Construction Of Data Aggregation Tree Research Articles

Hypergraph-Based Active Minimum Delay Data Aggregation Scheduling in Wireless-Powered IoT

Thanks to the promising wireless power transmission (WPT) technology, wireless-powered Internet of Things (WPIoT) can significantly improve the sustainable service ability of Internet of Things (IoT) with low personnel maintenance costs, and thus, shows remarkable and broad prospects in many applications, especially under the abominable and dangerous environment. Minimum delay data aggregation scheduling (MAS) is a problem of cardinal significance in WPIoT with the objective of timely collecting the data of IoT devices. However, due to the residual energy limitation of IoT devices, WPIoT shows the special feature of adopting the store-charge-and-forward communication mode, which brings new research challenges on designing efficient solutions to the MAS problem. We show that the MAS problem under the physical interference model in WPIoT is NP-hard. To tackle this problem, we propose a delay-efficient data aggregation scheduling algorithm called HADA based on an active data aggregation tree construction method and a novel hypergraph-based link scheduling method. Extensive numerical experiments are conducted to evaluate the performance of our proposed algorithm. The results demonstrate that our HADA algorithm can efficiently improve the performance compared with the existing baseline algorithms.

Scheduling of data aggregation trees using Local Heuristics to enhance network lifetime in sensor networks

Data gathering is a basic requirement in many applications of Wireless Sensor Networks (WSNs). In tree based data gathering, Data Aggregation Tree (DAT) is constructed by the sink or by the nodes in a distributed manner. In this paper, we study the problem of enhancing Network Lifetime (NL) using hybrid DAT construction methods. In hybrid methods of DAT construction, the sink and the nodes collaboratively construct the DAT. We propose three algorithms for Scheduling DATs using Local Heuristics with Ordering (SDLHO), with Randomization (SDLHR) and with Tree factor (SDLHT) techniques.These techniques avoid disparity in energy levels of the nodes and increase the survivability of the network. In addition, to address imperfect link quality, we propose an algorithm for Scheduling DATs using Local Heuristics with Ordering based on Link Quality (SDLHO-LQ). Rigorous simulation results demonstrate the efficacy of the proposed algorithms; and their ability to scaleup to suit deployment of applications in harsh regions. Further, their performances evaluated to quantify the amount of enhancements of NL with the existing state of art is propitious to suit the distributed environments.

Communication-Efficient Data Aggregation Tree Construction for Complex Queries in IoT Applications

Data aggregation is a fundamental operation in Internet of Things (IoT) applications, e.g., distributed Internet-based industrial control and computing systems. As IoT devices are increasingly connected to the system via resource-constrained wireless communication links, it is critical to perform communication-efficient data aggregation to answer complex queries (e.g., skyline queries and equality joins) from IoT applications. In this paper, we investigate the problem of constructing an aggregation tree (AT) for complex queries with the minimum communication cost. As complex queries have a dynamic size of intermediate results, existing Steiner tree-based approaches for traditional query operators, e.g., MIN and top- ${k}$ , cannot be directly applied. We first formalize the aggregation gain by jointly considering the data pruning power (the size of data points that can be pruned during the aggregation for complex queries) and aggregation cost (the size of data points transmitted for the aggregation). By maximizing the aggregation gain, the data set that has a higher pruning power and a smaller size is selected and transferred for data aggregation at succeeding nodes. We then propose to construct the AT by connecting a set of aggregation operations with maximum aggregation gain. Extensive evaluation shows that our proposed framework achieves the promising results.

Lifetime enhancement in wireless sensor networks using binary search tree based data aggregation

Energy efficient data aggregation is a key solution to enhance the lifetime of wireless sensor networks since sensor nodes are battery-powered and deployed in remote environments. This article explore a two-hop data aggregation tree construction algorithm using binary search tree to reduce the total energy consumption of sensor nodes in wireless sensor networks. An adaptive and hybrid routing algorithm for simultaneous data aggregation and exploit the data correlation between nodes using the two-hop data aggregation tree framework is proposed. Routes are chosen based on the shortest response time for the broadcasting request to minimize the total energy expended by the network. This paper also proposes a high secure asymmetric key cryptography algorithm to provide the secure data communication among the network. The data aggregation function that is used in the proposed routing algorithm enhances the lifetime of sensor network by resolving the delay, collision and security issues. Simulations results show that the binary tree based data aggregation can appreciably reduces the total energy consumption and resolves the maximum data aggregation issues in wireless sensor network.

