Resource Allocation In Internet Of Things Research Articles

Resource Allocation Control of UAV-Assisted IoT Communication Device

This work aims to explore the effect of Resource Allocation and Control (RAC) of the Unmanned Aerial Vehicle (UAV)-assisted Internet of Things (IoT) communication equipment. The communication congestion problem is caused by insufficient computing resources of macro base stations when many user terminals access the IoT system. To this end, this work innovatively constructs the IoT system combined with UAVs to satisfy the user’s communication experience and allocate channel resources simultaneously. Finally, a UAV-assisted IoT resource allocation and control model is constructed and tested through experiments on the simulation software to analyze its performance. The results indicate that the transmission delay of the model reported here is significantly lower than that of other algorithms with the increased number of end users and the proportion of tasks. When the number of users is 500, the transmission delay of data resources is 47.5s. Besides, it requires lower energy consumption and shorter task completion time while maintaining lower throughput. At the same time, the uplink transmission rate of the model is generally stable at 3.10bps; the uplink transmission rate of the entire wireless communication system shows an increasing trend with the increase of the transmit power. Therefore, it is found that the model constructed here has strong robustness and achieves an excellent data resource allocation effect while ensuring lower data delay. This model provides an experimental reference and contribution to the improvement of the performance of IoT systems and the accurate allocation of network resources.

IoT Resource Allocation and Optimization Using Improved Reptile Search Algorithm

The Internet of Things (IoT) is a dispersed network system that connects the world through the Internet. The architecture of IoT consists of more gateways and resources which cannot be allocated in a manual process. The allocation of resources in IoT is a challenging process due to the higher consumption of energy and high latency rate. To overcome the challenges in existing works, this research introduced an Improved Reptile Search Algorithm (IRSA) to solve the optimization problem which occurs during the time of allocation resources among IoT networks. IRSA employs the methodology of levy flight and cross-over to update the candidate position and enhance the search speed in a single iteration. The proposed method consumes less energy and has low latency during data transmission from User equipment (UE) to the base station.IRSA has been compared with the existing Scalable Resource Allocation Framework (SRAF) and Improved Chaotic Firefly Algorithm (ICFA). The obtained experimental results show that the proposed IRSA attained better performance with an allocation rate of 96.40% which is comparatively higher than SRAF and ICFA with 92.40% and 91.67% respectively.

SecIoTComm: An Actor-Based Model and Framework for Secure IoT Communication.

Internet of Things (IoT) ecosystems are becoming increasingly ubiquitous and heterogeneous, adding extra layers of complexity to secure communication and resource allocation. IoT computing resources are often located at the network edge and distributed across many heterogeneous sensors, actuators, and controller devices. This makes it challenging to provide the proper security mechanisms to IoT ecosystems in terms of manageability and maintainability. In an IoT ecosystem, computational resources are naturally distributed and shareable among their constituency, which creates an opportunity to distribute heavy tasks to them. However, resource allocation in IoT requires secure and complex communication and coordination mechanisms, which existing ones do not adequately support. In this paper, we present Secure Actor-based Model for IoT Communication (SecIoTComm), a model for representing secure IoT communication. SecIoTComm aims to represent secure IoT communication properties and design and implement novel mechanisms to improve their programmability and performance. SecIoTComm separates the communication and computation concerns, achieving design modularity in building IoT ecosystems. First, this paper presents the syntax and operational semantics of SecIoTComm. Then, we present an IoT framework implementing the key concepts of the model. Finally, we evaluate the developed framework using various performance and scalability metrics.

Resource Allocation for Efficient IOT Application in Fog Computing

When it comes across problems in creating Internet of Things (IOT) architecture, the major problem that arises is an automatic stipulation of resources. At the same time in today’s era, it is very important to integrate this problem with better Quality of Services (QoS) because of which the cloud computing is taking a shift. As being well acquainted that in fog computing, network’s bandwidth is limited, therefore it becomes quite important to build a joint architecture with resource allocation problem giving it a better quality of services with enhanced efficiency and low latency communication. Priority of QoS is determined by Systematic Ladder Process (SLP) and decision parameter evaluation by RECK algorithm. In this paper, there will be a design of a better framework for IOT resource allocation scheme with better efficiency and better QoS. The paper too highlights the comparison of the previous works of the resource allocation algorithms and schemes with RECK algorithm.

A Review of Internet of Things—Resource Allocation

As one of emerging technologies, Internet of Things (IoT) has become popular. This article reviews the literature regarding resource allocation (RA) of the Internet of Things systematically. It identifies the key techniques, state-of-the-art, challenges, and trend associated with the IoT resource allocation, and its impact on the enterprise architecture.

