Data Aggregation Tree Research Articles

On maximizing the lifetime for data aggregation in wireless sensor networks using virtual data aggregation trees

Data gathering is a basic requirement in many applications of wireless sensor networks (WSNs). Because the energy of sensors is limited, the data-gathering mechanism must be carefully designed to save the energy consumption of sensors to prolong the network lifetime. Recently, many researchers have studied gathering data efficiently in WSNs to minimize the total energy consumption when a fixed number of data are allowed to be aggregated into one packet. However, when the total energy consumption is minimized, the energy consumption of sensors for data gathering cannot be guaranteed to be balanced, and thus, the network lifetime cannot be guaranteed to be maximized. This motivates us to study the problem of scheduling virtual data aggregation trees to maximize the network lifetime when a fixed number of data are allowed to be aggregated into one packet, termed the Maximum Lifetime Data Aggregation Tree Scheduling (MLDATS) problem. The MLDATS problem is shown to be NP-complete in the paper. In addition, a local-tree-reconstruction-based scheduling algorithm (LTRBSA) is proposed for the MLDATS problem. We use simulations to evaluate and demonstrate the performance of the LTRBSA when the sink has 2-hop, 3-hop, and all information in the networks. Simulation results show that the LTRBSA of using sink’s 3-hop information provides comparable performances to that of using all information in the networks, and outperforms other methods proposed in the simulation.

Hybrid Tree Construction for Sustainable Delay Aware Data Aggregation in Wireless Sensor Networks

Data aggregation is a process of collecting data from all the sensor nodes with a sink node to generate a statistic report in a wireless sensor network. Data aggregation can be performed in various ways that resolves many issues like aggregation delay, collision, security and energy utilization. This paper proposes a Hybrid tree construction (HTC) algorithm to perform the delay efficient data aggregation in wireless sensor network. In HTC a node which has high renewable energy is chosen as root. Each node chooses their corresponding parent and child node among their neighbors by implementing a two-hop tree construction method. In this HTC left child will be selected based on its residual energy and least distance from the other nodes and right child is the second least distance node from the root. By applying a delay efficient data aggregation algorithm on the data aggregation tree constructed by HTC, the performance of the proposed algorithm has been studied. The HTC algorithm has been applied on different network scenarios to improve its performance through the simulation models and the results are verified by comparing with other models.

Load balancing and data aggregation tree routing algorithm in wireless sensor networks

Load balancing and data aggregation tree routing algorithm in wireless sensor networks

Delay-Constrained Data Aggregation in VANETs

Data aggregation has been recognized as an effective technique for reducing communication costs while obtaining useful aggregated information. In this paper, we study the crucial problem of delay-constrained data aggregation in vehicular ad hoc networks (VANETs), which has not been well studied in the literature. With the analysis based on real traces, we observe that there is heterogeneity with node contact patterns, which indicates that some nodes contact other nodes more frequently. Motivated by this observation, we propose an approach called aTree. The centralized aTree first constructs a data aggregation tree based on the shortest path tree and then assigns a waiting time budget to each node on the tree based on dynamic programming. We further develop a distributed aTree, in which a shortest path tree is built in a distributed fashion, and nodes determine their waiting time budgets collaboratively. We have performed extensive simulations on real taxi traces, and results show that our aTree schemes incur much lower transmission overhead while achieving the same performance compared with other schemes.

Hierarchical Data Aggregation Using Compressive Sensing (HDACS) in WSNs

Energy efficiency is one of the key objectives in data gathering in wireless sensor networks (WSNs). Recent research on energy-efficient data gathering in WSNs has explored the use of Compressive Sensing (CS) to parsimoniously represent the data. However, the performance of CS-based data gathering methods has been limited since the approaches failed to take advantage of judicious network configurations and effective CS-based data aggregation procedures. In this article, a novel Hierarchical Data Aggregation method using Compressive Sensing (HDACS) is presented, which combines a hierarchical network configuration with CS. Our key idea is to set multiple compression thresholds adaptively based on cluster sizes at different levels of the data aggregation tree to optimize the amount of data transmitted. The advantages of the proposed model in terms of the total amount of data transmitted and data compression ratio are analytically verified. Moreover, we formulate a new energy model by factoring in both processor and radio energy consumption into the cost, especially the computation cost incurred in relatively complex algorithms. We also show that communication cost remains dominant in data aggregation in the practical applications of large-scale networks. We use both the real-world data and synthetic datasets to test CS-based data aggregation schemes on the SIDnet-SWANS simulation platform. The simulation results demonstrate that the proposed HDACS model guarantees accurate signal recovery performance. It also provides substantial energy savings compared with existing methods.

