Automated Composition, Analysis and Deployment of IoT Applications

Abstract

Building IoT applications of added-value from a set of available devices with minimal human intervention is one of the main challenges facing the IoT. This is a difficult task that requires models for specifying objects, in addition to user-friendly and reliable composition techniques which in turn prevent the design of erroneous applications. In this work, we tackle this problem by first describing IoT applications using abstract models obtained from existing models of concrete devices. Then, we propose automated techniques for building compositions of devices using a repository of available devices, and an abstract goal of what the user expects from such compositions. Since the number of possible solutions can be quite high, we use both filtering and ranking techniques to provide the most pertinent solutions to users. The provided solutions satisfy the given goal and may be analysed with respect to properties such as deadlock-freeness or unmatched send messages. Finally, the application can be deployed using existing execution engines.

IoT Capabilities Composition and Decomposition: A Systematic Review

“…In the same effort [20], eight atomic services addressing smart city challenges in data analytics, evaluation, integration, validation, and visualization were pointed out (parking data prediction atomic service, traffic flow predictor; 2 atomic visualization services; 1 data elaboration atomic service; and 3 data transformation atomic services). Functions performed on atomic capabilities include the composition function Cf , and the decomposition functions Df , which can be synchronous [21], asynchronous [22], serial or sequential, probabilistic or alternative, parallel, circular, or cyclic [23] [2]. The success of Cf and Df requires multiple processes, including computations, filtering, ranking, composing, and verifying atomic and composite features [21].…”

“…Functions performed on atomic capabilities include the composition function Cf , and the decomposition functions Df , which can be synchronous [21], asynchronous [22], serial or sequential, probabilistic or alternative, parallel, circular, or cyclic [23] [2]. The success of Cf and Df requires multiple processes, including computations, filtering, ranking, composing, and verifying atomic and composite features [21].…”

“…• mDNS resolves hostnames to IP addresses within small networks that do not include a local name server. [21] • mDNS is used to enable the discoverability of IoT devices.…”

“…[86] , deadlock freeness [94], consistency [100] , non-conflict [58], conformance [56] , completeness [100], fairness [26], trustworthiness [41] , and communication properties such as unmatched send messages [21]. It is worth mentioning that verification of some of these properties can be automatically achieved when other properties are verified.…”

Quantifying the Similarity of Non-bisimilar Labelled Transition Systems

“…The goal is an abstract specification of what the user expects from the resulting composition. It can be modelled using an LTS too using interactions as labels (synchronization of actions) as suggested in [5]. A composition is satisfactory if it satisfies the goal.…”