Zeitschriftenartikel zum Thema „IoT device provisioning“

Through the active development of industrial internet of things (IIoT) technology, there has been a rapid increase in the number of different industrial wireless sensor networks (IWSNs). Accordingly, the security of IWSNs is also of importance, as many security problems related to IWSN protocols have been raised and various studies have been conducted to solve these problems. However, the provisioning process is the first step in introducing a new device into the IIoT network and a starting point for IIoT security. Therefore, leakage of security information in the provisioning process makes exposure of secret keys and all subsequent security measures meaningless. In addition, using the exploited secret keys, the attacker can send false command to the node or send false data to the network manager and it can cause serious damage to industrial infrastructure depending on the IWSN. Nevertheless, a security study on the provisioning process has not been actively carried out, resulting in a provisioning process without guaranteed security. Therefore, in this paper, we analyzed security issues of the provisioning process in IWSN by researching prominent IWSN standards, including ISA 100.11a, WirelessHART, and Zigbee, and also an ISA 100.11a-certified device and provisioning process-related studies. Then, we verified the security issues of the provisioning process through testing and analyzing the provisioning process using the ISA 100.11a standard-implemented devices and ISA 100.11a-certified devices. Finally, we discuss security considerations and the direction of future research on provisioning security for IWSN in the IIoT era.

With the development of Internet of Things (IoT), large-scale of resources and applications atop them emerge. However, most of existing efforts are “silo” solutions, there is a tight-coupling between the device and the application. The paradigm for IoT and its corresponding infrastructure are required to move away from isolated solutions towards cooperative models. Recent works have focused on applying Service Oriented Architecture (SOA) to IoT service provisioning. Other than the traditional services of cyberspace which are oriented to a two-tuple problem domain, IoT services are faced with a three-tuple problem domain of user requirement, cyberspace and physical space. One challenge of existing works is lacking of efficient mechanism to on-demand provisioning the sensing information in a loosely-coupled, decentralized way and then dynamically coordinate the relevant services to rapidly respond to changes in the physical world. Another challenge is how to systematically and effectively access (plug) the heterogeneous devices without intrusive changing. This paper proposes a service provisioning platform which enables to access heterogeneous devices and expose device capabilities as light-weighted service, and presents an event-based message interaction mode to facilitate the asynchronous, on-demand sharing of sensing information in distributed, loosely-coupled IoT environment. It provides the basic infrastructure for IoT application pattern: inner-domain high-degree autonomy and inter-domain dynamic coordination. The practicability of platform is validated by experimental evaluations and a District Heating Control and Information System (DHCIS).

Although numerous consumer devices use Transmission Control Protocol/Internet Protocol (TCP/IP) protocol stack to connect and communicate over the Internet, their integration into a single Internet of Things (IoT) solution represents a challenge, primarily due to the lack of the standardized interoperability framework on the application layer. Devices produced by different manufacturers cannot operate together out of the box, thus raising the cost of system setup and maintenance. In this paper, a novel protocol is proposed that aims to bridge this gap. It is based on Message Queuing Telemetry Transport (MQTT) protocol and allows the seamless integration of different kinds of IP devices into the connected system. The proposed protocol is complete as it covers the aspects of device discovery and association in the IoT network. It provides mechanisms for IoT network maintenance and defines the abstract device model and communication patterns to enable system-wide device interoperability. The other goal of the protocol is to be portable to resource-constrained platforms. To validate the proposed protocol, it was integrated into the existing smart home hub, and for testing and validation purposes, prototype devices were developed.

The increasing pervasiveness of the Internet of Things is resulting in a steady increase of cyberattacks in all of its facets. One of the most predominant attack vectors is related to its identity management, as it grants the ability to impersonate and circumvent current trust mechanisms. Given that identity is paramount to every security mechanism, such as authentication and access control, any vulnerable identity management mechanism undermines any attempt to build secure systems. While digital certificates are one of the most prevalent ways to establish identity and perform authentication, their provision at scale remains open. This provisioning process is usually an arduous task that encompasses device configuration, including identity and key provisioning. Human configuration errors are often the source of many security and privacy issues, so this task should be semi-autonomous to minimize erroneous configurations during this process. In this paper, we propose an identity management (IdM) and authentication method called YubiAuthIoT. The overall provisioning has an average runtime of 1137.8 ms ±65.11+δ. We integrate this method with the FIWARE platform, as a way to provision and authenticate IoT devices.

Due to the rapid growth of the Internet of Things (IoT), applications such as the Augmented Reality (AR)/Virtual Reality (VR), higher resolution media stream, automatic vehicle driving, the smart environment and intelligent e-health applications, increasing demands for high data rates, high bandwidth, low latency, and the quality of services are increasing every day (QoS). The management of network resources for IoT service provisioning is a major issue in modern communication. A possible solution to this issue is the use of the integrated fiber-wireless (FiWi) access network. In addition, dynamic and efficient network configurations can be achieved through software-defined networking (SDN), an innovative and programmable networking architecture enabling machine learning (ML) to automate networks. This paper, we propose a machine learning supervised network traffic classification scheduling model in SDN enhanced-FiWi-IoT that can intelligently learn and guarantee traffic based on its QoS requirements (QoS-Mapping). We capture the different IoT and non-IoT device network traffic trace files based on the traffic flow and analyze the traffic traces to extract statistical attributes (port source and destination, IP address, etc.). We develop a robust IoT device classification process module framework, using these network-level attributes to classify IoT and non-IoT devices. We tested the proposed classification process module in 21 IoT/Non-IoT devices with different ML algorithms and the results showed that classification can achieve a Random Forest classifier with 99% accuracy as compared to other techniques.

Whether you consider “IoT” as a real thing or a buzzword, there’s no doubt that connected devices, data analysis and automation are transforming industry. CERN is no exception: a network of LoRa-based radiation monitors has recently been deployed and there is a growing interest in the advantages connected devices could bring—to accelerator operations just as much as to building management. Connected devices bring risks as well as advantages, however, and the last thing any business needs is an unsafe, uncoordinated and unmanaged sensor environment. To support the deployment of the LoRa-based radiation sensors, CERN has established both a Low Power Wireless Area Network (LPWAN) to complement the existing wired and wireless networks and a service infrastructure to manage the provisioning, orchestration, data transfer, security and operation for connected devices.

The rise of fast communication media both at the core and at the edge has resulted in unprecedented numbers of sophisticated and intelligent wireless IoT devices. Tactile Internet has enabled the interaction between humans and machines within their environment to achieve revolutionized solutions both on the move and in real-time. Many applications such as intelligent autonomous self-driving, smart agriculture and industrial solutions, and self-learning multimedia content filtering and sharing have become attainable through cooperative, distributed, and decentralized systems, namely, volunteer computing. This article introduces a blockchain-enabled resource sharing and service composition solution through volunteer computing. Device resource, computing, and intelligence capabilities are advertised in the environment to be made discoverable and available for sharing with the aid of blockchain technology. Incentives in the form of on-demand service availability are given to resource and service providers to ensure fair and balanced cooperative resource usage. Blockchains are formed whenever a service request is initiated with the aid of fog and mobile edge computing (MEC) devices to ensure secure communication and service delivery for the participants. Using both volunteer computing techniques and tactile internet architectures, we devise a fast and reliable service provisioning framework that relies on a reinforcement learning technique. Simulation results show that the proposed solution can achieve high reward distribution, increased number of blockchain formations, reduced delays, and balanced resource usage among participants, under the premise of high IoT device availability.

