Aggregation In Wireless Sensor Networks Research Articles

Design and implementation of secure data aggregation in wireless sensor networks

Design and implementation of secure data aggregation in wireless sensor networks

Efficient Hierarchical Data Aggregation in Wireless Sensor Networks in the Presence of Attacks

Efficient Hierarchical Data Aggregation in Wireless Sensor Networks in the Presence of Attacks

Treelet-Based Clustered Compressive Data Aggregation for Wireless Sensor Networks

Compressive sensing (CS)-based data aggregation has become an increasingly important research topic for large-scale wireless sensor networks since conventional data aggregations are shown to be inefficient and unstable in handling huge data traffic. However, for CS-based techniques, the discrete cosine transform, which is the most widely adopted sparsification basis, cannot sufficiently sparsify real-world signals, which are unordered due to random sensor distribution, thus weakening advantages of CS. In this paper, an energy-efficient CS-based scheme, which is called “treelet-based clustered compressive data aggregation” (T-CCDA), is proposed. Specifically, as a first step, treelet transform is adopted as a sparsification tool to mine sparsity from signals for CS recovery. This approach not only enhances the performance of CS recovery but reveals localized correlation structures among sensor nodes as well. Then, a novel clustered routing algorithm is proposed to further facilitate energy saving by taking advantage of the correlation structures, thus giving our T-CCDA scheme. Simulation results show that the proposed scheme outperforms other reference approaches in terms of communication overhead per reconstruction error for adopted data sets.

Toward cluster-based weighted compressive data aggregation in wireless sensor networks

Conventional Compressive Sampling (CS)-based data aggregation methods require a large number of sensor nodes for each CS measurement leading to an inefficient energy consumption in Wireless Sensor Networks (WSNs). To solve this problem, we propose a new scheme in the network layer, called “Weighted Compressive Data Aggregation (WCDA)”, which benefits from the advantage of the sparse random measurement matrix to reduce the energy consumption. The novelty of the WCDA algorithm lies in the power control ability in sensor nodes to form energy efficient routing trees with focus on the load-balancing issue. In the second part, we present another new data aggregation method namely “Cluster-based Weighted Compressive Data Aggregation (CWCDA)” to make a significant reduction in the energy consumption in our WSN model. The main idea behind this algorithm is to apply the WCDA algorithm to each cluster in order to reduce significantly the number of involved sensor nodes during each CS measurement. In this case, candidate nodes related to each collector node are selected among the nodes inside one cluster. This yields in the formation of collection trees with a smaller structure than that of the WCDA algorithm. The effectiveness of these new algorithms is evaluated from the energy consumption, load balancing and lifetime perspectives of the network. A comprehensive numerical evaluation is performed which shows that the performance of the proposed WCDA and CWCDA algorithms is significantly better than some existing data aggregation methods such as plain-CS, hybrid-CS and the Minimum Spanning Tree Projection (MSTP) schemes.

Lightweight and Reliable Routing Approachfor In-Network Aggregation in WirelessSensor Networks

Wireless Sensor Networks (WSNs) in a large scale, will be increasingly deployed in different classes of applications for accurate monitoring. Due to the high density of nodes in these networks, it is likely that redundant data will be detected by nearby nodes when sensing an event. Since energy conservation is a key issue in WSNs, data fusion and aggregation should be exploited in order to save energy. In this case, redundant data can be aggregated at intermediate nodes reducing the size and number of exchanged messages and, thus, decreasing communication costs and energy consumption. In this work, we propose a Routing algorithm for aggregation of nodes in the network, that has some key aspects such as a reduced number of messages number, high aggregation rate, and reliable data aggregation and transmission. The proposed Routing algorithm was extensively compared to two other known solutions: the Information Fusion-based Role Assignment (InFRA) and Shortest Path Tree (SPT) algorithms. Our results indicate clearly that the routing tree built by Routing algorithm provides the best aggregation quality when compared to these other algorithms. The obtained results show that our proposed solution outperforms these solutions in different scenarios and in different key aspects required by WSNs.

