Dynamic Resource Allocation for Load Balancing in Fog Environment

Internet of Things (IoT) applications are generally developed in a vertical manner, i.e., each IoT application is developed for a certain scenario which generally does not share data with IoT applications for recommendation services. This leads to an inefficient exploitation of other IoT service applications. In fact, such recommendation services can be achieved with the help of Social IoT (SIoT) by using data generated by various IoT applications. SIoT builds a profile of objects based on IoT applications data that can be exchanged with SIoT network for accessible to other IoT applications. In this manner, the SIoT network provides recommendation services for reusability of IoT applications' data among various IoT applications, as well as adapting IoT services according to users' needs which improve user experience. Additionally, the profiles built by a SIoT network can also help a single IoT application by looking for similar conditions that have been addressed in the past for the same IoT application. We propose a concept for exploiting the SIoT for recommendation services among various IoT applications with the help of a sample application scenario that highlights how the SIoT can help by providing recommendations. We also provide some implementation challenges for this concept.

With the rapid increase of Internet of Things (IoT) devices and applications, the ordinary cloud computing paradigm soon becomes outdated. Fog computing paradigm extends services provided by a cloud to the edge of network in order to satisfy requirements of IoT applications such as low latency, locality awareness, low network traffic, mobility support, and so forth. Task scheduling in a Cloud-Fog environment plays a great role to assure diverse computational demands are met. However, the quest for an optimal solution for task scheduling in the such environment is exceedingly hard due to diversity of IoT applications, heterogeneity of computational resources, and multiple criteria. This study approaches the task scheduling problem with aims at improving service quality and load balancing in a merged computing system of Edge-Fog-Cloud. We propose a Multi-Objective Scheduling Algorithm (MOSA) that takes into account the job characteristics and utilization of different computational resources. The proposed solution is evaluated in comparison to other existing policies named LB, WRR, and MPSO. Numerical results show that the proposed algorithm improves the average response time while maintaining load balancing in comparison to three existing policies. Obtained results with the use of real workloads validate the outcomes.

The correct implementation and behavior of Internet of Things (IoT) applications are seldom investigated in the literature. This paper shows how the simulation mechanism can be integrated well into an IoT application development platform for correct implementation and behavior investigation. We use an IoT application development platform called IoTtalk as an example to describe how the simulation mechanism called SimTalk can be built into this IoT platform. We first elaborate on how to implement the simulator for an input IoT device (a sensor). Then we describe how an output IoT device (an actuator) can be simulated by an animated simulator. We use a smart farm application to show how the simulated sensors are used for correct implementation. We use applications including interactive art (skeleton art and water dance) and the pendulum physics experiment as examples to illustrate how IoT application behavior investigation can be achieved in SimTalk. As the main outcome of this paper, the SimTalk simulation codes can be directly reused for real IoT applications. Furthermore, SimTalk is integrated well with an IoT application verification tool in order to formally verify the IoT application configuration. Such features have not been found in any IoT simulators in the world.

A decentralized service placement policy plays a key role in distributed systems, such as fog computing, where sharing workloads fairly among active computing nodes is critical. A decentralized policy is an inherent feature of the service placement process that may improve load balancing among computers and can reduce the latency in many real‐time Internet of Things (IoT) applications. This article proposes reinforcement optimization for a decentralized service placement policy, which attempts to mitigate some of the drawbacks of existing service placement policies. Matching task size with node specifications and the allocation of less popular but time‐sensitive applications in the fog layer are the primary contributions of this study. Extensive experimental comparisons are made between the proposed algorithm and other well‐known algorithms over service latency, network usage, and computing usage using the iFogSim simulator. A microservice‐based application with varying sizes of computing requests are tested experimentally and show that the proposed algorithm effectively serves computing instances that are closer to users, reducing service latency and network usage. Compared to the existing models, the proposed modified algorithm reduces service latency by 24.1%, network usage by 4%, and computing usage by 20%, thus highlighting positive outcomes when using the proposed algorithm for fog analytics in future real‐time IoT applications.

