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

In this paper, we address the problem of minimizing energy consumption and balancing energy in a wireless sensor network, using a topology control algorithm. Such an algorithm is able to minimize and balance energy consumption by reasonably tuning the transmission power level while preserving network connectivity. This paper proposes an energy welfare topology control using game theory approach, which adopts the welfare function from social sciences to compute energy welfare as a goodness measure for energy populations. When each node tries to maximize the energy welfare of its local society, it collectively leads to energy balancing. We show that the resulting game is a potential game and that it possesses a unique Nash equilibrium, which is Pareto optimal. To evaluate the performance of the proposed algorithm, extensive simulations were carried out, and the results were compared with the existing algorithm. The results demonstrated the superiority of the proposed algorithm over the existing algorithm.

A game theoretic framework for strategic refinery production planning is presented in which strategic planning problems are formulated as non‐cooperative potential games whose solutions represent Nash equilibria. The potential game model takes the form of a nonconvex nonlinear program (NLP) and we examine an additional scenario extending this to a nonconvex mixed integer nonlinear program (MINLP). Tactical planning decisions are linked to strategic decision processes through a potential game structure derived from a Cournot oligopoly‐type game in which multiple crude oil refineries supply several markets. Two scenarios are presented which illustrate the utility of the game theoretic framework in the analysis of production planning problems in competitive scenarios. Solutions to these problems are interpreted as mutual best responses yielding maximum profit in the competitive planning game. The resulting production planning decisions are rational in a game theoretic sense and are robust to deviations in competitor strategies. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2751–2763, 2017

In this paper, we see the objectives of spectrum allocation as evidence information, and propose a novel channel selection method based on Dempster-Shafer (DS) theory. This channel selection method is used in potential game model, and we finally obtain the dynamic spectrum allocation engine based on DS theory and game theory. The simulation results satisfy the requirements of spectrum allocation, which indicate that the method we use is suitable for solving channel selection problems.

For multi-transmission rate environments, access point (AP) connection methods have been proposed for maximizing system throughput, which is the throughput of an entire system, on the basis of the cooperative behavior of users. These methods derive optimal positions for the cooperative behavior of users, which means that new users move to improve the system throughput when connecting to an AP. Typically, the arrival of two or more new users causes signal interference for other users. Specifically, traditional methods have not considered interference and the number of allowable mobile positions is so large that it is difficult to find the optimal position to maximize system throughput. In this paper, we propose an AP connection method that maximizes system throughput by considering interference between users, with a potential game model. This game can find at least one optimal user position. Moreover, numerical analyses show the effectiveness of our method.

Existing methods based on statistical models ignore the detail effect of the real environment on path loss, leading to some discrepancies with the actual results in real urban environments. In order to reduce the discrepancies and improve the unmanned aerial vehicle (UAV) coverage effect, a multi-UAV coverage method based on deterministic radio wave propagation is proposed. First, the ray-tracing method, as one of the deterministic models, is used to simulate the path loss. Next, the cooperative search and coverage problem of multiple unmanned aerial vehicles is modeled as a potential game model, and the utility function is designed by path loss. After that action set is designed to constrain the motion of unmanned aerial vehicles. Then, the coverage problem is solved using a binary log-linear learning algorithm, which combines ray-tracing method and deterministic model. Finally, experiments are conducted in local areas of two cities, Haikou and Guangzhou by proposed method and statistic models. The experimental results show that the proposed method is capable of multi-UAV cooperative search and coverage tasks in urban areas and can achieve better performance than the statistical model-based methods.

Wireless ad hoc networks are power constrained since nodes operate with limited battery energy. Thus, energy consumption is crucial in the design of new ad hoc routing protocols. In order to maximize the lifetime of ad hoc networks, traffic should be sent via a route that can be avoid nodes with low energy. In addition, considering that the nodes of ad hoc networks are mobile, it is possible that a created path is broken because of nodes mobility and establishment of a new path would be done again. This is because of sending additional control packets, accordingly, energy consumption increases. Also, it should avoid nodes which have more buffered packets. Maybe, because of long queue, some of these packets are dropped and transmitted again. This is the reason for wasting of energy. In this paper we propose a new energy efficient algorithm, that uses a new cost function and avoid nodes with characteristics which mentioned above. We show that this algorithm improves the network energy consumption by using this new cost function.