Fog computing is one of the promising technologies for realizing global-scale Internet of Things (IoT) applications as it allows moving compute and storage resources closer to IoT devices, where data is generated, in order to solve the limitations in cloud-based technologies such as communication delay, network load, energy consumption, and operational cost. However, this technology is still in its infancy stage containing essential research challenges. For instance, what is a suitable fog computing scheme where effective service provision models can be deployed is still an open question. This paper proposes a novel multitier fog computing architecture that supports IoT service provisioning. Concretely, a solid service placement mechanism that optimizes service decentralization on fog landscape leveraging context-aware information such as location, response time, and resource consumption of services has been devised. The proposed approach optimally utilizes virtual resources available on the network edges to improve the performance of IoT services in terms of response time, energy, and cost reduction. The experimental results from both simulated data and use cases from service deployments in real-world applications, namely, the intelligent transportation system (ITS) in Ho Chi Minh City, show the effectiveness of the proposed solution in terms of maximizing fog device utilization while reducing latency, energy consumption, network load, and operational cost. The results confirm the robustness of the proposed scheme revealing its capability to maximize the IoT potential.

The last few decades have seen a large proliferation in the prevalence of cyber-physical systems. This has been especially highlighted by the explosive growth in the number of Internet of Things (IoT) devices. Unfortunately, the increasing prevalence of these devices has begun to draw the attention of malicious entities which exploit them for their own gain. What makes these devices especially attractive is the various resource constraints present in these devices that make it difficult to add standard security features. Therefore, one intriguing research direction is creating security solutions out of already present components such as sensors. Physically Unclonable Functions (PUFs) are one potential solution that use intrinsic variations of the device manufacturing process for provisioning security. In this work, we propose a novel weak PUF design using thermistor temperature sensors. Our design uses the differences in resistance variation between thermistors in response to temperature change. To generate a PUF that is reliable across a range of temperatures, we use a response-generation algorithm that helps mitigate the effects of temperature variation on the thermistors. We tested the performance of our proposed design across a range of environmental operating conditions. From this we were able to evaluate the reliability of the proposed PUF with respect to variations in temperature and humidity. We also evaluated the PUF’s uniqueness using Monte Carlo simulations.

<span>Wireless sensor network (WSN) has attained wide adoption across various sectors and is considered to be key component of future real-time application such as BigData, Internet of things (IoT) etc. The modern application requires low latency and scalable real-time data access considering heterogeneous network. However, provisioning low latency real-time data access incurs energy overhead among sensor device. Clustering technique aided in providing scalability and minimizing energy consumption among sensor device. However, it incurs energy overhead among cluster head and sensor device closer to sink. To address, many optimization technique is been presented in recent time for optimal cluster selection. However, these technique are designed considering homogenous network. To address, this work presented Low Latency and Energy Efficient Routing (LLEER) design for heterogeneous WSN. The LLEER adopts multi-objective function such as</span><span>connectivity, connection time, radio signal strength, coverage time, and network traffic for cluster head and hop node selection. Experiment are conducted to evaluate LLEER design shows significant performance improvement over state-of-art model in terms of network lifetime considering total node death, first node death, and loss of connectivity, communication overhead, and packet transmission latency. Proposed LLEER brings a good trade-off between energy efficiency, and latency requirement of future real-time application.<span> </span></span>

Bedsores are one of the severe problems which could affect a long-term lying subject in the hospitals or the hospice. To prevent lying bedsores, we present a smart Internet of Things (IoT) system for detecting the position of a lying person using novel textile pressure sensors. To build such a system, it is necessary to use different technologies and techniques. We used sixty-four of our novel textile pressure sensors based on electrically conductive yarn and the Velostat to collect the information about the pressure distribution of the lying person. Using Message Queuing Telemetry Transport (MQTT) protocol and Arduino-based hardware, we send measured data to the server. On the server side, there is a Node-RED application responsible for data collection, evaluation, and provisioning. We are using a neural network to classify the subject lying posture on the separate device because of the computation complexity. We created the challenging dataset from the observation of twenty-one people in four lying positions. We achieved a best classification precision of 92% for fourth class (right side posture type). On the other hand, the best recall (91%) for first class (supine posture type) was obtained. The best F1 score (84%) was achieved for first class (supine posture type). After the classification, we send the information to the staff desktop application. The application reminds employees when it is necessary to change the lying position of individual subjects and thus prevent bedsores.

Internet of things (IoT) has developed into an interconnected platform infrastructure for providing everyday services. Emerging end-to-end IoT services are being developed for local and multiple distributed regions. To realize the on-demand services in a timely and economically beneficial way, programmability and reusability are crucial for provisioning and reusing IoT resources. Existing IoT platforms are rigid and cannot be easily adapted to accommodate new services. This paper proposes a programmable large-scale software-defined IoT model for provisioning IoT services on demand with two levels of management and orchestration. One orchestrates services over geographically distributed clusters and the other orchestrates services over IoT devices within a cluster. The model entails the design of IoT-specific controllers, software-defined virtual sensors, and a new protocol for managing resource-constrained but enriched devices. The model allows provisioning and resource-sharing of end-to-end IoT services on demand. Implementation results demonstrate the feasibility and efficiency of the proposed model.

The new era of technology is a rollback in which massive information comes from interconnected devices. These types of things that have Internet connectivity are called the Internet of Things (IoT). IoT and its applications have enabled novel service provisioning, whereas at the same time it has raised complex challenges of managing such network. Resource limitation such as computational power and energy limitation defines the characteristics of IoT that require a novel design of management framework. Designing a management framework that meets the IoT requirements is a daunting task. Recently, Software-Defined Networking (SDN) paradigm has eased the management of traditional Internet by provisioning centralized view of the network. This makes SDN a potential candidate for managing IoT. However, traditional design of the SDN paradigm needs to be redefined to adapt it for the IoT. Hence, in this paper, we have discussed trends and research of management architecture for IoT based on Software-Defined Networking principles. In addition, we discuss non-SDN-based approaches. In this paper, we compare all of the efforts in the last five years in addressing issues of IoT management that involves security service provisioning, fault tolerance, energy management, and load balancing. We also discuss future research directions that evolved from our detailed survey and taxonomy of the Software-Defined IoT (SDIoT) management frameworks.

This demo will show the important thing capabilities of a Platform as-a-provider (PaaS) we've proposed in a research paper widely wide-spread for presentation at IEEE LANMAN 2016 conference. The proposed PaaS allows IoT packages provisioning in hybrid cloud/fog environments. Two desires are assigned to the demo. On the one hand, we will highlight how IoT programs may be provisioned in such environments. However, we will display concretely the benefits those hybrid environments have over conventional cloud environments. The provisioning in these hybrid environments permits latency discount and processing performance enhancement. Certainly, a good way to lessen latency, the programs can have a number of its additives strolling in cloud and interacting with the other additives going for walks within the fog, toward IoT devices.