Fog Computing (FC) paradigm offers significant potential for hosting diverse delay-sensitive Internet of Things (IoT) applications. However, the limited resources of fog devices pose significant challenges for deploying multiple applications, particularly in heterogeneous and dynamic IoT scenarios, due to the absence of effective mechanisms for resource estimation and discovery. An efficient resource allocation strategy is crucial for meeting the Quality of Service (QoS) requirements of IoT applications while enhancing overall system performance. Identifying the optimal allocation strategy for IoT applications with multiple QoS parameters is a complex and computationally intensive challenge, classified as an NP-complete problem. This paper proposes a Multi-Objective Optimization Algorithm (MOOA) for optimal resource allocation using the Osprey Optimization Algorithm (OOA) to efficiently allocate available resources. The proposed algorithm was evaluated against existing approaches, including the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), under varying task loads ranging from 100 to 500 tasks. The simulation results demonstrate significant performance improvements, including an average reduction in execution time by 12.45% compared to PSO and 22.97% compared to GA, response time by 32.57% compared to GA and 24.45% compared to PSO, and completion time by 44.39% compared to GA and 33.23% compared to PSO. These findings highlight the proposed algorithm’s ability to efficiently handle task allocation in dynamic FC environments and its potential to address complex QoS requirements in real-world IoT applications.

As a major agricultural province in China, Shandong Province has always played an important role. At the same time, it is also closely related to the strong support of science and technology. As a new technology, the Internet of things (IoT) has made positive contributions. Agricultural IoT is a mode that uses information technology, wireless communication technology and radio frequency automatic identification technology to share the identified agricultural information to the network. The application of agricultural IoT is a major change in agricultural development, which provides convenient conditions for the development of agriculture, provides a favorable environment for the agricultural products, promotes the transformation of agricultural technology, realizes the informatization of agricultural production, improves the efficiency of rural land, integrates agricultural resources and provides a foundation for food safety. In this paper, the application of IoT in the field of agriculture in Shandong Province is described from two practical cases. One is about how to use wireless sensor network technology to transmit data in the big data platform based on the Bohai granary project. The other describes how to use IoT for food quality and safety traceability. It takes pigs as the research object, runs through all links of the whole industrial chain of pig breeding, breeding, slaughtering, processing, logistics and sales, collects the data of the whole industrial chain with the help of IoT, and realizes the information tracking and traceability of the whole industrial chain. At the end of the paper, it discusses the countermeasures.

With the advancement of computing technology, we are witnessing the dawn of a new era of the Internet of Things (IoT) paradigm in which objects equipped with sensors, actuators and processing capabilities communicate with each other to serve a given goal. The IoT’s intrinsic nature, which uses heterogeneous devices, resources and different communication protocols, complicates IoT applications’ design, development, and validation. Reducing the complexity of building IoT applications is one of the current challenges in this area.To address this challenge, we focus on a model-driven approach to support IoT systems’ management and the development of IoT applications. In particular, we propose the FloWare approach and its toolchain, which combine Software Product Line and Flow-Based Programming paradigms to manage the complexity in the various stages of the IoT application development process. An automatic transformation procedure generates the final IoT application, an executable Node-RED flow, starting from a configuration of the designed Feature Models.KeywordsIoTModel-drivenFeature modelFlow-based programmingNode-RED

The internet of things (IoT) is focused in offering new technologies which is changing the business environment by showing the potential in different sectors like retail, transportation, healthcare etc. Through IoT, the managers can gather and process data and information in an effective manner, also they can use them to automate in order to enhance productivity and efficiency. With the usage of such technologies, human resource managers can access information for possible candidates which can be accessed through IoT. The overall behaviour of potential candidate tends to help them in shortlisting and choosing the desirable ones. The application of IoT has emerged in enhancing the recruitment of candidates in search of candidates based on large volume of information, assist in screening the candidates, selecting the most suitable ones. The application of IoT technology can modify the HR roles, enable in changing the company perspective in enhancing recruitment process, source the candidates effectively and select them so as to meet the organisational requirements. The implementation of IoT driven technologies support in removing the biases in screening the candidates and aids in candidate selection with the support of other technologies like artificial intelligence, predictive analytics etc. Furthermore, IoT is also involved in better inventory management as it supports the organisation in converting the data which are sourced through RFID (Radio Frequency Identification) into more insights related to inventory data, location of the products, tracking the movement of goods until it reaches the hands of the customers. With the application of IoT inventory managers possess better control in managing the inventory through automated ordering request and thereby reduce manual time and cost for the business. This paper is focused in presenting a conceptual understanding on the application of environmental IoT in recruitment process and also the overall impact created by the technology in managing the inventory efficiently.KeywordsInternet of thingsRecruitment processInventory managementPredictive analytics

Under the background of prosperity of Internet of things (IoT), in order to accelerate the training of talented persons of China for IoT, we propose to design and develop a new course for Chinese students: Essential Technologies, Applications, and Commercial Patterns of Internet of Things. Aiming at three-dimensional target, which has dimensionalities of attitude and thoughts, knowledge and skills, process and methods, the contents of the course include the following parts: system and application architectures, key technologies of IoT, applications of IoT, commercial patterns of IoT, IoT industry and social significance etc. The course highlights the training of abilities of innovation, creativity, comprehensive analysis and practical operations. The construction of teaching resources, such as textbooks, lecture notes, teaching equipments, practice bases, are also emphasized in the process of development of this course.