On the Construction of Data Aggregation Tree with Minimum Energy Cost in Wireless Sensor Networks: NP-Completeness and Approximation Algorithms

In many applications, it is a basic operation for the sink to periodically collect reports from all sensors. Since the data gathering process usually proceeds for many rounds, it is important to collect these data efficiently, that is, to reduce the energy cost of data transmission. Under such applications, a tree is usually adopted as the routing structure to save the computation costs for maintaining the routing tables of sensors. In this paper, we work on the problem of constructing a data aggregation tree that minimizes the total energy cost of data transmission in a wireless sensor network. In addition, we also address such a problem in the wireless sensor network where relay nodes exist. We show these two problems are NP-complete, and propose O(1)-approximation algorithms for each of them. Simulations show that the proposed algorithms each have good performance in terms of the energy cost.

On the Construction of Data Aggregation Tree With Maximizing Lifetime in Large-Scale Wireless Sensor Networks

Data aggregation protocols are generally utilized to extend the lifetime of sensor networks by reducing the communication cost. Traditionally, tree-based structured approaches that are a basic operation for the sink to periodically collect reports from all sensors were concerned about many applications. Since the data aggregation process usually proceeds for many rounds, it is important to collect these data efficiently, that is, to reduce the energy cost of data transmission. Under such applications, a tree is usually adopted as the routing structure to save the computation costs when maintaining the routing tables of sensors. In our previous work, we have demonstrated that multiple trees, as well as split trees, can provide additional lifetime extensions when certain nodes are deployed in a wireless sensor network. In this paper, we explore how the number of the family-set of trees influences the lifetime gain, and we work on the problem of constructing data aggregation trees that minimizes the total energy cost of data transmission under diverse set of scenarios and network query region. Through dividing query area, the sensory and aggregation data have been returned through a number of different forwarding trees within each sub query area, which reduces the network hot spots. To evaluate the performance of the proposed approach, we have compared and analyzed an angular division routing algorithm and query region division routing with LEACH. Theoretical and experimental results illustrate that the query region division algorithm based on angle leads to lower energy cost in comparison with the models reported in the literature.

Maximise success probability for real-time sensor networks using contention-based protocols

We study real-time data aggregation problem for wireless sensor networks that use CSMA/CA MAC layer protocols. The problem is, for a given sink, a set of sensor nodes and a delay bound, to maximise the average transmission success probability of all sensor nodes within the delay bound. In CSMA/CA protocols, the success probability and the expected transmission delay are highly sensitive to node interference. We divide the system time into time-frames with fixed size and schedule the transmission of nodes into time-frames. The transmissions of all child nodes under the same parent are scheduled in the same time-frame. Therefore, the construction of data aggregation trees becomes very important in maximising the success probability of data collection. We solve the joint routing and scheduling problem by first constructing an aggregation tree that minimises the node interference. Then, we propose an efficient greedy scheduling method to assign time-frames to sensor nodes.

Construction of Data Aggregation Tree for Multi-objectives in Wireless Sensor Networks through Jump Particle Swarm Optimization

As a typical data aggregation technique in wireless sensor networks, the spanning tree has the ability of reducing the data redundancy and therefore decreasing the energy consumption. However, the tree construction normally ignores some other practical application requirements, such as network lifetime, convergence time and communication interference. In this case, the way how to design a tree structure subjected to multi-objectives becomes a crucial task, which is called as multi-objective steiner tree problem (MOSTP). In view of this kind of situation, a multi-objective optimization framework is proposed, and a heuristic algorithm based on jump particle swarm optimization (JPSO) with a specific double layer encoding scheme is introduced to discover Pareto optimal solution. Furthermore, the simulation results validate the feasibility and high efficiency of the novel approach by comparison with other approaches.

A Novel Energy-Efficient MAC Aware Data Aggregation Routing in Wireless Sensor Networks #

Embedding data-aggregation capabilities into sensor nodes of wireless networks could save energy by reducing redundant data flow transmissions. Existing research describes the construction of data aggregation trees to maximize data aggregation times in order to reduce data transmission of redundant data. However, aggregation of more nodes on the same node will incur significant collisions. These MAC (Media Access Control) layer collisions introduce additional data retransmissions that could jeopardize the advantages of data aggregation. This paper is the first to consider the energy consumption tradeoffs between data aggregation and retransmissions in a wireless sensor network. By using the existing CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) MAC protocol, the retransmission energy consumption function is well formulated. This paper proposes a novel non-linear mathematical formulation, whose function is to minimize the total energy consumption of data transmission subject to data aggregation trees and data retransmissions. This solution approach is based on Lagrangean relaxation, in conjunction with optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithms could construct MAC aware data aggregation trees that are up to 59% more energy efficient than existing data aggregation algorithms.