IJRIT International Journal of Research in Information Technology, Volume 2, Issue 2, February 2014, Pg: 64- 70

International Journal of Research in Information Technology (IJRIT) www.ijrit.com

ISSN 2001-5569

Secure Data Aggregation for Multiple Applications in Wireless Sensor Network R.Sasikala PG Student Department of CSE, Vivekananda College of Technology for Women, Tiruchengode, Tamil Nadu, India [email protected]

V.Bhoopathy Professor Department of CSE, Vivekananda College of Technology for Women, Tiruchengode, Tamil Nadu, India [email protected] Abstract Wireless sensor networks are designed to gather real-time physical and environmental information. However, major constraints such as energy consumption, memory, usage, computational speed and bandwidth are experienced by sensor nodes, which restrict them from transferring point-to-point data. In order to overcome this, various data aggregation techniques have been proposed which increase the lifetime of sensor nodes by merging and redistributing data. Techniques such as the structured approach lead to high maintenance cost for event driven applications. The implementation of a structure-free approach allows optimal communication between sensor nodes, thereby, improving the efficiency of sensor networks. Thus, the fixed-grid and fuzzy-logic structure-free approach enable data aggregation based purely on correlation. This project introduces a varied-grid approach for structure-free data aggregation, which tests sensor nodes for data packet size, communication speed and energy efficiency. Simulation results show a decrease in congestion with a substantial increase in throughput, suggesting the ability of sensor nodes to adapt to the dynamic changes in networks.

Keywords: Wireless Sensor Networks, Structure-free Data Aggregation I. INTRODUCTION Advancement in wireless communication technology has led to the development of sensor networks. Sensor network comprises of sensor nodes which are small and cost effective and can be easily deployed. They are used for various military, industrial, consumer and machine health monitoring applications. Each sensor node is a mini computer in itself and uses a multi-hop network to route data. When an event occurs, these sensor nodes gather information from the surrounding environment and send data to other nodes, base station or the sink for analysis generating wireless traffic. Traffic congestion increases with an increase in the number of nodes which in turn deteriorates the routing performance. This results in an increase in the response time of the sensor nodes which may cause information loss. Data aggregation is defined as a systematic collection of data that is gathered from the sensor nodes and is sent to the base station for processing. This technique is used to address the issue of voluminous data by reducing its packet size. Information is collected from the surrounding sensor nodes and only the processed information is sent to the sink, which reduces redundancy. Due to this, the end points can have accurate information in a timely manner. In this paper, we focus on data aggregation mechanism which will increase the efficiency of sensor nodes by reducing

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 2, February 2014, Pg: 64- 70

the number of transmissions to the end point. The goal of our work is to design a varied-grid approach, with may lead to data aggregation without explicit maintenance of a structure. II. EXISTING SYSTEM

Transfer of packet to destination

Malicious node enter into network

Loss of packet Generally, aggregative operations are algebraic, such as the addition multiplication of received data, or statistical operation, such as a median, a minimum, or a maximum of a data set SIA: Secure Information Aggregation in Sensor Networks. Although data aggregation could significantly reduce transmission, it is vulnerable to some attacks. Compromising a CH will allow adversaries to forge aggregated results Security in Wireless Sensor Networks as similar as compromising all its cluster members. A. PRIVACY HOMOMORPHISM Homomorphic encryptions allow complex mathematical operations to be performed on encrypted data without compromising the encryption. In mathematics, homomorphic describes the transformation of one data set into another while preserving relationships between elements in both sets. The term is derived from the Greek words for "same structure." Because the data in a homomorphic encryption scheme retains the same structure, identical mathematical operations -- whether they are performed on encrypted or decrypted data will yield equivalent results. Similar to conventional encryption schemes, PH schemes are classified to symmetric cryptosystem when the encryption and decryption keys are identical, or asymmetric cryptosystem (also called public key cryptosystem) when the two keys are different. Symmetric PH schemes, such as Domingo-Ferrer scheme [17] . The most notable asymmetric PH schemes are based on elliptic curve cryptography (ECC).Compared with RSA cryptosystems, ECC provides the same security with a shorter key size and shorter cipher texts. A 160-bit ECC cryptosystem provides the same security as a 1,024-bit RSA cryptosystem [18]. In energy constraint WSNs, constructing PH via ECC is more efficient. Up to now, the PH schemes on ECC include elliptic curve Okamoto-Uchiyama (EC-OU), elliptic curve Nac cacheStern, elliptic curve Paillier, and elliptic curve El Gamal Encryption Schemes (EC-EG) [11]. B. ELLIPTIC CURVE CRYPTOGRAPHY Elliptical curve cryptography (ECC) is a public key encryption technique based on elliptic curve theory that can be used to create faster, smaller, and more efficient cryptographic keys. ECC generates keys through the properties of the elliptic curve equation instead of the traditional method of generation as the product of very large prime numbers. The technology can be used in conjunction with most public key encryption methods, such as RSA, and Diffie-Hellman. According to some researchers, ECC can yield a level of security with a 164-bit key that other systems require a 1,024-bit key to achieve. Because ECC helps to establish equivalent security with lower computing power and battery resource usage, it is becoming widely used for mobile applications. ECC was R.Sasikala, IJRIT