Coal heavy-haul railway has been aiming at maximizing capacity utilization, but ignoring energy consumption for a long time. With the focus on green production, heavy-haul railways need transportation organization plans that can balance energy consumption and capacity utilization. Based on this, this paper proposes a data mining + optimization framework that uses train trajectory data to estimate train energy consumption and then uses a mixed integer programming model to simultaneously optimize plans from energy and capacity aspects. We use Gaussian distribution to describe features of energy consumption under different situations, and build a multi-dimensional cube to store these features to connect with the optimization model. In addition, a branch-and-bound algorithm is design to solve the optimization model. From the sensitivity analyses we can conclude that (1) shortening the departure interval from 13 min to 9 min will generate more energy consumption, about 3.6%; (2) combining short-form trains (50 units) with long-form trains (100 units) while increasing the carrying capacity will generate more energy consumption, about 5~14%; and (3) by controlling weights of the optimization model, capacity–energy-balanced plans can be obtained. The results can contribute to improving the sustainability of railways.

In recent years, mobile sinks are used more and more efficiently in sensor networks to collect data for the mobility advantage in balancing energy consumption than static sinks. However, it is still a challenge in both efficiency and network cost to avoid generating large amounts of overheads and lots of unnecessary energy consumption, when data source forward sensing data to mobile sink proactively according to their location information broadcasted all over the network. To reduce the overhead and balance the energy consumption in a network, we propose a clue-based data collection routing (CBDCR) protocol for mobile sensor networks. In CBDCR, a mobile sink moves randomly other than the following predesigned trajectories, during which it only broadcasts its location messages by limited hops instead of the whole network. The nodes getting these messages are called watchers who can obtain the upstream or downstream relations and infer the hop(s) from them to the mobile sink, and then a watcher stores this information as a “clue” to the location of mobile sink for data forwarding. As the movement of the mobile sink, more and more nodes are becoming watchers, and so a sensing data can be efficiently forwarded to the mobile sink according to these clues. Numerous simulations are conducted with mobile sinks in network to evaluate the performance of CBDCR, which demonstrate that CBDCR can both reduce the redundant transmission messages significantly and balance the network energy consumption.

Balanced energy consumption in WSNs is the key to maximize network lifetime. After analyzing laws of energy consumption of WSNs, a heterogeneous node deployment model based on area topology control is proposed in this paper. It utilizes predetermined heterogeneous node deployment to balance area energy consumption ratio and the topology control algorithm based on area energy consumption ratio to maintain dynamic equilibrium energy consumption. Simulation results show that our model takes a superior performance on maintaining network coverage, balancing energy consumption, and maximizing network lifetime. Compared with similar WSNs deployment models, our model can be more efficient to prolong network lifetime and has advantages in reducing dependence on deployment accuracy, balancing dynamic energy consumption, and improving lifetime in poor network environment.

Energy-efficient transmission and bit allocation schemes are investigated in multi-source single-sink Wireless Sensor Networks (WSNs). For transmission over Additive White Gaussian Noise (AWGN) channels with path loss, this work shows that the overall energy consumption can be minimised if each sensor transmits with the minimum power and cooperates with others in Time-Division Multiple Access (TDMA) mode. From the efficient correlated source coding perspective, the Slepian-Wolf coding theorem is applied. Jointly considering the two aspects, we propose a closed form bit allocation scheme to minimise the overall energy consumption. The underlying idea is to assign more bits to nodes with better channel conditions. Additionally, based on the definition of network lifetime as the time before the first sensor fails, we further maximise the network lifetime by developing a heuristic algorithm to balance energy consumption among sensors. The superiority of the proposed scheme is validated by both analytical and simulation results.