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.

Water Drop Algorithms

This article presents a review of a novel nature-inspired adaptive optimization algorithm known as Water Drop Algorithm (WDA) which imitates the dynamics of river systems and the behavior of water drops when they are moving in a river system such as the variation of velocity, the change of sediment/soil in the river bed, the change of direction of the flow, and so on. Recent improved, modified, and adaptive WDAs are examined in this paper. Various applications of WDA are described briefly including the vehicle routing problem, economic load dispatch problem, economic and emission dispatch problem, optimal data aggregation tree in wireless networks, reactive power dispatch problem service selection, and reservoir operation.

Data Aggregation Based on Saving Energy and Preserving Privacy in Wireless Sensor Networks

Privacy preserving plays an important role in application of the Internet of Things (IoT). As apart of the IT, 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 Wireless Sensor Networks, which uses characteristic of the data aggregation tree structure to reduce communication overhead. It assigns the random time pieces to nodes with aim to avoid collision, and limits the scope of collusion data to reinforce data—loss resilience. Compared with the SMART algorithm have less communication overhead. ESPART uses data-dependent fusion characteristics of the tree structure to eliminate SMART algorithm for unnecessary communication node, so that in case of the same privacy protection security, the data transmission amount is reduced to about 48% of SMART.

Building Maximum Lifetime Shortest Path Data Aggregation Trees in Wireless Sensor Networks

In wireless sensor networks, the spanning tree is usually used as a routing structure to collect data. In some situations, nodes do in-network aggregation to reduce transmissions, save energy, and maximize network lifetime. Because of the restricted energy of sensor nodes, how to build an aggregation tree of maximum lifetime is an important issue. It has been proved to be NP-complete in previous works. As shortest path spanning trees intuitively have short delay, it is imperative to find an energy-efficient shortest path tree for time-critical applications. In this article, we first study the problem of building maximum lifetime shortest path aggregation trees in wireless sensor networks. We show that when restricted to shortest path trees, building maximum lifetime aggregation trees can be solved in polynomial time. We present a centralized algorithm and design a distributed protocol for building such trees. Simulation results show that our approaches greatly improve the lifetime of the network and are very effective compared to other solutions. We extend our discussion to networks without aggregation and present interesting results.

Constructing Load-Balanced Data Aggregation Trees in Probabilistic Wireless Sensor Networks

Data Gathering is a fundamental task in Wireless Sensor Networks (WSNs). Data gathering trees capable of performing aggregation operations are also referred to as Data Aggregation Trees (DATs). Currently, most of the existing works focus on constructing DATs according to different user requirements under the Deterministic Network Model (DNM). However, due to the existence of many probabilistic lossy links in WSNs, it is more practical to obtain a DAT under the realistic Probabilistic Network Model (PNM). Moreover, the load-balance factor is neglected when constructing DATs in current literatures. Therefore, in this paper, we focus on constructing a Load-Balanced Data Aggregation Tree (LBDAT) under the PNM. More specifically, three problems are investigated, namely, the Load-Balanced Maximal Independent Set (LBMIS) problem, the Connected Maximal Independent Set (CMIS) problem, and the LBDAT construction problem. LBMIS and CMIS are well-known NP-hard problems and LBDAT is an NP-complete problem. Consequently, approximation algorithms and comprehensive theoretical analysis of the approximation factors are presented in the paper. Finally, our simulation results show that the proposed algorithms outperform the existing state-of-the-art approaches significantly.