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 2, February 2014, Pg: 64- 70

developed by Certicom, a mobile e-business security provider, and was recently licensed by Hifn, a manufacturer of integrated circuitry (IC) and network security products. III. PROPOSED SYSTEM

Transfer of

packet to destination

Use watch dog timer

Prevent from loss

We propose a new concealed data aggregation scheme extended from Bonehet al.’s homomorphic public encryption system. The proposed scheme has three contributions. First, it is designed for a multi-application environment. The base station extracts application-specific data from aggregated cipher texts. Next, it mitigates the impact of compromising attacks in single application environments .Finally, it degrades the damage from unauthorized aggregations. A. STRUCTURE BASED DATA AGGREGATION IN WSNS The following section gives an overview of the tree-based, grid-based, cluster-based and chain-based approach for data aggregation. Tree-based Data Aggregation In a tree-based approach, data aggregation is performed at the intermediate nodes. The parent nodes receive information propagated by the intermediate nodes, which acquire data from the child nodes. It is very essential to construct an energy efficient aggregation tree. The Tiny Aggregation (TAG) framework allows the user to perform queries for data aggregation . TAG works in two phases, in the distribution phase the query is distributed and in the collection phase the parent nodes use a specified aggregation function to fuse data aggregated from the child nodes. Another tree based approach is the Energy-Aware Data Aggregation Tree (EADAT) which is proposed. A broadcast control message is periodically sent to the nodes form the base station. When the message is received, the timers in the nodes get activated. The expiration time of the transfer period is inversely proportional to the node’s residual energy. The timer may be refreshed each time node receives the message during the timer count down. Grid-based Data Aggregation This technique is very useful for WSN applications like military surveillance and weather forecasting, where an event occurs in a very short span of time. The network is divided into a pre-defined set of grids and each grid is responsible for observing and reporting, the events that occur inside that region to the sink. The sensors within a grid may not communicate with each other. In the case of an event, each sensor sends data to the aggregator. Then the aggregator filters information and sends critical information to the sink. This is highly adaptable in dynamic environments. It reduces the overall traffic by making sure that critical information is transmitted to the nodes R.Sasikala, IJRIT

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 2, February 2014, Pg: 64- 70

interested in the data, which increases the throughput in such environments. It may lead to an increase in the communication time thereby increasing congestion due to an increase in the number of packets. Cluster-based Data Aggregation Data packets are organized in groups called cluster-heads. These designated nodes are the aggregation points which combine the data sensed by cluster members and propagate the aggregated data to the sink . Examples of cluster based aggregation are LEACH and COUGAR . LEACH based protocols assume that the base station is accessible within one hop which limits the size of the network. This approach also increases the communication overhead for cluster information and maintenance. Chain-based Data Aggregation The key idea behind chain-based data aggregation is that the sensors transmit information only to its closest neighbor. Power-Efficient Data Gathering Protocol for Sensor Information Systems (PEGASIS) was proposed by Lindsey et al. In PEGASIS, nodes are organized linearly forming a chain. The nodes assume that all other nodes have a global knowledge of the network. The information is transmitted step by step from the farthest node, forming a chain, to its consecutive node in the chain. For each piece of information transferred, a node receives data from its neighbors, fuses the data with its own, and transmits the fused data to its other neighbors along the chain. Figure 1.4 shows the chain-based data aggregation in PEGASIS. Since the information is transferred in a hierarchical fashion, it leads to excessive energy consumption, thus reducing the lifetime of WSNs. Structure-free Data Aggregation in WSNs In order to overcome the hierarchical issue, introduced a structure-free aggregation mechanism, which does not require structure maintenance in case of nodal failure. A message authentication code (MAC) layer called the DataAware Any cast (DAA) was proposed which helps data to aggregate early on its route to the sink. If some nodes wait for other nodes to transfer information i.e. Randomized Waiting (RW) the efficiency and accuracy of data aggregation drastically increases. It does not produce any communication overhead which makes it applicable for dynamic networks. However, this protocol is based on the assumption that there is only one data sink in the network. B. STRUCTURE -FREE DATA AGGREGATION Fixed-grid Data Aggregation The average speed of segments is periodically broadcasted to its 1-hop neighbors. The traffic update is gradually disseminated over multi hops when the neighbors consider the updated information in their next broadcast . Fuzzy-logic based Data Aggregation This approach allows flexible aggregation covering all aspects of data aggregation. All influences on aggregation decisions are considered by applying fuzzy set theory . The input values of these influences are calculated as real values using the atomic values of the two aggregates under construction. Then the real values are mapped using fuzzy reasoning. Therefore, the bandwidth consumption of all three systems is performed . The major drawback was the potential loss of accuracy compared to individual reports which lead to testing the accuracy of the aggregation mechanisms in a given situation. The system uses fixed size segmentation to employ a simple aggregator which combines two items of information whenever they fall into the same segment . The fusion mechanism and dissemination mechanism involve parameters such as packet size, dissemination periodicity for both compared systems. In the fuzzy-logic approach, data aggregation is highly application dependent. For the hierarchical calculation of an aggregate, standard deviation allows the user to fully exploit fuzzy reasoning process in take application requirements like the maximum tolerable aggregation error into account. C. VARIED-GRID APPROACH FOR STRUCTURE-FREE DATA AGGREGATION In most sensor applications, either the grid-based or the fuzzy-logic based application can be used. However, the fuzzy-logic is preferred over grid-based approach in localized environments. The only concern in the two approaches is the performance provided by each of them. We introduce a varied-grid approach for data aggregation R.Sasikala, IJRIT

