The rapid urbanization necessitates effective management, driving the development of smart cities. A key component of smart cities is a secure and efficient transportation system, which is vital for residents’ well-being. However, the growing complexity of road networks and increased vehicle volume present significant challenges in maintaining service quality. Pedestrian crossings, especially for children and the elderly, remain problematic in urban traffic worldwide. To address this, we propose an intelligent pedestrian crossing system that leverages advanced technology and real-time data processing to improve service quality and sustainability. Despite challenges such as data management, infrastructure costs, and privacy concerns, smart cities are adopting cloud and fog-based solutions, like smart traffic signals and intelligent pedestrian crossings, to enhance traffic flow and safety. This work presents an Edge-AI-assisted framework for pedestrian crossings, using edge devices as roadside units to collect real-time data. Images captured by cameras are processed with YOLOv8, an object detection algorithm, to analyze road and pedestrian crossing traffic. The edge-AI assisted smart pedestrian crossings Framework adapts the flow of objects based on the real-time data and objects over pedestrian. Simulation results demonstrate its effectiveness across various complex road transport datasets.

The Internet of Vehicles (IoV), equipped with sensors, generates vast amounts of data, demanding rigorous computation and network. The cloud computing (CC) platform meets these stringent computation requirements, but it has a significant latency that fog computing (FC) effectively handles. Software defined network (SDN) has become the de facto standard for next-generation networking due to its unique and flexible features, which handle the network prerequisites in an agile manner. Task offloading (TO) is a crucial issue in software defined-IoV (SD-IoV), particularly when the vehicle’s resources are insufficient. Fog nodes schedule offloaded tasks; however, neglecting computational and network details during task scheduling can lead to longer completion times, thereby restricting the functionality of SD-IoV due to its time-sensitive nature. Therefore, this article introduces two proactive intelligent TO (ITO’ and ITO) schemes. These schemes schedule tasks onto fog nodes with enhanced computational capabilities, dynamically enable the network through the ovs-ofctl SDN utility, and consider vehicle mobility during task scheduling. Experiments with Mininet show that the proposed (ITO’ and ITO) schemes improve performance in terms of CPU availability, bandwidth, and throughput by 5.2 times, 50% and (30.7–13.9)% more than the existing scheme. They also reduce average packet loss, delay, round trip time (RTT), and offloaded node time selection by (45.35–21.3)%, (50–33.3)%, (53.9–9.9)% and (43.2–26.06)% which demonstrates the efficacy of the proposed scheme.

The necessity to address the growing pace of industrialization and resource constraints has underscored the importance of developing innovative Information and Communication Technology solutions. The prevailing “smart industry” paradigm is distinguished by its widespread cyber infrastructure for monitoring and controlling critical industrial assets. In case sudden failure of any equipment shot down the industry and apart from financial loss, it take ample amount of time to recover. To effectively manage the multitude of continuous services while maintaining their quality of service requirements, it is crucial to concurrently utilize sensor-based real-time assessment for predicting employed equipment status and lifespan become a challenging. This research explores the feasibility of enhancing the lifespan of sensors at the fog layer by implementing intelligent sleep and wake-up duty cycles. We propose a clustered-based duty-cycling approach that optimizes service continuity with a minimal number of active sensor nodes using a genetic algorithm. The abovementioned framework simulated in iFogSim simulator and obtained results demonstrate the reduced the energy consumption by approximately 16% to 20% over the energy efficient aware and around 38% to 42% over the K-mean clustering approach with 90% duty cycle.

As the Internet of Things (IoT) continues to advance as a technology, it has given rise to innovative and cross-domain IoT applications, particularly in smart cities. For IoT applications and services that are sensitive to latency and due resource constraints it affects the Quality of Service (QoS). To address these challenges, context-aware fog computing at the network edge requires an enhanced focus on optimizing resources for intelligent service management. Due to the dynamic change of workload at fog nodes, i.e., sudden rise in demand, an effective load balancing approach among fog nodes becomes essential. However, it’s crucial to execute load transfers, such as Virtual Machine (VM) migrations but improper migration can lead to a cascade of migrations and ultimately degrade system performance. In this paper, we introduce a resource-optimized intelligent service model (RoISM) designed to facilitate resource optimization through a forecasting technique. This technique predicts the requisite context instances and resource computation needed for efficient service delivery. The proposed hybrid approach to service management leverages context-sharing, context-migration, and live service migration strategies, all based on the forecast method. This method utilizes both current and predicted resource utilization data, as well as context availability, to fulfil service requests within the specified latency requirements for cross-domain IoT applications. To validate the effectiveness of our proposed service management algorithms, we conducted simulations using a CloudSim simulator. The results obtained from these simulations confirm the superiority of our proposed methods

