The widescale design, development, and deployment of connected, sensor-based devices have paved the way for Industry 4.0. This is characterized as sensor-based devices that gather data within the Fog and rely on the Cloud for processing and is termed the internet of things (IoT). IoT suffers from latency as it is dependent on the Cloud. Artificial intelligence of things (AIoT) enables low latency by processing sensor data within the Fog. This makes it a smart environment that exhibits context awareness, which is the ability of a system to acquire sensor data and process it using machine learning algorithms. Context awareness envisages that multiple smart personal spaces of different types and having distinct contexts should be able to interact with each other, thus enabling a synergy of distinct contexts. This paper presents a mathematical model of the synergy of distinct contexts. The contexts belong to distinct personal spaces, and each space gathers the contextual data and processes it using Bayesian classification. The participating personal spaces include garbage cans and garbage trucks, which share their contextual information to establish a synergy of distinct contexts. Furthermore, the system generates a priority list to schedule garbage removal. The results show that distinct contexts can interact with each other with low failure probability in the domain of solid waste management.

The advancements in urban commuting have enabled ease of travel for commuters. However, in the underdeveloped world, commuting has become a challenge for the mental health of commuters. A commuter who travels through public transport or their vehicle can develop depression and anxiety due to traffic congestion and unwanted delays. Symptoms of depression and anxiety can be mitigated through psychotherapeutic music. However, this music requires quiet rooms where a patient could listen to them. This can be overcome by playing music available on online streaming services via the commuters’ smart devices. The data from the sensors embedded in a commuter’s smart device is gathered and is termed the current context. The context includes both the data from the sensors and deduced data that is acquired through sensor services. The current context is then processed to determine the context of the commuter. The context is a label that is the outcome of a machine learning algorithm as part of context processing. The authors have utilized Bayesian probability to classify the current context of the commuter. Based on the classification outcome, which is termed context, a suitable playlist is generated and played on the commuters’ smart devices. A feedback loop enables improvement in classification as well as playlist generation. This proposed mechanism would improve the mental health of commuters including students, workers, and passengers, traveling to work and back frequently.

For the past few years, software security has become a pressing issue that needs to be addressed during software development. In practice, software security is considered after the deployment of software rather than considered as an initial requirement. This delayed action leads to security vulnerabilities that can be catered for during the early stages of the software development life cycle (SDLC). To safeguard a software product from security vulnerabilities, security must be given equal importance with functional requirements during all phases of SDLC. In this paper, we propose a policy-driven waterfall model (PDWM) for secure software development describing key points related to security aspects in the software development process. The security requirements are the security policies that are considered during all phases of waterfall-based SDLC. A framework of PDWM is presented and applied to the e-travel scenario to ascertain its effectiveness. This scenario is a case of small to medium-sized software development project. The results of case study show that PDWM can identify 33% more security vulnerabilities as compared to other secure software development techniques.

A smart personal space is a context-aware system that recognizes situations using contextual data. A user interacts within the personal space using smart devices that are mobile, and run-on batteries that have limited power. This paper proposes a Power-Constrained Context-Aware System (PCCA) that uses Markov Chain-based pre-classification to predict context change and defer context processing to conserve energy in an intelligent way. A new Markov Chain Module is added that creates a Markov Chain using history information. This enables PCCA to predict context change for the next observation. The results show that PCCA consumes 37% less power than a context-aware system.

The increase in the use of smart devices has led to the realization of the Internet of Everything (IoE). The heart of an IoE environment is a Context-Aware System that facilitates service discovery, delivery, and adaptation based on context classification. The context has been defined in a domain-dependent way, traditionally. The classical models of context have been focused on rich context and lack Cost of Context (CoC) that can be used for decision support. The authors present a philosophy-inspired mathematical model of context that includes confidence in activity classification of context, the actions performed, and the power information. Since a single recurring activity can lead to distinct actions performed at different times, it is better to record the actions. The power information includes the power consumed in the complete context processing and is a quality attribute of the context. Power consumption is a useful metric as CoC and is suitable for power-constrained context awareness. To demonstrate the effectiveness of the proposed work, example contexts are described, and the context model is presented mathematically in this study. The context is aggregated with power information, and actions and confidence on the classification outcome lead to the concept of situational context. The results show that the context gathered through sensor data and deduced through remote services can be made more rich with CoC parameters.

