Heterogeneous Smart Research Articles

The Smart Home: How Consumers Craft New Service Networks by Combining Heterogeneous Smart Domestic Products

Service research suggests homes are becoming increasingly connected as consumers automate and personalize new forms of service provision. Yet, large-scale empirical evidence on how and why consumers automate smart domestic products (SDPs) is lacking. To address this knowledge gap, we analyze 13,905 consumer-crafted, automated combinations of SDPs, totaling 1,144,094 installations, across 253 separate service providers on the web service IFTTT.com. An exploratory network analysis examines the topology of the network and an interpretive coding exercise reveals how consumers craft different styles of human-computer interaction to cocreate value. The results reveal that the SDP network is disassortative, is imbalanced, has a long-tailed degree distribution, and that popular services have high centrality across all product category combinations. We show that popular combinations of SDPs are primarily motivated by utilitarian value-seeking enacted through a preference for automated tasks outside of conscious attention, though more individualistic combinations are slightly more likely to be hedonistically inclined. We conclude by showing how these consumer-crafted forms of service provision within domestic environments reveal design redundancy and opportunities for service innovation.

Security Improvement Mechanisms in Software-Defined Internet of Things

The IoT contains millions of heterogeneous smart objects that are connected together through the Internet platform. These heterogeneous smart objects deal with different protocols, technologies and resources, therefore each of them requires diverse security services in heterogeneous environments. Therefore, providing security services in heterogeneous environments is a daunting task for network providers that cannot be guaranteed through the traditional network architecture. Wide distribution and openness of IoT smart objects makes them very vulnerable to attacks and it can be easily targeted by cyber-attacks. Software-Defined Networking (SDN) is a new paradigm that separates the control plane from data plane t a global network view by centralized controller. Integrating the software-defined network with the Internet of Things can provide better access control and security mechanisms. Software-defined networking provides better control and management possibilities to manage and secure Internet of Things in a good manner. In this paper, we discuss about IoT architecture, security challenges in IoT, SDN architecture, security challenges in each layers of the SDN and software-defined IoT. In addition, we provide solutions to security problems in IoT through software-defined networking approach.

SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things.

Nowadays, the integration of Wireless Sensor Networks (WSN) and the Internet of Things (IoT) provides a great concern for the research community for enabling advanced services. An IoT network may comprise a large number of heterogeneous smart devices for gathering and forwarding huge data. Such diverse networks raise several research questions, such as processing, storage, and management of massive data. Furthermore, IoT devices have restricted constraints and expose to a variety of malicious network attacks. This paper presents a Secure Sensor Cloud Architecture (SASC) for IoT applications to improve network scalability with efficient data processing and security. The proposed architecture comprises two main phases. Firstly, network nodes are grouped using unsupervised machine learning and exploit weighted-based centroid vectors for the development of intelligent systems. Secondly, the proposed architecture makes the use of sensor-cloud infrastructure for boundless storage and consistent service delivery. Furthermore, the sensor-cloud infrastructure is protected against malicious nodes by using a mathematically unbreakable one-time pad (OTP) encryption scheme to provide data security. To evaluate the performance of the proposed architecture, different simulation experiments are conducted using Network Simulator (NS3). It has been observed through experimental results that the proposed architecture outperforms other state-of-the-art approaches in terms of network lifetime, packet drop ratio, energy consumption, and transmission overhead.

Security of RPL Based 6LoWPAN Networks in the Internet of Things: A Review

Internet of Things (IoT) is one of the fastest emerging networking paradigms enabling a large number of applications for the benefit of mankind. Advancements in embedded system technology and compressed IPv6 have enabled the support of IP stack in resource constrained heterogeneous smart devices. However, global connectivity and resource constrained characteristics of smart devices have exposed them to different insider and outsider attacks, which put users' security and privacy at risk. Various risks associated with IoT slow down its growth and become an obstruction in the worldwide adoption of its applications. In RFC 6550, the IPv6 Routing Protocol for Low Power and Lossy Network (RPL) is specified by IETF's ROLL working group for facilitating efficient routing in 6LoWPAN networks, while considering its limitations. Due to resource constrained nature of nodes in the IoT, RPL is vulnerable to many attacks that consume the node's resources and degrade the network's performance. In this paper, we present a study on various attacks and their existing defense solutions, particularly to RPL. Open research issues, challenges, and future directions specific to RPL security are also discussed. A taxonomy of RPL attacks, considering the essential attributes like resources, topology, and traffic, is shown for better understanding. In addition, a study of existing cross-layered and RPL specific network layer based defense solutions suggested in the literature is also carried out.

