RECOVERABLE CONCEALED DATA AGGREGATION USING SHARED KEY SIGNATURE IN WIRELESS SENSOR NETWORKS

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1 RECOVERABLE CONCEALED DATA AGGREGATION USING SHARED KEY SIGNATURE IN WIRELESS SENSOR NETWORKS 1 SUCHITHRA M. S, 2 L. MALATHI 1,2 Vivekananda College of Engineering for Women, Tiruchengodu, Tamil Nadu, India suchithrams194@gmail.com, malasumathi@gmail.com Abstract Wireless sensor networks are undoubtedly one of the largest growing types of networks today. Wireless sensor networks consist of large number of sensor nodes with sensing and communication capabilities. The main goal of data aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. In privacy homomorphism encryption, the base station only retrieves the aggregated result, not individual data, which causes two problems. First, the usage of aggregation functions is constrained. Second, the base station cannot confirm data integrity and authenticity via attaching message digests or signatures to each sensing sample. To overcome the above two drawbacks, the base station can recover all sensing data even these data has been aggregated. This property is called recoverable. In this paper, we are going to recover the concealed data aggregation for authenticity and data integrity in wireless sensor networks. The aim of the project is to use shared key signature algorithm for providing security and authenticity in wireless sensor network. This technique is an enhancement over existing encryption and signature verification algorithms. Here the key used for each sensor is shared for these algorithms. It reduces memory usage and delay in communication. Keywords LEACH, Recoverable Concealed data aggregation, Shared key signature algorithm, Wireless sensor networks. I. INTRODUCTION Wireless sensor networks are fast becoming one of the largest growing types of networks today and, as such, have attracted quite a bit of research interest. These are used in many aspects of lives including environmental analysis and monitoring, battlefield surveillance and management, emergency response, medical monitoring and inventory management. Their reliability, cost-effectiveness, ease of deployment and ability to operate in an unattended environment, among other positive characteristics, make sensor networks the leading choice of networks for these applications. Sensor networks are collection of sensor nodes which co-operatively send sensed data to base station. As sensor nodes are battery driven, an efficient utilization of power is essential in order to use networks for long duration hence it is needed to reduce data traffic inside sensor networks, reduce amount of data that need to send to base station. The main goal of data aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. Wireless sensor networks (WSN) offer an increasingly Sensor nodes need less power for processing as compared to transmitting data. It is preferable to do in network processing inside network and reduce packet size. One such approach is data aggregation which attractive method of data gathering in distributed system architectures and dynamic access via wireless connectivity. Wireless sensor networks have limited computational power and limited memory and battery power, this leads to increased complexity for application developers and often results in applications that are closely coupled with network protocols. With advance in technology, sensor networks composed of small and cost effective sensing devices equipped with wireless radio transceiver for environment monitoring have become feasible. The key advantage of using these small devices to monitor the environment is that it does not require infrastructure such as electric mains for power supply and wired lines for Internet connections to collect data, nor need human interaction while deploying. These sensor nodes can monitor the environment by collecting information from their surroundings, and work cooperatively to send the data to a base station, or sink, for analysis. The main goal of data aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. Wireless sensor networks (WSN) offer an increasingly attractive method of data gathering in distributed system architectures and dynamic access via wireless connectivity as shown in figure 1.Data aggregation is an important mechanism used in sensor networks to conserve the already limited battery power of the sensor devices. Traditional approaches to data aggregation, which centered on the base station, required that all data be routed from sensor to sensor towards the base station where the aggregate of the data is calculated. This causes sensors to expend much of their energy transmitting data [1]. More recently, research on data aggregation in sensor networks has resulted in energy-conserving and less centralized approaches in which data is aggregated at each sensor and the resulting aggregates are forwarded to the base station [2]. Data aggregation is a process of aggregating the sensor data using aggregation approaches. The general data aggregation algorithm works as shown in the below figure. The algorithm uses the sensor data from the sensor node 81

