Catenarian-Trim Medley Routing System for Energy Balancing in Dispensed Computing Networks

Verma, Vinod Kumar; Sharma, Anupam; Ntalianis, Klimis; Verma, Kuldeep Chand

doi:10.1109/tnse.2021.3140139

Cited by 9 publications

(6 citation statements)

References 68 publications

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“…In summary, there have been many studies on the optimization of traffic signal control at home and abroad for many years [13][14][15][16][17][18]. In recent years, with the development of artificial intelligence, artificial intelligence algorithms in traffic signal control have also been formed on this basis.…”

Section: Prefacementioning

confidence: 99%

“…(4) According to the distribution form of the Bellman iterative equation (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14), the maximum action value distribution Z(s t , a t ) of the next state s t+1 is contracted by the discount coefficient γ and the reward r t+1 is shifted to obtain the true distribution T π Z ′ (s t+1 , a t+1 ). Due to shrinking and shifting, the sampling points between the real distribution and the estimated value distribution are different at this time.…”

Section: Simulation Experiments Designmentioning

confidence: 99%

“…(6) Use Adam optimizer to do gradient descent to the objective function, calculate the gradient according to formula (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17) and update the storage network weight;…”

Section: Simulation Experiments Designmentioning

confidence: 99%

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Research on signal control method of deep reinforcement learning based on value distribution

Chen

Zhou³

et al. 2022

J. Phys.: Conf. Ser.

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In the traditional DQN (Deep Q network)-based signalized intersection control strategy, this research introduces an improved Distributional DQN to establish signal optimization decision-making model utilizing reinforcement learning based on value distribution. Different from the DQN model based on expected values, proposed model makes full use of the intersection environment information in each phase action for the distribution of the future total return. At the same time, proposed model constructs an optimization through minimizing the KL divergence between the estimated distribution and the true distribution, which makes it easier minimize the loss and accelerate the convergence speed of the model. In addition, a fixed boundary is reasonably added to the discrete distribution of the reward of the phase action, which effectively suppresses the traffic flow. The reward shock caused by large randomness reduces the instability of the algorithm. The simulation results demonstrate that the Distributional DQN proposed in this paper has a faster convergence rate than the original DQN, and the cumulative delay at the intersection is reduced by about 13.1%, and the average driving speed is increased by 7.1%, which further improves the control efficiency of signalized intersections.

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Section: Prefacementioning

confidence: 99%

Section: Simulation Experiments Designmentioning

confidence: 99%

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Research on signal control method of deep reinforcement learning based on value distribution

Chen

Zhou³

et al. 2022

J. Phys.: Conf. Ser.

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“…Different application scenarios or environmental changes might make the topology of the WSN unbalanced, causing the lifetime of the nodes and the Qos to be reduced [ 9 ]. WSNs are essential in developing many applications for AI and the Internet of Things (AIoT).…”

Section: Introductionmentioning

confidence: 99%

A Near-Optimal Energy Management Mechanism Considering QoS and Fairness Requirements in Tree Structure Wireless Sensor Networks

Tai

Liu

Hsiao

et al. 2023

Sensors

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The rapid development of AIOT-related technologies has revolutionized various industries. The advantage of such real-time sensing, low costs, small sizes, and easy deployment makes extensive use of wireless sensor networks in various fields. However, due to the wireless transmission of data, and limited built-in power supply, controlling energy consumption and making the application of the sensor network more efficient is still an urgent problem to be solved in practice. In this study, we construct this problem as a tree structure wireless sensor network mathematical model, which mainly considers the QoS and fairness requirements. This study determines the probability of sensor activity, transmission distance, and transmission of the packet size, and thereby minimizes energy consumption. The Lagrangian Relaxation method is used to find the optimal solution with the lowest energy consumption while maintaining the network’s transmission efficiency. The experimental results confirm that the decision-making speed and energy consumption can be effectively improved.

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“…Verma et al [10] investigated the effects of several battery models on five routing protocols using a wireless sensor network system and provided an analytical approach for analysing important performance indicators such as average jitter, first and last packet received, total bytes received, average end-to-end delay, throughput, and energy consumption. These issues make it susceptible to eavesdropping and information interception [11].…”

Section: Introductionmentioning

confidence: 99%

Multiuser-scheduling and resource allocation using max-min technique in wireless powered communication networks

Thomas

Malarvizhi

2022

IJEECS

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Wireless <span>powered communication network (WPCN) is a promising research area for improving network security and speed. The transmission power of the source during the uplink functions as a random variable in WPCN due to the intrinsic power transfer process, whereas it is a constant in typical cooperative networks, culminating in the signal to noise ratio of the source-access point and all the source-relay-access point being mutually correlated. As a result of the massive increase in communication devices powered by battery, the goal of prolonging their life is critical. To get the most throughput in the shortest amount of time, the best uplink and downlink time allocations were calculated. For high throughput and secrecy performance, the proportional max-min fairness algorithm was used in this paper for secure communication in hybrid relays integrated with WPCN. This method allows for multi-user scheduling with optimum targets to provide reliable hybrid outage probability, secrecy outage probability, and energy outage probability. Efficacy of the proposed system was demonstrated regarding throughput, outage probability, and confidentiality. The performance of the model was compared to that of various energy harvesting models like random user scheduling, best user scheduling, and several others, and was found to outperform them for secure transmission.</span>

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Catenarian-Trim Medley Routing System for Energy Balancing in Dispensed Computing Networks

Cited by 9 publications

References 68 publications

Research on signal control method of deep reinforcement learning based on value distribution

Research on signal control method of deep reinforcement learning based on value distribution

A Near-Optimal Energy Management Mechanism Considering QoS and Fairness Requirements in Tree Structure Wireless Sensor Networks

Multiuser-scheduling and resource allocation using max-min technique in wireless powered communication networks

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