Energy-efficient collaborative transmission algorithm based on potential game theory for beamforming

Encouraging a certain number of users to participate in a sensing task continuously for collecting high-quality sensing data under a certain budget is a new challenge in the mobile crowdsensing. The users’ historical reputation reflects their past performance in completing sensing tasks, and users with high historical reputation have outstanding performance in historical tasks. Therefore, this study proposes a reputation constraint incentive mechanism algorithm based on the Stackelberg game to solve the abovementioned problem. First, the user’s historical reputation is applied to select some trusted users for collecting high-quality sensing data. Then, the two-stage Stackelberg game is used to analyze the user’s resource contribution level in the sensing task and the optimal incentive mechanism of the server platform. The existence and uniqueness of Stackelberg equilibrium are verified by determining the user’s optimal response strategy. Finally, two conversion methods of the user’s total payoff are proposed to ensure flexible application of the user’s payoff in the mobile crowdsensing network. Simulation experiments show that the historical reputation of selected trusted users is higher than that of randomly selected users, and the server platform and users have good utility.

show abstract

“…Thus, the other energy consumed by users is ignored. 28 Equation (3) represents the energy consumption of transmitting and receiving sensing data…”

Section: System Modelmentioning

confidence: 99%

Incentive mechanism based on Stackelberg game under reputation constraint for mobile crowdsensing

Yang

Zhang

Peng

et al. 2021

International Journal of Distributed Sensor Networks

Self Cite

View full text Add to dashboard Cite

show abstract

“…The expected number of sensors in an area I can be calculated as S = ρπd max . However, the output function of the area as f N (ℵ) = ρ κ , in the range κ ∈ 0 ≤ ℵ ≤ N of each of the ℵ of N grouped by clusters of collaborative sensors of probability of size 1 ℵ . Therefore, the expected number of collaborative sensors in each cluster is given by ℵ = S N Linear beamforming technique implies that every sensor directs its mainbeam towards the geometric center of the network, where the connection can be easily established if any two sensors are aligned within each other's mainbeam.…”

Section: Connectivity Modelmentioning

confidence: 99%

“…Moreover, CB distributes power consumption overall sensors when the collaboration involves ℵ κ sensors up to an N κ [41]. The gain G r is defined as the maximum in mainlobe depending on the network topology as well as the minimum value of an 1 N κ fold with the reduction in the received power everywhere else [41]. An example of IoT application is monitoring rural areas by deploying sensors.…”

Section: Connectivity Modelmentioning

confidence: 99%

“…Beamforming techniques are gaining an increasing research interest in recent years due to the growth of Internet of Things (IoT) applications that require establishing connections for long distances among sensors and a remote base station [1]. The popularity of these applications stems from the demand of increasing throughput accompanied by decreasing size of electronic devices [2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Beamforming Optimization in Internet of Things Applications Using Robust Swarm Algorithm in Conjunction with Connectable and Collaborative Sensors

Hasan

Al‐Rizzo

2020

Sensors

View full text Add to dashboard Cite

The integration of the Internet of Things (IoT) with Wireless Sensor Networks (WSNs) typically involves multihop relaying combined with sophisticated signal processing to serve as an information provider for several applications such as smart grids, industrial, and search-and-rescue operations. These applications entail deploying many sensors in environments that are often random which motivated the study of beamforming using random geometric topologies. This paper introduces a new algorithm for the synthesis of several geometries of Collaborative Beamforming (CB) of virtual sensor antenna arrays with maximum mainlobe and minimum sidelobe levels (SLL) as well as null control using Canonical Swarm Optimization (CPSO) algorithm. The optimal beampattern is achieved by optimizing the current excitation weights for uniform and non-uniform interelement spacings based on the network connectivity of the virtual antenna arrays using a node selection scheme. As compared to conventional beamforming, convex optimization, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), the proposed CPSO achieves significant reduction in SLL, control of nulls, and increased gain in mainlobe directed towards the desired base station when the node selection technique is implemented with CB.

show abstract

“…The energy consumption of MU i mainly includes the energy consumption of sending and receiving data during the sensing task, and other energy consumption used by MUs can be ignored [ 29 ]. Equation (6) represents the energy consumption of transmitting and receiving data.…”

Section: System Model and Game Formulationmentioning

confidence: 99%

A Joint Constraint Incentive Mechanism Algorithm Utilizing Coverage and Reputation for Mobile Crowdsensing

Zhang

Yang

Feng

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Selection of the optimal users to maximize the quality of the collected sensing data within a certain budget range is a crucial issue that affects the effectiveness of mobile crowdsensing (MCS). The coverage of mobile users (MUs) in a target area is relevant to the accuracy of sensing data. Furthermore, the historical reputation of MUs can reflect their previous behavior. Therefore, this study proposes a coverage and reputation joint constraint incentive mechanism algorithm (CRJC-IMA) based on Stackelberg game theory for MCS. First, the location information and the historical reputation of mobile users are used to select the optimal users, and the information quality requirement will be satisfied consequently. Second, a two-stage Stackelberg game is applied to analyze the sensing level of the mobile users and obtain the optimal incentive mechanism of the server center (SC). The existence of the Nash equilibrium is analyzed and verified on the basis of the optimal response strategy of mobile users. In addition, mobile users will adjust the priority of the tasks in time series to enable the total utility of all their tasks to reach a maximum. Finally, the EM algorithm is used to evaluate the data quality of the task, and the historical reputation of each user will be updated accordingly. Simulation experiments show that the coverage of the CRJC-IMA is higher than that of the CTSIA. The utility of mobile users and SC is higher than that in STD algorithms. Furthermore, the utility of mobile users with the adjusted task priority is greater than that without a priority order.

show abstract

Energy-efficient collaborative transmission algorithm based on potential game theory for beamforming

Cited by 6 publications

References 35 publications

Incentive mechanism based on Stackelberg game under reputation constraint for mobile crowdsensing

Incentive mechanism based on Stackelberg game under reputation constraint for mobile crowdsensing

Beamforming Optimization in Internet of Things Applications Using Robust Swarm Algorithm in Conjunction with Connectable and Collaborative Sensors

A Joint Constraint Incentive Mechanism Algorithm Utilizing Coverage and Reputation for Mobile Crowdsensing

Contact Info

Product

Resources

About