A lot of access points (APs) in wireless mesh networks (WMNs) are battery powered. In order to minimize their energy consumption, the APs can adaptively adjust their transmission power and time based on various network conditions. In this paper, the problem of minimizing the AP energy consumption is formulated as an optimization problem with the constraint to satisfy the throughput requirements of associated mobile stations (MSs). Balancing the energy consumption of the APs can be important to prolong the network lifetime, particularly in a network with high and random user mobility, which can easily result in unbalanced traffic load and energy consumption among the APs. Two distributed handoff schemes are proposed for a WMN with adaptive transmission power and rate. One scheme attempts to achieve balanced energy consumption among the APs during each scheduling interval, and the other scheme balances the AP energy consumption over a longer term. Numerical results show that both schemes achieve much longer network lifetime than the traditional distance-based handoff scheme, while the long-term based handoff scheme can achieve the objective with approximately the same number of handoffs as the distance-based handoff.

In wireless sensor networks, in order to satisfy the requirement of long working time of energy-limited nodes, we need to design an energy-efficient and lifetime-extended medium access control (MAC) protocol. In this paper, a node cooperation mechanism that one or multiple nodes with higher channel gain and sufficient residual energy help a sender relay its data packets to its recipient is employed to achieve this objective. We first propose a transmission power optimization algorithm to prolong network lifetime by optimizing the transmission powers of the sender and its cooperative nodes to maximize their minimum residual energy after their data packet transmissions. Based on it, we propose a corresponding power-optimized cooperative MAC protocol. A cooperative node contention mechanism is designed to ensure that the sender can effectively select a group of cooperative nodes with the lowest energy consumption and the best channel quality for cooperative transmissions, thus further improving the energy efficiency. Simulation results show that compared to typical MAC protocol with direct transmissions and energy-efficient cooperative MAC protocol, the proposed cooperative MAC protocol can efficiently improve the energy efficiency and extend the network lifetime.

Field stations and observation systems in cold and arid areas are mostly distributed in harsh natural environments which lead to problems such as the poor real-time data collection, transmission and processing, lower accuracy of data and failure to form integrated network observation, etc. To solve these problems which severely restrict scientific monitoring and research in these areas, a Layered and Heterogeneous Clustering routing algorithm LHC is proposed for field observation instrument networks based on the classical clustering routing algorithm LEACH. First, the LHC algorithm adopts the mechanism of heterogeneous node energy to divide nodes into advanced nodes and normal nodes. The advanced nodes have more initial energy than normal nodes, which increases their probability to be elected as cluster heads (CHs). Then, a hierarchical structure is used to divide the network into several layers, each layer elects a fixed number of CHs, and the distribution of CHs is improved. Finally, by analyzing the influence of the residual energy of the node and the distance between the node and the base station (BS) of the network, the mechanism based on energy and distance factors is introduced into each round of CH election to improve the CH election method. In Matlab experiments, the improved LHC algorithm was compared with LEACH, SEP, and DEEC through analysis and comparison from the aspects of network life cycle, energy consumption and CH number. The experimental results show that the LHC algorithm has the advantages of uniform CH distribution and balanced node energy consumption which effectively improve the energy efficiency and data transmission capacity, and prolong the life cycle of the observing instrument network. The LHC algorithm provides an important routing protocol for observing instrument network and real-time reliable data transmission.

Analysis of cumulative energy consumption in an oxy-fuel combustion power plant integrated with a CO2 processing unit

The energy of sensor nodes is usually limited, and it is difficult to supplement energy frequently. Multi-hop communication, which can lead to unbalanced energy consumption in the whole network and seriously affect the life cycle of wireless sensor network, is generally used for infrastructure and testing linear network. Optimization of regionalization node deployment was used to balance energy consumption among all the nodes and establish the network optimization model. Combined with engineering application, hybrid particle swarm was used as model for the linear network transmission strategy. Finally, simulation was used for modeling verification. The results of simulation showed that the energy consumption of optimizing transmission strategy is lower with balanced energy consumption among all the nodes, which aims to reduce energy consumption so that the life cycle of the whole network can be improved.