Enable Efficient Data Aggregation for Region-Based Top-K Queries in Wireless Sensor Networks

We study data aggregation for region-based top-k queries in wireless sensor networks, which is one kind of internet of things. Because the energy of sensor nodes is limited and a sensor node will die if it has no energy left, one of the important targets for all protocols in wireless sensor networks is to decrease the energy consumption of the sensor nodes. For a sensor node, communication cost is much more than other kinds of energy cost such as energy cost on computation and data storage. Thus, a very efficient way to decrease the energy cost of the sensor nodes is to decrease the quality of the sensing data that will be transmitted to the base station. In this paper, we use the technique of data aggregation to achieve this goal, and propose an algorithm to construct a novel Data Aggregation Tree (DAT) in the query region. To check the efficiency of DAT, we have made a simulation on OMNET, and the results show that DAT can shrink large quality of data when they are transmitted to the base station, and the life time of the sensor networks can thus be prolonged..

Energy Efficient Wireless Sensor Network using Genetic Algorithm based Association Rules

Wireless Sensor Networks (WSN) usually contains thousands or hundreds of sensors which are randomly deployed. Sensors are powered by battery, which is an important issue in sensor networks, since routing consumes a lot of energy. Such nodes are deployed in thousands to form a network with capacity to report to a data collection sink (base station). An efficient routing scheme in sensor network is also important. Networking unattended sensor nodes are expected to have significant impact on the efficiency of many military and civil applications such as combat field surveillance, security and disaster management. Genetic algorithm (GA) based data aggregation trees are used where the sensors receive data from neighboring nodes, aggregate the incoming data packets, and forward the aggregated data to a suitable neighbor. GA is used to create energy efficient data aggregation trees. In this work, the amount of data sent to sink is reduced using association rule mining and in turn to further reduce the energy consumption of the network; optimal routes are chosen to transmit data to the sink based on energy consumption. The proposed method is able to discover the association rules to make predictive analysis on node failure, asymmetric links. The rules found form the basis for coding solutions in the proposed genetic algorithm. GA is applied to generate balanced and energy efficient data aggregation spanning trees for wireless sensor networks. E-Span, which is an energy-aware spanning tree algorithm and Lifetime-Preserving Tree (LPT) are used to create data aggregation trees. The proposed GA extends network lifetime.

The Degree-Constrained Adaptive Algorithm Based on the Data Aggregation Tree

In the PEDAP algorithm, a minimum spanning tree considering the energy consumption is established based on the Kruskal algorithm, and updated every 100 rounds. There exists a defect that the energy of some nodes rapidly expires because the degrees of nodes differ significantly, and the delay time is not considered. Based on the above analysis, a new algorithm called DADAT (a Degree-based Adaptive algorithm for Data Aggregation Tree) is proposed. The energy consumption and the delay time are both considered, and a weight model to construct a minimum spanning tree is established. Furthermore, the node degree on the tree is readjusted according to the average degree of the network, and nodes are labeled by red, yellow, and green colors according to their remaining energy; the child nodes of the red nodes are adaptively transferred to their neighbor nodes which are labeled as green. Finally, we discuss the weight and the update rounds' impact on the network lifetime. Experimental results show that the algorithm can effectively balance the energy consumption and prolong the lifetime of the network, as well as achieving a lower latency.

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.

Secure and energy-efficient data aggregation with malicious aggregator identification in wireless sensor networks

Data aggregation in wireless sensor networks is employed to reduce the communication overhead and prolong the network lifetime. However, an adversary may compromise some sensor nodes, and use them to forge false values as the aggregation result. Previous secure data aggregation schemes have tackled this problem from different angles. The goal of those algorithms is to ensure that the Base Station (BS) does not accept any forged aggregation results. But none of them have tried to detect the nodes that inject into the network bogus aggregation results. Moreover, most of them usually have a communication overhead that is (at best) logarithmic per node. In this paper, we propose a secure and energy-efficient data aggregation scheme that can detect the malicious nodes with a constant per node communication overhead. In our solution, all aggregation results are signed with the private keys of the aggregators so that they cannot be altered by others. Nodes on each link additionally use their pairwise shared key for secure communications. Each node receives the aggregation results from its parent (sent by the parent of its parent) and its siblings (via its parent node), and verifies the aggregation result of the parent node. Theoretical analysis on energy consumption and communication overhead accords with our comparison based simulation study over random data aggregation trees.

