Hierarchical flow balancing protocol for data aggregation in wireless sensor networks

This paper deals with information discovery and aggregation in large scale wireless sensor networks applied for mission-critical applications like military reconnaissance. To support query processing based on the gathered information, an efficient and reliable information discovery mechanism is proposed for sensor networks. We extend the basic Comb-Needle Discovery Support Model [3] by including Cluster-based data aggregation mechanism, which helps minimize the communication cost. Clusterbased approach groups the sensor nodes in the sensor network. Each node of a group will send information to its Cluster Head, which then aggregates and forwards the information to the base station (Sink). We compare the performance of proposed Cluster-based Comb-Needle Model with the basic Comb-Needle Model using the parameters, namely, energy consumption and communication cost. The experimental results through Avrora Simulator on TinyOS Platform reveal that Clusterbased Comb-Needle Model for Data Aggregation helps a sensor network in conserving its energy, and thereby extending its life time.

We present a protocol for secure data aggregation in wireless sensor networks that offers end-to-end data confidentiality by using homomorphic functions and interleaved encryption. Hop-by-hop aggregation in sensor networks is an efficient way to save energy. Node compromises in hostile environments require protocols for data aggregation where the intermediate nodes contribute with their own values to the aggregated data without getting access to it. Homomorphic encryption schemes allow aggregation on ciphertext and thus can provide end-to-end data confidentiality. We propose a layered data aggregation protocol which ensures that, in the presence of less than n captured nodes, an attacker cannot get access to any aggregated data from the network. When more than n nodes are captured, the attacker can only get access to the aggregated values received by the captured nodes. Our protocol is resilient to node failure and no pre-built tree for data aggregation is needed.

This paper attempts to identify the reliability assessment in terms of data aggregation in wireless sensor networks. With the sensor nodes' Bayesian lifetime model, the fault ratio of sensing data among a pair of nodes in the worst case is derived. Leveraging the property of exponential aggregation latency distribution, the optimizing strategy, namely fault-tolerant scheduling for aggregation (FTSA) is proposed. Quantitative analysis of the task scheduling is also presented, which is used for reducing the latency of data aggregation. Moreover, the sensor fault probability and aggregation latency are initially introduced into the data collection scheduling process, and it mathematically shows that the fused local estimate can obtain a global estimation with the reliable data acquisition framework. Performance evaluations of the proposed approach with both sensor node faults and scheduling policy are carried out. These results show that the proposed approach outperforms the earlier algorithms in terms of latency designed without regard to sensor faults.

In wireless sensor networks, sensor nodes have limited battery power. Data aggregation is an important technique in a wireless sensor network to reduce the energy consumption by eliminating the redundant data and delivering useful data to the user. Data aggregation technique is used to gather the data sensed by the sensor nodes in the network and aggregates the sensed data in an energy-efficient way so that overall network lifetime can be increased. Cluster, tree, chain, and grid are some of the architectures for data aggregation in wireless sensor networks. In this paper, authors present an analysis-based survey of data aggregation protocols for tree-based architecture in wireless sensor networks. The authors analyze each algorithm on the basis of performance measurements such as network lifetime, energy consumption, and node distance. Authors also compare these algorithms on the basis of the preferred parameter and a key feature of each algorithm. In the fifth section of the paper, authors described the proposed approach to construct the data aggregation tree to maximize the network lifetime. In the proposed approach, authors used distance parameter to construct minimum spanning tree, and other parameters such as load and energy are preferred in a balanced manner to reduce the energy consumption and to maximize the network lifetime.

In‐network data aggregationin wireless sensor networks (WSNs) is a technique aimed at reducing the communication overhead—sensed data are combined into partial results at intermediate nodes during message routing. However, in the above technique, some sensor nodes need to send their individual sensed values to an aggregator node, empowered with the capability to decrypt the received data to perform a partial aggregation. This scenario raises privacy concerns in applications like personal health care and the military surveillance. A few other solutions exist where the data are not disclosed to the aggregator (e.g., using privacy homomorphism (PH)), but these solutions are not robust to node or communication failure.The contributions of this paper are two‐fold: first, we design a private data aggregation protocol that does not leak individual sensed values during the data aggregation process. In particular, neither the base station (BS) nor the other nodes are able to compromise the privacy of an individual node's sensed value. Second, the proposed protocol is robust to data‐loss; if there is a node‐failure or communication failure, the protocol is still able to compute the aggregate and to report to the base station the number of nodes that participated in the aggregation. To the best of our knowledge, our scheme is the first one that efficiently addresses the above issues all at once. Copyright © 2009 John Wiley & Sons, Ltd.

In wireless sensor networks, measurements from neighboring sensor nodes are typically cross-correlated and can be aggregated and compressed locally. This process, referred to as in-network data aggregation, saves a lot of energy by reducing the amount of data that needs to be transferred to the data sink. We consider the problem of using a TDMA schedule in order to perform data aggregation in a network in which the wireless links are unreliable and heterogeneous. In order to balance the energy consumption of different sensor nodes, the nodes with the weakest links should discard more packets than nodes with the strongest links. The existing packet discarding policies are unsuitable for data aggregation because they fail to consider the dependencies between different packets. We propose three packet discarding policies and show that they are appropriate for different kinds of networks. For large networks, among the policies that we propose, the best policy is discard the oldest packet, and to transmit the oldest packet that has not been discarded.

