Secure Data Aggregation Techniques Research Articles

A Data Aggregation Technique for WSNs Using Lightweight Cryptographic Algorithm

: Many SNs are dispersed throughout Wireless Sensor Networks (WSN) to collect data from other nodes. Various secure data aggregation techniques are employed in WSN; however, the authentication procedure is not addressed. Implementing authentication while preserving WNS’s energy usage is difficult. The current approaches focus on various restrictions like key length and key sharing to improve authentication. However, it exposes the data aggregation network to criminal activity. This paper introduces a unique technique called a secure, Lightweight cryptographic data-aggregation algorithm to address security and energy challenges in WSN (SLC-DAA). The proposed SLC-DAA uses XOR operations and hash functions as cryptographic primitives. Using a stronger security and energy consumption paradigm, this technique offers potential options for cluster-based data aggregation. The suggested paradigm is less complex than current methods and offers superior computing efficiency and privacy protection.

Enhancing the efficiency of Iterative Filtering Algorithm and sensors material analysis

Wireless Sensor Networks are exposed to hostile and malignant environments, so they are vulnerable to collusion attacks leading to compromised nodes and hence false data injection. Hence there is a requirement of secure data aggregation techniques. Iterative Filtering Algorithms provide secure data aggregation by imparting trustworthiness values to the nodes in the form of weight factors. In Iterative filtering algorithm, the most significant aspect for calculating weights is the selection of proper discriminant function. Different converging rates are furnished by different discriminant functions leading to different efficiencies. In this paper, we will be observing the effect of implementing the Iterative Filtering Algorithm with two new discriminant functions and comparing the convergence rates and hence the efficiency imparted by these two new discriminant functions with the existing discriminant functions. Also, we will be investigating and discussing the various material aspects for selecting suitable material for sensor nodes. We will also be considering the possibility of using biodegradable materials to manufacture sensor nodes.

SAC-TA: A Secure Area Based Clustering for Data Aggregation Using Traffic Analysis in WSN

Clustering is the most significant task characterized in Wireless Sensor Networks (WSN) by data aggregation through each Cluster Head (CH). This leads to the reduction in the traffic cost. Due to the deployment of the WSN in the remote and hostile environments for the transmission of the sensitive information, the sensor nodes are more prone to the false data injection attacks. To overcome these existing issues and enhance the network security, this paper proposes a Secure Area based Clustering approach for data aggregation using Traffic Analysis (SAC-TA) in WSN. Here, the sensor network is clustered into small clusters, such that each cluster has a CH to manage and gather the information from the normal sensor nodes. The CH is selected based on the predefined time slot, cluster center, and highest residual energy. The gathered data are validated based on the traffic analysis and One-time Key Generation procedures to identify the malicious nodes on the route. It helps to provide a secure data gathering process with improved energy efficiency. The performance of the proposed approach is compared with the existing Secure Data Aggregation Technique (SDAT). The proposed SAC-TA yields lower average energy consumption rate, lower end-to-end delay, higher average residual energy, higher data aggregation accuracy and false data detection rate than the existing technique.

Comparison of the effect of different discriminant functions on the convergence rate of if algorithm for secure data aggregation technique in presence of collusion attacks

Comparison of the effect of different discriminant functions on the convergence rate of if algorithm for secure data aggregation technique in presence of collusion attacks

EASDAG – Energy Aware Secure Data Aggregation in Wireless Sensor Networks

Wireless sensor networks have a complex and totally differ technique of communication compared with the conventional networks. In Wireless Sensor Network, Clustering architecture plays a vital role to reduce power consumption. In a clustered Architecture a Cluster Head (CH) node act as Aggregator node, will collect all the information from its neighboring nodes and give a condensed summary of the data and send to the next level of communication. This enormously reduces the communication cost and conserve power utilization of sensor nodes. In this proposal an Energy Aware SecureData Aggregation (EASDAG) is implemented and performed experiments for evaluation. The main aim of data aggregation technique is to collect and aggregate data in an energy efficient manner so that network lifetime is enhanced. The simulation results proves that Energy Aware SecureData Aggregationtechnique is much efficient than Cluster head election Strategy used in LEACH and can make the network stay reliable for longer time. Simulation results shows that throughput increases and battery consumption remains lesser when compared with LEACH.

