## 5 Increasing Robustness with Location Verification and Computation
### Methods for Location Verification  
One of the earliest solutions to the problem of secure location verification is _distance bounding_, which attempts to prove that a pervasive device, a _prover_, is within a bounded distance of a trusted _verifier_[65, 66]. The physical space in which a verifier will attest to a prover's location is known as that verifier's _Region of Acceptance_, or ROA. The distance bounding that a verifier performs on a prover involves distance ranging. The ability of a verifier to perform its job is based on the assumption that either 1) the prover has only limited capability to distort the distance ranging process (e.g. the prover can increase but not decrease his calculated range from a verifier) or 2) the prover does not know the location of verifiers.  
Probabilistic methods for location verification have been proposed [69, 70]. These methods utilize the fact that it is possible to probabilistically relate the hop count (or number of devices through which a message passes) between nodes of a randomly dispersed Wireless Sensor Network (WSN) to Euclidean distance [71]. Namely, they work by bounding the Euclidean distance between two nodes according to the number of network hops between them. Unfortunately, these methods impose sufficiency limitations on network density; malicious or malfunctioning nodes are more difficult to detect in sparse networks.  
Attacks against such probabilistic methods of location verification are possible. As the methods rely on reputation information communicated between peers, an attacker can use a variant of the Sybil attack to disrepute a victim node and cause blacklisting of that node. Similarly, a malicious node can alter the per-packet hop count information on which probabilistic location verification methods are dependent. Methods of securing verification systems against these and other attacks have been proposed. Inferring hop count from packet length might help in securing networks against nodes which maliciously alter the hop count of packets [69].  
Two algorithms for secure location verification have been proposed in [72]. These algorithms attempt to verify that estimations of the same parameters (e.g. node location) are sufficiently close. The first algorithm looks for inconsistencies in four derived matrices (representative of network layout information), while the second is an iterative algorithm which utilizes an indicator value based on node neighbor consistency.  
Neighboring sensors may be utilized for location verification. A method of position verification tailored to Vehicular Ad Hoc networks (VANETs) has been proposed [73]. This method utilizes a weighting of multiple _sensors_. Importantly, in the work's context, sensors refer to parameters that are used to heuristically determine whether or not a node is reporting the wrong location. As an example of the work's defined sensors, the _acceptance range_ sensor utilizes the fact that VANET transceiver hardware typically has a well defined range; communications received from nodes known to be outside this range are flagged as likely to be lying about their position. As another example, the _proactive exchange of neighbor tables_ sensor communicates neighboring node information across nodes, using it to find and flag inconsistencies in the reported network topology. Another location-based neighborhood authentication scheme was proposed by [PERSON] _et al._ that takes advantage of a group of special mobile anchors [74]. The anchors may be mobile robots and can perform coordinated group movement for collecting and measuring sensor data for location verification.