2 and then aggregates the data by using some aggregation algorithms such as centralized approach, LEACH (low energy adaptive clustering hierarchy),tag(tiny Aggregation)etc. This aggregated data is transfer to the sink node by selecting the efficient path. For secure data aggregation, several schemes, such as ESPDA and SRDA have been proposed [3] [4]. Although this type of protocols yields sub-optimal results, these are more efficient for large-scale networks and thus more suitable for distributed environments. However, these schemes restrict the data type of aggregation or cause extra transmission overhead. Besides, an adversary can still obtain the sensing data of its cluster members after capturing a cluster head. To solve above problems completely, two ideas are used in recent research. First, data are encrypted during transmission. Second, cluster heads directly aggregate encrypted data without decryption. A well-known approach named Concealed Data Aggregation (CDA) has been proposed based on these two ideas. CDA provides both end-to-end encryption and in-networking processing in WSN [5]. Since CDA applies privacy homomorphism (PH) encryption with additive homomorphism, cluster heads are capable of executing addition operations on encrypted numeric data. constrained. For example, these schemes only allow cluster heads to perform additive operations on cipher texts sent by sensors; therefore, these are ineffective if the base station desires to query the maximum value of all sensing data. Second, the base station cannot verify the integrity and authenticity of each sensing data [7]. These problems seem to be solved if the base station can receive all sensing data rather than aggregated results, but this method is in direct contradiction to the concept of data aggregation that the base station obtains only aggregated results. Thus, attempt to design an approach that allows the base station to receive all sensing data but still reduce the transmission overhead. Here introduce a concept named Recoverable Concealed Data Aggregation (RCDA) [13]. In this paper, recover the concealed aggregated data with authenticity and data integrity in wireless sensor networks. The base station can recover all sensing data even these data has been aggregated in cluster head. The aim of this paper is to use shared key signature algorithm for providing security and authenticity in wireless sensor network. II. ARCHITECTURE MODEL The architecture model forms the networks with 100 nodes. Initialize the node size, position, and color in the network. It is shown in the figure 3. Finally it analyzes the performance of simulation of nodes with speed with respect to time. The simulation work has been done with The Network Simulator ns-2, Version In the simulation 100 nodes are randomly distributed within the network field of size 1000 m * 1000m. Then vary the node speed from 5m/s to 30m/s. Wireless Architecture model follows following steps Define Properties (Channel, MAC, Antenna types) Create NS Instant (Simulator object) Create Nam & Trace Figure 1. General architecture of the data aggregation algorithm Later, several PH-based data aggregation schemes have been proposed to achieve higher security levels [6]. In the above PH-based schemes, the base station receives only the aggregated results. However, it brings two problems. First, the usage of aggregation functions is Figure. 2. Homogeneous WSN environment 82

3 With P as the cluster-head probability, r as the number of the current round and G as the set of nodes that have not been cluster-heads in the last 1/P rounds. This algorithm ensures that every node becomes a cluster-head exactly once within 1/P rounds. III. Figure. 3. Simulated result of architecture model Create Topology Define GOD Set Properties to Topology Create Node Create Node Position Create Node Mobility Create Agents Create application and/or traffic sources Start simulation CLUSTER FORMATION The process of grouping the sensor nodes in a densely deployed large-scale sensor network is known as clustering. The intelligent way to combine and compress the data belonging to a single cluster is known as data aggregation in cluster based environment. There are some issues involved with the process of clustering in a wireless sensor network. First issue is, how many clusters should be formed that could optimize some performance parameter. Second could be how many nodes should be taken in to a single cluster. Third important issue is the selection procedure of cluster-head in a cluster. Another issue is that user can put some more powerful nodes, in terms of energy, in the network which can act as a cluster-head and other simple node work as cluster-member only. In this study, it form the network as cluster as shown in figure 4 and choosing cluster head, collect the