Semantic analysis and verification of context-driven adaptive applications in intelligent environments

Abstract

With the advent of the Internet of Things (IoT) and Cyber-Physical Systems (CPS), assistive applications and smart environments can tap into a growing amount of context information to learn about their users, their surroundings and typical behavior. This information is useful to adapt intelligently, autonomously and non-intrusively to a myriad of circumstances. However, trustworthiness and reliable adaptation to changes in line with user expectationsespecially to situations that the developers did not anticipate-remain key concerns. Understanding the impact of changes from a developer (design time) and system (runtime) perspective, and ensuring that no undesired side effects take place are two non-trivial research challenges to increase the adoption of such applications. Given the limited tool support for anticipating change at design time and runtime, we present our change impact analysis (CIA) methodologyfound in the formal semantic modeling of intelligent environments and rule-based application behavior-to contribute to the development and deployment of reliable context-aware adaptive applications. We validate our contributions on nontrivial smart home and office scenarios, and demonstrate how our framework helps increase trust in intelligent environment applications by anticipating change implications upfront at design time and by minimizing the occurrence of undesired side effects at runtime.

An IoT sensor and scenario survey for data researchers

“…Complete list of surveyed papers, with scenario code and application classification Paper Application Scenario Paper Application Scenario [18] Smart agriculture 0000000 [19] Smart home 1032102A [20] Smart home 0000103 [21] Smart home 1101133 [22] Smart home 0011010 [23] Smart home 1132020 [24] Smart building 0020000 [25] Smart building 1132110 [26] Smart agriculture 0020000A [27] Smart home 1232120 [28] Water management 0021000 [29] Disaster management 1301210 [30] Smart home 0021000A [31] Smart vehicle 1332300 [32] Energy monitoring 0030000 [33] Playful furniture 2012010 [34] Energy monitoring 0031000 [35] Smart agriculture 2023200 [36] Smart home 0101003 [37] Smart city 2030103 [38] Playful furniture 0101123 [39] Smart city 2032013 [40] Smart healthcare 0130030 [41] Smart healthcare 2033020 [42] Smart healthcare 0131113 [43] Robot movement 2130033 [44] Learning device 0230023 [45] Smart healthcare 2133030 [46] Smart healthcare 0231020 [47] Smart building 3001200 [48] Smart healthcare 0312020 [49] Smart home 3022103 [50] Smart healthcare 0321210 [51] Smart city 3032100 [52] Smart healthcare 0322310 [53] Robot movement 3032200 [54] Smart healthcare 0331020 [55] Smart security 3032203 [56] Smart home 1002013...…”

Non-monotonic reasoning rules for energy efficiency

“…Another completely different approach, based on conflict adaptation, namely on the attempt of providing some space for context-based reasoning, has been adopted by Preuveneers in [32].…”

Design framework for reliable and environment aware management of smart environment devices

“…This approach also enables the detection of inconsistencies by verifying the actions actual effects. In a more recent work [44], the authors consider environment dependencies for the detection of implicit errors. However, using this approach requires to solve the detected errors.…”