Energy based-Genetically Derived Secure Cluster-based Data Aggregation in Wireless Sensor Networks

Wireless sensor networks (WSNs), are spatially distributed autonomous sensors used to monitor physical and environmental conditions, such as temperature, sound, pressure, etc. Each device can sense, process, and talk to its peers. All the sensor nodes are considered as little objects and therefore the data communication between sensor nodes is maximum. Extending of network period and information measure utilization becomes critical. This is achieved through the existing technique, routing protocol technique called destination sequenced distance-vector routing (DSDV). In the existing technique, initially it performs clustering and cluster head election process. Finally, the aggregated data from the cluster heads is transmitted to the sink. In the existing technique, the energy utilization is not efficient and it does not concentrate on network security. Therefore, in order to overcome these issues, a technique called, Energy-Based Genetically Derived Secure Cluster Based Data Aggregation (EB-GDSDA) clustering technique has been proposed for WSN. This technique highly minimizes the energy utilization and increases the life span of the network. In the proposed technique, the selection of CH is static throughout the simulation. To make cluster head selection dynamic, a Dynamic Selection-CH technique has also been implemented, where the user can have a choice of selecting the CH in each cluster based on the threshold residual energy. By simulation results, the performance of the Dynamic Selection-CH is more efficient than the existing and proposed static cluster head techniques, based on the simulation parameters. General Terms EB-GDSDA, RSA encryption-decryption, node connectivity, fitness function.

Hierarchical Approach to Secure Data Aggregation in Wireless Sensor Networks

The main objective of this paper is to aggregate the data in a secure way such that the tampering caused by the adversaries can be avoided and these secure authentication mechanisms ensure that the base station does not accept forged aggregation value. Previous data aggregation schemes make sure that base stations do not receive and agree with the false aggregation values. The authentication mechanisms are used to detect malicious nodes. But these mechanisms may not be efficient and they can be easily bypassed by hacking the keys used for encryption. A secure mechanism for aggregating the data has been put forward in which the two stages of verification are performed. In the first stage, Level based scheme is applied as the verification technique at each level. In this scheme, the keys are updated dynamically which enhances the security. Next stage of verification is the encryption of the packets by using its private key. The proposed secure data aggregation mechanism improves the performance and efficiency of the aggregation process. Analysis of experimental results indicates that the performance of the proposed scheme is twice that of the previous schemes.

Improved trust management based on the strength of ties for secure data aggregation in wireless sensor networks

Secure data aggregation exerts more and more important effects on the research of wireless sensor network. There occurs many groping research subject, via managing nodes' trust and reputation to keep data aggregation secure. The nodes in wireless sensor networks are often compromised and those compromised nodes will do a lot of harmful behaviors to decrease the security of wireless sensor networks. These behaviors include impersonate legal nodes to join routing paths, selectively forward data to an adversary, inject erroneous data, and disrupt data transmission. Thus, the conception of nodes' trust is introduced into data aggregation to judge and evaluate whether the nodes are trustable or not. In this paper, we propose an improved trust management method for data aggregation based on the relationship between nodes that is called the strength of the ties between the nodes. The improved method is developed from the trust model in the iRTEDA protocol and increasing the utilization efficiency of second-hand information coming from neighboring nodes. The aim of the proposed trust model is to obtain a higher level of security for data aggregation. The simulation results show us that data aggregation, based on proposed trust management method, can get higher accuracy of evaluating nodes' trust and reputation and achieve higher data accuracy of aggregating.

