A novel efficient approach for protecting integrity of data aggregation in wireless sensor networks

Data aggregation is an important method to reduce the energy consumption in wireless sensor networks (WSNs), however, performing data aggregation while preserving data confidentiality and integrity is mounting a challenge. The existing solutions either have large communication and computation overheads or produce inaccurate results. This paper proposes a novel secure data aggregation scheme based on homomorphic encryption in WSNs. The scheme adopts a symmetric-key homomorphic encryption to protect data privacy and combines it with homomorphic signature to check the aggregation data integrity. In addition, during the decryption of aggregated data, the base station is able to classify the encrypted and aggregated data based on the encryption keys. Simulation results and performance analysis show that our mechanism requires less communication and computation overheads than previously known methods. It can effectively preserve data privacy, check data integrity, and achieve high data transmission efficiency. Also, it performs accurate data aggregation rate while consuming less energy to prolong network lifetime.

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.

Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this paper, we present two privacy-preserving data aggregation schemes for additive aggregation functions. The first scheme -cluster-based private data aggregation (CPDA)-leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme -Slice-Mix-AggRegaTe (SMART)-builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme -TAG, where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes. To the best of our knowledge, this paper is among the first on privacy-preserving data aggregation in wireless sensor networks.

Data aggregation in wireless sensor networks using ant colony algorithm

With the exponential rise of pervasive computing applications, data privacy has become much more of an important issue than before. When data is aggregated at each hop in a sensor network, it becomes harder to protect its privacy. A number of privacy preserving data aggregation algorithms have recently appeared for wireless sensor networks (WSNs), very few of them however also address the issue of data integrity along with privacy. Data privacy and integrity are two contrasting objectives to achieve in general. In a privacy preserved data aggregation, it becomes easier for an attacker to inject false data hence, we suggest that both privacy and integrity of data should be treated together. In this paper, we present an energy efficient, privacy preserving data aggregation algorithm which also preserves data integrity in WSNs. We analyze the security of the algorithm and provide proofs for confidentiality and integrity. We enhance this algorithm further to localize, to a certain degree, the corrupt aggregator. We provide the results of our implementation of the algorithm on TelosB motes, illustrating that both the computational overhead and the energy consumption are very low. Finally, we compare our algorithm with other schemes having similar objectives demonstrating that our algorithm performs better in terms of band with usage and energy consumption in a WSN environment.

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.

To address the contradiction between data aggregation and data security in wireless sensor networks, an integrityassured concealed data aggregation scheme is proposed based on the privacy homomorphism and the aggregate message authentication code techniques.The proposed scheme provides both end-to-end privacy and data integrity in data aggregation for wireless sensor networks.Besides, it supports any type of aggregation functions since the base station can recover each sensing data collected by all sensors even if these data have been aggregated by aggregators.The performance analysis shows that the proposed scheme is efficient in computation and communication, and is feasible for resource limited sensor networks.

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.

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.

Integrity protecting hierarchical concealed data aggregation for wireless sensor networks

Wireless sensor networks consists of sensors nodes used for the communication purpose. Wireless sensor networks are self-organized and data-centric. In Wireless sensor networks, there are some issues like data redundancy, high energy conservation and injecting unnecessary data. Data aggregation method overcomes the data redundancy problem. In this paper, a data integrity protection strategy has been discussed based on watermarking technologies to protect the data integrity. At the end of the paper comparison of various schemes is also provided based on characteristics like the method adopted by each scheme and the position at which integrity is provided.

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.

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.

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.