Table Lookup

[…] a Node can perform a simple table lookup operation augmented with extrapolation or interpolation techniques to determine and apply the best 𝛼 and 𝛽 settings in response to dynamically changing conditions. The lookup time is O(1) and can be efficiently applied at runtime.

The lookup table as shown in Figure 8 below would store key-value pairs where the “keys” are combinations of input parameter values, and the “values” are the best 𝛼 and 𝛽 design parameter values for achieving the desirable accuracy and maximizing application performance under the input parameter values.

Figure 8: Lookup Table Mechanism.

The “sensed input parameters” on the left are input to be sensed at runtime. The “design parameters” on the right are output as a result of a table lookup operation. Depending on data granularity, a set of input parameter values may not directly map to a set of output parameter values. Extrapolation or interpolation techniques may be used to produce the matching output.


CHEN, Ing-Ray, BAO, Fenye and GUO, Jia, 2016. Trust-Based Service Management for Social Internet of Things Systems. IEEE Transactions on Dependable and Secure Computing. November 2016. Vol. 13, no. 6, p. 684–696. DOI 10.1109/TDSC.2015.2420552.

A social internet of things (IoT) system can be viewed as a mix of traditional peer-to-peer networks and social networks, where “things” autonomously establish social relationships according to the owners’ social networks, and seek trusted “things” that can provide services needed when they come into contact with each other opportunistically.

We propose and analyze the design notion of adaptive trust management for social IoT systems in which social relationships evolve dynamically among the owners of IoT devices. >> adaptive trust management

We reveal the design tradeoff between trust convergence versus trust fluctuation in our adaptive trust management protocol design. With our adaptive trust management protocol, a social IoT application can adaptively choose the best trust parameter settings in response to changing IoT social conditions such that not only trust assessment is accurate but also the application performance is maximized.

We propose a table-lookup method to apply the analysis results dynamically and demonstrate the feasibility of our proposed adaptive trust management scheme with two real-world social IoT service composition applications. >> table lookup method

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