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which has been compared with the other methods of aggregation on the parameters such as the data packet size, communication speed and energy efficiency. In this approach, each sensor maintains a history of past events and the corresponding signal strengths the sensors detected. In case of an event, each sensor checks in its data for the previous entry to identify if the event is mobile or stationary. To reduce data aggregation switching, instead of switching the data aggregation method every unit of data traffic measurement, we can configure the switch to occur once every two units of data traffic measurement. This means that the data aggregation method will only change if data traffic is over or under the threshold value for two consecutive time units. IV. APPLICATIONS A. MULTI-APPLICATION WSNS Compared with the multi-application WSNs, the scenario of a single application is more commonly discussed in WSNs. However, the scenario of multiple applications working concurrently is more realistic in most cases. The deploying multiple applications in a shared WSN can reduce the system cost and improve system flexibility. The reason is because an SN supports multiple applications and can be assigned to different applications dynamically. For example, UC Berkerly’s MICA node is capable of sensing different data, e.g., temperature, light, accelerometer, and magnetometer. For instance, three different kinds of SNs, smoke detectors, temperature collectors, and light detectors, are deployed in the same building. Fig. 1 shows this typical case. Each room contains an AG and some SNs. A big challenge for the AGs, AG1 to AG4, is to aggregate the sensed readings from the different applications to a mixed aggregated result. Unfortunately, two limitations make the aggregation more difficult: 1. To maintain data privacy and reduce the communication overhead, sensed reading should be encrypted by SNs and the corresponding cipher texts must be aggregated. The solution satisfying this requirement has already been proposed, called CDA. 2. Even if aggregation on cipher texts is possible, aggregation of multi-application is still hard because the decryption cannot extract application-specific aggregated result from a mixed cipher text.

V. CONCLUSION AND FUTURE WORK Drastic growth in the area of wireless communication technology has led to the production of wireless sensors which are capable to adapt to the dynamic changes in the environment. They can observe and report real time information. However, these systems may encounter technical difficulties such as the bandwidth, energy and throughput constraints. Data aggregation is addressed to alleviate these problems, but is limited due to its lack of adaptation to dynamic changes in the network and unpredictable traffic patterns. This project proposes a varied-grid data aggregation approach which is able to overcome some of these issues. This approach allows the nodes to interact efficiently in a dynamic environment. There is a substantial decrease in energy dissipation which increases the lifetime of the sensor nodes. Although our work in this approach is very promising, a lot more can be done in this area. Different scenarios with sensors deployed sparsely for the wireless network can be tested. Multiple events occurring at the same time also need to be tested. R.Sasikala, IJRIT

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[18] L. Oliveira, D. Aranha, E. Morais, F. Daguano, J. Lopez, and R. Dahab, “TinyTate: Computing the Tate Pairing in Resource- Constrained Sensor Nodes,” Proc. IEEE Sixth Int’l Symp. Network Computing and Applications (NCA ’07), pp. 318-323, 2007. [19] L. Washington, Elliptic Curves: Number Theory and Cryptography.Chapman & Hall/CRC, 2008. [20] S. Zhu, S. Setia, and S. Jajodia, “LEAP+: Efficient Security Mechanisms for Large-Scale Distributed Sensor Networks,” ACM Trans. Sensor Networks, vol. 2, no. 4, pp. 500-528, 2006.

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