Ubiquitous computing is envisioned to seamlessly integrate computing devices into our environment for the purpose of providing a wealth of information, in a real, or near real-time manner. This vision can be realized via the embedding of wireless transceivers into a large class of computing devices that are wirelessly networked in self-organized and self-maintained networks. These networks will enable a range of applications including but not limited to, home, community and enterprise networking, vehicular and transportation networks, inventory tracking, patient monitoring, environmental control, surveillance networks and tactical communications.

        The unobtrusive and robust network operation combined with constant and universal availability, emerge as key elements for successful commercialization of the aforementioned applications. In an era where the use and management of information provides a significant competitive advantage, information warfare is expected to escalate with the expansion of information availability. Provision of network services resilient to malicious adversaries that attempt to disrupt the continuous flow of valid information, while violating the privacy of the interacting parties is an area of national interest and of highest priority. However, the key properties of ubiquitous systems, that is, lack of pre-deployed infrastructure (ad hoc networks), portability and user mobility, un-tethered operation due to self-organization and self-healing processes pose, at the same time, great challenges in realizing secure networking.

         
         The adversarial models that have migrated from the wired networks to the infrastructureless networks, such as the Dolev-Yao and the Byzantine adversarial model, are known to be insufficient to capture the adversary capabilities and goals. The unsupervised operation of the wireless devices, enables a series of side-channel attacks such as, device tampering (hardware or software), device cloning, physical displacement or removal of nodes, environment alteration, node impersonation, compromise and even collusion among compromised entities. Furthermore, intelligent adversaries are able to adapt their strategies to the attack prevention mechanisms thus, evading timely detection and significantly disrupting the network functionality.

        In addition, the cross-layer designs adopted in resource-constrained networks for the purpose of resource efficiency, generate cross-layer network vulnerabilities. Adversaries disrupting protocols at one layer, can significantly impact performance at another layer due to the cross-layer interaction. Securing vital network processes such as neighbor discovery, localization, time synchronization, data aggregation and dissemination, cluster formation and fair access to the common medium, still remain open problems. It is critical that adequate adversarial models that span the space of attacks with respect to the elementary network functions are proposed, before any detection and prevention mechanisms are developed.

        Furthermore, the environment uncertainty due to mobility or topology change, device malfunction or poor performance of the wireless medium can trigger false alarms that are indistinguishable from attacks. An intelligent adversary can take advantage of the inability to differentiate between network faults and attack and adaptively masquerade its attacks to degrade network performance. Hence, a multimodal approach is required that combines consistency checks based on invariant network and physical properties, such as the network deployment statistics, or the propagation speed of electromagnetic waves.
   

        One of the primary tasks of wireless sensor networks (WSN) is to monitor a Field of Interest (FoI). The availability of observations is directly related to the number of sensors able to sense a particular event, and can be quantified by computing the fraction of the FoI covered by at least a threshold number of sensors, also know as k-coverage. Previous work on evaluating the k-coverage, assumed that sensors have identical sensing areas and/or conform to the idealized unit disk model. However, sensors of multiple sensing modalities such as acoustic, optical, infrared, CCD, magnetic, or thermal, have sensing areas significantly different than the unit disk model and may be concurrently deployed, thus forming a heterogeneous WSN.
 
        Alternatively, for applications such as area surveillance and habitat monitoring the network performance is related on how well the deployed network can monitor mobile targets that cross the FoI. The latter can be quantified by computing the probability of detecting a target crossing the FoI. As in the case of k-coverage, analytically computing the target detection probability assuming a heterogeneous WSN is a challenge.