A self-aware paradigm for autonomous architectural systems within the Internet of Things

This research explores a newfangled approach for the notion that architecture is no longer concerned with the organization of space and matter, but rather a system of systems with self-organizational behavior and progressively complex programmatic interventions. Such system represents an Internet of Things (IoT) paradigm that has the capacity to self-organize a pre-defined architecture acclimating dynamically to the demands of the environment through a process of self-awareness and re-configurability. This paper is an attempt to develop spaces that bring computation into the physical world by introducing artificial intelligence into building systems so as to communicate, exchange information, and allow right responses and decisions within a sustainable manner. Environments with embedded computational systems and adaptive reconfiguration behavior will be precisely studied. With an intensive focus on smart, self reconfiguring architecture that has embraced kinetic motion as an approach for environmental adaptation and responsiveness. This study addresses the networked organization of sensory systems that incorporate computational platforms in relation to user’s desires, where architecture turns into an interactive intermediary between human and computation. Correspondingly, the authors are particularly aiming to propose three conceptual frameworks for delivering a smart system at the architectural scale which is capable of reconfiguring and interacting constantly in real time, precisely focusing on an initial implementation for the first framework utilizing an experimental customized prototype, while the other two frameworks would be posteriorly studied through future work. Thus the IoT will foster a rapid development in intelligent systems which would directly introduce an advanced technological leap forward in accommodating a new way of demonstrating human-computer interaction within a novel architectural design approach

Wireless energy harvesting for Internet of Things

Internet of Things (IoT) is an emerging computing concept that describes a structure in which everyday physical objects, each provided with unique identifiers, are connected to the Internet without requiring human interaction. Long-term and self-sustainable operation are key components for realization of such a complex network, and entail energy-aware devices that are potentially capable of harvesting their required energy from ambient sources. Among different energy harvesting methods such as vibration, light and thermal energy extraction, wireless energy harvesting (WEH) has proven to be one of the most promising solutions by virtue of its simplicity, ease of implementation and availability. In this article, we present an overview of enabling technologies for efficient WEH, analyze the life-time of WEH-enabled IoT devices, and briefly study the future trends in the design of efficient WEH systems and research challenges that lie ahead

Towards low cost prototyping of mobile opportunistic disconnection tolerant networks and systems

Fast emerging mobile edge computing, mobile clouds, Internet of Things (IoT) and cyber physical systems require many novel realistic real time multi-layer algorithms for a wide range of domains, such as intelligent content provision and processing, smart transport, smart manufacturing systems and mobile end user applications. This paper proposes a low cost open source platform, MODiToNeS, which uses commodity hardware to support prototyping and testing of fully distributed multi-layer complex algorithms over real world (or pseudo real) traces. MODiToNeS platform is generic and comprises multiple interfaces that allow real time topology and mobility control, deployment and analysis of different self-organised and self-adaptive routing algorithms, real time content processing, and real time environment sensing with predictive analytics. Our platform also allows rich interactivity with the user. We show deployment and analysis of two vastly different complex networking systems: fault and disconnection aware smart manufacturing sensor network and cognitive privacy for personal clouds. We show that our platform design can integrate both contexts transparently and organically and allows a wide range of analysis

SONATA: Self-adaptive Evolutionary Framework for Hardware-aware Neural Architecture Search

Recent advancements in Artificial Intelligence (AI), driven by Neural Networks (NN), demand innovative neural architecture designs, particularly within the constrained environments of Internet of Things (IoT) systems, to balance performance and efficiency. HW-aware Neural Architecture Search (HW-aware NAS) emerges as an attractive strategy to automate the design of NN using multi-objective optimization approaches, such as evolutionary algorithms. However, the intricate relationship between NN design parameters and HW-aware NAS optimization objectives remains an underexplored research area, overlooking opportunities to effectively leverage this knowledge to guide the search process accordingly. Furthermore, the large amount of evaluation data produced during the search holds untapped potential for refining the optimization strategy and improving the approximation of the Pareto front. Addressing these issues, we propose SONATA, a self-adaptive evolutionary algorithm for HW-aware NAS. Our method leverages adaptive evolutionary operators guided by the learned importance of NN design parameters. Specifically, through tree-based surrogate models and a Reinforcement Learning agent, we aspire to gather knowledge on 'How' and 'When' to evolve NN architectures. Comprehensive evaluations across various NAS search spaces and hardware devices on the ImageNet-1k dataset have shown the merit of SONATA with up to 0.25% improvement in accuracy and up to 2.42x gains in latency and energy. Our SONATA has seen up to sim$93.6% Pareto dominance over the native NSGA-II, further stipulating the importance of self-adaptive evolution operators in HW-aware NAS.Comment: 13 pages, 9 figure

An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge

The advent of edge devices dedicated to machine learning tasks enabled the execution of AI-based applications that efficiently process and classify the data acquired by the resource-constrained devices populating the Internet of Things. The proliferation of such applications (e.g., critical monitoring in smart cities) demands new strategies to make these systems also sustainable from an energetic point of view. In this paper, we present an energy-aware approach for the design and deployment of self-adaptive AI-based applications that can balance application objectives (e.g., accuracy in object detection and frames processing rate) with energy consumption. We address the problem of determining the set of configurations that can be used to self-adapt the system with a meta-heuristic search procedure that only needs a small number of empirical samples. The final set of configurations are selected using weighted gray relational analysis, and mapped to the operation modes of the self-adaptive application. We validate our approach on an AI-based application for pedestrian detection. Results show that our self-adaptive application can outperform non-adaptive baseline configurations by saving up to 81\% of energy while loosing only between 2% and 6% in accuracy

