PSKA: usable and secure key agreement scheme for body area networks.

A body area network (BAN) is a wireless network of health monitoring sensors designed to deliver personalized healthcare. Securing intersensor communications within BANs is essential for preserving not only the privacy of health data, but also for ensuring safety of healthcare delivery. This paper presents physiological-signal-based key agreement (PSKA), a scheme for enabling secure intersensor communication within a BAN in a usable (plug-n-play, transparent) manner. PSKA allows neighboring nodes in a BAN to agree to a symmetric (shared) cryptographic key, in an authenticated manner, using physiological signals obtained from the subject. No initialization or predeployment is required; simply deploying sensors in a BAN is enough to make them communicate securely. Our analysis, prototyping, and comparison with the frequently used Diffie-Hellman key agreement protocol shows that PSKA is a viable intersensor key agreement protocol for BANs.

Communication scheduling to minimize thermal effects of implanted biosensor networks in homogeneous tissue.

A network of biosensors can be implanted in a human body for health monitoring, diagnostics, or as a prosthetic device. Biosensors can be organized into clusters where most of the communication takes place within the clusters, and long range transmissions to the base station are performed by the cluster leader to reduce the energy cost. In some applications, the tissues are sensitive to temperature increase and may be damaged by the heat resulting from normal operations and the recharging of sensor nodes. Our work is the first to consider rotating the cluster leadership to minimize the heating effects on human tissues. We explore the factors that lead to temperature increase, and the process for calculating the specific absorption rate (SAR) and temperature increase of implanted biosensors by using the finite-difference time-domain (FDTD) method. We improve performance by rotating the cluster leader based on the leadership history and the sensor locations. We propose a simplified scheme, temperature increase potential, to efficiently predict the temperature increase in tissues surrounding implanted sensors. Finally, a genetic algorithm is proposed to exploit the search for an optimal temperature increase sequence.