Data Aggregation Tree Research Articles

An Efficient Clustering Algorithm in Wireless Sensor Networks Using Cooperative Communication

Processing the gathered information efficiently is a key functionality for wireless sensor networks. In generally, the sensor networks often use in-network data aggregation and clustering to optimize network communication. The set of aggregating nodes forms a dominating set of the network graph. Finding the weakly connected dominating set (WCDS) is a promising approach for clustering the WSN. However, finding a minimum WCDS is NP-hard problem for most graphs, and a host of approximation algorithm has been proposed. The aim of the paper is to construct a minimum WCDS as a clustering scheme for WSN. Our clustering schemes construction algorithm includes two phases. First of all, we construct a maximal data aggregation tree (DAT) of the network. The second phase of the algorithm is to choose the nodes (called connectors) to make the WCDS connected. The correctness and performance of our algorithms are confirmed through theoretical analysis and comprehensive simulations.

Optimal data aggregation tree in wireless sensor networks based on intelligent water drops algorithm

Energy conservation is an important aspect in wireless sensor networks (WSNs) to extend the network lifetime. In order to obtain energy-efficient data transmission within the network, sensor nodes can be organised into an optimal data aggregation tree with optimally selected aggregation nodes to transfer data. Various nature-inspired optimisation methods have been shown to outperform conventional methods when solving this problem in a distributed manner, that is, each sensor node makes its own decision on routing the data. In this study, a novel optimisation algorithm called intelligent water drops (IWDs) is adopted to construct the optimal data aggregation trees for the WSNs. Further enhancement of the basic IWD algorithm is proposed to improve the construction of the tree by attempting to increase the probability of selecting optimum aggregation nodes. The computational experiment results show that the IWD algorithm is able to obtain a better data aggregation tree with a smaller number of edges representing direct communication between two nodes when compared with the well-known optimisation method such as ant colony optimisation. In addition, the proposed improved version of the IWD algorithm provides better performance in comparison with the basic IWD algorithm for saving the energy of WSNs.

A Tree Based Data Aggregation Scheme for Wireless Sensor Networks Using GA

Energy is one of the most important items to determine the network lifetime due to low power energy nodes included in the network. Generally, data aggregation tree concept is used to find an energy efficient solution. However, even the best aggregation tree does not share the load of data packets to the transmitting nodes fairly while it is consuming the lowest possible energy of the network. Therefore, after some rounds, this problem causes to consume the whole energy of some heavily loaded nodes and hence results in with the death of the network. In this paper, by using the Genetic Algorithm (GA), we investigate the energy efficient data collecting spanning trees to find a suitable route which balances the data load throughout the network and thus balances the residual energy in the network in addition to consuming totally low power of the network. Using an algorithm which is able to balance the residual energy among the nodes can help the network to withstand more and consequently extend its own lifetime. In this work, we calculate all possible routes represented by the aggregation trees through the genetic algorithm. GA finds the optimum tree which is able to balance the data load and the energy in the network. Simulation results show that this balancing operation practically increases the network lifetime.

Energy-efficient routing for correlated data in wireless sensor networks

In this paper, we investigate the reduction in the total energy consumption of wireless sensor networks using multi-hop data aggregation by constructing energy-efficient data aggregation trees. We propose an adaptive and distributed routing algorithm for correlated data gathering and exploit the data correlation between nodes using a game theoretic framework. Routes are chosen to minimize the total energy expended by the network using best response dynamics to local data. The cost function that is used for the proposed routing algorithm takes into account energy, interference and in-network data aggregation. The iterative algorithm is shown to converge in a finite number of steps. Simulations results show that multi-hop data aggregation can significantly reduce the total energy consumption in the network.

An Energy-Saving Privacy-Preserving Data Aggregation Algorithm

Privacy preserving plays an important role in application of the Internet of Things(IoT).As a part of the IoT,Wireless Sensor Networks(WSNs) should provide the privacy preserving in data aggregation.This paper presents a novel energy-saving private-preserving aggregation scheme(ESPART) for WSNs,which uses characteristic of the data aggregation tree structure to reduce communication overhead,assigns the random time pieces to nodes to avoid collision,and limits the scope of collusion data to reinforce data_loss resilience.Compared with the SMART algorithm,the simulation results show that ESPART can preserve data privacy,get accurate data aggregation results while taking the same epoch duration as TAG,and have less communication overhead.