Addressing Data Aggregation Using Polynomial Regression in WSNs

One of the scarcest resources in wireless sensor networks (WSNs) is energy. Thus, a major issue of WSN is to prolong the network lifetime. The energy dissipation of sensor nodes should be minimized within the network for prolong network lifetime. For the sake of this, data aggregation emerged as an important field of research in modern era. Intensive research has been carried out for providing novel energy efficient and secure data aggregation techniques. Unlike conventional data aggregation techniques, polynomial regression based data aggregation technique increases the network lifetime by transmitting compressed data packets of constant size. Data compression has been carried out by performing regression function on the sensed data. These techniques provide good approximation of the sensed value and are scalable as the data compression ratio is nearly constant. In this paper, we present a detailed review on different polynomial regression based data aggregation techniques and provide a comparison among them. Keywords: Data aggregation, Data correlation, Polynomial regression, Security, Wireless sensor networks.

Energy Efficient, Integrity Preserving and Secure Data Aggregation Technique for Wireless Sensor Network

Energy Efficient, Integrity Preserving and Secure Data Aggregation Technique for Wireless Sensor Network

Secure Data Aggregation Technique for Wireless Sensor Networks in the Presence of Collusion Attacks

Due to limited computational power and energy resources, aggregation of data from multiple sensor nodes done at the aggregating node is usually accomplished by simple methods such as averaging. However such aggregation is known to be highly vulnerable to node compromising attacks. Since WSN are usually unattended and without tamper resistant hardware, they are highly susceptible to such attacks. Thus, ascertaining trustworthiness of data and reputation of sensor nodes is crucial for WSN. As the performance of very low power processors dramatically improves, future aggregator nodes will be capable of performing more sophisticated data aggregation algorithms, thus making WSN less vulnerable. Iterative filtering algorithms hold great promise for such a purpose. Such algorithms simultaneously aggregate data from multiple sources and provide trust assessment of these sources, usually in a form of corresponding weight factors assigned to data provided by each source. In this paper we demonstrate that several existing iterative filtering algorithms, while significantly more robust against collusion attacks than the simple averaging methods, are nevertheless susceptive to a novel sophisticated collusion attack we introduce. To address this security issue, we propose an improvement for iterative filtering techniques by providing an initial approximation for such algorithms which makes them not only collusion robust, but also more accurate and faster converging.

Secure Data Aggregation Using Reliable Nodes for Wireless Sensor Networks

Generally, aggregation techniques in Wireless Sensor Networks (WSNs) are defenseless against various attacks. The aggregator and aggregated data has to be secured to assure integrity and confidentiality. In this study, we propose a secure data aggregation technique with reliable nodes using key predicate test protocol for sensor network. This technique specialize some nodes as Reliable nodes (R-nodes) to monitor the process of aggregation. Initially, for each node, a secret key is shared between base station and neighboring nodes. Then, an aggregation tree is constructed for transmitting data to the base station in a hierarchical fashion. The aggregator encrypts the data using secret key and forwards to a level up aggregator in aggregation tree. By enhancing broadcasting feature of R- nodes, the aggregated value is verified for ensuring integrity. As keys are shared between neighboring nodes, the nodes are validated using self-key predicate test. The proposed technique is proved through simulation results. It increases the throughput by reducing the packet drops significantly.

Energy Constrained Secure Hierarchical Data Aggregation 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.

Fuzzy Based Secure Data Aggregation Technique in Wireless Sensor Networks

Problem statement: Secure data aggregation is a challenging task in wireless sensor network due to the facts like more complexity, greater overhead in the case of cryptographic techniques. These issues need to be overcome using efficient technique. Approach: We propose a fuzzy based secure data aggregation technique which was having 3 phases. In its first phase, it performs clustering and cluster head election process. In the second phase, within each clusters, power consumed, distance and trust values were calculated for each member. In the third phase, based on these parameters, fuzzy logic technique was used to select the secure and non-faulty node members for data aggregation. Finally, the aggregated data from the cluster heads was transmitted to the sink. Results: By simulation results we show that our technique had improved throughput and packet delivery ratio with reduced packet drop and less energy consumption. Conclusion: The proposed technique efficiently checks for malicious nodes based on the system parameters and maintains a secure aggregation process in the network.

Fuzzy Logic Based Performance Optimization with Data Aggregation in Wireless Sensor Networks

Wireless sensor networks are characterized by multi hop wireless lossy links and resource constrained nodes. Energy efficiency is a major concern in such networks. Due to their constraints in computation, memory, and power resources, the performance optimization is a challenging task in these networks. In this paper, fuzzy based secure data aggregation technique is used to select the secure and non-faulty node members for data aggregation. The aggregated data from the cluster heads is transmitted to the sink. This technique efficiently checks for malicious nodes based on the system parameters and maintains a secure aggregation process in the network. By simulation results, we show that our technique has improved throughput and packet delivery ratio with reduced packet drop and less energy consumption based on rate.