The wide-spread Internet of Things (IoT) utilization in almost every scope of our life made it possible to automate daily life tasks with no human intervention. This promising technology has immense potential for making life much easier and open new opportunities for newly developed applications to emerge. However, meeting the diverse Quality of Service (QoS) demands of different applications remains a formidable topic due to diverse traffic patterns, unpredictable network traffic, and resource-limited nature of IoT devices. In this context, application-tailored QoS provisioning mechanisms have been the primary focus of academic research. This paper presents a literature review on QoS techniques developed in academia for IoT applications and investigates current research trends. Background knowledge on IoT, QoS metrics, and critical enabling technologies will be given beforehand, delving into the literature review. According to the comparison presented in this work, the commonly considered QoS metrics are Latency, Reliability, Throughput, and Network Usage. The reviewed studies considered the metrics that fit their provisioning solutions.

The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence. A network-wide flexible control and management are missing in IoT networks. In addition, huge numbers of devices and large amounts of data are involved in IoT, but none of them have been tuned for supporting network management and control. In this paper, we argue that Software-defined Networking (SDN) together with the data generated by IoT applications can enhance the control and management of IoT in terms of flexibility and intelligence. We present a review for the evolution of SDN and IoT and analyze the benefits and challenges brought by the integration of SDN and IoT with the help of IoT data. We discuss the perspectives of knowledge-driven SDN for IoT through a new IoT architecture and illustrate how to realize Industry IoT by using the architecture. We also highlight the challenges and future research works toward realizing IoT with the knowledge-driven SDN.

The Internet of Things (IoT) is a promising paradigm in which the objects are fused with the capabilities of sensing, networking, identifying and processing to make it feasible for them to communicate with one another and with the other dedicated devices over the Internet to acquire some goals. Wireless Sensor Network (WSN)-based IoT has proliferated its manifold applications in almost every sector of human lives. Quality of Service (QoS) provisioning in WSN-based IoT still requires significant research attention. In this paper, QoS provisioning-based routing protocols are proposed that use multiple data sinks in WSN-based IoT. The proposed protocols are named as Optimized-Energy and Threshold Sensitive Stable Election Protocol (O-ETSSEP) and Multiple data Sinks-based Optimized-ETSSEP (MSO-ETSSEP). The Cluster Head (CH) selection is optimized by incorporating distance, residual energy, node density and energy threshold factors in both protocols. Both protocols use three levels of energy heterogeneity for the energy balancing in the network. MSO-ETSSEP employs multiple (four) data sinks around each periphery of the square-shaped network to mitigate the hot-spot problem by circumventing multi-hop communication. The performance evaluation of these protocols is done based on the QoS provisioning performance metrics. The simulation results obtained confirm the superiority of the proposed protocols over the state-of-the-art protocols.

With the exponential growth of the Internet of Things (IoT) devices connected to the internet, resource provisioning for such the heterogeneous network is a challenging task for the traditional network architecture. In this context, the Software-Defined Networking (SDN) introduces many opportunities and provides the potential to overcome challenges associated with traditional network architecture. This work presents a Software-Defined IoT (SDIoT) architecture. The main focus of this research is to design a control plane (CP) for the SDIoT. The scope of this work is limited to the introduction of an overlay SDN CP in the traditional IoT network architecture. The proposed architecture focuses on resource provisioning while ensuring the quality of service (QoS) satisfaction for the network. A comparative analysis between the traditional and the SDN network approach was done in terms of Jitter, Latency and Throughput. From the latency, delay and throughput performance results, the SDN-based IoT network improves network efficiency by reducing network overheads generated from frequent communication between the nodes and the controllers. Precisely, the average latency and average jitter percentile improvement from the traditional IoT network to the SDIoT for all the nodes is 574% and 600% respectively. Also, an overall throughput improvement is recorded for the SDIoT when compared to traditional IoT network for all the nodes.

Internet of Things (IoT) enables inters connectivity among devices and platforms. IoT devices such as sensors, or embedded systems offer computational, storage, and networking resources and the existence of these resources permits to move the execution of IoT applications to the edge of the network and it is known as fog computing. It is able to handle billions of Internet-connected devices and is well situated for real-time big data analytics and provides advantages in advertising and personal computing. The main issues in fog computing includes fog networking, QoS, interfacing and programming model, computation offloading, accounting, billing and monitoring, provisioning and resource management, security and privacy. A particular research challenge is the Quality of Service metric for fog services. Thus, this paper gives a survey of cloud computing, discusses the QoS metrics, and the future research directions in fog computing.

In recent years, we observed the proliferation of cloud data centers (CDCs) and the Internet of Things (IoT). Cloud computing based on CDCs has the drawback of unpredictable response times due to variant delays between service requestors (IoT devices and end devices) and CDCs. This deficiency of cloud computing is especially problematic in providing IoT services with strict timing requirements and as a result, gives birth to fog/edge computing (FEC) whose responsiveness is achieved by placing service images near service requestors. In FEC, the computing nodes located close to service requestors are called fog/edge nodes (FENs). In addition, for an FEN to execute a specific service, it has to be provisioned with the corresponding service image. Most of the previous work on the service provisioning in the FEC environment deals with determining an appropriate FEN satisfying the requirements like delay, CPU and storage from the perspective of one or more service requests. In this paper, we determined how to optimally place service images in consideration of the pre-obtained service demands which may be collected during the prior time interval. The proposed FEC environment is scalable in the sense that the resources of FENs are effectively utilized thanks to the optimal provisioning of services on FENs. We propose two approaches to provision service images on FENs. In order to validate the performance of the proposed mechanisms, intensive simulations were carried out for various service demand scenarios.

Internet of Things (IoT) has emerged as a computing paradigm to develop smart applications such e-health care systems, smart city, smart waste management systems, etc. It contains a large number of different devices and heterogeneous networks, which make it difficult to provide secure and fast response to the end user. To provide the faster response services, there is a need to use the concept of Fog computing Recently, the use of fog computing is a rapidly increasing in many industries for the development of applications such as manufacturing, e-health, oil and gas, As more and more users have started to store/process their real-time data in Fog-based Cloud environments, resource provisioning and scheduling of IoT based applications becomes a key element of consideration for efficient execution of these applications. This article will help to select the most suitable technique for processing smart IoT based applications in Fog computing environments.

The number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. Machine Learning (ML) plays a pivotal role in QoS enhancement, connectivity, and provisioning of smart applications. Therefore, this survey focuses on the use of ML for enhancing IoT applications. We also provide an in-depth overview of the variety of IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare. For each application, we introduce the advantages of using ML. Finally, we shed light on ML challenges for future IoT research, and we review the current literature based on existing works.