Energy efficient joint data aggregation and link scheduling in solar sensor networks

Solar sensor nodes equipped with micro-solar subsystems [1] provide a novel approach to harvest ambient energy, which partially alleviated the energy-limitation in traditional wireless sensor networks. However, it also poses new challenges that the amounts of energy harvested by nodes are dynamic and unbalanced among them thus network life cannot be necessarily prolonged if no well-designed energy-scheduling is adopted. Herein, we present an algorithm to construct Energy-efficient Data Aggregation Tree (EDAT) based on a Maximum-Weighted Connected Dominating Set (MaCDS). The EDAT aims to prolong network life by minimizing differences in energy consumption among sensor nodes. Here we assume that the amount of harvested energy H randomly and uniformly distributes in the interval [ H min , H max ] , where H max and H min are respectively the maximum and the minimum of H . The total energy consumption difference of an EDAT is at most 5 H ∼ | S | 2 n - 1 , where H ∼ = | [ H min , H max ] | , and S is the dominating set and n the total number of nodes. Furthermore, we designed a link-scheduling algorithm to minimize the number of time slots necessary for scheduling all links in the whole network based on EDAT. The number of time slots is bounded in the interval [4 δ − 1, 2 l k ϱ 2], where l k is determined by hops k , ϱ = max Δ , H max H min , and δ and Δ are respectively the minimal and maximal degree of the network. We found the most efficient work period is not determined by the node with the minimum harvested energy but by the one with the minimum H d , where d is the node’s degree in the EDAT. We determine the necessary condition required for every node to have sufficient energy to support consecutive operation.

Predictive model-aided filtering scheme of data-collection in WSN

The paper proposes a prediction-mode-based filtering mechanism (PMF) to solve the problems of transmission energy wasting caused by time-redundant data in wireless sensor networks (WSN), according to the characteristic of spatio-temporal correlations on sampling series in data-collection. Prior works have suggested several approaches to decrease energy cost during data transmission process via data aggregation tree structure. Distinguish from those methods in above researches, our proposed scheme mainly focus on reducing the temporal redundant degree in event-source to achieve energy-saving effect via self-adaptive filtering structure. The framework of PMF for energy-efficient collection is composed of prediction module for mining the change law of time domain, self-learning module for updating model, and driving module for controlling data filtering operation. Combined with the design of error driving rule and threshold distributing rule, which is the middleware in the above filtering mechanism, the quantity of transmission load in networks can be greatly inhibited on the premise of quality of service (QoS) assurance and energy consumption can be reduced consequently. Finally, the experimental results show that the performance of PMF can significantly outperform some classical data-collection algorithms on energy-saving effect and self-adaptability.

Computing Localized Power-Efficient Data Aggregation Trees for Sensor Networks

We propose localized, self organizing, robust, and energy-efficient data aggregation tree approaches for sensor networks, which we call Localized Power-Efficient Data Aggregation Protocols (L-PEDAPs). They are based on topologies, such as LMST and RNG, that can approximate minimum spanning tree and can be efficiently computed using only position or distance information of one-hop neighbors. The actual routing tree is constructed over these topologies. We also consider different parent selection strategies while constructing a routing tree. We compare each topology and parent selection strategy and conclude that the best among them is the shortest path strategy over LMST structure. Our solution also involves route maintenance procedures that will be executed when a sensor node fails or a new node is added to the network. The proposed solution is also adapted to consider the remaining power levels of nodes in order to increase the network lifetime. Our simulation results show that by using our power-aware localized approach, we can almost have the same performance of a centralized solution in terms of network lifetime, and close to 90 percent of an upper bound derived here.

Energy efficient clustering algorithm for data aggregation in wireless sensor networks

Energy problem is a key issue for Wireless Sensor Network. It's a good idea to select a head node for data aggregation to reduce the energy of data transmission. However, many algorithms don't consider the whole integrated network situation for head node selection. An energy efficient clustering algorithm for data aggregation is proposed in this paper. The cluster header is selected by considering node's residual energy as well as the distance between this node and its neighbors. Furthermore, a type of data aggregation tree is constructed with the help of the clusters to reduce the amount of data transmission which effectively extends the network lifetime. Simulation results show that this method obtains satisfactory performance on prolonging the networks lifetime.

