A sensor network is a collection of wireless devices that are able to monitor physical or environmental conditions. These devices (nodes) are expected to operate autonomously, be battery powered and have very limited computational capabilities. This makes the task of protecting a sensor network against misbehavior or possible malfunction a challenging problem. In this document we discuss performance of Artificial immune systems (AIS) when used as the mechanism for detecting misbehavior.We show that (i) mechanism of the AIS have to be carefully applied in order to avoid security weaknesses, (ii) the choice of genes and their interaction have a profound influence on the performance of the AIS, (iii) randomly created detectors do not comply with limitations imposed by communications protocols and (iv) the data traffic pattern seems not to impact significantly the overall performance.We identified a specific MAC layer based gene that showed to be especially useful for detection; genes measure a network's performance from a node's viewpoint. Furthermore, we identified an interesting complementarity property of genes; this property exploits the local nature of sensor networks and moves the burden of excessive communication from normally behaving nodes to misbehaving nodes. These results have a direct impact on the design of AIS for sensor networks and on engineering of sensor networks.

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We study the interaction between communication protocols, network topology and packet traffic in wireless static radio networks. A particular interest is to empirically characterize the effect of interaction between the routing layer and the MAC layer on overall system performance. Three well known MAC protocols: 802.11, CSMA, and MACA are considered. Similarly three recently proposed routing protocols: AODV, DSR and LAR scheme 1 are considered. The performance of the protocols is measured with regard to three important parameters: (i) number of packets received (ii) average latency of each packet and (iii) long term fairness.We use a simple statistical technique based on ANOVA (Analysis of Variance), to characterize the effect of interaction between protocols and various input parameters on network performance. This technique is of independent interest and can be utilized in other simulation studies. Using our methodology, we conclude that different combinations of routing and MAC protocols yield varying performance under varying network topology and traffic situations. Our results show that no combination of routing protocol and MAC protocol is the best over all situations. An important implication of the study is that the performance analysis of protocols at a given level in the protocol stack needs to be studied not locally in isolation but as a part of the complete protocol stack.