The technological development of Internet of Things (IoT) applications is emerging and attracting the attention of the real world in the automated industry, agriculture, environment, and scientific community. In most scenarios, extending the network lifetime of an IoT network is highly challenging because of constrained nodes. The wireless sensor network (WSN) is the core component of IoT applications. In addition, the WSN nodes are required for the network processes, particularly routing, energy maintenance, load balance, congestion control, packet delivery, quick response, and more. The failure of any of the above network processes will affect the entire network operation. IPv6 Routing Protocol for Low-power and Lossy network (RPL) provides high routing solutions to IoT applications requirements. The load balance, congestion control, traffic load, and bottleneck problems are still open issues in the RPL. To resolve the load balance issue, we propose a weighted sum method objective function (WSM-OF), which provides the ability to select the alternative parent in routing by RPL metrics. WSM-OF adopts congestion control and load balancing to avoid heavy traffic and extend the network's node lifetime. The network parameters of control overhead, jitter, packet delivery ratio, parent switching, energy consumption, latency, and network lifetime are implemented and analyzed through the COOJA simulator. The result shows that the WSM-OF improves the network performance and significantly enhances the network lifetime by up to 7.8%.

Fog computing has evolved as a promising computing paradigm to support the execution of latency-sensitive Internet of Things (IoT) applications. The mobile devices connected to the fog environment are resource constrained and non-stationary. In such environments, offloading mobile user’s computational task to nearby fog servers is necessary to satisfy the QoS requirements of time-critical IoT applications. Moreover, the fog servers are also susceptible to numerous attacks which induce security and privacy issues.Offloading computation task to a malicious fog node affects the integrity of users’ data. Despite the fact that there are many integrity-preserving strategies for fog environments, the majority of them rely on a reliable central entity that might have a single point of failure. Blockchain is a promising strategy that maintains data integrity in a decentralized manner. The state-of-art blockchain offloading mechnanisms have not considered the mobility during secure offloading process. Besides, it is necessary to ensure QoS constraints of the IoT applications while considering mobility of user devices. Hence, in this paper, Blockchain assisted Mobility-aware Secure Computation Offloading (MSCO) mechanism is proposed to choose the best authorized fog servers for offloading task with minimal computational and energy cost. To address the optimization issue, a hybrid Genetic Algorithm based Particle Swarm Optimization technique is employed. Experimental results demonstrated the significant improvement of MSCO when compared to the existing approaches in terms of on average 11 % improvement of total cost which includes the parameters of latency and energy consumption.

SummaryIn recent years, applying Internet of Things (IoT) applications has significantly increased to facilitate and improve quality of human life activities in various fields such as healthcare, education, industry, economics, etc. The energy aware cloud‐edge computing paradigm has developed as a hybrid computing solution to provide IoT applications using available cloud service providers and fog nodes for the smart devices and mobile applications. Since the IoT applications are developed in the form of several IoT services with various quality of service (QoS) metrics which can deploy on the cloud‐edge providers with different resource capabilities, finding an efficient placement solution as one of challenging topics to be measured for IoT applications. The service placement issue arranges IoT applications on the cloud‐edge providers with various capabilities of atomic services though sufficient different QoS factors to support service level agreement (SLA) contracts. This paper presents a technical analysis on the cloud‐edge service placement approaches in IoT systems. The key point of this technical analysis is to identify substantial studies in the service placement approaches which need additional consideration to progress more efficient and effective placement strategies in IoT environments. In addition, a side‐by‐side taxonomy is proposed to categorize the relevant studies on cloud‐edge service placement approaches and algorithms. A statistical and technical analysis of reviewed existing approaches is provided, and evaluation factors and attributes are discussed. Finally, open issues and forthcoming challenges of service placement approaches are presented.