Energy-Efficiency based Clustering and Data Aggregation for Wireless Sensor Networks

wireless sensor network is maximizing the lifetime of the network. All sensor nodes directly send information to the base station so the energy requirement is very high. The data collection mechanism comes at the cost of power consumption and packet collision. We introduce automatic time series modeling based data aggregation scheme in wireless sensor networks. The decrease in the number of transmitting data values between sensor node and aggregation by using the time series predication model. We proposed in the paper energy-efficient cluster for wireless sensor network which is better suited periodical data gathering from the application. We proposed Energy -Efficiency Clustering and Data aggregation for sensor Network. There are four phases: cluster head, cluster head selection, data aggregation and maintenance. Important issued in wireless sensor network is minimize the total energy consumption requires to collect data. Keywordssensor network, data aggregation, clustering, energy

A Balance Privacy-Preserving Data Aggregation Model in Wireless Sensor Networks

Wireless sensor networks are always deployed in remote and hostile environments to gather sensitive information, in which sensor nodes are apt to encounter some serious leakage of sensitive data. Hence, privacy-preserving is becoming an increasingly important issue in security data aggregation for wireless sensor networks. In this paper, we propose a balance privacy-preserving data aggregation model (BPDA) based on slicing and mixing technology. Compared to fixed or random slicing, BPDA model gives a balance slicing mechanism to ensure that slice can be sent to the nodes which have lower privacy preservation and enhance the privacy-preserving efficacy. Furthermore, according to the influence of the node degree and energy, three different schemes are presented to keep the privacy-preserving data aggregation balance. Theoretical analysis and simulation show that BPDA model demonstrates a good performance in terms of privacy-preserving efficacy and communication overhead and prolongs the lifetime of network.

SDAW: secure data aggregation watermarking-based scheme in homogeneous WSNs

Redundant data retransmission problem in wireless sensor networks (WSNs) can be eliminated using the data aggregation process which combines similar data to reduce the resource-consumption and consequently, saves energy during data transmission. In the recent days, many researchers have focused on securing this paradigm despite the constraints it imposes such as the limited resources. Most of the solutions proposed to secure the data aggregation process in WSNs are essentially based on the use of encryption keys to protect data during their transmission in the network. Indeed, the key generation and distribution mechanisms involve additional computation costs and consume more of energy. Considering this, in this paper, we propose a new security mechanism to secure data aggregation in WSNs called SDAW (secure data aggregation watermarking-based scheme in homogeneous WSNs). Our mechanism aims to secure the data aggregation process while saving energy. For this, the mechanism uses a lightweight fragile watermarking technique without encryption to insure the authentication and the integrity of the sensed data while saving the energy. The links between the sensor nodes and the aggregation nodes, and also the links between the aggregation nodes and the base station are secured by using the watermarking mechanism.

Malleability Resilient Concealed Data Aggregation in Wireless Sensor Networks

The objective of concealed data aggregation is to achieve the privacy preservation at intermediate nodes while supporting in-network data aggregation. The need for privacy preservation at intermediate nodes and the need for data aggregation at intermediate nodes can be simultaneously realized using privacy homomorphism. Privacy homomorphism processes the encrypted data without decrypting them at intermediate nodes. However, privacy homomorphism is inherently malleable. Although malicious adversaries cannot view transmitted sensor readings, they can manipulate them. Hence, it is a formidable challenge to realize conflicting requirements, such as end-to-end privacy and end-to-end integrity, while performing en route aggregation. In this paper, we propose a malleability resilient concealed data aggregation protocol for protecting the network against active and passive adversaries. In addition, the proposed protocol protects the network against insider and outsider adversaries. The proposed protocol simultaneously realizes the conflicting objectives like privacy at intermediate nodes, end-to-end integrity, replay protection, and en route aggregation. As per our knowledge, the proposed solution is the first that achieves end-to-end security and en route aggregation of reverse multicast traffic in the presence of insider, as well as outsider adversaries.

