Optimal service provisioning in IoT fog-based environment for QoS-aware delay-sensitive application

Introduction: Nowadays, fog computing has emerged as a promising solution for handling the prompt processing of tasks in Internet of Things (IoT)-based applications. One of the key advantages of fog computing is that it reduces service completion time by offloading tasks from IoT devices to the fog server. Therefore, scheduling of tasks becomes vital, where emergency and non-emergency tasks can be prioritized to offload data to the nearby fog servers, which improves the Quality of Service (QoS). Due to the dynamic nature of the IoT environment, traffic load varies over time, making it difficult to select the optimal fog server for task offloading. Objectives: This research introduces a novel task offloading for the Fog Cloud scenario using the improved Coati Optimization Algorithm based on Genetic Operators (ICOA-GO) algorithm. Methods: Initially, the Delay aware Four Queue model with Fuzzy logic (DFQM-Fuzz) is designed to queue incoming tasks into four different priorities. In the DFQM-Fuzz, Highly urgent (HU) and Urgent (U) tasks are considered as first and second priorities and those tasks are offloaded through the Fog Server. The Non Urgent (NU) and No Deadline (ND) tasks are offloaded through the Cloud Server. Furthermore, when there is no resource to offload through the Fog Server, the task is offloaded through the Cloud Server. Results: The proposed improved Coati Optimization Algorithm based on Genetic Operators (ICOA-GO) algorithm optimizes the selection of Cloud and Fog Servers. The analysis based on Energy Utilization, Task Rejection Ratio, and Makespan yielded values of 93.589, 1, and 1.96073. Conclusions: By integrating the DFQM-Fuzz and ICOA-GO improves task prioritizing and offloading efficiency while reducing execution time, energy consumption, and cost.

Cognitive vehicular network (CVN) is proposed to improve the spectrum efficiency and enhance the Quality of Service (QoS) performance of diverse intelligent transportation service (ITS) applications. The road traffic jams and accidents will result in heavy communication load and QoS degradation in the hot cells, and then bring about asymmetric bandwidth requirements among the cells. In order to formulate the inter-cell asymmetric resource allocation in CVNs, two alternative bargaining solutions, such as Generalized Nash Bargaining Solution (GNBS) and Generalized Raiffa-Kalai-Smorodinsky Bargaining Solution (GRKSBS), are proposed to achieve a good tradeoff between efficiency and weighted fairness. Then, GNBS-S and GRKSBS-S schemes are proposed to guarantee the QoS of safety services in the heavy load case, where the bandwidth demand and supply can not be matched well. In these resource allocation schemes, a two-band partition algorithm is developed to reduce complexity. Furthermore, the primary user activity (PUA) which can cause rate loss to secondary users is also considered to alleviate its influence to fairness. Simulation results indicate that the proposed schemes outperform both the Max-min and Max-rate schemes, and they can also improve the communication reliability of safety service considerably in CVNs.

The internet of things (IoT) has revolutionized the way we interact with technology, with the proliferation of interconnected devices leading to an increase in the volume of data transmitted over the network. One of the key applications of IoT is in multimedia communication, where real-time audio and video data is transmitted over the network. However, the diverse nature of applications and the sheer volume of data in IoT networks can lead to network congestion, latency, and variable quality of service (QoS) at the internet side, resulting in a degradation of the overall QoS for multimedia traffic. In this paper, we propose a cross-layer multimedia optimization solution for multi-point to point IoT networks that incorporates service differentiation and bandwidth reservation techniques to improve the QoS of multimedia traffic. We evaluate the performance of our proposed solution using simulations and compare it with existing solutions. Our results show that our proposed solution can significantly improve the QoS of multimedia traffic in IoT networks, even during periods of high network congestion or variable QoS at the internet side.

To facilitate the development of Internet of Things (IoT) applications, numerous middleware protocols and APIs have been introduced. Such applications built atop reliable or unreliable protocols and they expose different characteristics. Additionally, with regard to the application context (e.g., emergency response operations), several Quality of Service (QoS) requirements must be satisfied. To study QoS in IoT applications, the provision of a generic performance analysis methodology is required. Queueing network models offer a simple modeling environment, which can be used to represent IoT interactions by combining multiple queueing model types for building queueing networks. The resulting networks can be used for performance analysis through analytical or simulation models. In this paper, we present several types of queueing models that represent different QoS settings of IoT interactions, such as intermittent mobile connectivity, message drop probabilities, message availability/validity and resource constrained devices. Using MobileJINQS, we simulate our models demonstrating the significant effect on response times and message success rates when varying QoS settings.

