Abstract. Implanted biological sensors are a special class of wireless sensor networks that are used in-vivo for various medical applications. One of the major challenges of continuous in-vivo sensing is the heat generated by the implanted sensors due to communication radiation and circuitry power consumption. This paper addresses the issues of routing in implanted sensor networks. We propose a thermal-aware routing protocol that routes the data away from high temperature areas (hot spots). With this protocol each node estimates temperature change of its neighbors and routes packets around the hot spot area by a withdraw strategy. The proposed protocol can achieve a better balance of temperature rise and only experience a modest increased delay compared with shortest hop, but thermal-awareness also indicates the capability of load balance, which leads to less packet loss in high load situations.

Abstract-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.