Verifying system behavior before deployment is a necessity, especially with self-adaptive software. However, exhaustive verification is impractical due to both the scale of the input/output space and uncertainties that may not be captured in pre-deployment testing. Run-time monitors have been used to measure operational health after deployment but are unlikely to have a one-to-one correspondence with pre-deployment testing. Problematically, adaptation strategies can be defined for managing adverse run-time measurements due to runtime monitors, however, it is infeasible to provide adaptation triggers for untested behaviors with only static pre-deployment tests. This paper introduces contextual metamorphic relations that are applicable to specific contexts of a system’s behavior and implementation artifacts. These contextual metamorphic relations are a subset of general metamorphic relations and can be used for both pre-deployment testing and post-deployment run-time monitoring to trigger context specific adaptations in order to improve tolerance to specific faults. We illustrate our approach by triggering adaptation at run-time using contextual metamorphic relations for two real-world inspired responsive cyber-physical systems: a robotic drone system and a set of proximity-enabled responsive systems.

The increased growth of the aging population (i.e., 65 years or older) has led to emerging technologies in health care that provide in-home support to patients using devices throughout the household. Such smart home environments can monitor and interact with patients and their doctors/caregivers to augment patient medical data for diagnosis than can be generated via traditional doctor visits. Moreover, smart homes are enabling older adults to stay at home longer as opposed to permanent moves to assisted living or nursing facilities, increasing health and well-being and decreasing overall costs to the individual and society at large. This paper proposes Cognitive Assisted Living (CAL), a cyber-physical system comprising a network of embedded devices for collecting and analyzing patient speech patterns over time for monitoring cognitive function beginning in the early stages of Alzheimer's disease. Specifically, CAL will analyze patient speech patterns and spatial abilities, via a set of daily interactions, to provide a longitudinal analysis of speech deterioration, a significant indicator of cognitive decline resulting from Alzheimer's disease. Understanding the rate of cognitive decline can enable caregivers and health care professionals to better manage the patient's daily care and medical requirements. Additionally, the patient's cognitive state can be shared across household devices to increase the patient's comfort and better accommodate lifestyle changes. To these ends, we describe the architecture of the proposed system, the methods to which we will detect cognitive decline, and specify how the system will provide continuing fault tolerance and data security at run time.

A self-adaptive system (SAS) can reconfigure at run time in response to adverse combinations of system and environmental conditions in order to continuously satisfy its requirements. Moreover, SASs are subject to cross-cutting non-functional requirements (NFRs), such as performance, security, and usability, that collectively characterize how functional requirements (FRs) are to be satisfied. In many cases, the trigger for adapting an SAS may be due to a violation of one or more NFRs. For a given NFR, different combinations of hierarchically-organized FRs may yield varying degrees of satisfaction (i.e., satisficement). This paper presents Providentia , a search-based technique to optimize NFR satisficement when subjected to various sources of uncertainty (e.g., environment, interactions between system elements, etc.). Providentia searches for optimal combinations of FRs that, when considered with different subgoal decompositions and/or differential weights, provide optimal satisficement of NFR objectives. Experimental results suggest that using an SAS goal model enhanced with search-based optimization significantly improves system performance when compared with manually- and randomly-generated weights and subgoals.

Road Side Units (RSUs) within the connected vehicle infrastructure are vulnerable to security and access control challenges. RSUs may share resources with unreliable SPs that could lead to information leakage due to an insecure messaging infrastructure. To mitigate these concerns, we introduce an automated service provision mechanism that enables a controlled messaging infrastructure using a Distributed Security Framework (DSF). Service provision is accomplished by providing RSUs with publish/subscribe brokers that enable authorized SPs to distribute their services as topics and define access rights through the DSF. The DSF acts as a secure middle layer that is hosted by fog computing nodes to ensure close proximity to RSUs, handles resource authorization (i.e., topic creation in specific brokers), and provides identity authentication of both RSUs and SPs. The DSF uses an attribute-based access control model to enable both SPs and RSUs to define and dynamically manage attribute-based access policies to cope with run-time changes of protection requirements. We validate the DSF framework in a simulated smart highway environment comprising interconnected RSUs and SPs to demonstrate our technique's effectiveness.