Privacy‐preserving robust 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 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 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.

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.

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.

Data aggregation in wireless sensor networks using ant colony algorithm

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 is a complex task in wireless sensor networks because of its limited power and computing capabilities. Due to large scale deployment of sensor nodes for various applications, multiple nodes are required to aggregate the relevant information from huge data sets. This data aggregation process is vulnerable to many security threats due to loss of cryptographic keys, node compromise etc. This eventually leads to breach of confidentiality. In this paper we propose a scheme called Secure Data Aggregation using Privacy Homomorphism (SDAPH) which offers high degree of confidentiality through encrypted data processing. It has lesser computing overhead as it uses additive privacy homomorphism which is a symmetric key homomorphic encryption technique.

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.

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.

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.

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.

Wireless sensor networks (WSNs) are increasingly used in many applications, such as volcano and fire monitoring, urban sensing, and perimeter surveillance. In a large WSN, in-network data aggregation (i.e., combining partial results at intermediate nodes during message routing) significantly reduces the amount of communication overhead and energy consumption. The research community proposed a loss-resilient aggregation framework called synopsis diffusion, which uses duplicate-insensitive algorithms on top of multipath routing schemes to accurately compute aggregates (e.g., predicate count or sum). However, this aggregation framework does not address the problem of false subaggregate values contributed by compromised nodes. This attack may cause large errors in the aggregate computed at the base station, which is the root node in the aggregation hierarchy. In this paper, we make the synopsis diffusion approach secure against the above attack launched by compromised nodes. In particular, we present an algorithm to enable the base station to securely compute predicate count or sum even in the presence of such an attack. Our attack-resilient computation algorithm computes the true aggregate by filtering out the contributions of compromised nodes in the aggregation hierarchy. Extensive analysis and simulation study show that our algorithm outperforms other existing approaches.

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.

The wireless communication nature of remotely deployed sensor nodes make the attacks more easily to be happened in wireless sensor networks (WSNs). But traditional security algorithms are infeasible in WSNs due to the limited computing, communication power, storage, band width and energy of sensor nodes. So energy efficient secure data aggregation schemes are necessary in resource constrained WSNs. Concealed Data Aggregation (CDA) based on privacy homomorphism (PH) gives a critical solution for energy efficient secure data aggregation in WSNs. PH based algorithms allow aggregation to be happened on cipher texts. Thus, it eliminates the power consuming decryption operations at the aggregator node for the data aggregation and further encryption for the secure transmission of aggregated data. It also avoids the aggregator node from the burden of keeping the secret key information and thereby it achieves energy efficiency and reduces the frequency of node compromise attacks in aggregator nodes. Among the CDA techniques, asymmetric PH based CDA techniques are exploring due to their combination with elliptic curve cryptography having reduced key size. We present an overview of asymmetric concealed data aggregation techniques that achieve both end to end data confidentiality and non delayed data aggregation.