A roadmap towards energy‐efficient data fusion methods in the Internet of Things

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 recent rapid development of artificial intelligence [1,2], machine learning [3], optical networks [4,5], smart grids [6], cloud computing [7,8], 5G connectivity [9], Blockchain, and Internet of Things (IoT) [10,11] is leading to an exponential growth in data usage across a wide range of engineering and commerce disciplines. Over the last decade, the IoT has been recognized for its remarkable potential in computer science.…”

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.

“…Recent rapid growth in artificial intelligence [1,2], machine learning [3], optical networks [4,5], smart grids [6], cloud computing [7], 5G connectivity [8], Blockchain [9,10], and Internet of Things (IoT) [11,12] have resulted in an explosion of data in almost all fields of engineering and commerce. With cloud computing, large-scale applications can be deployed quickly and efficiently, affordably on a per-use basis [13].…”

Section: Introductionmentioning

confidence: 99%

“…( 6) calculated the response time parameter, and Eq. ( 7) to (11) are used to calculate delay, energy, reliability, availability, and successability parameters in the composing services, respectively.…”

mentioning

confidence: 99%

Cloud Service Composition using Firefly Optimization Algorithm and Fuzzy Logic

Wang¹,

Liu²

2023

Cloud computing involves the dynamic provision of virtualized and scalable resources over the Internet as services. Different types of services with the same functionality but different non-functionality features may be delivered in a cloud environment in response to customer requests, which may need to be combined to satisfy the customer's complex requirements. Recent research has focused on combining unique and looselycoupled services into a preferred system. An optimized composite service consists of formerly existing single and simple services combined to provide an optimal composite service, thereby improving the quality of service (QoS). In recent years, cloud computing has driven the rapid proliferation of multi-provision cloud service compositions, in which cloud service providers can provide multiple services simultaneously. Service composition fulfils a variety of user needs in a variety of scenarios. The composite request (service request) in a multi-cloud environment requires atomic services (service candidates) located in multiple clouds. Service composition combines atomic services from multiple clouds into a single service. Since cloud services are rapidly growing and their Quality of Service (QoS) is widely varying, finding the necessary services and composing them with quality assurances is an increasingly challenging technical task. This paper presents a method that uses the firefly optimization algorithm (FOA) and fuzzy logic to balance multiple QoS factors and satisfy service composition constraints. Experimental results prove that the proposed method outperforms previous ones in terms of response time, availability, and energy consumption.