A novel class based searching algorithm in small world internet of drone network

Internet of Drones (IoD) is a new area which is based on incorporation of social networks into Internet of Things (IoT). In near future the social network of IoT devices is expected to be highly convoluted due to the exponential growth of heterogeneous smart devices. Searching the right objects and services in optimal time through best path will be a challenging task. Information searching techniques in IoD need efficient schemes and innovative research. The Small World phenomenon reveals some alluring facts and has been motivating many researchers to understand the behavior of social networks. Inspired by information searching mechanisms in drone networks, an information searching algorithm which exploits rating of services in IoD, is proposed in this work. Results show that the proposed information searching algorithm proves to be efficient with its mean execution time not over than 2.5 ms on two different machines.

Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions

The Internet of Things (IoT) has gained significant recognition to become a novel sensing paradigm to interact with the physical world in this Industry 4.0 era. The IoTs are being used in many diverse applications that are part of our life and is growing to become the global digital nervous systems. It is quite evident that in the near future, hundreds of millions of individuals and businesses with billions will have smart-sensors and advanced communication technology, and these things will expand the boundaries of current systems. This will result in a potential change in the way we work, learn, innovate, live and entertain. The heterogeneous smart sensors within the Internet of Things are indispensable parts, which capture the raw data from the physical world by being the first port of contact. Often the sensors within the IoT are deployed or installed in harsh environments. This inevitably means that the sensors are prone to failure, malfunction, rapid attrition, malicious attacks, theft and tampering. All of these conditions cause the sensors within the IoT to produce unusual and erroneous readings, often known as outliers. Much of the current research has been done in developing the sensor outlier and fault detection models exclusively for the Wireless Sensor Networks (WSN), and adequate research has not been done so far in the context of the IoT. Wireless sensor network’s operational framework differ greatly when compared to IoT’s operational framework, using some of the existing models developed for WSN cannot be used on IoT’s for detecting outliers and faults. Sensor faults and outlier detection is very crucial in the IoT to detect the high probability of erroneous reading or data corruption, thereby ensuring the quality of the data collected by sensors. The data collected by sensors are initially pre-processed to be transformed into information and when Artificially Intelligent (AI), Machine Learning (ML) models are further used by the IoT, the information is further processed into applications and processes. Any faulty, erroneous, corrupted sensor readings corrupt the trained models, which thereby produces abnormal processes or outliers that are significantly distinct from the normal behavioural processes of a system. In this paper, we present a comprehensive review of the detecting sensor faults, anomalies, outliers in the Internet of Things and the challenges. A comprehensive guideline to select an adequate outlier detection model for the sensors in the IoT context for various applications is discussed.

Vulnerability and Cost Analysis of Heterogeneous Smart Contract Programs in Blockchain Systems

The first generation blockchain designed for Bitcoin cryptocurrency maintains a record of transactions across several...

Suicidal behavior among Iranian psychiatric patients

Recent advances in Internet of Things (IoT) technologies require a new type of IoT security environment. Various heterogeneous smart devices have easy access to IoT environment, and as the number of users increases, they are exposed to various threats such as malicious attacks on IoT devices and IoT infrastructure, and data tampering by malicious code. Malware detection in IoT requires data and models for continuous and changing learning of smart devices. Methods/Statistical analysis: To minimize these security threats, various malware detection techniques in the field of IoT security have been studied. Malware detection in IoT environment is important for data derivation and learning model required for continuous and changing learning of smart devices. The metadata of malware detection can be normalized by the value of device id, time, behavior, location and state. This paper proposes behavior-based malware detection using deep learning (BMD-DL). Findings: BMD-DL was able to collect metadata about behavior-based malicious behavior and learn and detect malicious codes through deep learning. In addition, through the learned model, IoT Security is provided by disconnecting malicious devices that cause malicious behavior in the IoT environment. Improvements/Applications: BMD-DL collects behavioral data generated from multiple devices in the IoT and applies the results learned through deep learning to detect persistent malware.

