The expanding computer landscape leads us toward ubiquitous computing, in which smart gadgets seamlessly provide intelligent services anytime, anywhere. Smartphones and other smart devices with multiple sensors are at the vanguard of this paradigm, enabling context-aware computing. Similar setups are also known as smart spaces. Context-aware systems, primarily deployed on mobile and other resource-constrained wearable devices, use a variety of implementation approaches. Rule-based reasoning, noted for its simplicity, is based on a collection of assertions in working memory and a set of rules that regulate decision-making. However, controlling working memory capacity efficiently is a key challenge, particularly in the context of resource-constrained systems. The paper’s main focus lies in addressing the dynamic working memory challenge in memory-constrained devices by introducing a systematic method for content removal. The initiative intends to improve the creation of intelligent systems for resource-constrained devices, optimize memory utilization, and enhance context-aware computing.

Smart spaces are physical environments equipped with sensors, actuators, and other computing devices to gather data and provide intelligent services to users. These spaces are made possible by ubiquitous computing, particularly context-aware computing. Although these systems are mainly implemented on mobile and other resource-constrained wearable devices, different techniques have been adopted for their implementation. Rule-based reasoning is a relatively easy-to-implement approach that can solve real-world problems. Rule-based systems rely on a set of assertions that constitute the working memory and a set of rules that govern what should be done with the set of assertions. Despite its relative simplicity, the working memory size is a critical factor in developing these systems, particularly for resource-constrained devices. In this paper, we propose techniques for efficiently calculating the working memory size. Our results show that all three techniques, DWM, APS, and SAPS, performed well in different ways. However, APS and SAPS consumed from 25% to 100% less memory than existing techniques.

Smart indoor kitchen garden environments have a significant potential to provide for monitoring of plants where a user can grow selected plants without any prior knowledge. Such environments are typically equipped with a number of heterogeneous sensors that monitor environmental parameters. This paper presents a formal system development framework, called Planquarium , consisting of an agent-based context-aware infrastructure, which provides information with an unambiguous, shared meaning across sensing devices and end-users. The main focus is on collecting, modelling, reasoning, and distribution of context in relation to sensor data. The ontologies and semantic web rules are used in order to enable semantic interpretation of context awareness. A simple but realistic example system shows how activities are deduced using distributed rule-based reasoning based on the situations that occur in the indoor kitchen garden. The novelty of the proposed approach lies in the formal ontology usage within the design stage and the combination of different semantic and reasoning technologies providing a clear benefit for the application scenario under consideration.

Recent years have witnessed the rapid advances of smart computing paradigms in a ubiquitous environment. These paradigms make human life much easier, comfortable, secure and hassle free. In a smart computing environment, it is a fact that human users interact with the systems dynamically with or without human intervention using different modalities. The core emphasize is given on the intelligent systems that run in a highly decentralized environment with different communication mechanism. Literature highlighted numerous formalisms to bridge the communication modalities for different knowledge sources. Among others, Multi‐context System (MCS) has been advocated as one of the most suitable formalism to interlink different contexts (domains) dynamically in the distributed environment. However, interaction of these knowledge sources sometime may produce inconsistent and conflicting results. In this work, we presents a contextual defeasible reasoning based multi‐agent formalism to handle the inconsistency issues. This framework relies on the semantic knowledge sources which allow us to model context‐aware non‐monotonic reasoning agents to infer the desired goals using the extracted rules from the ontologies and handles inconsistencies using conflicting contextual information. We illustrate the validity and correctness of the proposed formalism using a simple case study of a smart healthcare system with the prototypal implementation of the system.

The context-aware computing paradigm introduces environments, known as smart spaces, which can unobtrusively and proactively assist their users. These systems are currently mostly implemented on mobile platforms considering various techniques, including ontology-driven multi-agent rule-based reasoning. Rule-based reasoning is a relatively simple model that can be adapted to different real-world problems. It can be developed considering a set of assertions, which collectively constitute the working memory, and a set of rules that specify how to act on the assertion set. However, the size of the working memory is crucial when developing context-aware systems in resource constrained devices such as smartphones and wearables. In this paper, we discuss rule-based context-aware systems and techniques for determining the required working memory size for a fixed set of rules.

Context-aware computing is a mobile computing paradigm that helps designing and implementing next generation smart applications, where personalized devices interact with users in smart environments. Development of such applications are inherently complex due to these applications adapt to changing contextual information and they often run on resource-bounded devices. Most of the existing context-aware development frameworks are centralized, adopt clientserver architecture, and do not consider resource limitations of context-aware devices. This thesis presents a systematic framework to modelling and implementation of multi-agent context-aware rule-based systems on resource-constrained devices, which includes a lightweight efficient rule engine and a wide range of user preferences to reduce the number of rules while inferring personalized contexts. This shows rules can be reduced in order to optimize the inference engine execution speed, and ultimately to reduce total execution time and execution cost. The use of the proposed framework is illustrated using five different case scenarios considering different smart environment domains.

Planquarium is a context-aware indoor kitchen garden system, where a user can grow fresh plants and vegetables without prior knowledge. Further, the Planquarium will take care of the plants that are inside it using a Rule-Based Context-Aware environment, that is capable to monitor different aspects of the plant and can provide an ideal environment for the plant inside. Different plants have different requirements therefore, we have integrated profiling systems so that a person can select a plant and the Planquarium will adjust itself accordingly. Initially, we have deployed temperature, humidity, moisture, water and light(Artificial sunlight full spectrum) sensors to monitor the plants. In the future, we can further add soil quality monitors to optimum growth. Planquarium is suitable for congested smart cities, smart homes and for people who care for organic food.