information from cluster members who reduce the energy and produce aggregated result to the sink. It is shown in figure 2. It is based on level of sensors. Elect the cluster head based upon energy of the nodes. Assume divide the level of sensors based on complexity. In order to select cluster-heads each node n determines a random number between 0 and 1. If the number is less than a threshold T (n), the node becomes a cluster-head for the current round. The threshold is set as follows: LEACH forms clusters by using a distributed algorithm, where nodes make autonomous decisions without any centralized control. It is an energy-efficient routing protocol. The goal is to design a cluster formation algorithm such that there are a certain number of clusters, during each round. In addition, if nodes begin with equal energy, the goal is to try to evenly distribute the energy load among all the nodes in the network so that there are no overlyutilized nodes that will run out of energy before the others. As being a cluster head node is much more energy intensive than being a non-cluster head node, this requires that each node take its turn as cluster head. LEACH is a cluster-based protocol, which includes distributed cluster formation. LEACH randomly selects a few sensor nodes as cluster heads (CHs) and rotates this role to evenly distribute the energy load among the sensors in the network. The operation of LEACH is separated into two phases, the setup phase and the steady state phase. In the setup phase, the clusters are organized and CHs are selected. The operation of LEACH is divided into rounds. In the steady state phase, the actual data transfer to the base station takes place. In setup phase, each node chooses a random number between 0 and 1. If the number is less than the threshold, the node is the cluster head. It will broadcast news to other nodes, and each node decides to join which cluster node according to signal strength. In steady state phase, cluster nodes will process data which is sent from nodes such as data fusion and aggregation, and transmit it to sink. Cluster formed as dynamically, in ns2 simulation it is performed by movement random selection. The simulated cluster architecture is shown in figure 5. Figure. 4. Cluster Formation 83

4 V. SIGNATURE VERIFICATION It performs the signature verification process. After receiving the data from cluster head it decrypt the data from aggregated result. Then decode the data and then verify the signature of individual nodes of the network. The base station decrypts the data and verifies the signature. Based on signature it retrieves the data. Boneh proposed an aggregate signature scheme which merges a set of distinct signatures into one aggregated signature. This scheme consists of five procedures: key generation (KeyGen), signing (Sign), verifying (Verify), aggregation (Agg), and verifying aggregated signature (Agg-Verify). IV. Figure. 5. Simulated result of Cluster Architecture ENCRYPTION In this study, it encrypts the sensing data of cluster members and sends it to cluster head. And also add the signature to all the sensing data of the node. Then aggregate the result and send to the cluster head. First generate the keys for each sensor node by using encryption algorithm and add the signature for that data of sensor node in the network. After adding cipher text and signature, send to the cluster head. Then aggregate the data which sent by all nodes of the network. Mykletun proposed a concealed data aggregation scheme based on the elliptic curve ElGamal (EC-EG) cryptosystem as shown in figure 6. It consists of four procedures: key generation (KeyGen), encryption (Enc), aggregation (Agg), and decryption (Dec). RCDA-HOMO is composed of four procedures: Setup, Encrypt-Sign, Aggregate, and Verify. The Setup procedure is to prepare and install necessary secrets for the BS and each sensor. When a sensor decides to send sensing data to its CH, it performs Encrypt-Sign and sends the result to the CH. Once the CH receives all results from its members, it activates Aggregate to aggregate what it received, and then sends the final results (aggregated cipher text and signature) to the BS. The last procedure is Verify. The BS first extracts individual sensing data by decrypting the aggregated cipher text. Afterward, the BS verifies the authenticity and integrity of the decrypted data based on the corresponding aggregated signature. VI. ANALYSIS It is used to analyze the performance of the network parameters such as Packet delivery ratio, Throughput, Delay. It focuses on the performance of the algorithm. It evaluates the performance using the tool X graph in ns2. A. SECURITY ANALYSIS: Figure 6. Simulated result of Data transmission We first assume that an adversary does not compromise sensors. The proposed schemes are secure because sensing data are encrypted. In RCDA- HOMO, each sensor encrypts their data with private key of base station before transmitting. Besides, our design generates the corresponding signature for each sensing data. Consequently, an adversary cannot modify messages and inject forged messages since he cannot sign forged messages without private keys. If an adversary has the ability to compromise sensors, we consider the following situations. An adversary can compromises a sensor and perform it as a legal one. Detecting compromised sensors that still act normally is infeasible in all existing detection mechanisms in WSN. Also, if the value of a forged message is in a reasonable range, detecting it is still infeasible. An adversary can also try to manipulate the aggregated result. He may generate false data, modify legal messages or impersonate other sensors. 84