The advent of Green Computing and its recognition in UN-GA 2030 Agenda establishing affordable and clean energy as a goal, has led many researchers and institutes to explore energy consumption and power conservation of algorithms. This paper establishes the energy consumption of insertion sort to ascertain whether the consumption matches the time complexity of space complexity when the input size is increased. The tests have been performed on Ubuntu and the code has been written in Java. The tests have been run on two different machines running on battery power. The test shows that the energy consumption follows the execution time.

The advent of smart devices, interacting with each other as well as remote services, has paved the way for the Internet of Everything (IoE). IoE is a direct successor of Internet of Things (IoT), composed of smart devices interacting with remote services. The devices in an IoE environment are power constrained. At the core of an IoE environment, there is a context-aware system that gathers the context and classifies it. Various datasets have been published by authors for context-aware systems. This paper presents a mechanism that gathers a dataset of contextual information along with power information using smartphones. An Android application “PowerIpsum” is developed for gathering contextual information, power information, and user input activity labels. The dataset includes the sensor data as the contextual data, timestamps, average current, and average voltage as well as user activity labels. Time elapsed and power consumption is forecasted using Monte Carlo method. The results provide useful insights and demonstrate the advantages of power information within a context-aware system.

The advent of the 4th industrial revolution has realized smart environments composed of cloud and fog. These have been improved to enable intelligence at the fog, where numerous, inexpensive devices communicate with each other and provide computation capabilities to solve domain-specific problems in a distributed fashion. One of these domains is smart traffic flow with a focus on emergency vehicle (EV) transit. The increase in traffic congestion in third-world countries is a hindrance in EV transit to save human lives. This paper proposes an Artificial Intelligence of Things– (AIoT–) based, distributed, EV transit system developed on Raspberry PI as a rule-based system with minimal sensors. The sensors include an infrared sensor to detect emergency light and directional microphones to detect siren. The departure direction of the EV is shared with adjacent intersections to further reduce the transit time.

Fleet maintenance management requires adequate data and timely information regarding vehicle systems so that early diagnostics can be performed to avoid unscheduled maintenance and breakdowns which affect fleet productivity and performance. In this article, we present a fleet maintenance system called Car e-Talk that uses Internet-of-Things technology and cloud computing to monitor vehicle health and report any anomalies along with information about the nearest maintenance center. Different sensors are attached to the vehicle for monitoring the vehicle's health. Data from sensors are received on the driver's smartphone through a microcontroller and, after processing, useful information is displayed on the driver's mobile screen. The same information is uploaded to a cloud server, where a history of the system is maintained and analyzed for predictive maintenance. The advantages of our system are that it is able to monitor real-time vehicle health statistics, predict fleet health and maintenance, improve vehicle diagnostics, and perform automatic reporting, thus increasing the usable life of the vehicle, fleet productivity, and performance.

Widespread use of numerous hand-held smart devices has opened new avenues in computing. Internet of things (IoT) is the next big thing resulting in the 4th industrial revolution. Coupling IoT with data collection, storage, and processing leads to Internet of everything (IoE). This work outlines the concept of smart device and presents an IoE ecosystem. Characteristics of IoE ecosystem with a review of contemporary research is also presented. A comparison table contains the research finding. To realize IoE, an object-oriented context aware model is presented. This model is based on Unified Modelling Language (UML). A case study of a museum guide system is outlined that discusses how IoE can be implemented. The contribution of this chapter includes review of contemporary IoE systems, a detailed comparison, a context aware IoE model, and a case study to review the concepts.

With the advent of smart, inexpensive devices and a highly connected world, a need for smart service discovery, delivery, and adaptation has appeared. This interconnection is composed of sensors within devices or placed externally in the surrounding environment. Our research addresses this need through a context aware system, which adapts to the users’ context. Given that the devices are mobile and battery operated, the main challenge in a context awareness approach is power conservation. The devices are composed of small sensors that consume power in the order of a few mW. However, their consumptions increase manifold during data processing. There is a need to conserve power while delivering the requisite functionality of the context aware system. Therefore, this feature is termed as ‘power awareness.’ In this paper, we describe different power awareness techniques and compare them in terms of their conservation effectiveness. In addition, based on the investigations and comparison of the results, a power aware framework is proposed for a context aware system.

Context aware systems strive to facilitate better usability through advanced devices, interfaces and systems in day to day activities. These systems offer smart service discovery, delivery and adaptation all based on the current context. A context aware system must gather the context prior to context inference. This gathered context is then stored in a tagged, platform independent format using Extensible Markup Language (XML) or Web Ontology Language (OWL). The hierarchy is enforced for fast lookup and contextual data organization. Researchers have proposed and implemented different contextual data organizations a large number of which has been reviewed in this chapter. The chapter also identifies the tactics of contextual data organizations as evident in the literature. A qualitative comparison of these structures is also carried out to provide reference to future research.