Performance tradeoffs in resource provisioning for mixed internet-of-things (IoT) and human-oriented-communications (HoC) services over cellular networks are investigated. First, we present a low-complexity model of cellular connectivity in the uplink direction in which both access reservation and scheduled data transmission procedures are included. This model is employed subsequently in deriving analytical expressions for energy efficiency, spectral efficiency, and experienced delay in data transmission of connected devices as well as energy consumption of base stations. The derived expressions indicate that the choice of uplink resource provisioning strategy introduces tradeoffs between battery lifetime for IoT communications, quality of service (QoS) for HoC, spectral efficiency and energy consumption for the access network. Then, the impacts of system and traffic parameters on the introduced tradeoffs are investigated. Performance analysis illustrates that improper resource provisioning for IoT traffic not only degrades QoS of high-priority services and decreases battery lifetime of IoT devices, but also increases energy consumption of the access network. The presented analytical and simulations results figure out the ways in which spectral/energy efficiency for the access network and QoS for high-priority services could be traded to prolong battery lifetimes of connected devices by compromising on the level of provisioned radio resources.

Denial-of-service attacks have grown bigger and sophisticated over the years. The alarming number of online services became the targets of DDoS attacks. Emergence of Internet of Things (IOT) has made the situations devastating for the organizations. The flooding attacks are frequently launched by attackers. These attacks consume vital resources of the target resulting into degradation in quality of service and huge financial losses. Hence, it became necessary to design lightweight detection and mitigation approach. In this paper, the presented approach is based on IP address. The IPs are classified as known and unknown based on lists maintained at the target end. The packet filtering decisions are taken based on arrival of known/unknown source IP addresses, volume of incoming requests from these IPs and CPU utilization threshold of the target. The approach mitigates the attack impact for different patterns of attacks. The performance evaluation of the proposed mechanism is done using qualitative and quantitative parameters.

The Internet of Things (IoT) is rapidly gaining popularity as a result of the advancements in portable embedded devices and wireless protocols, enabling a new class of services. On the other hand, edge clouds provide IoT services as a new paradigm called fog computing. As the number of available IoT devices increases, more efficient methods are required to select the optimal combination of services out of several existing candidates in edge clouds while composing more complex IoT workflow tasks. So, cloud-assisted fog computing requires a platform for management, composition and provisioning of IoT services for IoT–cloud integration. Resent works have some weaknesses and did not consider some aspects of fog computing such as low latency, low energy and efficient resource allocation. We propose a cloud-based platform for management of IoT service selection and composition in fog computing to enhance QoS parameters such as bandwidth usage, latency and distributed resource utilization. In particular, we propose a multi-objective evolutionary game theory, enhanced by evaporation-based water cycle algorithm (EG-ERWCA) to optimize CPU usage, power consumption and latency of the IoT workflows in cloud-assisted fog computing environments. Many different real IoT workflows are used for evaluation of the proposed method in comparison with the state-of-art algorithms. Simulation results show that the overall quality of service is improved by 2.66 times compared to rival algorithms.

The Internet of Things (IoT) interconnects diverse objects and service platforms for providing ubiquitous application support through diverse communication technologies. Quality of service and experience required application support is ensured through precise resource allocation and request scheduling in this platform. Considering the densely populated user scenario and service demand, this article introduces a Rational Process Offloading Method (RPOM) for reducing the service backlogs in IoT. This method distinguishes the scheduled and offloading of required application requests for preventing additional service delays. The decisions for offloading and time-sensitive application responses are performed using the state learning process. In this learning, the offloading and scheduling states are validated using current and previous state analysis for independent and congestion-less responses. The state is trained using the time and offloading demand observed between the request and response. However, the varying state modeling is used for determining a forecast-based service allocation, improving the utilization. The RPOM’s performance is validated using the metrics of resource utilization, offloading ratio, service delay, and backlogs

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.

Infrastructure of fog is a complex system due to the large number of heterogeneous resources that need to be shared. The embedded devices deployed with the Internet of Things (IoT) technology have increased since the past few years, and these devices generate huge amount of data. The devices in IoT can be remotely connected and might be placed in different locations which add to the network delay. Real time applications require high bandwidth with reduced latency to ensure Quality of Service (QoS). To achieve this, fog computing plays a vital role in processing the request locally with the nearest available resources by reduced latency. One of the major issues to focus on in a fog service is managing and allocating resources. Queuing theory is one of the most popular mechanisms for task allocation. In this work, an efficient model is designed to improve QoS with the efficacy of resource allocation based on a Queuing Theory based Cuckoo Search (QTCS) model which will optimize the overall resource management process.