The emerging network computing technologies have significantly extended the abilities of the resource-constrained IoT devices through the network-based service sharing techniques. However, such a flexible and scalable service provisioning paradigm brings increased security risks to terminals due to the untrustworthy exogenous service codes loading from the open network. Many existing security approaches are unsuitable for IoT environments due to the high difficulty of maintenance or the dependencies upon extra resources like specific hardware. Fortunately, the rise of blockchain technology has facilitated the development of service sharing methods and, at the same time, it appears a viable solution to numerous security problems. In this paper, we propose a novel blockchain-based secure service provisioning mechanism for protecting lightweight clients from insecure services in network computing scenarios. We introduce the blockchain to maintain all the validity states of the off-chain services and edge service providers for the IoT terminals to help them get rid of untrusted or discarded services through provider identification and service verification. In addition, we take advantage of smart contracts which can be triggered by the lightweight clients to help them check the validities of service providers and service codes according to the on-chain transactions, thereby reducing the direct overhead on the IoT devices. Moreover, the adoptions of the consortium blockchain and the proof of authority consensus mechanism also help to achieve a high throughput. The theoretical security analysis and evaluation results show that our approach helps the lightweight clients get rid of untrusted edge service providers and insecure services effectively with acceptable latency and affordable costs.

Providing Internet of Things (IoT) environments with service level guarantee is a challenging task for improving IoT application usage experience. We specify in this paper an IoT architecture enabling an IoT Service Level Agreement (iSLA) achievement between an IoT Service Provider (IoT-SP) and an IoT Client (IoT-C). In order to guarantee the IoT applications’ requirements, Quality of Service (QoS) mechanisms should be implemented within all the layers of the IoT architecture. Thus, we propose a specific mechanism for the lowest layer of our service level based IoT architecture (i.e., sensing layer). It is an adaptation of the IEEE 802.15.4 slotted CSMA/CA mechanism enabling to take into consideration the requirements of real-time IoT services. Our access method called QBAIoT (QoS based Access for IoT) extends IEEE 802.15.4 systems by creating a new contention access period for each specified traffic class in the iSLA. Furthermore, due to the huge number of IoT connected devices, self-configuring capability provisioning is necessary for limiting human intervention and total cost of ownership (TCO). Thus, we integrate a self-configuring capability to the QBAIoT access method by implementing the MAPE-K closed control loop within the IoT High Level Gateway (HL-Gw) of our proposed QoS based IoT architecture.

A huge amount of smart devices which have capacity of computing, storage, and communication to each other brings forth fog computing paradigm. Fog computing is a model in which the system tries to push data processing from cloud servers to “near” IoT devices in order to reduce latency time. The execution orderings and the deployed places of services make significant effect on the overall response time of an application. Beside new research directions in fog computing, e.g., fog-cloud collaboration, service scalability, fog scalability, mobile fog computing, fog federation, trade-off between energy consumption and communication efficiency, duration of storing data locally, storage security and communication security, and semantic-aware fog computing, the service deployment problem is one of the attractive research fields of fog computing. The service deployment is a multiobjective optimization problem; there are so many proposed solutions for various targets, such as response time, communication cost, and energy consumption. In this paper, we focus on the optimization problem which minimizes the overall response time of an application with awareness of network usage and server usage. Then, we have conducted experiments on two service deployment strategies, called cloudy and foggy strategies. We analyze numerically the overall response time, network usage, and server usage of those two strategies in order to prove the effectiveness of our proposed foggy service deployment strategy.

Realization of Internet of Things (IoT) has revolutionized the scope of connectivity and reachability ubiquitously. Under the umbrella of IoT, every object which is smart enough to communicate with other object leads to the enormous data generation of varying sizes and nature. Cloud computing (CC) employs centralized data centres for the provisioning of remote services and resources. However, for the reason of being far away from client devices, CC has their own limitations especially for time and resource critical applications. The remote and centralized characteristics of CC often result in creating bottle necks, being latent, and hence deteriorate the quality of service (QoS) in the provisioning of services. Here, the concept of fog computing (FC) emerges that tends to leverage CC and end devices for data congestion and processing locally in a distributed and decentralized way. However, addressing latency and bottleneck issues for time critical applications are still challenging. In this work, a lightweight framework is proposed which employs the concept of fog head node that keeps track of other fog nodes in terms of user registrations and location awareness. The proposed lightweight location-aware fog framework (LAFF) persistently satisfies QoS by providing an accurate location-aware algorithm. A comparative analysis is also presented to analyse network usage, service time, latency, and RAM and CPU utilization. The comparison results depicts that the LAFF reduces latency, network use, and service time by 11.01%, 7.51%, and 14.8%, respectively, in contrast to the state-of-the-art frameworks. Moreover, considering RAM and CPU utilization, the proposed framework supersedes IFAM and TPFC targeting IoT applications. The RAM consumption and CPU utilization are reduced by 8.41% and 16.23% as compared with IFAM and TPFC, respectively, making the framework lightweight. Hence, the proposed LAFF improves QoS while accessing remote computational servers for the outsourced applications in fog computing.

Smart cities use the Internet of Things (IoT) devices such as connected sensors, lights, and meters to collect and analyze data to improve infrastructure, public utilities, and services. However, the true potential of smart cities cannot be leveraged without addressing many security concerns. In particular, there is a significant challenge for provisioning a reliable access control solution to share IoT data among various users across organizations. We present a novel entitlement-based blockchain-enabled access control architecture that can be used for smart cities (and for any ap-plication domains that require large-scale IoT deployments). Our proposed entitlement-based access control model is flexible as it facilitates a resource owner to safely delegate access rights to any entities beyond the trust boundary of an organization. The detailed design and implementation on Ethereum blockchain along with a qualitative evaluation of the security and access control aspects of the proposed scheme are presented in the paper. The experimental results from private Ethereum test networks demonstrate that our proposal can be easily implemented with low latency. This validates that our proposal is applicable to use in the real world IoT environments.

This article describes how cloud computing has emerged as a strong competitor against traditional IT platforms by offering low-cost and “pay-as-you-go” computing potential and on-demand provisioning of services. Governments, as well as organizations, have migrated their entire or most of the IT infrastructure to the cloud. With the emergence of IoT devices and big data, the amount of data forwarded to the cloud has increased to a huge extent. Therefore, the paradigm of cloud computing is no longer sufficient. Furthermore, with the growth of demand for IoT solutions in organizations, it has become essential to process data quickly, substantially and on-site. Hence, Fog computing is introduced to overcome these drawbacks of cloud computing by bringing intelligence to the edge of the network using smart devices. One major security issue related to the cloud is the DDoS attack. This article discusses in detail about the DDoS attack, cloud computing, fog computing, how DDoS affect cloud environment and how fog computing can be used in a cloud environment to solve a variety of problems.

The advancements in the technologies and the increase in the digital miniaturization day by day are causing devices to become smarter and smarter and the emergence of the internet of things and the cloud has made things even better with insightful suggestions for organization as well as the way the people work and lead their life. The limitations in the cloud paradigm in terms of processing complexity, the latency in the service provisioning and improper resource scheduling, remains as a reason leading to shifting of applications from cloud to edge. More over the emergence of the artificial intelligence in the edge computing has turned out to be center of attention as it improves the speed and the range of the IOT applications. The paper also puts forth the design of the AI-enabled Edge computing for developing a Smart Farming.

