CEDAR: A cluster-based energy-aware data aggregation routing protocol in the internet of things using capuchin search algorithm and fuzzy logic

Authenticated key exchange mechanisms are critical for security-sensitive Internet of Things (IoT) and Wireless Sensor Networks (WSNs). In this area, the Bloom Filter (BF) plays a crucial role directly and indirectly, which has a significant advantage in space and time. Light-weight input authentication is one of the most challenging tasks in IoT. Weak or inefficient defense algorithms can allow fake information to enter the system, share information, send unnecessary messages, and reduce network efficiency. The utilization of an augmented Bloom filter for creating an authentication prominent called Enroute Authentication Bitmap (EAB) has a substantial advantage over traditional methods that involve direct usage of Message Authentication Codes (MAC). This effective method of EAB picks the fake information almost accurately, thereby reducing the feeding attacks within not more than two steps taken by the attacker. EAB necessarily needs only a few bytes of bandwidth for efficient defense against at least ten forward steps of the adversary. Without hesitation, the Augmented Bloom filter and its components are becoming more common in network defense mechanisms.

Section: Introductionmentioning

confidence: 99%

A Light-weight Authentication Scheme in the Internet of Things using the Enhanced Bloom Filter

Huo¹

2023

“…The IoT tries to link the physical and virtual worlds by equipping physical devices with processing, networking, detection, and identification functions. The IoT objects are employed in different applications, such as vehicle networks, energy management, traffic control, medical treatment, and healthcare, aiming to gather information about the physical world [13,14]. In this regard, to obtain valuable information and fulfil the assigned tasks, a massive amount of data is produced that brings challenging problems for efficient information processing, especially for the specific scenarios requiring real-time data handling.…”

Section: Introductionmentioning

confidence: 99%

A New Task Scheduling Framework for Internet of Things based on Agile VNFs On-demand Service Model and Deep Reinforcement Learning Method

Yang¹

2023

Recent innovations in the Internet of Things (IoT) have given rise to IoT applications that require quick response times and low latency. Fog computing has proven to be an effective platform for handling IoT applications. It is a significant challenge to deploy fog computing resources effectively because of the heterogeneity of IoT tasks and their delay sensitivity. To take advantage of idle resources in IoT devices, this paper presents an edge computing concept that offloads edge tasks to nearby IoT devices. The IoT-assisted edge computing should meet two conditions, edge services should exploit the computing resources of IoT devices effectively and edge tasks offloaded to IoT devices do not interfere with local IoT tasks. Two main phases are included in the proposed method: virtualization of edge nodes, and task scheduling based on deep reinforcement learning. The first phase offers a layered edge framework. In the second phase, we applied deep reinforcement learning (DRL) to schedule tasks taking into account the diversity of tasks and the heterogeneity of available resources. According to simulation results, our proposed task scheduling method achieves higher levels of task satisfaction and success than existing methods.

“…Combining cloud computing with IoT technology is far more efficient than regular cloud-based applications. This combination of technologies can be used in emerging medical, military, and banking applications [18]. Cloud-based IoT is particularly useful in medical applications that require tracking and remote access to records [19].…”

Section: Introductionmentioning

confidence: 99%

A Cloud and IoT-enabled Workload-aware Healthcare Framework using Ant Colony Optimization Algorithm

Liu¹,

Deng²

2023

In recent years, smart cities have gained in popularity due to their potential to improve the quality of life for urban residents. In many smart city services, particularly those in the field of smart healthcare, big healthcare data is analyzed, processed, and shared in real time. Products and services related to healthcare are essential to the industry's current state, which increases its viability for all parties involved. With the increasing popularity of cloud-based services, it is imperative to develop new approaches for discovering and selecting these services. This paper follows a two-stage process. The first step involves designing and implementing an Internet-enabled healthcare system incorporating wearable devices. A new load-balancing algorithm is presented in the second stage, based on Ant Colony Optimization (ACO). ACO distributes tasks across virtual machines to maximize resource utilization and minimize makespan time. In terms of both makespan time and processing time, the proposed method appears to be more efficient than previous approaches based on statistical analysis.