only current channel information. To further reduce the latency, we also introduce a new data aggregation mode for CRN, i.e., Data Aggregation Scheduling in The Dark, to utilize the spectrum opportunities of scheduled nodes. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.

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

ABSTRACT 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 approximation algorithms is very important for this problem. In this paper, we present new heuristic algorithms based on genetic local search and variable neighbourhood search metaheuristics. We conducted an extensive simulation that demonstrates the superiority of our algorithms compared with the best of the previous approaches.

Minimum-latency data aggregation scheduling based on multi-path routing structures under physical interference model

Minimum-Latency Aggregation Scheduling is a significant problem in wireless sensor networks. The key challenge is to find an effective solution to aggregate data from all sensors to the sink with minimum aggregation latency. In this article, we propose a novel data aggregation scheduling algorithm under the physical interference model. First, the algorithm partitions the network into square cells according to the communication range of a sensor. Second, a node is selected randomly as the aggregated node to receive the data from the other nodes in the same cell. Finally, a data aggregation tree, which consists of multiple disjoint paths, is constructed to aggregate data from all aggregated nodes to the sink. We empirically proved that the delay of the aggregation schedule generated by our algorithm is ( K+1)2Δ− K−1+2λ time-slots at most, where K is a constant depending on the sensors transmitting power, the signal-to-interference-plus-noise-ratio threshold, and the path-loss exponent; [Formula: see text] represents the maximal number of nodes in a cell; and [Formula: see text] denotes the number of cells at a row/column in a square network area. Simulation results also show that our algorithm achieves lower average latency than the previous works.

An Adaptive Aggregation Scheduling Algorithm Based on the Grid Partition in Large-Scale Wireless Sensor Networks

Data aggregation algorithm aims to reduce the redundant information by gathering the sensed data, save energy, and prolong the lifetime of the network. However, the data aggregation technology will increase the network transmission delay of wireless sensor networks. Minimum-latency aggregation scheduling is designed to minimize the number of scheduled time slots to perform an aggregation. In this paper, we present an Adaptive Aggregation Scheduling Algorithm based on the Grid Partition (AASA-GP) in large-scale wireless sensor networks. By dividing the network into grids based on the geographical information, we allocate the channels according to the grid coordinates. Nodes with the same grid coordinates use the same channel and the adjacent grids use the different channels, so we can effectively avoid the wireless media transmission interference, increase the parallel transfer rate, and reduce the aggregation latency. Our extensive evaluation results demonstrate the superiority of the AASA-GP. For small-scale networks, the resultant latency is comparable with the best practice, and it is more suitable for large-scale wireless sensor networks.

A note on the complexity of minimum latency data aggregation scheduling with uniform power in physical interference model

In this paper we prove that the Minimum Latency Aggregation Scheduling (MLAS) problem in the Signal-to-Interference-Noise-Ratio (SINR) model is APX-hard in the uniform power model.

Minimum-latency aggregation scheduling in wireless sensor network

Data aggregation is an essential yet time-consuming task in wireless sensor networks. This paper studies the well-known minimum-latency aggregation schedule problem and proposes an energy-efficient...

MC-MLAS: Multi-channel Minimum Latency Aggregation Scheduling in Wireless Sensor Networks

Data aggregation is a key, yet time-consuming functionality in wireless sensor networks (WSNs). Multi-channel design is a promising technique to alleviate interference as a primary reason for long latency of TDMA aggregation scheduling. Indeed, it provides more potential of parallel transmissions over different frequency channels, thus minimizing time latency. In this paper, we focus on designing a multi-channel minimum latency aggregation scheduling protocol, named MC-MLAS, using a new joint approach for tree construction, channel assignment, and transmission scheduling. To our best knowledge, this is the first work in the literature which combines orthogonal channels and partially overlapping channels to consider the total latency involved in data aggregation. Extensive simulations verify the superiority of MC-MLAS in WSNs.

Improved minimum latency aggregation scheduling in wireless sensor networks under the SINR model

Wireless sensor networks are attracting much attention from the world and Minimum Latency Aggregation Scheduling (MLAS) has become one of the most significant fundamental problems in wireless sensor networks. However, there are few results about efficient data aggregation algorithms under the Signal–to–Interference–plus–Noise–Ratio (SINR) model. In this paper, we propose a centralised algorithm to aggregate data from all sources in O(log²n) time slots where n is the total number of nodes. To the best of our knowledge, this is the current best result for the problem. This algorithm uses round scheduling, topology construction and non–linear power assignment as the main techniques. We give a detailed proof of correctness, also an aggregation latency analysis of the algorithm as well as the parameter constraints to achieve our result.

Neither Shortest Path Nor Dominating Set: Aggregation Scheduling by Greedy Growing Tree in Multihop Wireless Sensor Networks

Data aggregation is a fundamental task in multihop wireless sensor networks. Minimum-latency aggregation scheduling (MLAS) seeks to minimize the number of scheduled time slots to perform an aggregation. In this paper, we present the first work on a solvable mathematical formulation of the MLAS problem. The optimal solution of small example networks suggests that an optimal scheduling can be neither shortest path nor dominating set based. Instead of yet another theoretical analysis with provable bounds, our work focuses on reducing the average latency of general random topologies. Inspired by backward induction theory, we propose to schedule the aggregation in a reverse order, and the tree construction of Greedy Growing Tree (GGT) is directly guided by the scheduling algorithm in a step-by-step way. By following priority rules when we schedule candidate 〈sender, receiver 〉 pairs, the opportunity of parallel transmission is maximized. As a result, the aggregation latency can be minimized. Our extensive evaluation results demonstrate the superiority of the GGT algorithm: For sparse networks, the resultant latency is comparable with the best practice, whereas for high-degree networks, the latency is only half of that using state-of-art competitors.