Exponential utility function based criteria for network selection in heterogeneous wireless networks

Khan, Shoaib; Ahmad, Mian Ilyas; Hussain, Farooq

doi:10.1049/el.2017.2673

Cited by 7 publications

(2 citation statements)

References 7 publications

(17 reference statements)

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“…Owing to the uncertainty and no boundary of node parameters, it is very difficult to find the optimal weight coefficient for the decision algorithm based on utility function [19] and multiple-attribute decision-making [20]. The decision algorithm based on machine learning and game theory [21] has a higher complexity and longer time delay.…”

Section: Flgr Protocolmentioning

confidence: 99%

Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks

Ding

et al. 2019

Sensors

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The geographic routing protocol only requires the location information of local nodes for routing decisions, and is considered very efficient in multi-hop wireless sensor networks. However, in dynamic wireless sensor networks, it increases the routing overhead while obtaining the location information of destination nodes by using a location server algorithm. In addition, the routing void problem and location inaccuracy problem also occur in geographic routing. To solve these problems, a novel fuzzy logic-based geographic routing protocol (FLGR) is proposed. The selection criteria and parameters for the assessment of the next forwarding node are also proposed. In FLGR protocol, the next forward node can be selected based on the fuzzy location region of the destination node. Finally, the feasibility of the FLGR forwarding mode is verified and the performance of FLGR protocol is analyzed via simulation. Simulation results show that the proposed FLGR forwarding mode can effectively avoid the routing void problem. Compared with existing protocols, the FLGR protocol has lower routing overhead, and a higher packet delivery rate in a sparse network.

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Section: Flgr Protocolmentioning

confidence: 99%

Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks

Ding

et al. 2019

Sensors

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“…Exponential utility function is also used by some previous works to model the satisfaction level of the agents in multi-agent systems. Examples include [25], [26], [27], [28].…”

Section: Definition 1 (Allocation) An Allocation Of State Contingent ...mentioning

confidence: 99%

Distributed Task Management in Cyber-Physical Systems: How to Cooperate Under Uncertainty?

Schaar

2019

IEEE Trans. Cogn. Commun. Netw.

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We consider the problem of task allocation in a network of cyber-physical systems (CPSs). The network can have different states, and the tasks are of different types. The task arrival is stochastic and state-dependent. Every CPS is capable of performing each type of task with some specific state-dependent efficiency. The CPSs have to agree on task allocation prior to knowing about the realized network's state and/or the arrived tasks. We model the problem as a multi-state stochastic cooperative game with state uncertainty. We then use the concept of deterministic equivalence and sequential core to solve the problem. We establish the non-emptiness of the strong sequential core in our designed task allocation game and investigate its characteristics including uniqueness and optimality. Moreover, we prove that in the task allocation game, the strong sequential core is equivalent to Walrasian equilibrium under state uncertainty; consequently, it can be implemented by using the Walras' tatonnement process.

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A Hybrid MADM Algorithm Based on Attribute Weight and Utility Value for Heterogeneous Network Selection

Huang

Zhang

2018

J Netw Syst Manage

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Exponential utility function based criteria for network selection in heterogeneous wireless networks

Cited by 7 publications

References 7 publications

Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks

Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks

Distributed Task Management in Cyber-Physical Systems: How to Cooperate Under Uncertainty?

A Hybrid MADM Algorithm Based on Attribute Weight and Utility Value for Heterogeneous Network Selection

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