Recent advances in low-power computing and communication technologies have given rise to the proliferation of wireless sensor networks that often have a large number of low-cost sensor nodes with limited bandwidth, battery power, and processing capacity. Wireless sensor networks are expected to be used in a wide range of applications from habitat monitoring to military surveillance. For many of these applications, security is an essential requirement, and therefore the lack of proper security can curtail the widespread deployment of sensor networks.;This dissertation addresses the security issues in wireless sensor networks, focusing on secure data aggregation and false data detection. A novel secure data aggregation protocol is proposed to prevent the redundant data transmission from sensor nodes to clusterheads. Data redundancy is eliminated using representative data items rather than actual sensor data. Representative data items also enable the proposed protocol to work in conjunction with security protocols in the sense that sensor nodes can send the actual data in encrypted form, without any need for decryption in cluster heads. Since secure data aggregation necessitates the existence of pairwise secret keys among neighboring sensor nodes and data aggregators, two key establishment protocols are proposed to provide secret key establishment for neighboring sensor nodes. In the key establishment protocols, memory and communication overhead of sensor nodes are reduced using the group-based key distribution and the events detected in the overlapping sensing regions of sensor nodes.;False data detection is addressed in the presence of compromised data aggregators and sensor nodes. To detect the false data injected by compromised data aggregators and sensor nodes, false data detection and secure data aggregation are integrated. In addition to providing data integrity and confidentiality, the proposed algorithms also reduce significantly the amount of transmitted data with the help of data aggregation and early detection and dropping of false data.
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