A Game Theoretic Optimization Method for Energy Efficient Global Connectivity in Hybrid Wireless Sensor Networks

Based on the connectivity and energy consumption problems in wireless sensor networks, this paper proposes a kind of new network algorithm called the connectivity and energy efficiency (CEE) algorithm to guarantee the connectivity and connectivity probability, and also to reduce the network energy consumption as much as possible. Under the premise that all sensors can communicate with each other in a specific communication radius, we obtained the relationship among the connectivity, the number of sensor nodes, and the communication radius because of the theory of probability and statistics. The innovation of the paper is to maximize the network connectivity and connectivity probability, by choosing which types of sleeping nodes to wake up. According to the node’s residual energy and the relative value of distance, the algorithm reduces the energy consumption of the whole network as much as possible, and wakes up the number of neighbor nodes as little as possible, to improve the service life of the whole network. Simulation results show that this algorithm combines the connectivity and the energy efficiency, provides a useful reference value for the normal operation of the sensors networks.

Section: Network Modelmentioning

confidence: 99%

On Connectivity and Energy Efficiency for Sleeping-Schedule-Based Wireless Sensor Networks

Wang

Jia

Deng

2019

“…The interactive relationship among things or objects, either competitive or cooperative, arises in countless types of fields. Game theory gives a mathematical framework to study the interactions among related individuals in interactive decision situations where the aims, goals and preferences of the participating agents are potentially in conflict [ 19 , 20 , 21 ] and has achieved great success in multiple of disciplines and fields [ 22 , 23 , 24 , 25 , 26 ]. As a game model representing the most conflicting and competitive relationship, the two-person zero-sum game models the case where players’ interests are completely opposed [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Zero-Sum Matrix Game with Payoffs of Dempster-Shafer Belief Structures and Its Applications on Sensors

Deng

Jiang

Zhang

2017

The zero-sum matrix game is one of the most classic game models, and it is widely used in many scientific and engineering fields. In the real world, due to the complexity of the decision-making environment, sometimes the payoffs received by players may be inexact or uncertain, which requires that the model of matrix games has the ability to represent and deal with imprecise payoffs. To meet such a requirement, this paper develops a zero-sum matrix game model with Dempster–Shafer belief structure payoffs, which effectively represents the ambiguity involved in payoffs of a game. Then, a decomposition method is proposed to calculate the value of such a game, which is also expressed with belief structures. Moreover, for the possible computation-intensive issue in the proposed decomposition method, as an alternative solution, a Monte Carlo simulation approach is presented, as well. Finally, the proposed zero-sum matrix games with payoffs of Dempster–Shafer belief structures is illustratively applied to the sensor selection and intrusion detection of sensor networks, which shows its effectiveness and application process.

“…It has recently received an increasing interest in the context of wireless sensor networks [11,12]. It can be used to solve the optimal energy management problems in wireless powered sensor networks, to meet the perceived QoS (Quality of Services) performance [13].…”

Section: Introductionmentioning

confidence: 99%

Optimal Power Control in Wireless Powered Sensor Networks: A Dynamic Game-Based Approach

Guo

Zhang

2017

In wireless powered sensor networks (WPSN), it is essential to research uplink transmit power control in order to achieve throughput performance balancing and energy scheduling. Each sensor should have an optimal transmit power level for revenue maximization. In this paper, we discuss a dynamic game-based algorithm for optimal power control in WPSN. The main idea is to use the non-cooperative differential game to control the uplink transmit power of wireless sensors in WPSN, to extend their working hours and to meet QoS (Quality of Services) requirements. Subsequently, the Nash equilibrium solutions are obtained through Bellman dynamic programming. At the same time, an uplink power control algorithm is proposed in a distributed manner. Through numerical simulations, we demonstrate that our algorithm can obtain optimal power control and reach convergence for an infinite horizon.