5 The proposed schemes are still secure against above attacks because of the signature required for each generated message. A malicious sender cannot impersonate other legal sensors because he lacks the corresponding keys of legal sensors. Based on the attack model, data aggregation scheme must satisfy the following security requirements [8] [9] [10]. 1) Data Privacy: All sensing data must be encrypted and concealed from CH, especially in homogeneous WSN. It implies that a CH must have an ability to aggregate the cipher texts without decryption. 2) Data Integrity/Authenticity: The integrity ensures the base station can detect altered data by unauthorized entity during transmission. Besides, authenticity can trace the source of data; the base station can verify the data sender. 3) Prevent Decryption with Compromised Secrets: An adversary cannot decrypt the cipher text or aggregated results after compromising sensors[11]. 4) Prevent Encryption with Compromised Secrets: An adversary cannot generate the forged cipher text through compromised secrets. 5) Prevent Unauthorized Aggregation: An adversary cannot aggregate the cipher text and modify aggregate results if he does not compromise sensors or cluster heads[12]. These security requirements may broaden the scope of the assumptions defined in the past research. For example, CDA cannot satisfy all these requirements, but it can be solved by combining other mechanisms such as node authentication approaches [13]. However, in this analysis, our design can achieve all security requirements without collaborating with other mechanisms. It seems more cost efficient. VII. SIMULATIONS To simulate a WSN, we select NS2 as our simulation platform due to its popularity. We allocate 100, 500, and 1000 sensors to a predefined square with a random topology. Also, we adjust the density of a WSN to evaluate the impact from different degree. Simulation program begins to deploy sensors with random coordinates. When the deployment ends, the program builds links with estimated distances (Euclidean Distance) and estimated signal. Once possible links are created, the network triggers the routing procedure to form the cluster-based topology and finishes the bootstrapping. When the base station launches the data query task, all leaf nodes send their data (in our setting, each leaf send only one packet during each simulation) to the internal nodes. Then packets finally reach the base station. During transmission, cluster heads (or internal nodes) receive the packets and then perform 85 aggregation or directly forwarding based on the mechanism aggregation or directly forwarding [14]. To measure the pros and cons on aggregation, we defined the following metrics: 1) Energy consumption: Evaluate the communication energy consumed on internal nodes (or called cluster heads). This metric could measure the efficiency of energy saving by aggregation mechanism offered by the proposed scheme. 2) Delay time: Evaluate the longest delay during transmission. This metric gives us an estimated delays caused by RCDA-HOMO and RCDA-HETE, respectively. CONCLUSION In this paper, recoverable concealed data aggregation using shared key signature providing secure and authenticated data aggregation in wireless sensor network. The idea behind this scheme is the ability to share the key used in each sensors for encryption and signature verification. These approaches have the advantages like reducing the memory usage and avoid the communication delay. It also reduces the transmission overhead in wireless sensor network. The modifications of Mykletun et al. s Encryption Scheme and Boneh et al. s Signature Scheme are implemented to enhance security and authenticity in wireless sensor network. To summarize the above results, we obtain the following conclusions. First, applying the proposed scheme, RCDA-HOMO can reduce the energy consumption through aggregation significantly (at least 50%) because the CHs (or internal nodes) deliver fewer packets during communication. However, delay is the major cost when we apply the proposed schemes. Meanwhile, we ensure that processing delay causes a great impact than aggregation delay. Total delay is acceptable when the proportion of leaf is less than 50%, in the scheme. 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