Security approaches for data aggregation in Wireless Sensor Networks against Sybil Attack

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.

Communication in wireless sensor networks uses the majority of a sensor's limited energy. Using aggregation in wireless sensor network reduces the overall communication cost. Security in wireless sensor networks entails many different challenges. Traditional end-to-end security is not suitable for use with in-network aggregation. A corrupted sensor has access to the data and can falsify results. Additively homomorphic encryption allows for aggregation of encrypted values, with the result being the same as the result when unencrypted data was aggregated. Using public key cryptography, digital signatures can be used to achieve integrity. We propose a new algorithm using homomorphic encryption and additive digital signatures to achieve confidentiality, integrity and availability for in-network aggregation in wireless sensor networks. We prove that our digital signature algorithm which is based on the elliptic curve digital signature algorithm (ECDSA) is as secure as ECDSA.

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.

A scheme for WSNs (wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper.The sensing tetrahedron is divided into a number of small grids.All those sensor nodes, both ordinary sensor nodes and heterogeneous sensor nodes are distributed in the sensing tetrahedron.In a grid, all ordinary sensor nodes and heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.All the heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.At last, all sensor nodes establish their keys directly or indirectly in the whole sensing tetrahedron.Analysis and comparison demonstrate this scheme enhances the WSN security, has good network connectivity, saves node storage, reduces network computing load, and extends the network lifetime.

Being one of the main technologies for Internet of Things (IoT), wireless sensor network is absolutely critical and plays an increasingly important role in the development of Internet of Things. Wireless sensor network is mobile network composed of sensor nodes with limited resources, like computation capability, energy, memory storage, and communication capability. Its characteristic results in that the security of wireless sensor network faces a great challenge. Hence, the security of wireless sensor network is an important issue for the development of IoT and shall be focused on. In this paper, a novel authentication scheme is proposed for wireless sensor networks based on theory of quadratic residues. It is making the use of master key to achieve simple symmetric cryptographic primitives and authentication operation and reach the aims of great resistance against the attacks and low energy consumption. Then the proposed scheme is compared with other network-wide key management schemes, obtaining better results in the aspects of security, efficiency, and scalability.

Wireless sensor networks are being used more and more. Sensor nodes can be distributed in hazardous environments to collect data. As these environments are usually unsecured, provide security in wireless sensor networks is vital. Detecting attacks in sensor networks is inevitable to provide security. Various attacks threaten sensor networks one of which is Sybil attack, which can be a big threat for geographical routing protocols and multi-path routing. In this paper, a distributed method has been presented using mobile agents and local information of each sensor to detect Sybil attack. The results of simulation show the efficacy of this method in comparison with previous methods.

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.

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.

This paper considers security in wireless sensor networks (WSNs), focusing at the routing layer. We propose to analyze the behavior of some routing protocols according to attacks stemming from compromised nodes. Such malicious nodes could disrupt the routing functionality (node replication, Sybil attacks or Black-Grey-Sink holes). For such adversary models traditional cryptographic solutions are not enough by themselves but need to be completed by algorithmic solutions considering “beyond cryptography” approaches. Emphasizing internal attacks, with security features of routing in mind, we define the resiliency as the ability of a network to continue to operate in presence of k compromised nodes, i.e. the capacity of a network to endure and overcome internal attacks. In this context, we analyze four particular routing protocols (DSR, Gradient based, Greedy forwarding and Random walk routing). Using intensive simulations, we test their resiliency in presence of several compromised nodes in several adversary models.

Data aggregation in wireless sensor networks using ant colony algorithm

Wireless sensor networks have become integral components of the monitoring systems for critical infrastructures such as the power grid or residential microgrids. Therefore, implementation of robust Intrusion Detection Systems (IDS) at the sensory data aggregation stage has become of paramount importance. Key performance targets for IDS in these environments involve accuracy, precision, and the receiver operating characteristics which is a function of the sensitivity and the ratio of false alarms. Furthermore, the interplay between machine learning and networked systems has led to promising opportunities, particularly for the system level security of wireless sensor networks. Pursuant to these, in this paper, we propose Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS) for wirelessly connected sensor clusters that monitor critical infrastructures. The proposed ASCH-IDS mechanism is built on a hybrid IDS framework, and transforms the previous work by continuously monitoring the behavior of the receiver operating characteristics, and adaptively directing the incoming packets at a sensor cluster towards either misuse detection or anomaly detection module. We evaluate the proposed mechanism by introducing real attack data sets into simulations, and show that our proposal performs at 98.9% detection rate and approximately 99.80% overall accuracy to detect known and unknown malicious behavior in the sensor network.

Data aggregation plays an important role in energy constrained wireless sensor networks (WSN). Redundant data can be aggregated at intermediate nodes of a WSN reducing the number of messages exchanged and consequently reducing communication costs. In this work we consider the problem of constructing a dynamic and scalable structure for data aggregation in WSN. Although there are many proposed solutions to data aggregation in WSN, most of them build the data aggregation structure based on the order in which events occur. This kind of structure leads to low quality routing trees and does not address the load balancing problem, since the same tree is used throughout the network life. To tackle these challenges we propose a novel routing protocol called Dynamic and Scalable Tree (DST), which reduces the number of messages necessary to set up a routing tree, maximizes the number of overlapping routes, and selects routes with the highest aggregation rate. The routing tree created by DST does not depend on the order of events and is not held fixed along the occurrence of events. DST was extensively compared with two solutions reported in the literature regarding communication costs, aggregation rate and quality of the routing tree. Results show that the routing tree built by DST provides the best aggregation quality compared with other algorithms outperforming them for different scenarios in all evaluations performed.

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 this paper, wireless sensor network (WSN) security issue in the presence of an eavesdropper is analysed. The sensor-sink as well as sensor-eavesdropper channels are assumed to be subjected to generalized-K fading. According to the physical layer security framework, we employ round-robin, optimal sensor and cumulative distribution function-based scheduling scheme to enhance secure connection between sensor nodes and to attain low probability of intercept. Novel analytical results are given in a form of easy-to-compute analytical expressions for the intercept probability. Additionally, asymptotic closed-form expressions are derived and diversity orders of scheduling schemes are determined. Numerical results demonstrate important impacts of fading and shadowing parameters of main and wiretap links, network size and selected scheme on WSN security. Presented results are validated by independent Monte Carlo simulations.

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.