 Abstract— Wavelength Division Multiplexing (WDM) is used in optical networks to implement data circuits. These circuits allow exchange of information as a measure of wavelength in optical domain. Quality of Service (QoS) provisioning is one of the issues in WDM optical networks. This paper discusses different QoS aware Routing and Wavelength Assignment (RWA) algorithms. Some unaddressed issues are identified that include the effects of degraded performance, traffic patterns and type of QoS service for users. A software module is proposed that calculates a 'D' factor facilitating in the wavelength assignment for QoS provisioning. This module is designed to work in conjunction with existing RWA algorithms.

Context Awareness is the mechanism through which systems can adapt to the needs of a user by monitoring the context. Context includes environment, spatial, temporal, etc information that is used to infer the current activity. UML is used to design a context aware system. The context aware system is viewed as an Object Oriented software product. The UML model is generated through ArgoUML, a free UML modelling tool. The Use Case Diagram, the Sequence Diagrams and the Class Diagram are modelled using this tool. The Class Diagram is subjected to CK metrics to identify the strengths and weaknesses of the design. The measurements show that the proposed model is within the recommended range.

The production of smart, seamless and economical devices has paved the way for the development of context aware systems. Context awareness allows a system to classify the current activity based on the context as measured by the sensors present in the devices. With the presence of multiple devices and services in the environment security threats are expected. The threats can be modelled during the design phase. This article presents a flow model of a context aware system that classifies current activity based on the context. The threat modelling of the context aware system is carried out using STRIDE classification. The common countermeasures are also presented.

Context Awareness is the task of inferring contextual data acquired through sensors present in the environment. 'Context' encompasses all knowledge bounded by a scope and includes attributes of machines and users. A general context aware system is composed of context gathering and context inference modules. This paper proposes a Context Inference Engine (CiE) that classifies the current context as one of several recorded context activities. The engine follows a distance measure based classification approach with standard deviation based ranks to identify likely activities. The paper presents the algorithm and some results of the context classification process.

Pervasive Computing integrates numerous, casually accessible and inexpensive mobile devices with traditional distributed systems. The foremost issue of pervasive computing is Context-Awareness. Context-awareness requires flexible context sensing and context interpretation mechanism that are used in smart service discovery and its subsequent delivery to the mobile user. The proposed research, CAPP, is a Service Oriented Architecture (SOA) that enhances smart service discovery in a pervasive environment. Objective of CAPP is to deliver the best service available, among a pool of similar services, to the user. The interpreted high-level context is then used to discover the best available service for the user. The proposed system is implemented in Java and simulated through test data. Results show that the proposed technique is promising.

Context-awareness is the ability of computing systems to acquire and reason about the situational context and adapt application accordingly. Context-aware system start with gathering of raw, low-level contextual data, interpret the raw contextual data into high-level interpreted context, reason the interpreted context to derive implications and adapt the application behavior on the basis of the implications. The paper identifies the components of contextaware computing and elaborates the context-aware process with the issues in each stage. In pervasive computing, a user might be part of various security domains at any particular instant of time having various authentication mechanisms and different privileges in different security domains. The paper presents a threat modeling approach for pervasive computing and presents the model for threat modeling and risk analysis in pervasive environment. A history module using modular approach is presented in conjunction with existing context-aware systems to provide user preference on the basis of usage history.

Context awareness enables smart service discovery and adaptation for mobile and wireless hosts. The contextual data is acquired from sensors present in the smart space, which may be absent. The inherent noisy nature of wireless environments does not guarantee the gathering of correct data. A history module is thus required in conjunction with existing context-aware systems that overcomes these limitations by predicting the data. We present a modular approach that when coupled with existing context managers will be able to provide user preference on the basis of usage history.

This paper presents the threat modeling approach for pervasive environment's security. In pervasive computing, a user might be part of various security domains at any particular instant of time having various authentication mechanisms and different privileges in different security domains. A number of threat modeling approaches and methods have been defined in literature and are in use. However, because of the nature of the pervasive computing and ubiquitous networks, these approaches do not handle the inherent security problems and perspective of pervasive computing. The paper examines in detail the threat modeling and analysis approaches being developed at Microsoft and other methods used for threat modeling. The paper present a novel approach for addressing the threat modeling in pervasive computing and presents the model for threat modeling and risk analysis in pervasive environment.