Design, Resource Management, and Evaluation of Fog Computing Systems: A Survey

A steady increase in Internet-of-Things (IoT) applications needing large-scale computation and long-term storage has lead to an overreliance on cloud computing. The resulting network congestion in the cloud, coupled with the distance of cloud data centers from IoT, contributes to unreliable end-to-end response delay. Fog computing has been introduced as an alternative to cloud, providing low-latency service by bringing processing and storage resources to the network edge. In this survey, we sequentially present the phases required in the implementation and realization of practical fog computing systems: 1) design and dimensioning of a fog infrastructure; 2) fog resource provisioning for IoT application use and IoT resource allocation to fog; 3) installation of fog frameworks for fog resource management; and 4) evaluation of fog infrastructure through simulation and emulation. Our focus is on determining the implementation aspects required to build a practical large-scale fog computing infrastructure to support the general IoT landscape.

EdgeABC: An architecture for task offloading and resource allocation in the Internet of Things

The evolving Internet of Things (IoT) faces considerable challenges in terms of sensitive delay requirements for tasks, reasonable allocation requirements of resources, and reliability requirements of resource transactions. In the paper, considering these problems, we propose an emerging IoT architecture EdgeABC introduced the blockchain to ensure the integrity of resource transaction data and the profits of service provider, and we also propose a Task Offloading and Resource Allocation (TO-RA) algorithm, where the TO-RA algorithm is implemented on the blockchain in the form of smart contracts. In other words, the architecture proposed optimizes the resource allocation in IoT based on the advantages of the blockchain. Specifically, we first propose a subtask-virtual machine mapping strategy to complete the Task Offloading (TO) and the first allocation of resources; then aiming at the possible load imbalance problem of the system, we propose a stack cache supplement mechanism to complete the Resource Allocation (RA) based on the TO strategy. Finally, simulation experiments verify that the fairness, user satisfaction, and system utility of the TO-RA algorithm are superior to traditional algorithms.

IoT Resource Allocation and Optimization Based on Heuristic Algorithm.

The Internet of Things (IoT) is a distributed system that connects everything via internet. IoT infrastructure contains multiple resources and gateways. In such a system, the problem of optimizing IoT resource allocation and scheduling (IRAS) is vital, because resource allocation (RA) and scheduling deals with the mapping between recourses and gateways and is also responsible for optimally allocating resources to available gateways. In the IoT environment, a gateway may face hundreds of resources to connect. Therefore, manual resource allocation and scheduling is not possible. In this paper, the whale optimization algorithm (WOA) is used to solve the RA problem in IoT with the aim of optimal RA and reducing the total communication cost between resources and gateways. The proposed algorithm has been compared to the other existing algorithms. Results indicate the proper performance of the proposed algorithm. Based on various benchmarks, the proposed method, in terms of “total communication cost”, is better than other ones.

Resource allocation mechanisms and approaches on the Internet of Things

Internet of Things (IoT) as a novel paradigm is an environment with a vast number of connected things and applications. The IoT devices are used to generate data, which transforms into useable information and provides applied resources to end-users and this process is the main goal of IoT. Therefore, one of the important subjects in the IoT is resource allocation which aims is load balancing and minimizing operational cost, and power consuming. In addition, the resources should be allocated in such a way to be a balanced efficiency that can increase the system performance, Quality of Service (QoS) and Service Level Agreement (SLA). Although the resource allocation is very important in the IoT, there is no systematic review in this field. Therefore, in this paper, a Systematic Literature Review (SLR) is provided and the resources allocation methods in the IoT and used algorithms are investigated. Different classification, including cost-aware, context-aware, efficiency-aware, load-balancing-aware, power-aware, QoS-aware, SLA-based and utilization-aware resource allocation mechanisms are organized to investigate the resource allocation techniques. We present several parameters and describe them in each category. In addition, the used parameters in different articles are evaluated and the major developments in each category are surveyed and are outlined the new challenges. Furthermore, an SLR is provided in each of these eight categories. In this paper, a structure of different technical keys in the scope of resource allocation in the IoT and its platforms are presented and the important areas for improving the resource allocation methods in the future is highlighted and the open issues about resource allocation in IoT to achieve a better utilization of this technology are focused. The future direction is useful for academic researchers that work on IoT. This study shows that an independent technique does not exist to address all issues and challenges in resource allocation for IoT.

SEIRA: An effective algorithm for IoT resource allocation problem

To provide a high performance internet of things (IoT) system by integrating cloud computing, data mining, and other intelligent methods to all the people has been a promising trend in recent years. However, the performance of such a system is restricted by several factors, namely, various user requirements, various types of appliances, enormous need of communications, limited network bandwidth, and limited computation power. Considering these restrictions, IoT resource allocation problem (IRAP) is one of the most critical problems because a good solution to this problem will be very useful for enhancing the performance of such a system. That is why an effective algorithm will be presented in this paper for solving the IoT resource allocation problem of the internet of things. The concepts of data clustering and metaheuristic are employed for the proposed algorithm to allocate the large-scale devices and gateways of IoT to reduce the total communication cost between them. The simulation results show that the proposed algorithm is significantly better than the other resource allocation algorithms compared in this paper in terms of the total data communication costs.