Edge computing (EC) is a promising paradigm for providing ultra-low latency experience for IoT applications at the network edge, through pre-caching required services in fixed edge nodes. However, the supply-demand mismatch can arise while meeting the peak period of some specific service requests. The mismatch between capacity provision and user demands can be fatal to the delay-sensitive user requests of emerging IoT applications and will be further exacerbated due to the long service provisioning cycle. To tackle this problem, we propose the mobile-assisted edge computing framework to improve the QoS of fixed edge nodes by exploiting mobile edge nodes. Furthermore, we devise a CRI (Credible, Reciprocal, and Incentive) auction mechanism to stimulate mobile edge nodes to participate in the services for user requests. The advantages of our mobile-assisted edge computing framework include higher task completion rate, profit maximization, and computational efficiency. Meanwhile, the theoretical analysis and experimental results guarantee the desirable economic properties of our CRI auction mechanism.

The Internet of Things (IoT) is an emerging technology that aims to enable the interconnection of a large number of smart devices and heterogeneous networks. Ad hoc networks play an important role in the designing of IoT-enabled platforms due to their efficient, flexible, low-cost and dynamic infrastructures. These networks utilize the available resources efficiently to maintain the Quality of Service (QoS) in a multi-hop communication. However, in a multi-hop communication, the relay nodes can be malicious, thus requiring a secured and reliable data transmission. In this paper, we propose a QoS-aware secured communication scheme for IoT-based networks (QoS-IoT). In QoS-IoT, a Sybil attack detection mechanism is used for the identification of Sybil nodes and their forged identities in multi-hop communication. After Sybil nodes detection, an optimal contention window (CW) is selected for QoS provisioning, that is, to achieve per-flow fairness and efficient utilization of the available bandwidth. In a multi-hop communication, the medium access control (MAC) layer protocols do not perform well in terms of fairness and throughput, especially when the nodes generate a large amount of data. It is because the MAC layer has no capability of providing QoS to prioritized or forwarding flows. We evaluate the performance of QoS-IoT in terms of Sybil attack detection, fairness, throughput and buffer utilization. The simulation results show that the proposed scheme outperforms the existing schemes and significantly enhances the performance of the network with a large volume of data. Moreover, the proposed scheme is resilient against Sybil attack.

Processing IoT applications directly in the cloud may not be the most efficient solution for each IoT scenario, especially for time-sensitive applications. A promising alternative is to use fog and edge computing, which address the issue of managing the large data bandwidth needed by end devices. These paradigms impose to process the large amounts of generated data close to the data sources rather than in the cloud. One of the considerations of cloud-based IoT environments is resource management, which typically revolves around resource allocation, workload balance, resource provisioning, task scheduling, and QoS to achieve performance improvements. In this paper, we review resource management techniques that can be applied for cloud, fog, and edge computing. The goal of this review is to provide an evaluation framework of metrics for resource management algorithms aiming at the cloud/fog and edge environments. To this end, we first address research challenges on resource management techniques in that domain. Consequently, we classify current research contributions to support in conducting an evaluation framework. One of the main contributions is an overview and analysis of research papers addressing resource management techniques. Concluding, this review highlights opportunities of using resource management techniques within the cloud/fog/edge paradigm. This practice is still at early development and barriers need to be overcome.

Due to centralized nature for cloud computing and some other reasons, high mobility cannot be supported and low latency requirements for some applications such as Internet of Things (IoT) that require real time and mobility support. To satisfy such requirements new technologies, fog computing is a good solution, where we use edges of network for service provisioning instead of far datacenters allocated in clouds. Low latency response is the most attractive property for fog computing, which is very suitable for IoT multi-billion devices, sensors and actuators generates huge amount of data that need processing and analysis for smart decision generation. The main objective of this article is to show the super ability of fog computing over cloud-only computing. The authors present a patient monitoring system as a case study for simulation; they evaluated the performance of the system using: latency, network usage, power consumption, cost of execution and simulation execution time performance metrics. The results show that the Fog computing is superior over Cloud-only paradigm in all performance measurements.

Recently, multi-access edge computing (MEC) is a promising paradigm to offer resource-intensive and latency-sensitive services for IoT devices by pushing computing functionalities away from the core cloud to the edge of networks. Most existing research has focused on effectively improving the use of computing resources for computation offloading while neglecting non-trivial amounts of data, which need to be pre-stored to enable service execution (e.g., virtual/augmented reality, video analytics, etc.). In this paper, we, therefore, investigate service provisioning in MEC consisting of two sub-problems: (i) service placement determining services to be placed in each MEC node under its storage capacity constraint, and (ii) request scheduling determining where to schedule each request considering network delay and computation limitation of each MEC node. The main objective is proposed to ensure the quality of experience (QoE) of users, which is also yet to be studied extensively. A utility function modeling user perception of service latency is used to evaluate QoE. We formulate the problem of service provisioning in MEC as an Integer Nonlinear Programming (INLP), aiming at maximizing the total utility of all users. We then propose a Nested-Genetic Algorithm (Nested-GA) consisting of two genetic algorithms, each of whom solves a sub-problem regarding service placement or request scheduling decisions. Finally, simulation results demonstrate that our proposal outperforms conventional methods in terms of the total utility and achieves close-to-optimal solutions.

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.

Application placement in the fog environment is becoming one of the major challenges because of its distributed, hierarchical, and heterogeneous nature. Also, user expectations and various features of IoT devices further increase the complexity of the problem for the placement of applications in the fog computing environment. Therefore, to improve the QoE of various end-users for the use of various system services, proper placement of applications in the fog computing environment plays an important role. In this paper, the authors have proposed a service placement methodology for the fog computing environment. For a better selection of application services, AHP technique has been used which provides results in the form of ranks. The performance evaluation of the proposed technique has been done by using a customized testbed that considers the parameters like CPU cycle, storage, maximum latency, processing speed, and network bandwidth. Experimental results obtained for the proposed methodology improved the efficiency of the fog network.

A common design of the Internet of Things (IoT) system relies on distant Cloud for management and processing, which faces the challenge of latency, especially when the application requires rapid response in the edge network. Therefore, researchers have proposed the Fog computing architecture, which distributes the computational data processing tasks to the edge network nodes located in the vicinity of data sources and end-users to reduce the latency. Although the Fog computing architecture is promising, it still faces a challenge in mobility when the tasks come from ubiquitous mobile applications in which the data sources are moving objects. In order to address the challenge, this article proposes a proactive Fog service provisioning framework, which hastens the task distribution process in Mobile Fog use cases. Further, the proposed framework provides an optimization scheme in task allocation based on runtime context information. A proof-of-concept prototype has been implemented and tested on real devices.

This paper presents cooperative transmission (CT), where multiple relays are used to achieve array and diversity gains, as an enabling technology for Internet of Things (IoT) networks with hardware-limited devices. We investigate a channel coding aided decode-and-forward (DF) relaying network, considering a two-hop multiple-relay network, where the data transmission between the source and the destination is realized with the help of DF relays. Low density parity check (LDPC) codes are adopted as forward error correction (FEC) codes to encode and decode the data both at the source and relays. We consider both fixed and variable code rates depending upon the quality-of-service (QoS) provisioning such as spectral efficiency and maximum energy efficiency. Furthermore, an optimal power allocation scheme is studied for the cooperative system under the energy efficiency constraint. We present the simulation results of our proposed scheme, compared with conventional methods, which show that if decoupled code rates are used on both hops then a trade-off has to be maintained between system complexity, transmission delay, and bit error rate (BER).

