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Reputation systems are, however, somewhat more general because the decisions to be made can come from multiple sensor streams and reputation scores can to some extent be abstracted away from the particular physical models Part of the commonality with the calibration problem is the need for an outlier detection protocol With strong prior information concerning the model of the physical system, model-based outlier detection can be employed in sensor networks [87, 136, 137] The reputation rating of data points is made proportional to its conformance to this model The model could be deterministic or statistical, or even constructed using adaptive techniques in a phase where all nodes are trusted (eg, following calibration) With only partial knowledge of the physical situation, one may apply simple criteria such as rejection of values outside particular ranges, or sequences which change too quickly, or sequences that change too little (indicating stuck-at faults).

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Apart from the dif culty of dealing with dynamic environments, model-based techniques suffer from the dif culty of modeling the fault behavior of nodes An alternative is to use consensus-based techniques, which use much weaker assumptions concerning models In particular, it is assumed that neighboring nodes have correlated readings and that nonfaulty nodes are the large majority of those still permitted to participate in decisions To plan a sensor node density suf cient for the rst of these conditions to be met of course requires some knowledge of the physical model In operation, readings are compared to the consensus of neighboring nodes in order to compute a reputation score, rather than being compared to the model predictions There are two basic consensus criteria: (a) outlier designation based on distance from the consensus or (b) density-based outlier detection.

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In distance-based mechanisms, the absolute difference in sensor readings from the (reputation-weighted) average of the remaining nodes is used as the basis for outlier rejection [138, 139] To set the outlier detection thresholds, some knowledge of the statistical model of the system (phenomenon and sensor behavior) is thus required Density-based outlier rejection [140, 141] operates similarly, except that now the data can be clustered into groups with differing spreads, for which different distance thresholds then apply Any outlier detection scheme [142] can be used to produce the reputation score Having accomplished this, there are then several possibilities for how these scores will be accumulated and used One can assign scores from any transaction as being binary (satisfactory/unsatisfactory) or give more levels A particularly convenient framework is to accumulate reputation metrics using a beta distribution [96, 143] since only two parameters are needed to characterize the entire probability distribution.

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