Abstract
A hybrid Unicast joint Broadcast Aggregation (UBA) schedule scheme is proposed for maximizing aggregation information and minimizing delay for wireless sensor networks (WSNs). UBA scheme adopts following regimes for maximizing aggregation information and minimizing delay. (a) The nodes in the far to sink region adopt broadcast manner, which can not only efficiently collect more sensing information within the same time slot but also reduce overall network delay. (b) The nodes in the near to sink region use unicast manner to collect sensing information, which can efficiently save energy consumption to extend network lifetime. This is UBA scheme with a great advantage that it can collect more sensing information and reduce delay without shortening network lifetime. Simulation and theoretical analytical results clearly indicate that the proposed scheme can significantly improve sensing information by 25% and reduce delay by 14% to 18% under the same network lifetime.
1. Introduction
The key function of wireless sensor networks (WSNs) is sensing and collecting surrounding information periodically through sensor nodes constituting the network and aggregating to sink node for further processing [1–5]. In those applications, all of the node samples are in each sample cycle. For each sample cycle, all sensor nodes do sampling once [2,5]. Data aggregation refers to the situation in which two data packets meet with each other at a node in the routing procedure and aggregated into one new data packet [1,6].
According to WSNs for real-time requirements, data aggregation can be divided into two categories. (1) Convergecast [7]. It is one of the most used non-real-time data aggregation patterns in WSNs. (2) Real-time data collection. In the real-time data aggregation network, sink node needs to gather as much sensing information as possible in a predetermined sample cycle [1]. For the node, if sensing information collected by node has no available time-slot to be sent in this sample cycle, it will cause data loss. The reason is that this node will generate a new piece of sensing information in the next sample cycle and it becomes meaningless to transmit the old sensing information [1]. After this happens, there is no doubt that sensing information aggregated by sink node will be reduced due to data loss. In general, sample cycle of real-time network is much less than non-real-time network. Real-time network time-slot schedule will not wait to receive all the sensing information from node’s children before sending them in accordance with the method of aggregation convergecast.
6. Conclusion and future work
In this paper, a hybrid Unicast joint Broadcast Aggregation (UBA) Schedule scheme is proposed for maximizing aggregation information and minimizing network delay in data collection multi-hop wireless sensor networks. The two main mechanisms of UBA scheme are the adoption of a mixed scheduling mode and the use of residual energy of nodes, the former mechanism adopts broadcast strategy in the far sink region and unicast strategy in the near sink region, and the latter is making the best use of the residual energy of nodes in non-hotspots to implement broadcast strategy. What’s more, Time-Slot Distribution Algorithm for Children (TSDAC) algorithm is also proposed, which can guide to assigning optimal time slots for children nodes and reduce network delay efficiently. Through simulation and detailed theoretical analyses of the proposed scheme, the evaluation results show that UBA scheme can improve sensing information by 25% and reduce delay by 14% - 18% under the same lifetime compared with EASDC and DAS scheme.
To maximize aggregation information and minimize delay is two pivot issues for in-network aggregation in lossy wireless sensor networks. Most existing in-network aggregation schedules designed for a fixed allocated time slot which the total aggregation information and delay can not be optimized at same time. For future work, we yearn to design a integrating unallocated time slot in-network aggregation schedule scheme that is capable of maximizing aggregation information and minimizing delay for lossy WSNs.