Capacitated Multicommodity Flow Problem for Heterogeneous Smart Electricity Metering Communications Using Column Generation

This paper addresses the planning and deployment of wireless heterogeneous networks (WHNs) for smart metering, based on a cross-layer solution. We combine the constraints of the network layer that considers routing and flow demands at each link in the WHN, while at the same time, we account for the restrictions of the physical layer referred to the capacity of a short range technology when used in a multi-hop fashion. We propose a model based on a column generation approach to solve the capacitated multicommodity flow problem (CMCF); the model includes wireless links capacities, coverage, and cost. The work integrates the multi-hop routing of packets in a mesh network formed by smart meters and concentrators connected to a cellular network via base stations. The traffic of each link is represented in a multigraph with the occupation percentage, and we build a scalable routing tree on a georeferenced map to represent a real deployment. The results describe the behavior of the proposed model in terms of the traffic load per concentrator, the network coverage, and the reduction of energy consumption. We demonstrate that an infrastructure cost reduction is achieved with the inclusion of multi-hop short range technology, which reduces the number of smart meters that require a direct connection to cellular technology. The model guarantees 100% coverage of the smart meters analyzed in each scenario. The calculation time of the CMCF for advanced metering infrastructure (CMCF-AMI) based on the column generation algorithm as the population increases is reduced by 10%, and this is the expected return when the population is considerable.

Predicting the security threats on the spreading of rumor, false information of Facebook content based on the principle of sociology

With the rapid development of Internet of Things (IOT), frequent communication among a huge amount of heterogeneous smart devices over the Online Social Networks(OSN) becomes viable and efficient. Increasing user submissions including online contents, videos and comments are gradually affecting people’s lives, leading to an explosive propagation of information and posing security threats on the spreading of rumor, false information and inappropriate online speech. The goal of popularity prediction of online content is accurate predict the popularity in the future based on the early diffusion status. Existing models for popularity prediction are mostly based on discovering network features or fitting the equation into a varying time function which seldom introduces the principle of sociology. In this paper, we find that there exists a high linear correlation between the proportion of faithful fans in Facebook homepage with frequent shares in the early and the future popularity. The statistical results about Facebook remind us that the principle of mainstream fatigue plays an important role in prediction task. Furthermore, an experimental study clearly illustrates that the effectiveness of the proposed method.

An Integrated Heterogeneous Smart Agriculture System

It is very essential to use smart agriculture in present days. This will solve various issues occur in agriculture. With Internet of Things (IoT), wireless sensors and fog computing an integrated system providing the smart agriculture is running in present villages. Various issues are identified on this smart agriculture. Parameters such as irrigation scheduling and inefficient utilization of water resources are two of several ubiquitous parameters restricting production in many agricultural regions. To solve these issues, energy consumption of the sensors plays major role to send and receive the data on various parameters. In this paper, an integrated energy efficient sensors by using thermal imaging to maintain the constant data flow from sensors to fog and cloud server.

Load‐Balancing and QoS Based Dynamic Resource Allocation Method for Smart Gird Fiber‐Wireless Networks

Real-time network resource allocation based on virtualization technology is an important method to solve the solidification problem of Fiber-wireless (FiWi) access networks. To increase the resource utilization and meet the specific QoS requirements of smart gird communication services, a Load-balancing and QoS based dynamic resource allocation method (LbQ-DR) is proposed with three sub-mechanisms. A time-window based substrate network resource update mechanism is designed to describe the realtime resource consumption of substrate networks, which can balance the accuracy of resource status and the complexity of the allocation algorithm. A QoS-based Virtual network request (VNR) sorting mechanism is presented to precisely calculate the priority of VNRs and reasonably sort the incoming services. A load-balancing based resource allocation mechanism is designed to avoid unbalanced resource consumption. Especially, the channel interference is considered in the cost of embedding and a collision domain mechanism is introduced to decrease interference. Simulation results demonstrate that the proposed method can provide heterogeneous smart grid businesses with differentiated service, improve the utilization and economic benefits of the network and make the network more balanced.