Time synchronization algorithm of wireless sensor networks based on data aggregation tree

Time synchronization is a critical technique of wireless sensor networks (WSNs). Aimed at the disadvantages of hierarchical time synchronization algorithm, this paper proposes an improved algorithm used for WSNs based on data aggregation tree. Based on collection and selection principles of multiple time data point tuples, the relative time drift and phase offset of the node are calculated through linear programming method, and time synchronization is achieved eventually during the establishment of data aggregation tree. Performance analysis and simulation results prove that, compared with the existing time synchronization algorithms, the proposed algorithm can reduce the energy cost of nodes and shorten the time of synchronization effectively.

Building Representative-Based Data Aggregation Tree in Wireless Sensor Networks

Data aggregation is an essential operation to reduce energy consumption in large-scale wireless sensor networks (WSNs). A compromised node may forge an aggregation result and mislead base station into trusting a false reading. Efficient and secure aggregation scheme is critical in WSN applications due to the stringent resource constraints. In this paper, we propose a method to build up the representative-based aggregation tree in the WSNs such that the sensing data are aggregated along the route from the leaf cell to the root of the tree. In the cinema of large-scale and high-density sensor nodes, representative-based aggregation tree can reduce the data transmission overhead greatly by directed aggregation and cell-by-cell communications. It also provides security services including the integrity, freshness, and authentication, via detection mechanism in the cells.

Delay QoS and MAC Aware Energy-Efficient Data-Aggregation Routing in Wireless Sensor Networks

By eliminating redundant data flows, data aggregation capabilities in wireless sensor networks could transmit less data to reduce the total energy consumption. However, additional data collisions incur extra data retransmissions. These data retransmissions not only increase the system energy consumption, but also increase link transmission delays. The decision of when and where to aggregate data depends on the trade-off between data aggregation and data retransmission. The challenges of this problem need to address the routing (layer 3) and the MAC layer retransmissions (layer 2) at the same time to identify energy-efficient data-aggregation routing assignments, and in the meantime to meet the delay QoS. In this paper, for the first time, we study this cross-layer design problem by using optimization-based heuristics. We first model this problem as a non-convex mathematical programming problem where the objective is to minimize the total energy consumption subject to the data aggregation tree and the delay QoS constraints. The objective function includes the energy in the transmission mode (data transmissions and data retransmissions) and the energy in the idle mode (to wait for data from downstream nodes in the data aggregation tree). The proposed solution approach is based on Lagrangean relaxation in conjunction with a number of optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithm outperforms existing heuristics that do not take MAC layer retransmissions and the energy consumption in the idle mode into account.

Approximation algorithm for constructing data aggregation trees for wireless sensor networks

This paper considers the problem of constructing data aggregation trees in wireless sensor networks (WSNs) for a group of sensor nodes to send collected information to a single sink node. The data aggregation tree contains the sink node, all the source nodes, and some other non-source nodes. Our goal of constructing such a data aggregation tree is to minimize the number of non-source nodes to be included in the tree so as to save energies. We prove that the data aggregation tree problem is NP-hard and then propose an approximation algorithm with a performance ratio of four and a greedy algorithm. We also give a distributed version of the approximation algorithm. Extensive simulations are performed to study the performance of the proposed algorithms. The results show that the proposed algorithms can find a tree of a good approximation to the optimal tree and has a high degree of scalability.

Optimization-Based Channel Constrained Data Aggregation Routing Algorithms in Multi-Radio Wireless Sensor Networks

In wireless sensor networks, data aggregation routing could reduce the number of data transmissions so as to achieve energy efficient transmission. However, data aggregation introduces data retransmission that is caused by co-channel interference from neighboring sensor nodes. This kind of co-channel interference could result in extra energy consumption and significant latency from retransmission. This will jeopardize the benefits of data aggregation. One possible solution to circumvent data retransmission caused by co-channel interference is to assign different channels to every sensor node that is within each other's interference range on the data aggregation tree. By associating each radio with a different channel, a sensor node could receive data from all the children nodes on the data aggregation tree simultaneously. This could reduce the latency from the data source nodes back to the sink so as to meet the user's delay QoS. Since the number of radios on each sensor node and the number of non-overlapping channels are all limited resources in wireless sensor networks, a challenging question here is to minimize the total transmission cost under limited number of non-overlapping channels in multi-radio wireless sensor networks. This channel constrained data aggregation routing problem in multi-radio wireless sensor networks is an NP-hard problem. I first model this problem as a mixed integer and linear programming problem where the objective is to minimize the total transmission subject to the data aggregation routing, channel and radio resources constraints. The solution approach is based on the Lagrangean relaxation technique to relax some constraints into the objective function and then to derive a set of independent subproblems. By optimally solving these subproblems, it can not only calculate the lower bound of the original primal problem but also provide useful information to get the primal feasible solutions. By incorporating these Lagrangean multipliers as the link arc weight, the optimization-based heuristics are proposed to get energy-efficient data aggregation tree with better resource (channel and radio) utilization. From the computational experiments, the proposed optimization-based approach is superior to existing heuristics under all tested cases.