A survey on the privacy-preserving data aggregation in wireless sensor networks

Wireless sensor networks (WSNs) consist of a great deal of sensor nodes with limited power, computation, storage, sensing and communication capabilities. Data aggregation is a very important technique, which is designed to substantially reduce the communication overhead and energy expenditure of sensor node during the process of data collection in a WSNs. However, privacy-preservation is more challenging especially in data aggregation, where the aggregators need to perform some aggregation operations on sensing data it received. We present a state-of-the art survey of privacy-preserving data aggregation in WSNs. At first, we classify the existing privacy- preserving data aggregation schemes into different categories by the core privacy-preserving techniques used in each scheme. And then compare and contrast different algorithms on the basis of performance measures such as the privacy protection ability, communication consumption, power consumption and data accuracy etc. Furthermore, based on the existing work, we also discuss a number of open issues which may intrigue the interest of researchers for future work.

GABs: A Game-Based Secure and Energy Efficient Data Aggregation for Wireless Sensor Networks

This paper endeavors to investigate the security and energy issues in wireless sensor networks from prior art such as game theory and various embodiments of methods like public-key cryptography. Due to the needs of interacting with the physical world, thanks to a plethora of innovative applications, the state of the art in wireless sensor networks (WSNs) is focusing on multiple purposes such as monitoring, tracking, and security while relying upon some assumptions about distributed data, whereas these assumptions may not hold in a real scenario. The content herein models homogeneous WSN environment of highly linked nodes, in a theoretical game with synchronized actions, where, in order to transmit their readings securely across the network in some level of hierarchy, using the technique of data aggregation, sensor nodes with communication and computational resources constraint should establish trusted and direct links between their neighbors for the privacy and integrity of their data against faulty nodes, while ensuring energy saving. The study considers the ZigBee known as IEEE 802.15.4 standard for its high trustworthiness and low power consumption in wireless sensor network and compares different topology models.

Learning automata based energy efficient data aggregation in wireless sensor networks

Recently, many algorithms have been proposed for data aggregation in wireless sensor networks which try to find routes towards the sink through which data can be aggregated. In addition to data aggregation, two more criteria are also used in many of these algorithms for finding the routes; remaining energies of the nodes and their numbers of hops to the sink. But to the best of our knowledge, no data aggregation algorithm has been presented in which all of these three criteria are considered together. In this paper, we propose a novel data aggregation algorithm, called LAG, which tries to mix all of these criteria for finding the routes. Furthermore, by considering the fact that the remaining energy of a sensor node and its possibility for aggregating data received from other nodes may change during the operation of the network, the proposed LAG algorithm tries to dynamically adapt itself with such changes and to select new routes towards the sink accordingly. The adaptive behavior of LAG is the result of using learning automata (LA). Each node is equipped with an LA which helps the node selects its next hop for forwarding data towards the sink considering all of the three mentioned criteria. The learning automaton used in LAG algorithm, called INCASE-LA, is introduced in this paper for the first time. Using computer simulations, we demonstrate that LAG aggregates data better, consumes less power and achieves higher network lifetime in comparison to other existing algorithms such as SPT, TAG, and ES LA.

Energy Efficient Data Aggregation in Wireless Sensor Networks Using Neural Networks

Energy efficiency is very important issue in wireless sensor networks (WSNs). In WSN, sensors are distributed in different places, where they can be exposed to contact with the environment. Data aggregation, eliminating of data redundancy and improve the accuracy of the collected data are essential points for these networks. This research has been suggested some algorithms such as MEDA, LMTBPN, RBDA and RGDA. The first algorithm is based on the moment estimation method and the other three algorithms aggregate the data based on backward propagation, radial basis and general regression. These algorithms use a three-layer neural network. Input layer neurons are located in members of each cluster while the hidden layer neurons are located in cluster heads and output layer neurons are located in base station. Simulation results show that the performance of data aggregation is improved and also energy consumption of the network is reduced.

