A survey of security issues in wireless sensor networks <s> SECURE DATA AGGREGATION <s> As sensor networks edge closer towards wide-spread deployment, security issues become a central concern. So far, much research has focused on making sensor networks feasible and useful, and has not concentrated on security. We present a suite of security building blocks optimized for resource-constrained environments and wireless communication. SPINS has two secure building blocks: SNEP and μTESLA SNEP provides the following important baseline security primitives: Data confidentiality, two-party data authentication, and data freshness. A particularly hard problem is to provide efficient broadcast authentication, which is an important mechanism for sensor networks. μTESLA is a new protocol which provides authenticated broadcast for severely resource-constrained environments. We implemented the above protocols, and show that they are practical even on minimal hardware: the performance of the protocol suite easily matches the data rate of our network. Additionally, we demonstrate that the suite can be used for building higher level protocols. <s> BIB001 </s> A survey of security issues in wireless sensor networks <s> SECURE DATA AGGREGATION <s> Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensors, however, have significant power constraint (battery life), making communication very expensive. Another important issue in the context of sensor-based information systems is that individual sensor readings are inherently unreliable. In order to address these two aspects, sensor database systems like TinyDB and Cougar enable in-network data aggregation to reduce the communication cost and improve reliability. The existing data aggregation techniques, however, are limited to relatively simple types of queries such as SUM, COUNT, AVG, and MIN/MAX. In this paper we propose a data aggregation scheme that significantly extends the class of queries that can be answered using sensor networks. These queries include (approximate) quantiles, such as the median, the most frequent data values, such as the consensus value, a histogram of the data distribution, as well as range queries. In our scheme, each sensor aggregates the data it has received from other sensors into a fixed (user specified) size message. We provide strict theoretical guarantees on the approximation quality of the queries in terms of the message size. We evaluate the performance of our aggregation scheme by simulation and demonstrate its accuracy, scalability and low resource utilization for highly variable input data sets. <s> BIB002
Data communication constitutes an important share of the total energy consumption of the sensor network. The simulation in BIB001 shows that data transmission accounts for 71 percent of the energy cost of computation and communication for the SNEP protocol. Thus, data aggregation can greatly help conserve the scarce energy resources by eliminating redundant data. Data aggregation (fusion) protocols aim at eliminating redundant data transmitted across the network and are essential for energy-constrained WSNs. Traditional data aggregation techniques include simple types of queries such as SUM, COUNT, AVERAGE, and MIN/MAX. Some researchers also extend data aggregation to median, the most frequent (consensus) data values, a histogram of the data distribution, and range queries BIB002 . Data aggregation can be divided into two stages: detection and data fusion. In a WSN, there are usually certain nodes, called aggregators, helping to aggregate information requested by queries. When an aggregator node is compromised, it is easy for the adversary to inject false data into sensor networks. Thus, the aggregators are vulnerable to attack. Another possible attack is to compromise a sensor node and inject forged data through a sensor node. Without authentication, the attackers can fool the aggregators into reporting false data to the base station. Secure data aggregation requires authentication, confidentiality, and integrity. Moreover, secure data aggregation also requires the cooperation of sensor nodes to identify the compromised sensors. However, requirements for confidentiality and data aggregation are at odds with each other. Confidentiality requires the data to be transmitted in encrypted text while data aggregation is usually based on plain text. A straightforward method is to invoke end-to-end encryption and decryption before evoking data aggregation. However, the tradeoff is that the end-to-end encryption and decryption operations consume more energy, which is of great concern in WSNs. An alternative way is to provide data aggregation on concealed data, which requires a particular class of encryption transformation. However, this method usually lowers the security level. Figure 4 shows a taxonomy of secure data aggregation protocols in WSNs. According to the protocol operation, secure data aggregation can be classified into two categories: plaintext based and cipher based. This section reviews the techniques for secure data aggregation.