Software-Defined Networks ( SDNs ), with their segregated data and control planes, has proved to be capable of managing massive amounts of data by leveraging distributed information available across the network for informed decision-making at the network controller. However, with the proliferation of next-generation, real-time Internet of Things ( IoT ) applications that vary greatly in terms of data frequency and volumes, data traffic classification can substantially assist SDN controllers toward efficient routing and traffic engineering decisions. Existing works on network classification are limited by their application-centric nature, thus overlooking the key criterion for real-time IoT applications, namely, Quality of Service ( QoS ). In this article, we focus on augmenting SDN controllers’ decision-making capacity and Underwater Sensor Networks with machine learning algorithms to achieve real-time, QoS-aware, network traffic classification. Three classifiers, namely, Feed-forward Neural Network, Naïve Bayes, and Logistics Regression have been employed with a novel Artificial Neural Network and Particle Swarm Optimization hybridization scheme by carrying first- and second-order stability analysis for performance improvement of these classifiers. In short, the proposed framework exploits optimization algorithms and semi-supervised machine learning ( ML ) for precise traffic classification while keeping communication overhead between controller and switches minimal. Results obtained from real-life datasets demonstrate the efficacy of our proposed scheme.

With the unprecedented advancements in the field of Internet of Things (IoT), novel increasingly complex services are getting conceived and implemented with the day. A new trend in this landscape of next-generation IoT applications has been those orchestrated by the seamless integration of numerous vertical IoT applications, also known as, unified IoT services. However, such unified IoT applications are resource hungry (computation, storage, as well as network resources) and are usually characterized by real-time constraints. These render cloud and fog enactments insufficient. This article employs a context-aware computing approach that helps alleviating massive data transfers across fog nodes in real time. User requests are managed at fog nodes were depending upon their required contexts, availability of such contexts in the fog node and respective deadline, the requests are serviced either locally or at other fog nodes and Quality of Service (QoS) is fulfilled. To this end, a distributed service management algorithm is proposed that services the user requests at every fog node by either sharing contexts among context requests within a fog node, or by bringing in unavailable contexts from other fog nodes or by migrating service requests to remote fog nodes with available contexts in a deadline-aware fashion. Accordingly, this article proposes a novel smart multichannel queuing (SMCQ) model that schedules requests to virtual machines (VMs) at individual fog nodes by assigning them to specific priority groups (based on deadline vicinity) and thus minimizes service delay. The proposed algorithms for delay-tolerant service migration have been presented and simulation results carried out to demonstrate the efficacy of the proposed methodology.

As the Internet of Things (IoT) paradigm is maturing, innovative, and novel services are being envisioned. An upcoming trend is the depiction of services enacted through seamless integration of multiple vertical IoT services, termed as cross-vertical or unified IoT services in this paper. Traditional Cloud-based centralized network architectures cannot cater to real-time responses demanded by such unified IoT applications. Moreover, introducing Fog nodes within the network architecture, though a promising alternative, cannot sustain the burden of a huge number of applications that culminates in massive data handling. In this paper, we envision employing lessons learned from context-aware computing, specifically context sharing among interdependent vertical IoT applications to address this delay requirement of such unified IoT applications by enacting context sharing among Fog nodes for minimizing system delay. The detailed network model and context sharing mechanism have been presented and the service time minimization has been framed as an optimization problem. Algorithms for context sharing and delay tolerant load balancing have been presented and simulation results carried out demonstrate the efficacy of the proposed methodology.

Cloud infrastructures are capable of leveraging massive computational as well as data processing capabilities in virtualized environments. Emerging applications on today's clouds are data intensive and this has led to the trend of employing data-parallel frameworks, like Hadoop and its myriad descendants, for handling such massive data requirements. Scheduling of jobs in such frameworks is in essence a two-step process, where the block-data distribution follows mapping of computations among those resources. Since most Hadoop-based systems make these two decisions independently, it seems a promising prospective to map computations within cloud resources based on data blocks already distributed to them. This paper proposes data partitioning and placement aware computation scheduling scheme (DPPACS), a data and computation scheduling framework that adopts the strategy of improving computation and data co-allocation within a Hadoop cloud infrastructure based on knowledge of data blocks availability. Accordingly, this paper proposes a data-partitioning algorithm, a novel partition-cum-placement algorithm and finally proposes a computational scheduling algorithm that exploits knowledge of data availability at different clusters. The proposed DPPACS has been implemented on a test bed and its comparative performance results with respect to Hadoop's default data placement strategy have been presented. Experiments conducted herein conclusively demonstrate the efficacy of the proposed DPPACS.