The increasing number of Internet of Things (IoT) devices with various sensors has resulted in a focus on Cloud-based sensing-as-a-service (CSaaS) as a new value-added service, for example, providing temperature-sensing data via a cloud computing system. However, the industry encounters various challenges in the dynamic provisioning of on-demand CSaaS on diverse sensor networks. We require a system that will provide users with standardized access to various sensor networks and a level of abstraction that hides the underlying complexity. In this study, we aim to develop a cloud-based solution to address the challenges mentioned earlier. Our solution, SenseCloud, includes asensor virtualizationmechanism that interfaces with diverse sensor networks, amultitenancymechanism that grants multiple users access to virtualized sensor networks while sharing the same underlying infrastructure, and adynamic provisioningmechanism to allow the users to leverage the vast pool of resources on demand and on a pay-per-use basis. We implement a prototype of SenseCloud by using real sensors and verify the feasibility of our system and its performance. SenseCloud bridges the gap between sensor providers and sensor data consumers who wish to utilize sensor data.

The rapid proliferation of low-power wireless devices enables the industrial users to improve the productivity and safety of the plants as well as efficient management of the system. This can be achieved through significant increase in data collection, remote monitoring, and control of the plants and promoting the development of industrial Internet of Things (IoT) applications. However, the industrial environment is typically harsh causing high link quality variations and topology changes. The wireless devices used in this environment are also resource constrained in terms of energy, memory, and processing power. In spite of their low-power and lossy nature, these networks demand provisioning of differentiated services for various industrial applications having diverse quality of service (QoS) requirements. Considering the unique characteristics of low-power and lossy networks (LLN), routing for low-power and lossy networks (RPL) is devised which was standardized by IETF in 2012. To meet the demand of diverse traffic, RPL supports multiple instances in a single network. This paper proposes MI-RPL, a multi-instance solution of RPL for industrial low-power and lossy networks (LLNs). MI-RPL defines four instances for four distinct traffic classes of industrial monitoring applications in terms of delay and reliability. MI-RPL also introduces composite routing metrics and proposes an objective function (OF) to compute the most suitable path for each instance. The performance of MI-RPL is investigated through simulations that exhibit MI-RPL has better delay and packet delivery performance for delay- and reliability-constrained traffic along with lower energy consumption compared to the standard RPL.

The authors present their experience and practices of introducing cloud services, as a means to simplify the adoption of ICT (Information Communication and Technology) in education, using Free/Open Source Software. The solution creates a hybrid cloud infrastructure, in order to provide a pre-installed (Ubuntu and Linux Terminal Server Project) virtual machine, acting as a server inside the school, providing desktop environment based on the Software as a Service cloud model, where legacy PCs act as stateless devices. Classroom management is accomplished using the application “Epoptes.” To minimize administration tasks, educational software is provided accordingly, either on-line or through repositories to automate software installation (including patches and updates). The advantages of the hybrid cloud implementation, include services that are not completely dependent on broadband connections’ state, minimal cost, reusability of obsolete equipment, ease of administration, centralized management, patches and educational software provisioning and, above all, facilitation of the educational procedure.

Optical access networks, metro networks and mobile data networks are facing rapidly evolving demands, not only is it essential to satisfy the unyielding need for increased user bandwidths, but future networks must also support the growing wide variation in traffic dynamics and characteristics, due to various emerging technologies, such as cloud-based services, the Internet-of-Things (IoT) and 5G mobile systems, and due to growing trends, such as the proliferation of mobile devices and the rapidly increasing popularity of video-on-demand services. To be cost-effective and commercially sustainable, future optical networks must offer features, such as, dynamic reconfigurability, highly efficient use of network resources, elastic bandwidth provisioning with fine granularity, network sliceabilty and software defined networking (SDN). To meet these requirements Cloud Access Networks (CANs) are proposed which require a number of flexible, adaptive and reconfigurable networking elements. By exploiting digital signal processing (DSP) we have proposed a digital orthogonal filter-based multiplexing technique to implement CANs with multiplexed, independent optical channels at the wavelength, sub-wavelength, and orthogonal sub-band levels. This paper reviews the overall CAN concept, the operating principles of the various CAN network elements and presents an overview of the research work we have undertaken in order to validate the feasibility of the proposed technologies which includes real-time DSP-based demonstrations.

The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e., smart transportation, smart community, smart healthcare, smart grid, etc. which are integrated within smart city architecture aims to enrich the quality of life (QoL) of urban citizens. However, real-time processing requirements and exponential data growth withhold smart city realization. Therefore, herein we propose a Big Data analytics (BDA)-embedded experimental architecture for smart cities. Two major aspects are served by the BDA-embedded smart city. Firstly, it facilitates exploitation of urban Big Data (UBD) in planning, designing, and maintaining smart cities. Secondly, it occupies BDA to manage and process voluminous UBD to enhance the quality of urban services. Three tiers of the proposed architecture are liable for data aggregation, real-time data management, and service provisioning. Moreover, offline and online data processing tasks are further expedited by integrating data normalizing and data filtering techniques to the proposed work. By analyzing authenticated datasets, we obtained the threshold values required for urban planning and city operation management. Performance metrics in terms of online and offline data processing for the proposed dual-node Hadoop cluster is obtained using aforementioned authentic datasets. Throughput and processing time analysis performed with regard to existing works guarantee the performance superiority of the proposed work. Hence, we can claim the applicability and reliability of implementing proposed BDA-embedded smart city architecture in the real world.

The past decades witnessed the fast and wide deployment of Internet. The Internet has bred the ubiquitous computing environment that is spanning the cloud, edge, mobile devices, and IoT. Software running over such a ubiquitous computing environment environment is eating the world. A recently emerging trend of Internet-based software systems is “ resource adaptive ,” i.e., software systems should be robust and intelligent enough to the changes of heterogeneous resources, both physical and logical, provided by their running environment. To keep pace of such a trend, we argue that some considerations should be taken into account for the future operating system design and implementation. From the structural perspective, rather than the “monolithic OS” that manages the aggregated resources on the single machine, the OS should be dynamically composed over the distributed resources and flexibly adapt to the resource and environment changes. Meanwhile, the OS should leverage advanced machine/deep learning techniques to derive configurations and policies and automatically learn to tune itself and schedule resources. This article envisions our recent thinking of the new OS abstraction, namely, ServiceOS , for future resource-adaptive intelligent software systems. The idea of ServiceOS is inspired by the delivery model of “ Software-as-a-Service ” that is supported by the Service-Oriented Architecture (SOA). The key principle of ServiceOS is based on resource disaggregation, resource provisioning as a service, and learning-based resource scheduling and allocation. The major goal of this article is not providing an immediately deployable OS. Instead, we aim to summarize the challenges and potentially promising opportunities and try to provide some practical implications for researchers and practitioners.

