A Benchmarking Algorithm to Determine Minimum Aggregation Delay for Data Gathering Trees and an Analysis of the Diameter-Aggregation Delay Tradeoff

Abstract

Aggregation delay is the minimum number of time slots required to aggregate data along the edges of a data gathering tree (DG tree) spanning all the nodes in a wireless sensor network (WSN). We propose a benchmarking algorithm to determine the minimum possible aggregation delay for DG trees in a WSN. We assume the availability of a sufficient number of unique CDMA (Code Division Multiple Access) codes for the intermediate nodes to simultaneously aggregate data from their child nodes if the latter are ready with the data. An intermediate node has to still schedule non-overlapping time slots to sequentially aggregate data from its own child nodes (one time slot per child node). We show that the minimum aggregation delay for a DG tree depends on the underlying design choices (bottleneck node-weight based or bottleneck link-weight based) behind its construction. We observe the bottleneck node-weight based DG trees incur a smaller diameter and a larger number of child nodes per intermediate node; whereas, the bottleneck link-weight based DG trees incur a larger diameter and a much lower number of child nodes per intermediate node. As a result, we observe a complex diameter-aggregation delay tradeoff for data gathering trees in WSNs.

An IoT System for Social Distancing and Emergency Management in Smart Cities Using Multi-Sensor Data

“…Importantly, the applicability of the WSNs mainly depends on the lifetime of the sensor nodes, and, for this reason, it is important to design this type of system while bearing in mind this crucial aspect and selecting the more convenient energy efficient routing protocol [9][10][11]. Other important design parameters are [10,12,13]: (i) the limited storing and computational resources of each sensing nodes, (ii) the costs (i.e., cheap sensors are prone to failure, while expensive sensors need good housing and cannot be used for dense deployments), (iii) the position of each sensing node, which cannot be predetermined and depends on the accessibility of the point where the node should be placed, (iv) the sensing nodes' deployment (to collect the needed data, to have the required coverage and connectivity, to extend the network lifetime, and to minimize energy consumption), and (v) the minimum number of time slots required to aggregate data along the edges of a data-gathering tree spanning all the nodes in a WSN (a.k.a., minimum aggregation delay), if the gathered data are aggregated before the transmission to the control center. The solution presented in this paper was designed while bearing in mind all the design parameters mentioned above, focusing, in particular, on maximizing the exploitation of the nodes' storing and computational resources, on minimizing the system cost and on optimizing the system deployment.…”

Neighborhood Overlap-based Stable Data Gathering Trees for Mobile Sensor Networks

“…Performance Metrics: We evaluated the following three performance metrics: (P-i) Tree Lifetime, TL: The tree lifetime is the number of rounds a DG tree exists before one or more of its links fail due to node mobility, averaged over the duration of a simulation session. (P-ii) Aggregation Delay per Round, ADR: The aggregation delay per round is the minimum number of timeslots (computed as per algorithm by Meghanathan, 2015b) it takes for data to get aggregated along the edges of the DG tree and reach the root node, averaged across all the rounds. (P-iii) Energy Consumption per Round, ECR: The energy consumed per round is the sum of the energy consumed at each of the nodes for data aggregation in the network plus the energy lost due to broadcast tree discoveries if the DG tree was reconfigured at the beginning of the round.…”

A Generic Algorithm to Determine Maximum Bottleneck Node Weight-based Data Gathering Trees for Wireless Sensor Networks

“…In this section, we describe a benchmarking algorithm [15] to determine the delay for data aggregation spanning over all the nodes of the data gathering (DG) tree. We first identify the level of each node in the DG tree, with the root node (leader node) set to be at level 0, its immediate child nodes at level 1 and so on.…”