A Survey of Dynamic Key Management Schemes in Sensor Networks

A Survey of Dynamic Key Management Schemes in Sensor Networks

Biswajit Panja (Morehead State University, USA) and Sanjay Kumar Madria (Missouri University of Science and Technology, USA)
DOI: 10.4018/978-1-60566-328-9.ch015
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In sensor networks, the large numbers of tiny sensor nodes communicate remotely or locally among themselves to accomplish a wide range of applications. However, such a network poses serious security protocol design challenges due to ad hoc nature of the communication and the presence of constraints such as limited energy, slower processor speed and small memory size. To secure such a wireless network, the efficient key management techniques are important as existing techniques from mobile ad hoc networks assume resource-equipped nodes. There are some recent security protocols that have been proposed for sensor networks and some of them have also been implemented in a real environment. This chapter provides an overview of research in the area of key management for sensor networks mainly focused on using a cluster head based architecture. First we provide a review of the existing security protocols based on private/public key cryptography, Kerberos, Digital signatures and IP security. Next, the authors investigate some of the existing work on key management protocols for sensor networks along with their advantages and disadvantages. Finally, some new approaches for providing key management, cluster head security and dynamic key computations are explored.
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Sensor networks are used in complex environments for large-scale, real-time data processing and their foreseeable applications in military and in monitoring critical infrastructures such as bridges, water resources etc. A sensor network consists of a collection of sensor nodes with computation, communication, and sensing capabilities that spread across a geographical area. They run on low power batteries, and thus their capabilities are limited. The sensor network is an ad hoc wireless network with nodes having limited hardware and software capabilities. For example, Table 1 shows the hardware configuration of crossbow mica2 (http://www.xbow.com/Products/Wireless_Sensor_Networks.htm) sensor nodes. In addition, their limited computing power, bandwidth and memory size restrict the types of data processing algorithms to be used, and intermediate results that can be stored on the sensor nodes (Carman, Kruus, & Matt, 2000; Chan, Perrig, & Song, 2003; Du, Deng, Han, Chen, & Varshney, 2004; Eschenauer & Gligor, 2002).

Table 1.
Hardware of Mica2 sensor nodes
Processor Speed7.3728Mhz
Program Memory (Flash)128Kb
Variable Memory (RAM)4Kb
On chip storage (EPROM)4Kb
Off chip storage (Flash)4Mb
Digital IO48
Analog to digital converters8 10 bit
Radio Frequency communications rate38.4kbit/sec
Radio power requirements16mA transmit 9mA receive
Operating systemTinyOS

Once deployed, sensor nodes may be moveable. They have sensing capabilities such as seismic, acoustic, magnetic, and infrared (Carman, Kruus, & Matt, 2000) . The architecture of the sensor network depends on the application environment. Raw data is processed locally, and data fusion is used to aggregate the number of data streams from multiple nodes to conserve the most limiting factor in a sensor node’s life expectancy, its battery capacity. Energy conservation is an important issue at node as well as at network level since it is not possible to recharge or change the battery in many applications. Figure 1 shows the architecture of a most common sensor network.

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