Nowadays, centralized energy grid systems are transitioning towards more decentralized systems driven by the need for efficient local integration of new deployed small scale renewable energy sources. The high limits for accessing the energy markets and also for the delivery of ancillary services act as a barrier for small scale prosumers participation forcing the implementation of new cooperative business models at the local level. This paper is proposing a fog computing infrastructure for the local management of energy systems and the creation of coalitions of prosumers able to provide ancillary services to the grid. It features an edge devices layer for energy monitoring of individual prosumers, a fog layer providing Information and Communication Technologies (ICT) techniques for managing local energy systems by implementing cooperative models, and a cloud layer where the service specific technical requirements are defined. On top, a model has been defined allowing the dynamical construction of coalitions of prosumers as Virtual Power Plants at the fog layer for the provisioning of frequency restoration reserve services while considering both the prosumers' local constraints and the service ones as well as the constituents’ profit maximization. Simulation results show our solution effectiveness in selecting the optimal coalition of prosumers to reliably deliver the service meeting the technical constraints while featuring a low time and computation overhead being feasible to be run closer to the edge.

Recent Trends and Future of Fog and Edge Computing, Services, and Enabling Technologies Cloud computing has been established as the most popular as well as suitable computing infrastructure providing on-demand, scalable and pay-as-you-go computing resources and services for the state-of-the-art ICT applications which generate a massive amount of data. Though Cloud is certainly the most fitting solution for most of the applications with respect to processing capability and storage, it may not be so for the real-time applications. The main problem with Cloud is the latency as the Cloud data centres typically are very far from the data sources as well as the data consumers. This latency is ok with the application domains such as enterprise or web applications, but not for the modern Internet of Things (IoT)-based pervasive and ubiquitous application domains such as autonomous vehicle, smart and pervasive healthcare, real-time traffic monitoring, unmanned aerial vehicles, smart building, smart city, smart manufacturing, cognitive IoT, and so on. The prerequisite for these types of application is that the latency between the data generation and consumption should be minimal. For that, the generated data need to be processed locally, instead of sending to the Cloud. This approach is known as Edge computing where the data processing is done at the network edge in the edge devices such as set-top boxes, access points, routers, switches, base stations etc. which are typically located at the edge of the network. These devices are increasingly being incorporated with significant computing and storage capacity to cater to the need for local Big Data processing. The enabling of Edge computing can be attributed to the Emerging network technologies, such as 4G and cognitive radios, high-speed wireless networks, and energy-efficient sophisticated sensors. Different Edge computing architectures are proposed (e.g., Fog computing, mobile edge computing (MEC), cloudlets, etc.). All of these enable the IoT and sensor data to be processed closer to the data sources. But, among them, Fog computing, a Cisco initiative, has attracted the most attention of people from both academia and corporate and has been emerged as a new computing-infrastructural paradigm in recent years. Though Fog computing has been proposed as a different computing architecture than Cloud, it is not meant to replace the Cloud. Rather, Fog computing extends the Cloud services to network edges for providing computation, networking, and storage services between end devices and data centres. Ideally, Fog nodes (edge devices) are supposed to pre-process the data, serve the need of the associated applications preliminarily, and forward the data to the Cloud if the data are needed to be stored and analysed further. Fog computing enhances the benefits from smart devices operational not only in network perimeter but also under cloud servers. Fog-enabled services can be deployed anywhere in the network, and with these services provisioning and management, huge potential can be visualized to enhance intelligence within computing networks to realize context-awareness, high response time, and network traffic offloading. Several possibilities of Fog computing are already established. For example, sustainable smart cities, smart grid, smart logistics, environment monitoring, video surveillance, etc. To design and implementation of Fog computing systems, various challenges concerning system design and implementation, computing and communication, system architecture and integration, application-based implementations, fault tolerance, designing efficient algorithms and protocols, availability and reliability, security and privacy, energy-efficiency and sustainability, etc. are needed to be addressed. Also, to make Fog compatible with Cloud several factors such as Fog and Cloud system integration, service collaboration between Fog and Cloud, workload balance between Fog and Cloud, and so on need to be taken care of. It is our great privilege to present before you Volume 20, Issue 2 of the Scalable Computing: Practice and Experience. We had received 20 Research Papers and out of which 14 Papers are selected for Publication. The aim of this special issue is to highlight Recent Trends and Future of Fog and Edge Computing, Services and Enabling technologies. The special issue will present new dimensions of research to researchers and industry professionals with regard to Fog Computing, Cloud Computing and Edge Computing. Sujata Dash et al. contributed a paper titled “Edge and Fog Computing in Healthcare- A Review” in which an in-depth review of fog and mist computing in the area of health care informatics is analysed, classified and discussed. The review presented in this paper is primarily focussed on three main aspects: The requirements of IoT based healthcare model and the description of services provided by fog computing to address then. The architecture of an IoT based health care system embedding fog computing layer and implementation of fog computing layer services along with performance and advantages. In addition to this, the researchers have highlighted the trade-off when allocating computational task to the level of network and also elaborated various challenges and security issues of fog and edge computing related to healthcare applications. Parminder Singh et al. in the paper titled “Triangulation Resource Provisioning for Web Applications in Cloud Computing: A Profit-Aware” proposed a novel triangulation resource provisioning (TRP) technique with a profit-aware surplus VM selection policy to ensure fair resource utilization in hourly billing cycle while giving the quality of service to end-users. The proposed technique use time series workload forecasting, CPU utilization and response time in the analysis phase. The proposed technique is tested using CloudSim simulator and R language is used to implement prediction model on ClarkNet weblog. The proposed approach is compared with two baseline approaches i.e. Cost-aware (LRM) and (ARMA). The response time, CPU utilization and predicted request are applied in the analysis and planning phase for scaling decisions. The profit-aware surplus VM selection policy used in the execution phase for select the appropriate VM for scale-down. The result shows that the proposed model for web applications provides fair utilization of resources with minimum cost, thus provides maximum profit to application provider and QoE to the end users. Akshi kumar and Abhilasha Sharma in the paper titled “Ontology driven Social Big Data Analytics for Fog enabled Sentic-Social Governance” utilized a semantic knowledge model for investigating public opinion towards adaption of fog enabled services for governance and comprehending the significance of two s-components (sentic and social) in aforesaid structure that specifically visualize fog enabled Sentic-Social Governance. The results using conventional TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction are empirically compared with ontology driven TF-IDF feature extraction to find the best opinion mining model with optimal accuracy. The results concluded that implementation of ontology driven opinion mining for feature extraction in polarity classification outperforms the traditional TF-IDF method validated over baseline supervised learning algorithms with an average of 7.3% improvement in accuracy and