A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic

Internet of Things (IoT) is expected to have a significant impact on city’s service provisioning and make a smart city more accessible and pragmatic since the deployment of heterogeneous smart devices in each infrastructure of cities is increasing. So far, the IPv6 routing protocol for low power and lossy networks (RPL) is considered to fit on IoT infrastructure for achieving the expected network requirements. While RPL meets the IoT network requirements quite well, there are some issues that need to be addressed, such as adaptability to network dynamics. This issue significantly limits the use of RPL in many smart city application scenarios, such as emergency alerts with high traffic flows. As part of a smart city vision, IoT applications are becoming more diverse, which requires context-awareness in routing protocols to support the behavior of the network. To address this issue, we design an objective function that performs the route selection based on fuzzy logic techniques while using contextual information from the application. For this, we present a new context-oriented objective function (COOF) that comprises both nodes as well as link metrics. Further, we suggest two new routing metrics, known as queue fluctuation index (QFI) and residual energy index (REI), which consider the status of queue utilization and remaining energy, respectively. The metrics used are designed to respond to the dynamic needs of the network. The proposed approach has been examined and evaluated in different scenarios when compared to other similar approach and default RPL objective functions. Simulation experiments are conducted in Cooja network simulator for Contiki OS. The evaluation results show that COOF can cope with network dynamics and IoT-based smart city application requirements.

Design, Deployment and Evolution of Heterogeneous Smart Public Lighting Systems

Street lighting characterizes many smart city initiatives around the world. In fact, significant savings can be achieved by not only replacing traditional luminaires with low-power LEDs, but also providing streetlights with smart light controllers and network connectivity, allowing the introduction of a sensible light intensity management and reduction of maintenance costs. Moreover, if designed with a far-looking view, smart lighting infrastructure could also support city-wide Internet of Things services, becoming key enablers of the smart city revolution, also in the 5G perspective. In this paper, we provide a thorough discussion on network architectures and communication technologies that could be adopted for smart public lighting applications, showing their benefits and downsides. Starting with significant activity on research, implementation and in-field testing, we also outline the steps required for the deployment of a smart public lighting infrastructure, each discussed in accordance with the network topology considered. Finally, we introduce some additional services that a smart public lighting infrastructure could support and discuss the benefits that would arise from integration with the upcoming 5G cellular network.

CloudIoT

The Internet of Things (IoT) is seen as a novel paradigm enabling ubiquitous and pervasive communication of objects with each other and with the physical/virtual world via internet. With the exponential rise of sensor and RFID-based communication, much data is getting generated; which becomes arduous to manage given the constrained power and computation of low-powered devices. To resolve this issue, the integration of Cloud and IoT, also known as CloudIoT, is seen as panacea to create more heterogeneous smart services and handle increasing data demands. In this article, the authors examine and survey literature with a focus on the integration components of CloudIoT and present diverse applications including driving factors for CloudIoT integration. The article also identifies security vulnerabilities implied by the integration of Cloud and IoT and outlines some suggested measures to mitigate the challenge. Finally, the article presents some open issues and challenges providing potential directions for future research in this area.

A development approach for collective opportunistic Edge-of-Things services

Technological advances have recently fostered the Internet of Things vision, in which systems of situated entities perceive and act upon the world, and interact with one another to provide novel kinds of services, which are inherently cyber-physical, increasingly contextual and opportunistic in nature, and possibly span different scales and domains. The requirements of such IoT applications, however, pose significant non/functional challenges to engineering efforts, mitigated by emerging computing paradigms. On the infrastructure side, Cloud, Fog, and Edge Computing provide virtualised, on-demand, elastic resource provisioning – at the distant data centres, Network core and Edge – supporting the abstraction and scalability needs of IoT settings while also altogether giving options for QoS-driven trade-offs. However, despite intense research in these fields, there is still a gap of approaches supporting the engineering of dynamic, heterogeneous smart environments, such as those involving “collectives” of devices coordinating in a complex fashion to provide “global” services. In this paper, we integrate the Aggregate Computing and Opportunistic IoT Service models and propose a full-fledged approach for the engineering – from analysis to simulation – of complex “Edge of Things” applications. We compare by simulation two deployment targets for the same collective application: one centralised/Cloud-based, and the other decentralised/Edge-based. We discuss the trade-offs each one introduces, and we draw recommendations on application-driven choices of the appropriate deployment.