Minimum Cost Data Aggregation for Wireless Sensor Networks Computing Functions of Sensed Data

We consider a problem of minimum cost (energy) data aggregation in wireless sensor networks computing certain functions of sensed data. We use in-network aggregation such that data can be combined at the intermediate nodes en route to the sink. We consider two types of functions: firstly the summation-type which includessum,mean, andweighted sum, and secondly the extreme-type which includesmaxandmin. However for both types of functions the problem turns out to be NP-hard. We first show that, forsumandmean, there exist algorithms which can approximate the optimal cost by a factor logarithmic in the number of sources. Forweighted sumwe obtain a similar result for Gaussian sources. Next we reveal that the problem for extreme-type functions is intrinsically different from that for summation-type functions. We then propose a novel algorithm based on the crucial tradeoff in reducing costs between local aggregation of flows and finding a low cost path to the sink: the algorithm is shown to empirically find the best tradeoff point. We argue that the algorithm is applicable to many other similar types of problems. Simulation results show that significant cost savings can be achieved by the proposed algorithm.

Symmetric-Key Based Homomorphic Primitives for End-to-End Secure Data Aggregation in Wireless Sensor Networks

In wireless sensor networks, secure data aggregation protocols target the two major objectives, namely, security and en route aggregation. Although en route aggregation of reverse multi-cast traffic improves energy efficiency, it becomes a hindrance to end-to-end security. Concealed data aggregation protocols aim to preserve the end-to-end privacy of sensor readings while performing en route aggregation. However, the use of inherently malleable privacy homomorphism makes these protocols vulnerable to active attackers. In this paper, we propose an integrity and privacy preserving end-to-end secure data aggregation protocol. We use symmetric key-based homomorphic primitives to provide end-to-end privacy and end-to-end integrity of reverse multicast traffic. As sensor network has a non-replenishable energy supply, the use of symmetric key based homomorphic primitives improves the energy efficiency and increase the sensor network’s lifetime. We comparatively evaluate the performance of the proposed protocol to show its efficacy and efficiency in resource-constrained environments.

Maximizing precision for energy-efficient data aggregation in wireless sensor networks with lossy links

Two main factors that impact the performance of data aggregation in wireless sensor networks (WSNs) are data quality and energy efficiency. This paper exploits the tradeoff between data quality and energy consumption to maximize the data aggregation precision under heterogeneous per-node energy constraints. Unlike previous work, we explicitly account for link loss in the optimization framework. To tackle link unreliability, we need to appropriately allocate the limited energy across the incoming and outgoing links of each individual node. We present a centralized algorithm based on the Immune-Genetic heuristic to find near-optimal energy allocation strategy such that the precision of the aggregated data received by the sink is maximized. The algorithmic complexity and implementation issues are also discussed. Furthermore, we develop a localized alternative algorithm based on the Gibbs sampler, which is more scalable and can adapt to large-scale distributed WSNs. Finally, we conduct numerical simulations to demonstrate the convergence as well as the data aggregation precision performance of the proposed algorithms.

Certificateless aggregate signature with efficient verification

Certificateless public key cryptography CL-PKC is a cryptosystem solving the key escrow problem of identity-based cryptography. One of the applications of CL-PKC is certificateless aggregate signature CLAS that in practice can be used to efficiently verify concealed data aggregation in wireless sensor networks. CLAS is referred to as an extension of certificateless signature, which in particular performs verification for many signatures efficiently. Therefore, not only plenty of CLAS schemes have been proposed but also the security models of CLAS were introduced in the literature. Recently, some CLAS schemes are extended from specific certificateless signature CLS schemes. However, we found that two certificateless signature CLS and their corresponding CLAS schemes are not secure. In this paper, we simplify the relation of security definitions of CLS and CLAS. Then, a new CLAS scheme is proposed, which leads to the advantages of both certificateless cryptography and aggregate signature. Moreover, our scheme only depends on constant pairing operations to verify a large number of signatures per time, because pairing is a complicated operation with high cost in computations. Copyright © 2014 John Wiley & Sons, Ltd.