Improved algorithm of reducing delay in data aggregation

Through studying the algorithm FHF of constructing the data aggregation tree,it is found that when the distant nodes are from the sink node transmit data to the sink node,the delay is longer.To reduce the transmission delay of abnormal data,particularly in large data packets occasions,the method of keeping a detection bit in the MAC layer was used to deal with unexpected events.Simulation results show that this method can reduce the delay of abnormal data transmission,and the emergency measures are relatively good.

Are there so many sons per node in a wireless sensor network data aggregation tree?

Some authors seem to believe that the average number of sons in a Wireless Sensor Network (WSN) data aggregation tree can be kept constant, even when the distances from the sink increase. But this assumption is impossible to stand in a dense and uniformly deployed network, as the number of nodes in a given level would grow exponentially, while the perimeter just grows linearly. In this letter, it is shown that the average number of sons of a given tree node is in average a quite low number, slightly higher than one, and that it tends to 1, as the distance to sink, or node depth, increases, both for 2-D, and for 3-D WSNs.

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.

Integrated channel assignment and data aggregation routing problem in wireless sensor networks

In wireless sensor networks, data aggregation routing could reduce the number of data transmission so as to achieve efficient total energy consumption. However, this kind of data aggregation introduces data retransmission that is caused by co-channel interference from neighbouring sensor nodes. Hence, more data aggregation leads to more extra energy consumption and significant retransmission delay from retransmission. This could jeopardise the benefits of data aggregation. One possible solution to circumvent retransmission caused by co-channel interference is to assign different channel to every sensor node that is within each other's interference range on the data aggregation tree. As the number of non-overlapping channels is limited in wireless networks, it is unlikely that we could assign a different channel to every sensor node on the data aggregation tree. Then, an interesting problem is to perform data aggregation routing in conjunction with channel assignment to minimise total transmission power under limited number of non-overlapping channels. This problem is an NP-complete problem. We devise heuristic algorithm, Iterative Channel Adjustment Data Aggregation Routing algorithm (ICADAR), and other three heuristics, to tackle this problem. From the simulation results, the ICADAR algorithm outperforms the other three algorithms under all experimental cases.

Data aggregation in wireless sensor networks using ant colony algorithm

Data aggregation is important in energy constraint wireless sensor networks which exploits correlated sensing data and aggregates at the intermediate nodes to reduce the number of messages exchanged network. This paper considers the problem of constructing data aggregation tree in a wireless sensor network for a group of source nodes to send sensory data to a single sink node. The ant colony system provides a natural and intrinsic way of exploring search space in determining data aggregation. Moreover, we propose an ant colony algorithm for data aggregation in wireless sensor networks. Every ant will explore all possible paths from the source node to the sink node. The data aggregation tree is constructed by the accumulated pheromone. Simulations have shown that our algorithm can reduce significant energy costs.

Timing control for delay‐constrained data aggregation in wireless sensor networks

AbstractTiming control for delay‐constrained data aggregation is critical for improving the performance of a wireless sensor network. In this paper, we propose an adaptive timing control (ATC) mechanism to determine the proper time for data aggregation and forwarding. Given the maximum delay requirements of an application, the ATC mechanism determines the aggregation time for sensor nodes based on both the locations of the nodes and the number of children in the data aggregation tree. The nodes with more children are allocated a longer waiting time. The ATC mechanism maximizes the opportunity for data aggregation and ensures sufficient time to process data from the children. Analytical and simulation results show that the ATC mechanism leads to a higher data delivery rate and lower energy costs compared with existing similar algorithms. Moreover, since a lot of data packets can arrive at the sink earlier, the ATC mechanism is capable of responding more quickly to users. Copyright © 2006 John Wiley & Sons, Ltd.