Maximizing Lifetime for Data Aggregation in Wireless Sensor Networks

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‐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.

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.

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.

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.

Data aggregation in wireless sensor networks using ant colony algorithm

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.

Major Wireless Sensor Network designs are focused on improving the network lifetime. The data aggregation process is the simplest way to achieve this. The PEGASIS routing protocol is a promising approach for efficient data aggregation in Wireless Sensor Networks. It is also reported in the literature that the PEGASIS performs better than LEACH and HEED protocol towards improving the network lifetime. However, in PEGASIS, the selection of a leader located far away from the base station still remains exposed to higher energy consumption for sending aggregated data. In this paper, we propose an enhancement to the chain formation process of PEGASIS to further improve its lifetime, and called it PEGASIS with Improved Network Lifetime (PEGASIS-INL). In contrast to original PEGASIS protocol, the PEGASIS-INL selects a node as a leader only if it is within a strong communication range of base station. A multiple overlapped chain formation and the aggregation technique is used for transmission of sensed information to the base station. Experimental results demonstrate that the PEGASIS-INL algorithm outperforms PEGASIS in terms of the product of consumed energy and delay, weighting the overall performance of both the energy consumption and the network lifetime.

Sensor networks are collection of sensor nodes which co-operatively send sensed data to base station. The major problem in the field of wireless network is draining of sensor's battery. As the senor nodes are driving high power so there is a need of an intelligent algorithm which could enable the sensors to utilize the energy in an energy efficient manner. It will also enable the network to work for longer duration of time as well as reduces trafficking inside network. Data Aggregation is a technique which can solve these problems. The main goal of data aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. It involves the selective transfer of data from the nodes to the base station. Wireless sensor networks (WSN) offer an increasingly Sensor nodes need less power for processing as compared to transmitting data. It is preferable to do in network processing inside network and reduce packet size. One such approach is data aggregation which attractive method of data gathering in distributed system architectures and dynamic access via wireless connectivity. In this paper, a data aggregation framework on wireless sensor networks is presented. The framework works as a middleware for aggregating data measured by a number of nodes within a network. Also the paper briefly discusses a case of sending the temperature reading from the source node to the destination. It also provides techniques in order to send the minimum data at the cost of a small error. The aim of the proposed work is to compare the performance of SPIN in terms of energy efficiency in comparison with and without data aggregation in wireless sensor networks and to assess the suitability of the protocol in an environment where resources are limited.

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.

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.

Problem statement: Wireless sensor networks, brings out variety of different challenges at energy level, integrity, authentication, communication cost. Approach: In the secure data aggregation techniques, reduction in the energy consumption was not elaborated in detail, since aggregator means of connection to sink was either direct or through other aggregators which need high energy level. Results: We suggest an Energy Constrained Secure Hierarchical Data Aggregation in Wireless Sensor Networks. At first the network was divided into clusters, each cluster begins with an aggregator and aggregator was connected to sink. Based on distance to sensor nodes and its energy level the aggregator detects the node. Separate keys were distributed to the two levels i.e., sensor node to the aggregator and aggregator to the sink. Whenever a data had to be sent from a sensor node to another node; initially the sensor node encrypts the data using a key and sends it to the aggregator. Conclusion/Recommendations: The digital signature algorithm that is based on the Elliptic Curve Digital Signature Algorithm is as secure and has reduced energy consumption.

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.

In wireless sensor networks, data aggregation protocols are employed to prolong the network lifetime. However, performing data aggregation while preserving security is a challenging problem. This paper presents a polynomial regression based secure data aggregation protocol in which sensor nodes represent their sensed data as polynomial functions. Instead of their original data, sensor nodes secretly send coefficients of these polynomial functions to data aggregators. Data aggregation is performed based on these coefficients and the base station is able to extract a good approximation of the network data from the aggregation result. The security analysis and simulation results show that the proposed scheme is able to reduce the amount of data transmission in the network without compromising data confidentiality.

Nodes in a wireless sensor network are normally constrained by hardware and environmental conditions and face challenges of reduced computing capabilities and system security vulnerabilities. This fact calls for special requirements for network protocol design, security assessment models, and energy-efficient algorithms. Data aggregation is an effective energy conservation technique, which removes redundant information from the data aggregated from neighbor sensor nodes. How to further improve the effectiveness of data aggregation plays an important role in improving data collection accuracy and reducing the overall network energy consumption. Unfortunately, sensor nodes are normally deployed in an open environment and thus are subject to various attacks conducted by adversaries. Consequently, data aggregation brings new challenges to wireless sensor network security. In this article, we propose a novel secure data aggregation solution based on autoregressive integrated moving average model, a time series analysis technique, to prevent private data from being learned by adversaries. We leverage the autoregressive integrated moving average model to predict the data volume in sensor nodes, and update and synchronize the model as needed. The experimental results demonstrate that our model provides accurate predictions and that, compared with competing methods, our solution achieves better security, lower computation and communication costs, and better flexibility.