A Data Aggregation Scheme For Timely Determining Compromised Sensor Nodes

Wireless sensor networks(WSNs) have a wide range of applications, such as habitat monitoring, precision agriculture, fleet management, security surveillance, forest fire detection,etc. The compromised sensor nodes lead to the network data and transmission interference. It is particularly important to determine the sensor nodes in time and takes appropriate measures to ensure the security of the entire network. In large WSNs, in-network data aggregation significantly reduces the amount of communication overhead and energy consumption. The aggregation framework needs to address the problem of false aggregate values contributed by compromised nodes. This attack may cause useless results in the aggregation computed at the base station. In this paper, we propose an approach secure against the above attack launched by compromised nodes. In particular, the scheme enables the base station to securely compute the aggregate of sensor data even in the presence of such an attack.In the proposed scheme, firstly, the sensor nodes use the state public key encryption, the symmetric public key encryption, the pseudo random function and the message authentication code to encrypt the plaintext twice. Secondly, the cluster head node authenticates the ciphertext and filters false data, and then, the cluster head node decrypts the ciphertext. The compromised nodes number is sent to the base station. At last, the base station decrypts the ciphertext to recover the plaintext and authenticate the data. The scheme proposed in this paper solves the problem of the error aggregation value problem caused by the compromised node, filters the false data in time and determines the compromised sensor nodes. The analysis shows the proposed scheme is secure under the secure one-way hash function, message authentication code and the assumption of the Discrete Logarithm Problem(DLP). Simultaneously, this scheme greatly reduces the network communication cost and computation cost. Simulation experiment analysis shows that the proposed scheme can filter the false data in time to get the correct aggregation value and can locate the compromised sensor nodes accurately.