Internet of Things (IoT) Cybersecurity Research: A Review of Current Research Topics

As an emerging technology, the Internet of Things (IoT) revolutionized the global network comprising of people, smart devices, intelligent objects, data, and information. The development of IoT is still in its infancy and many related issues need to be solved. IoT is a unified concept of embedding everything. IoT has a great chance to make the world a higher level of accessibility, integrity, availability, scalability, confidentiality, and interoperability. However, how to protect IoT is a challenging task. System security is the foundation for the development of IoT. This article systematically reviews IoT cybersecurity. The key considerations are the protection and integration of heterogeneous smart devices and information communication technologies (ICT). This review provides useful information and insights to researchers and practitioners who are interested in cybersecurity of IoT, including the current research of IoT cybersecurity, IoT cybersecurity architecture and taxonomy, key enabling countermeasures and strategies, major applications in industries, research trends and challenges.

A Fog-Based Application for Human Activity Recognition Using Personal Smart Devices

The diffusion of heterogeneous smart devices capable of capturing and analysing data about users, and/or the environment, has encouraged the growth of novel sensing methodologies. One of the most attractive scenarios in which such devices, such as smartphones, tablet computers, or activity trackers, can be exploited to infer relevant information is human activity recognition (HAR). Even though some simple HAR techniques can be directly implemented on mobile devices, in some cases, such as when complex activities need to be analysed timely, users’ smart devices can operate as part of a more complex architecture. In this article, we propose a multi-device HAR framework that exploits the fog computing paradigm to move heavy computation from the sensing layer to intermediate devices and then to the cloud. As compared to traditional cloud-based solutions, this choice allows to overcome processing and storage limitations of wearable devices while also reducing the overall bandwidth consumption. Experimental analysis aims to evaluate the performance of the entire platform in terms of accuracy of the recognition process while also highlighting the benefits it might bring in smart environments.

UAV Network and IoT in the Sky for Future Smart Cities

The paradigm of smart cities links the industry of telecommunications directly to sustainable economic growth and high quality of life. To meet this trend, this article develops a 5G IoT network with UAVs for future smart city architecture. We particularly emphasize IoT in the sky in a bid to unify the global world via heterogeneous smart devices through 3D connectivity. In our design, UAVs form a 5G hierarchical IoT network in the sky by connecting to a number of base stations on the ground. Simulation results reveal that the leader UAV has a significant impact on the performance of the whole system, and the proposed approach has an obvious advantage over the existing ones.

Gazing into the Crystal Ball: When the Future Internet Meets the Mobile Clouds

The latest advances in mobile devices and the widespread diffusion of networked objects are driving the evolution of traditional Mobile Cloud Computing (MCC) systems toward a new framework where storage, computing, sensing, and other device capabilities are offered as a service at the network edge. This visionary scenario, encompassing heterogeneous resources generated, shared, and consumed everywhere in the network, requires innovative architectural and protocol design. In this context, can the approaches recently formulated in the Future Internet research arena (e.g., middleware-based virtualization, Information Centric Networking, and Software-Defined Networking/Network Function Virtualization) support the evolution of mobile cloud systems? This paper provides an affirmative answer by proposing Future-MCC, a novel architecture that capitalizes on such promising approaches and re-thinks (when needed) their philosophy to better fit the evolution of MCC systems. The performance of Future-MCC has been investigated in a representative heterogeneous and dynamic Smart City scenario. Computer simulation results clearly demonstrate that the proposed solution ensures (i) a reduction of the bandwidth requirements spanning from 66 to 91 percent and (ii) an average energy saving equal to 99 percent with respect to a conventional cloud computing platform.