Over the last few years, context-aware computing has received a growing amount of attention among the researchers in the IoT and ubiquitous computing community. In principle, context-aware computing transforms a physical environment into a smart space by sensing the surrounding environment and interpreting the situation of the user. This process involves three major steps: context acquisition, context modelling, and context-aware reasoning. Among other approaches, ontology-based context modelling and rule-based context reasoning are widely used techniques to enable semantic interoperability and interpreting user situations. However, implementing rich context-aware applications that perform reasoning on resource-bounded mobile devices is quite challenging. In this paper, we present a context-aware systems development framework for smart spaces, which includes a lightweight efficient rule engine and a wide range of user preferences to reduce the number of rules while inferring personalized contexts. This shows rules can be reduced in order to optimize the inference engine execution speed, and ultimately to reduce total execution time and execution cost.

This paper presents a conceptual framework and multi-agent model for context-aware decision support in dynamic smart environments based on heterogeneous knowledge sources. A Protégé plug-in for rules extraction from distributed ontologies has been developed, which allows us to model context-aware agents using the notion of multi-context systems. Extracted rules can be annotated to match the users' needs and to develop a preference model to support their preferences so as to provide a user with a more personalized services. The use of the proposed framework is illustrated using a simple fact-based preference model developed from ontologies considering two different smart environment domains.

In mobile computing, context-awareness has recently emerged as an effective approach for building adaptive pervasive computing applications. Many of these applications exploit information about the context of use as well as incorporate personalisation mechanisms to achieve intended personalised system behaviour. Context-awareness and personalisation are important in the design of decision support and personal notification systems. However, personalisation of context-aware applications in resource-bounded devices are more challenging than that of the resource-rich desktop applications. In this paper, we enhance our previously developed approach to personalisation of resource-bounded context-aware applications using a derived context-based preference model.

Context-aware computing is a mobile computing paradigm that helps designing and implementing next generation smart applications, where personalized devices interact with users in smart environments. Development of such applications is inherently complex due to these applications adapt to changing contextual information and they often run on resource-bounded devices. Most of the existing context-aware development frameworks are centralized, adopt client–server architecture, and do not consider resource limitations of context-aware devices. This paper presents a systematic framework to modelling and implementation of resource-bounded multi-agent context-aware systems on Android devices. The proposed framework makes use of semantic technologies for context modelling and reasoning about resource-bounded context-aware agents, Android powered smartphones as development platform, a suitable communication model and declarative rule-based programming as a preferred development language.

Context-awareness is an essential component of mobile and pervasive computing. It refers to the concept that an application understands its context, reason about its current situation, and provide relevant information and/or services to the users. One of the main challenges of context-aware distributed mobile computing is the dynamic adaptation to changes in the resource-bounded operating environment with user preferences. For example, a depersonalized context-aware application may exhibit behavior that is not anticipated by its user in a given situation. In this paper, we present a personalized preference model for resource-bounded context-aware applications, which provides support for the development and execution of context-aware applications using a declarative language. We implement a simple example system that demonstrates the effectiveness of the approach in a real-world scenario.

This paper presents a conceptual framework and multi-agent model for context-aware decision support in dynamic smart environments based on heterogeneous knowledge sources. The framework relies on distributed ontologies and allows us to model context-aware agents which reason using rules that are derived from ontologies using the notion of multi-context systems. The use of the proposed framework is illustrated using a simple system developed from ontologies considering three different smart environment domains.

The recent advancement of mobile computing technology and smartphones have changed the way we live, communicate, interact, and understand the world. Smartphones have various salient features that make them promising system platforms for the development of context-aware applications, e.g., embedded sensors in smartphones make them more convenient to be used for making context-rich information available to applications. Although the state of the art development of smartphones has endued developers to build advanced context-aware applications, many challenges still remain. Those are mostly due to the limited resources available in the mobile devices including computational and communication resources. This paper surveys the recent advances in context-aware applications in mobile platforms, and proposes a decentralized context- aware computing model that makes use of the smartphone platform, a P2P communication model, and declarative rule-based programming.

The system proposed describe the global/universal database containing the information about the registered user's personal, financial, medical, family and almost every information about the user in multiple meta tag languages, duly verified by the users country authorities(citizen registration authorities)and protected by the users login name and password. In our proposed system this database will be used to register the user to other websites which need to register only the verified and genuine users i.e. no bots or fake users. The child site will send request to the UDB along with the user login information and UDB will send the required data to the child site and the user will get registered with in no time as well as providing the most trustable data. this method will completely remove the fake ids, autobots, frauds and various internet threats to child sites. However the system can be used globally as a wrap from the WWW to provide more security, sites restrictions can be achieved more efficiently specially protection from pornography and online child abuse. we checked the system with more then 50 fields of user information with five language support and the results were more then 97%.

The proposed paper describes the ease of security while on the go, by using the PDA from any where. The system will recognize the user and then connect the user with its server to handle the security devices installed on the station. In our proposed system we have two setups, one as client (PDA) and other as server (home base).The client will gain access through a WAP with the server using username and password, and can access the different devices attached with the server. In the proposed paper the main emphases are on security, i.e. locks, lights and camera etc. However more complex devices can be easily attached to it. The performance depends upon the service acquired for the communication between the two bodies. The better the quality/standard of the service, the higher will be the success rate. Statistically the system achieved 93% results with failures of two times and one disconnection error.