LBDEA: A Load Balanced and Delay Efficient Algorithm for Tree-based Data Aggregation in WSNs

Data aggregation is an essential operation in many applications of Wireless Sensor Networks (WSNs). For energy constrained and delay sensitive WSNs, how to prolong the lifetime and reduce the latency of the network are two critical problems. We deal with these two problems together in data aggregation by proposing a Load Balanced and Delay Efficient Algorithm (LBDEA), which consists of two phases. First, construct a load-balanced data aggregation tree based on Semi-Matching algorithm in the graph theory. Second, seek a collision-free scheduling based on greedy strategy for the transmission links in the aggregation tree. Through balancing the load of the sensor nodes, the lifetime of the network is optimized. By avoiding collisions and retransmissions of data delivery, the total latency is decreased. Simulations show that LBDEA not only improves the lifetime but also cuts down the delay for data aggregation in WSNs, compared with other related algorithms.

Optimal and Efficient Algorithms for Projection-Based Compressive Data Gathering

We investigate the problem of compressive data aggregation in wireless sensor networks. We propose a data gathering scheme using Compressive Sensing (CS) by building up data aggregation trees from sensor nodes to the sink. Our problem aims at minimizing the number of links in the trees to minimize the number of overall transmissions. We formulate the problem of constructing aggregation trees for forwarding the compressed data to the sink as a mixed integer linear program (MILP) and present efficient algorithms to solve the problem. We show that our algorithms have outstanding performance and order of magnitude faster than the optimal model.

A hybrid method of CSMA/CA and TDMA for real-time data aggregation in wireless sensor networks

We study the real-time data aggregation in contention-based wireless sensor networks that use CSMA/CA MAC layer protocols as defined in IEEE 802.15.4 or IEEE 802.11 standard. The problem is, for a given data aggregation tree and a delay bound, to maximize 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, while the node interference is often very high in the large scale sensor networks. We propose a hybrid method that combines the CSMA/CA protocol with TDMA scheduling of transmissions. We divide the child nodes of a parent into groups and schedule the groups into different “time-frames” for transmission. Within the group, the nodes still use the CSMA/CA protocol to compete for data transmission. By doing so, we divide a large collision domain (i.e., all child nodes competing to transmit to their parent) into several small collision domains (i.e., a group of nodes competing for transmission), and the success probability can thus be significantly improved. On the other hand, the “time-frame” used in our method is much larger than the timeslot used in pure TDMA protocols. It only requires loose synchronization of clocks, which is suitable for low-cost sensor networks. We transform our objective of maximizing the average success probability into minimizing the average node interference. We then convert our problem to the maximum weight k-cut problem, which is NP-hard. We propose two efficient heuristic algorithms to solve the problem. Simulation results have shown that our proposed method can improve the success probability significantly.

Data Aggregation Tree Approach in Aodv Protocol to Select Dynamic Route for Sensor Network

Design an energy efficient data aggregation tree approaches sensor network using localized power efficient data aggregation protocols. Using LMST and RNG topologies approximate minimum spanning tree and calculate position and distance information of one hop neighbors. A new node is added or fails route maintenance procedure is executed. The shortest weighted path – based approaches, can achieve 90 percent of the upper bound on lifetime. AODV proposed algorithm to assess the node lifetime and link lifetime utilizing the dynamic nature of energy drain rate and relative mobility estimation rate of nodes. Using node lifetime and link lifetime algorithm select least dynamic route with the longest lifetime. On prediction AODV protocol is a loop – free routing and has self – starting in network behavior as node mobility, link fail and packet losses.

A sensitive data aggregation scheme for body sensor networks based on data hiding

Body sensor networks (BSNs) enable the pervasive, long-term, and real-time monitoring in any environment and without restriction of activity. Security and privacy are extremely critical issues of BSNs, as sensitive information is transmitted through the wireless network. This paper proposes a novel sensitive data aggregation scheme based on data hiding for BSNs, namely SDAS. SDAS adopts the packet combination based on the sensitive data aggregation tree built in each BSN to reduce the transmission energy consumption by eliminating transmitted redundancy. After lossless compression, the compressed sensitive data are embedded into various ordinary data in the combined packet by using a lightweight data hiding algorithm to prevent the disclosure of the sensitive data and avoid arousing the attention of attackers. Extensive analysis demonstrates that our proposed scheme can ensure a high level of security and privacy of the sensitive data without affecting the ordinary data quality, while delivering low power consumption.