Data aggregation in wireless sensor networks using ant colony algorithm

Wireless sensor networks (WSNs) is a class of wireless ad hoc networks in which sensor nodes sense, process, and communicate data acquired from the physical environment to an external-base-station (BS). But the fundamental challenge in the design of wireless sensor networks (WSNs) is to maximise their lifetimes especially when they have a limited and non-replenish able energy supply. Energy-efficient communication is necessary to increase the lifetime of the sensor network. In this work solutions are for the data gathering and routing problem with in network aggregation in WSNs. The aim of our work is to focus on data gathering and redundancy elimination with in-network aggregation in WSNs. The objective is to maximise the network lifetime by utilising data aggregation with redundancy removal by increasing accuracy thereby improving packet delivery ratio and reducing energy consumption. This work focuses on the maximum lifetime data gathering and routing to avoid packet loss in wireless sensor networks.

We discuss the technology detail of data aggregation in wireless sensor networks (WSNs) in this paper, which consists of three parts. First, we apply the classical K-average clustering algorithm to the phase of cluster division which is the first step of the aggregate tree. Second, the cluster head node and the cluster group nodes are constructed based on the entropy formula H(S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</sub> |x)<;F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</sub> (c) and pseudo-random function. Finally, we study the secure data fusion method among the nodes.

This paper introduces a method that combines the K-means clustering genetic algorithm (GA) and Lempel-Ziv-Welch (LZW) compression techniques to enhance the efficiency of data aggregation in wireless sensor networks (WSNs). The main goal of this research is to reduce energy consumption, improve network scalability, and enhance data aggregation accuracy. Additionally, the GA technique is employed to optimize the cluster formation process by selecting the cluster heads, while LZW compresses aggregated data to reduce transmission overhead. To further optimize network traffic, scheduling mechanisms are introduced that contribute to packets being transmitted from sensors to cluster heads. The findings of this study will contribute to advancing packet scheduling mechanisms for data aggregation in WSNs in order to reduce the number of packets from sensors to cluster heads. Simulation results confirm the system's effectiveness compared to other compression methods and non-compression scenarios relied upon in LEACH, M-LEACH, multi-hop LEACH, and sLEACH approaches.

This paper studies energy efficient routing for data aggregation in wireless sensor networks. Our goal is to maximize the lifetime of the network, given the energy constraint on each sensor node. Using linear programming (LP) formulation, we model this problem as a multicommodity flow problem, where a commodity represents the data generated from a sensor node and delivered to a base station. A fast approximate algorithm is presented, which is able to compute (1-e)-approximation to the optimal lifetime for any e > 0. Then along this baseline, we further study several advanced topics. First, we design an algorithm, which utilizes the unique characteristic of data aggregation, and is proved to reduce the running time of the fastest existing algorithm by a factor of K, K being the number of commodities. Second, we extend our algorithm to accommodate the same problem in the setting of multiple base stations, and study its impact on network lifetime improvement. All algorithms are evaluated through both solid theoretical analysis and extensive simulation results.

In-network data aggregation plays an important role to reduce the transmitting energy and data redundancy in large scale sensor networks. However, the security of wireless sensor networks is a challenging problem in the process of data aggregation. An efficient secure data aggregation is proposed to enhance the data security of wireless sensor networks. Firstly, the secure in-network aggregation tree is introduced, then a judgment method based on trust schema was used to detect whether a sensor node has potential misbehavior. After the detection, a local recovery schema is proposed to reduce the possibility to become isolated nodes, which will increase the security level in data aggregation in wireless sensor networks. The performance analysis shows the efficiency of the schema.

A Distributed Cross-Layer Protocol for Sleep Scheduling and Data Aggregation in Wireless Sensor Networks

Concealed data aggregation (CDA) is important in wireless sensor networks, because it provides an energy efficient secure communication by allowing in network data aggregation on encrypted data. Privacy homomorphism (PH) based algorithms are the basis of CDA. However, it supports only a limited number of aggregation functions. Recoverability of individual sensor readings from the concealed data aggregation result at the BS overcomes the limitation of PH based algorithms on aggregation function. Thus, it allows authentication and integrity checking. But this technique reduces the node energy due to the computation overhead of encryption, signature operation as well as the transfer of both. So a mechanism that saves the energy of sensor nodes is required. The proposed technique overcomes this by transmitting the difference data rather than raw data from sensor node to cluster head. This differential data transfer achieves more energy and bandwidth efficiency than the existing recoverable concealed data aggregation scheme. The proposed differential data based recoverable data aggregation scheme increases the network lifetime by avoiding the redundant data transfer from each sensor node. Thus, it reduces the transmission overhead in the secure communication.

We propose two strategies to reduce energy consumption for secure data aggregation in wireless sensor networks. While the purpose of data aggregation is to reduce energy consumption, secure data aggregation introduces an overhead making the total energy consumption comparable to naive secure non-aggregation. Another aspect is that the secure data aggregation has higher node congestion than non-aggregation schemes for networks of sizes up to about 100 nodes. Considering a secure data aggregation protocol, our strategies are to reduce the height of the aggregation tree and limit its node degree. The former results in fewer verification messages and a lower total communication load, and the latter reduces node congestion. The most heavily burdened node sends a third of the messages required in the original protocol, which for small networks is lower than with non-aggregation, and the total communication load is lowered compared to non-aggregation.