Enhanced Dynamic Bandwidth Allocation Algorithm for Intelligent Home Networks

Internet of Things (IoT) has been seen playing a tremendous change in the Information Technology (IT) environments, and thus its importance has also been realized and played a vital role within Intelligent Home Networks (IHNs). This is because IoT establishes a connection between things and the Internet by utilizing different sensing devices to implement the intelligence to deal with the identification and management of the connected things. IHNs use intelligent systems to perform their daily operations. Meanwhile, these networks ensure comfort, safety, healthcare, automation, energy conservation, and remote management to devices and users. Apart from that, these networks provide assistance in self-healing for faults, power outages, reconfigurations, and more. However, we have realized that more and advanced devices and services continue to be introduced and used in these networks. This has led to competitions of the limited available network resources, services, and bandwidth. In this paper, therefore, we present the design and implementation of a Novel Dynamic Bandwidth Allocation (NoDBA) algorithm to solve the performance bottleneck incurred with IHNs. The proposed algorithm deals with the management of bandwidth and its allocation. In the proposed algorithm, this study integrates two algorithms, namely; Offline Cooperative Algorithm (OCA) and Particle Swarm Optimization (PSO) to improve Quality of Service (QoS). PSO defines the priority limits for subnets and nodes in the network. Meanwhile, OCA facilitates dynamic bandwidth allocation in the network. The Network Simulator-2 (NS-2) was used to simulate and evaluate the NoDBA and it showed improved results compared to the traditional bandwidth allocation algorithms. The obtained results show an average throughput of 92%, average delay of 0.8 seconds, and saves energy consumption of 95% compared to Dynamic QoS-aware Bandwidth Allocation (DQBA) and Data-Driven Allocation (DDA).   Keywords: IHNs, Dynamic Bandwidth Allocation, PSO, OCA, Qo

Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities

[EN] Energy efficiency is a significant characteristic of battery-run devices such as sensors, RFID and mobile phones. In the present scenario, this is the most prominent requirement that must be served while introducing a communication protocol for an IoT environment. IoT network success and performance enhancement depend heavily on optimization of energy consumption that enhance the lifetime of IoT nodes and the network. In this context, this paper presents a comprehensive review on energy efficiency techniques used in IoT environments. The techniques proposed by researchers have been categorized based on five different layers of the energy architecture of IoT. These five layers are named as sensing, local processing and storage, network/communication, cloud processing and storage, and application. Specifically, the significance of energy efficiency in IoT environments is highlighted. A taxonomy is presented for the classification of related literature on energy efficient techniques in IoT environments. Following the taxonomy, a critical review of literature is performed focusing on major functional models, strengths and weaknesses. Open research challenges related to energy efficiency in IoT are identified as future research directions in the area. The survey should benefit IoT industry practitioners and researchers, in terms of augmenting the understanding of energy efficiency and its IoT-related trends and issues.Kumar, K.; Kumar, S.; Kaiwartya, O.; Cao, Y.; Lloret, J.; Aslam, N. (2017). Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities. Energies. 10(12):1-40. https://doi.org/10.3390/en10122073S1401012Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for Smart Cities. 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Modeling context-aware systems using TOSCA

Cloud Computing is widely accepted as a provider of virtual resources over the Internet, it is due to its economical and technical benefits, such as on-demand self-service, resource pooling and rapid elasticity capabilities. To exploit these properties reliably, it is needed to automate their internal processes for application provisioning, configuration and management. One of the standards that has been developed in the last years with this aim is the Topology and Orchestration Specification for Cloud Applications (TOSCA) by OASIS. TOSCA is a standard which enables application developers to describe applications and their management as models that incorporate components and their relations among each other. Due to the emerging of new fields such as the Internet of Things, Industry 4.0 and the upcoming Fog Computing paradigm, which depend more and more on dynamically changing situations and therefore reconfiguration of applications in such a scenario, a systematic modelling approach which handles situational dependencies directly in the models of these applications is needed. The goal of the thesis is to identify suitable modeling concepts from the field of context- aware systems and apply one of these on the TOSCA language to incorporate the inherent nature of change of future applications in the field of Cloud/Fog Computing, Internet of Things and Industry 4.0

Smart objects as building blocks for the internet of things

The combination of the Internet and emerging technologies such as nearfield communications, real-time localization, and embedded sensors lets us transform everyday objects into smart objects that can understand and react to their environment. Such objects are building blocks for the Internet of Things and enable novel computing applications. As a step toward design and architectural principles for smart objects, the authors introduce a hierarchy of architectures with increasing levels of real-world awareness and interactivity. In particular, they describe activity-, policy-, and process-aware smart objects and demonstrate how the respective architectural abstractions support increasingly complex application

Federated Embedded Systems – a review of the literature in related fields

This report is concerned with the vision of smart interconnected objects, a vision that has attracted much attention lately. In this paper, embedded, interconnected, open, and heterogeneous control systems are in focus, formally referred to as Federated Embedded Systems. To place FES into a context, a review of some related research directions is presented. This review includes such concepts as systems of systems, cyber-physical systems, ubiquitous computing, internet of things, and multi-agent systems. Interestingly, the reviewed fields seem to overlap with each other in an increasing number of ways