Unique identity and localization based replica node detection in hierarchical wireless sensor networks☆
Introduction
A wireless sensor networks (WSN) is a group of specialized autonomous sensors or sensor node with communication framework. They are deployed to monitor and record any physical or environmental conditions at diverse locations. Commonly monitored parameters are temperature, humidity, pressure, illumination intensity, pollutant level, vibration, sound, and voltage. The architecture of WSN may be a partial peer to peer system (P2P) or clustered system. In partial P2P systems, nodes are peers which have similar type of operations and simple configurations [1]. In clustered systems, all the nodes within clusters are peers and they communicate to their cluster heads [2]. These cluster heads are elected among the peer nodes through election algorithms, and all the nodes take their turn, to be elected as a cluster head, in order to avoid a single node being encumbered. The final communication is towards a Base station, which is a powerful system like a laptop or an Access point. The un-tethered and openness of sensor network invites various types of attacks [3]. Due to the cost involved in deploying redundant number of nodes, the attack counter measures are not included physically into the single node architecture. The security attacks are broadly classified as Application Dependent and Application Independent attacks. Replication attack is a type of Application independent attack in WSN [3].
Replication attack is an attempt by an adversary in which one or more nodes are compromised or added into the network and these nodes have the same id as another node in the network. It is also known as clone attacks [4]. An attack similar to replication attack is Sybil attack [5]. In Sybil attack, a node gains multiple ids of many nodes and launches an attack. Replication attack is also treated as an intrusion and is detected using intrusion detection method (IDS) [6]. Node behaviors are monitored in IDS of WSN for corresponding applications and misbehavior or anomalous activities are identified.
The paper is organized as follows. The related works on replica node detection is discussed in Section 2. The proposed system is explained in Section 3. The background requirements of the proposed method and assumptions of the adversary are discussed in Sections 4 and 5. The proposed system implementation is explained in Section 6. The algorithm analysis is made available in Sections 7 and 8. The simulation results and conclusion is given in Sections 9 and 10.
Section snippets
Related works
The replication detection methods are broadly classified as network based and radio signal based [3]. The radio signal based detection methods take into account of the radio signal strength indicator (RSSI) or the radio finger print based on the received signals to detect the node replications in WSN [7]. This method cannot be used in hostile environments and geographically widespread WSN [8]. The networks based detection methods are classified as static based and mobile based. Static based
The proposed system
The proposed system identifies replicated nodes in overlapping clusters in WSN [14]. Replication attack detection in WSN is difficult in overlapping clusters as nodes may remain common between clusters. The process of obtaining unique parameters for node identifications is a challenging task in such situations. Fig. 1 shows replicas in overlapping clusters in which R1 and R2 are the replicated nodes. The replicated nodes may exist inside the clusters or can remain common between two clusters as
The K-OCHE clustering protocol
The proposed system uses K-connected Overlapping Clustering approach with Energy awareness protocol (K-OCHE) for clustering in WSN [22]. In the process of clustering, cluster head serves as the coordinator and is responsible for inter cluster communication. Cluster head election is performed to select a node in a cluster as its cluster head. The K-OCHE protocol is used for cluster formation and cluster head election. The aim of the protocol is to select a set of nodes as cluster heads and add
Adversary model and assumptions
The purpose of the adversary system is to replicate or clone the existing nodes in the network. These replicated or cloned nodes can behave like genuine nodes and may try to attack other nodes. The proposed system considers multi-hop homogenous wireless sensor network where all nodes are considered to be alike. The nodes are assumed to have unique ID. Except some anchor nodes, all the nodes are location-unaware. The system does not require base station or a system to coordinate the activities
Implementation
The nodes are grouped into clusters using K-OCHE protocol. Each cluster head creates a list of its nodes and forms the bloom filter. The Bloom filter is propagated to all the cluster heads in the network. Each cluster head performs the analysis of the results of bloom filter and detects for replicated nodes in its cluster or common nodes between clusters. A transaction revoke message is forwarded to all the clusters on identification of replica nodes. The cluster head would verify the
Communication complexity
The bloom filter message exchange occurs between the cluster heads. Hence the communication complexity of the system mainly depends on the number of cluster heads. Assuming the number of nodes in the network as n, the number of cluster heads c, the number of cluster members as cm, cluster head exchanges as 2(c-1) messages of b bit messages, the communication complexity can be summed as O(c2).
In N2NB, a node broadcast its neighbor id and its location claim to the neighboring nodes. The
Storage complexity
Each CH sends Its BF which is b bits in length to other peers or cluster heads in the network. With respect to a CH, it needs to store its own BF and the other peer CH's BF. Hence c number of BF will be stored in a CH. Thus the storage cost can be summed to be of the O(c). A major aspect of Computation cost in the cluster heads is that the proposed system requires hash computations. The performance of SHA-1 on a Pentium 4 is equal to 11.4 cycles per byte [23]. The computation of SHA-1 is slow
Simulation results
A set of simulations is made to run in NS2 to compare the proposed system with the protocols LSM and RM [26]. The tests are performed to find the detection rates, communication overheads and the energy gain. The tests are performed with the assumption that the cryptographic layer is not taken into consideration. Also the cost of cluster construction is not considered. The system is compared to a non cluster environment as in Fault Tolerant Virtual Back bone trees (FTVBT) [16] and k-coverage
Conclusion and future work
The replica node detection is a challenging task in overlapping clusters, as the cluster members are shared between the clusters. The methods RDBRFID and RDBLT address the issue using RFID and Localization. The methods are compared to existing systems RM and LSM and their performance is analyzed. The methods are further observed in non cluster environments FTVBT and K-coverage WSN for performance analysis and the results are found. It is observed that the proposed methods RDBRFID and RDBLT
T.P. Rani received her Master's in Computer Science and Engineering from Sathyabama University in 2005. Currently she is working as teaching faculty in the Department of Information Technology, Sri Sai Ram Engineering College, Chennai. She is a research scholar in the Department of Information and Communication Engineering, Anna University. Her area of interest includes sensor networks and network security.
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T.P. Rani received her Master's in Computer Science and Engineering from Sathyabama University in 2005. Currently she is working as teaching faculty in the Department of Information Technology, Sri Sai Ram Engineering College, Chennai. She is a research scholar in the Department of Information and Communication Engineering, Anna University. Her area of interest includes sensor networks and network security.
C. JayaKumar received his Master's and PhD degree in Computer Science and Engineering from Anna University. He has more than 20 years of teaching and research experience. Currently he is working as Professor and Head in the Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Chennai. His research interest includes ad hoc networks, sensor networks and cloud computing.
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Reviews processed and recommended for publication to the Editor-in-Chief by Associate Editor Dr. M. S. Kumar.