Trust dynamic task allocation algorithm with Nash equilibrium for heterogeneous wireless sensor network

2020

Sensors

Wireless Body Area Network (WBAN) is a highly promising technology enabling health providers to remotely monitor vital parameters of patients via tiny wearable and implantable sensors. In a WBAN, medical data is collected by several tiny sensors and usually transmitted to a server-side (e.g., a cloud service provider) for long-term storage and online/offline processing. However, as the health data includes several sensitive information, providing confidentiality and fine-grained access control is necessary to preserve the privacy of patients. In this paper, we design an attribute-based encryption (ABE) scheme with lightweight encryption and decryption mechanisms. Our scheme enables tiny sensors to encrypt the collected data under an access control policy by performing very few computational operations. Also, the computational overhead on the users in the decryption phase is lightweight, and most of the operations are performed by the cloud server. In comparison with some excellent ABE schemes, our encryption mechanism is more than 100 times faster, and the communication overhead in our scheme decreases significantly. We provide the security definition for the new primitive and prove its security in the standard model and under the hardness assumption of the decisional bilinear Diffie-Hellman (DBDH) problem.

Section: Related Workmentioning

confidence: 99%

Lightweight Fine-Grained Access Control for Wireless Body Area Networks

Ali

Sadeghi

2020

Sensors

“…In wireless communication networks, especially in heterogeneous sensor networks, the node energy is not static, and decreases gradually with time [18,19]. Therefore, if we randomly choose a fixed probability value to represent the roaming probability between nodes, it will undoubtedly deviate from the calculations of the network.…”

Section: Dynamic Node Probabilitymentioning

confidence: 99%

Energy-balance node-selection algorithm for heterogeneous wireless sensor networks

Khan

Singh

2018

ETRI Journal

To solve the problem of unbalanced loads and the short network lifetime of heterogeneous wireless sensor networks, this paper proposes a node‐selection algorithm based on energy balance and dynamic adjustment. The spacing and energy of the nodes are calculated according to the proximity to the network nodes and the characteristics of the link structure. The direction factor and the energy‐adjustment factor are introduced to optimize the node‐selection probability in order to realize the dynamic selection of network nodes. On this basis, the target path is selected by the relevance of the nodes, and nodes with insufficient energy values are excluded in real time by the establishment of the node‐selection mechanism, which guarantees the normal operation of the network and a balanced energy consumption. Simulation results show that this algorithm can effectively extend the network lifetime, and it has better stability, higher accuracy, and an enhanced data‐receiving rate in sufficient time.

“…The time complexity of Part 1 is

O true(k \times (true(2 N + m + n true) \times l o g N) true)

, where

m

is the number of obstacles and

n

denotes the number of edges that intersect with obstacles. The time complexity introduced by k can be significantly decreased by parallel skill (Guo, Chen, Chen, & Zheng, ). Delaunay triangulation of a simulated dataset S1 and the sub‐graphs rounded up by dotted colored borderlines are shown in Figure .…”

Section: The Adc+ Algorithmmentioning

confidence: 99%

An adaptive dual clustering algorithm based on hierarchical structure: A case study of settlement zoning

Wang

et al. 2016

Transactions in GIS

Traditional dual clustering algorithms cannot adaptively perform clustering well without sufficient prior knowledge of the dataset. This article aims at accommodating both spatial and non-spatial attributes in detecting clusters without the need to set parameters by default or prior knowledge. A novel adaptive dual clustering algorithm (ADC1) is proposed to obtain satisfactory clustering results considering the spatial proximity and attribute similarity with the presence of noise and barriers. In this algorithm, Delaunay triangulation is utilized to adaptively obtain spatial proximity and spatial homogenous patterns based on particle swarm optimization (PSO). Then, a hierarchical clustering method is employed to obtain clusters with similar attributes. The hierarchical clustering method adopts a discriminating coefficient to adaptively control the depth of the hierarchical architecture. The clustering results are further refined using an optimization approach. The advantages and practicability of the ADC1 algorithm are illustrated by experiments on both simulated datasets and real-world applications. It is found that the proposed ADC1 algorithm can adaptively and accurately detect clusters with arbitrary shapes, similar attributes and densities under the consideration of barriers. K E Y W O R D S adaptive dual clustering, data mining, Delaunay triangulation, hierarchical structure, rural settlement zoning 1 | I NTR OD U CTI ON Clustering algorithms aim to separate a dataset to minimize the within-group variance and maximize the betweengroup variance. As such, they are a powerful tool applied in various applications, such as in the unsupervised classification of image processing, hot spot analysis, and outlier detection. Many traditional clustering algorithms have been reported in the literature and can be grouped into six categories, i.e. density-based (Andrade et al.