approximately 38% reduction in features has been reported. Avinash Kaur and Pooja Gupta in the paper titled “Hybrid Balanced Task Clustering Algorithm for Scientific workflows in Cloud Computing” proposed novel hybrid balanced task clustering algorithm using the parameter of impact factor of workflows along with the structure of workflow and using this technique, tasks can be considered for clustering either vertically or horizontally based on value of impact factor. The testing of the algorithm proposed is done on Workflowsim- an extension of CloudSim and DAG model of workflow was executed. The Algorithm was tested on variables- Execution time of workflow and Performance Gain and compared with four clustering methods: Horizontal Runtime Balancing (HRB), Horizontal Clustering (HC), Horizontal Distance Balancing (HDB) and Horizontal Impact Factor Balancing (HIFB) and results stated that proposed algorithm is almost 5-10% better in makespan time of workflow depending on the workflow used. Pijush Kanti Dutta Pramanik et al. in the paper titled “Green and Sustainable High-Performance Computing with Smartphone Crowd Computing: Benefits, Enablers and Challenges” presented a comprehensive statistical survey of the various commercial CPUs, GPUs, SoCs for smartphones confirming the capability of the SCC as an alternative to HPC. An exhaustive survey is presented on the present and optimistic future of the continuous improvement and research on different aspects of smartphone battery and other alternative power sources which will allow users to use their smartphones for SCC without worrying about the battery running out. Dhanapal and P. Nithyanandam in the paper titled “The Slow HTTP Distributed Denial of Service (DDOS) Attack Detection in Cloud” proposed a novel method to detect slow HTTP DDoS attacks in cloud to overcome the issue of consuming all available server resources and making it unavailable to the real users. The proposed method is implemented using OpenStack cloud platform with slowHTTPTest tool. The results stated that proposed technique detects the attack in efficient manner. Mandeep Kaur and Rajni Mohana in the paper titled “Static Load Balancing Technique for Geographically partitioned Public Cloud” proposed a novel approach focused upon load balancing in the partitioned public cloud by combining centralized and decentralized approaches, assuming the presence of fog layer. A load balancer entity is used for decentralized load balancing at partitions and a controller entity is used for centralized level to balance the overall load at various partitions. Results are compared with First Come First Serve (FCFS) and Shortest Job First (SJF) algorithms. In this work, the researchers compared the Waiting Time, Finish Time and Actual Run Time of tasks using these algorithms. To reduce the number of unhandled jobs, a new load state is introduced which checks load beyond conventional load states. Major objective of this approach is to reduce the need of runtime virtual machine migration and to reduce the wastage of resources, which may be occurring due to predefined values of threshold. Mukta and Neeraj Gupta in the paper titled “Analytical Available Bandwidth Estimation in Wireless Ad-Hoc Networks considering Mobility in 3-Dimensional Space” proposes an analytical approach named Analytical Available Bandwidth Estimation Including Mobility (AABWM) to estimate ABW on a link. The major contributions of the proposed work are: i) it uses mathematical models based on renewal theory to calculate the collision probability of data packets which makes the process simple and accurate, ii) consideration of mobility under 3-D space to predict the link failure and provides an accurate admission control. To test the proposed technique, the researcher used NS-2 simulator to compare the proposed technique i.e. AABWM with AODV, ABE, IAB and IBEM on throughput, Packet loss ratio and Data delivery. Results stated that AABWM performs better as compared to other approaches. R.Sridharan and S. Domnic in the paper titled “Placement Strategy for Intercommunicating Tasks of an Elastic Request in Fog-Cloud Environment” proposed a novel heuristic IcAPER,(Inter-communication Aware Placement for Elastic Requests) algorithm. The proposed algorithm uses the network neighborhood machine for placement, once current resource is fully utilized by the application. The performance IcAPER algorithm is compared with First Come First Serve (FCFS), Random and First Fit Decreasing (FFD) algorithms for the parameters (a) resource utilization (b) resource fragmentation and (c) Number of requests having intercommunicating tasks placed on to same PM using CloudSim simulator. Simulation results shows IcAPER maps 34% more tasks on to the same PM and also increase the resource utilization by 13% while decreasing the resource fragmentation by 37.8% when compared to other algorithms. Velliangiri S. et al. in the paper titled “Trust factor based key distribution protocol in Hybrid Cloud Environment” proposed a novel security protocol comprising of two stages: first stage, Group Creation using the trust factor and develop key distribution security protocol. It performs the communication process among the virtual machine communication nodes. Creating several groups based on the cluster and trust factors methods. The second stage, the ECC (Elliptic Curve Cryptography) based distribution security protocol is developed. The performance of the Trust Factor Based Key Distribution protocol is compared with the existing ECC and Diffie Hellman key exchange technique. The results state that the proposed security protocol has more secure communication and better resource utilization than the ECC and Diffie Hellman key exchange technique in the Hybrid cloud. Vivek kumar prasad et al. in the paper titled “Influence of Monitoring: Fog and Edge Computing” discussed various techniques involved for monitoring for edge and fog computing and its advantages in addition to a case study based on Healthcare monitoring system. Avinash Kaur et al. elaborated a comprehensive view of existing data placement schemes proposed in literature for cloud computing. Further, it classified data placement schemes based on their assess capabilities and objectives and in addition to this comparison of data placement schemes. Parminder Singh et al. presented a comprehensive review of Auto-Scaling techniques of web applications in cloud computing. The complete taxonomy of the reviewed articles is done on varied parameters like auto-scaling, approach, resources, monitoring tool, experiment, workload and metric, etc. Simar Preet Singh et al. in the paper titled “Dynamic Task Scheduling using Balanced VM Allocation Policy for Fog Computing Platform” proposed a novel scheme to improve the user contentment by improving the cost to operation length ratio, reducing the customer churn, and boosting the operational revenue. The proposed scheme is learnt to reduce the queue size by effectively allocating the resources, which resulted in the form of quicker completion of user workflows. The proposed method results are evaluated against the state-of-the-art scene with non-power aware based task scheduling mechanism. The results were analyzed using parameters-- energy, SLA infringement and workflow execution delay. The performance of the proposed schema was analyzed in various experiments particularly designed to analyze various aspects for workflow processing on given fog resources. The LRR (35.85 kWh) model has been found most efficient on the basis of average energy consumption in comparison to the LR (34.86 kWh), THR (41.97 kWh), MAD (45.73 kWh) and IQR (47.87 kWh). The LRR model has been also observed as the leader when compared on the basis of number of VM migrations. The LRR (2520 VMs) has been observed as best contender on the basis of mean of number of VM migrations in comparison with LR (2555 VMs), THR (4769 VMs), MAD (5138 VMs) and IQR (5352 VMs).

IOT is going to offer large number of applications in various environments for improving the quality of our lives. Routing issues become more and more challenging for low-power and lossy radio-links, multi-hop mesh topologies, the battery supplied nodes and frequently changed network topologies. So that IOT routing protocols has some challenges like traffic Patterns, Energy efficiency, Scalability, Mobility Energy-aware metrics for routing protocol in IOT are node energy, throughput, latency, link quality. The multimedia transmission in IOT depends on a routing protocol to determine stable and resource efficient path, and to provide varying levels of QoS/QoE based on different requirements. The Quality- of-Service (QoS)/Quality-of- experience (QoE ) guarantee for multimedia in internet of things . The current research and development activities have been restricted to scalar sensor data based IOT systems and overlooked the challenges of provisioning multimedia devices over IOT.