Resource allocation in smart settings, more specifically in Internet of Things (IoT) transportation, is challenging due to the complexity and dynamic nature of fog computing. The demands of users may alter over time, necessitating more trustworthy resource allocation and administration. Effective resource allocation and management systems must be designed to accommodate changing user needs. Fog devices don’t just run fog-specific software. Resource and link failures could be brought on by the absence of centralised administration, device autonomy, and wireless communication in the fog environment. Resources must be allocated and managed effectively because the majority of fog devices are battery-powered. Latency-aware IoT applications, such as intelligent transportation, healthcare, and emergency response, are now pervasive as a result of the enormous growth of ubiquitous computing. These services generate a large amount of data, which requires edge processing. The flexibility and services on-demand for the cloud can successfully manage these applications. It’s not always advisable to manage IoT applications exclusively in the cloud, especially for latency-sensitive applications. Thus, fog computing has emerged as a bridge between the cloud and the devices it supports. This is typically how sensors and IoT devices are connected. These neighbouring Fog devices control storage and intermediary computation. In order to improve the Fog environment reliability in IoT-based systems, this paper suggests resource allocation and management strategy. When assigning resources, latency and energy efficiency are taken into account. Users may prioritise cost-effectiveness over speed in a fog. Simulation was performed in the iFogSim2 simulation tool, and performance was compared with one of the existing state-of-the-art strategy. A comparison of results shows that the proposed strategy reduced latency by 10.3% and energy consumption by 21.85% when compared with the existing strategy.

Purpose – The purpose of this paper is to study lightweight security scheme for Internet of Things (IoT) applications using Constrained Application Protocol (CoAP). Resource-constrained characteristics of IoT systems have ushered in compelling requirements for lightweight application protocol and security suites. CoAP has already been established as the candidate protocol for IoT systems. However, low overhead security scheme for CoAP is still an open problem. Existing security solutions like Datagram Transport Layer Security (DTLS) is not suitable, particularly due to its expensive handshaking, public key infrastructure (PKI)-based authentication and lengthy ciphersuite agreement process. Design/methodology/approach – This paper proposes a lightweight security scheme in CoAP using Advanced Encryption Standard (AES) 128 symmetric key algorithm. The paper presents an object security (payload embedded)-based robust authentication mechanism with integrated key management. The paper introduces few unique modifications to CoAP header to optimize security operation and minimize communication cost. Findings – It is resilient to number of security attacks like replay attack, meet-in-the-middle attack and secure under chosen plaintext attack. This scheme is generic in nature, applicable for gamut of IoT applications. The paper proves efficacy of our proposed scheme for vehicle tracking application in emulated laboratory setup. Specifically, it compares with DTLS-enabled CoAP to establish the lightweight feature of our proposed solution. Research limitations/implications – This paper mainly focuses on implementing in-vehicle tracking systems as an IoT application and used CoAP as the application protocol. Practical implications – Such a lightweight security scheme would provide immense benefit in IoT systems so that resource constraint-sensing devices and nodes can be made secure. This would impact IoT eco systems to a large extent. Originality/value – Such kind of security suite that provides both robustness and lightweight feature is hitherto not known to the authors, particularly in CoAP for IoT applications.

Cost-effective, low-power and wireless connectivity are among the key considerations for interconnecting the end-nodes or end-devices in the Internet of Things (IoT) applications. While wireless technologies such as WiFi, Bluetooth, Zigbee have been popular choices for last-mile connectivity, they however significantly restrict the distance between the communicating entities. While the use of additional network gear like repeaters or higher signal transmission power would increase the range of these wireless communication interfaces, the consequent increase in the cost has been a critical reason for not readily employing them in WAN based IoT applications such as Smart Cities. The emergence of the LoRa (Long Range) standard and the Low Power Wide Area Network (LPWAN) technology, in recent times, have however widened the scope and also broadened the deployment scenarios of the IoT end-nodes including the viability of the battery-powered and non-stationary ones. Yet another significance of these new long-distance-low-power interconnect standards is the empowerment for broader and independent Machine to Infrastructure (M2I) and Machine to Machine (M2M) communication for the end-nodes. This paper discusses details of the work that was carried out to explore the suitability of LoRa and LoRaWAN to address the short as well as the medium-range connectivity requirements of the IoT applications while also ensuring the flexibility and low-cost benefits with the help of results obtained from the representative experimental setups. The overall study and assessment were performed in steps, each with specific objectives. The first step was aimed at evaluating the performance of LoRa for communication between stationary as well as moving nodes. The next step was directed towards the validation of node-to-gateway and node-to-node communication, and also, with simultaneous use of different channels within LoRaWANs that were configured in star and mesh topologies. The subsequent step was devoted to assessing the viability of LoRa in broader IoT applications such as Smart Cities and Smart Irrigation, with the help of an illustrative setup comprising of nodes with sensors and actuators and, a gateway which was connected to an open-source cloud infrastructure to epitomize aspects of storage and analysis of data of a comprehensive IoT deployment.