Modern roundabouts are a type of intersection which is used successfully in many countries globally due to their advantages. More specifically, it is widely accepted that owing to their geometrical and operational characteristics the roundabouts enhance traffic capacity, cause an important reduction in delays and improve the road safety levels, for the benefit of the users. Moreover, their formation assures not only financial benefits, but environmental too, compared to other junction types, like signalized intersections. The engineering society launches research projects to develop guidelines for the assessment of such facilities in order to improve them. As a result, the concepts of level and quality of service have been introduced. The Level of Service (LoS) evaluates the traffic handling ability of a transportation facility and it is estimated by the use of specific techniques. The Quality of Service (QoS) assesses the performance of a transportation facility, as the drivers and the users perceive it. In this paper, the meaning of Quality of Service (QoS) of roundabouts is examined. A questionnaire’ s survey aimed at collecting data from users in Greece, regarding the factors that affect drivers’ satisfaction, during their driving through a roundabout. The results of the survey show that the quality of service perception on urban and rural roundabouts is influenced by several factors, such as the clarity of road signs, the presence of on-street parking, the lightning etc.

This paper proposes a framework for quality of service (QoS) control in WLAN-based converged personal network service (CPNS). First, we show that the CPNS devices in WLANs occupy the shared wireless channel in an unfair manner; and thus, QoS is degraded. The reasons of such problem are analyzed from two viewpoints of (i) channel access mechanism according to carrier sensing multiple access protocol of WLAN and (ii) TCP congestion control mechanism in response to packet loss. To improve QoS and assure fair channel sharing, we propose an integrated QoS control framework consisting of admission control and rate control. Based on the available capacity, the admission control determines whether or not a specific QoS service can be admitted. The rate control is implemented using congestion window control or token bucket algorithm. The proposed mechanism differentiates QoS service from best-effort (BE) service such that the QoS service is preferentially served to satisfy its QoS requirements and the BE service is served to share the remaining resource in a fair manner. The extensive simulation results confirm that the proposed scheme assures QoS and fair channel sharing for WLAN-based CPNS.

Thanks to the advent of the Internet of Things (IoT) concept, several areas have been extended to offer a new category of "smart" services, such as smart home, smart cities, smart grid or smart factory. The IoT is based on communicating devices (sensors, actuators, RFID tags) that are remotely manageable by business applications. In this new context, several traditional challenges have to be reconsidered, among them the quality of service (QoS) issue to meet the application requirements. As a part of our approach for an autonomic QoS management at the Middleware level within the IoT, we first propose an analytical model based on queueing theory of a Middleware platform, aimed at guiding the monitoring and planning steps of the proposed autonomic management. This model is then instantiated for an IoT entity (here a Gateway). We finally show the benefits induced by the use of this model to guide the activation of two adaptation actions aimed at satisfying QoS constraints of critical IoT application still optimizing energy consumption of the involved IoT entity.

Tracking systems are critical for air traffic control, as they provide aircraft trajectories to air controllers in real time. Therefore, the performance of these tracking systems must be carefully monitored to maintain traffic safety. Thales has thus developed a decision-making algorithm to precisely assess the quality of service (QoS) of tracking systems, by the aggregation of thirty metrics through a multi-criteria decision model. In particular, this QoS model provides a unique numeric value to quantify the tracking performance. The goal of this article is to show how to leverage sensitivity analysis to detect the specific metrics involved in a degradation of the QoS. The identification of the relevant metrics is a critical information for technical supervisors, who can then narrow their investigations to find the problem root causes, and ultimately restore an efficient tracking. Sensitivity analysis provides various methods to apportion the variability of a model output across the input variables, and thus identify the inputs with a strong impact on the QoS output. In particular, Shapley values have recently gain considerable momentum in the sensitivity analysis and machine learning communities as a powerful method to quantify the influence of input variables in correlated settings. Hence, we focus on a real dataset collected by Thales operational systems to show the efficiency of Shapley values. More precisely, we artificially introduce a random noise into the aircraft positions measured by the radars over a short time period, and show that Shapley values successfully identify the relevant metrics involved in the QoS degradation. Overall, this simulated analysis establishes a first level of demonstration of the approach efficiency to provide precise and useful information to the technical supervisor for the root cause identification of tracking problems.

DCSP: A delay and cost-aware service placement and load distribution algorithm for IoT-based fog networks