Energy efficiency is one of the critical challenges in wireless sensor networks (WSNs). WSNs collect and transmit data through sensor nodes. However, the energy carried by the sensor nodes is limited. The sensor nodes need to save energy as much as possible to prolong the network lifetime. This paper proposes a game theory-based energy-efficient clustering algorithm (GEC) for wireless sensor networks, where each sensor node is regarded as a player in the game. According to the length of idle listening time in the active state, the sensor node can adopt favorable strategies for itself, and then decide whether to sleep or not. In order to avoid the selfish behavior of sensor nodes, a penalty mechanism is introduced to force the sensor nodes to adopt cooperative strategies in future operations. The simulation results show that the use of game theory can effectively save the energy consumption of the sensor network and increase the amount of network data transmission, so as to achieve the purpose of prolonging the network lifetime.

Recently, locating emergency response facilities has been drawing increasing attention with the highly strategic nature of terrorist attacks. To this end, we present a game‐theoretic approach for the location of terror response facilities when both disruption risk and hidden information are taken into account. The game is described as a two‐stage game, in which the first stage allows the State, that is, defender, to locate the terror response facilities, including disclosed and undisclosed facilities, and assign them to the attacked city, while the second stage allows the terrorist, that is, attacker, to select one city to attack with partial information about facility location and assignment. We propose a mixed integer bi‐level nonlinear programming formulation, and in response, a heuristic algorithm is developed to find the equilibrium solution. Extensive computational tests on both synthetic data and a real‐world dataset of provincial capital cities in China demonstrate the effectiveness of the developed algorithm.

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In the field of edge computing, collaborative computing offloading, in which edge users offload tasks to adjacent mobile devices with rich resources in an opportunistic manner, provides a promising example to meet the requirements of low latency. However, most of the previous work has been based on the assumption that these mobile devices are willing to serve edge users, with no incentive strategy. In this paper, an online auction-based strategy is proposed, in which both users and mobile devices can interact dynamically with the system. The auction strategy proposed in this paper is based on an online approach to optimize the long-term utility of the system, such as start time, length and size, resource requirements, and evaluation valuation, without knowing the future. Experiments verify that the proposed online auction strategy achieves the expected attributes such as individual rationality, authenticity and computational ease of handling. In addition, the index of theoretical competitive ratio also indicates that the proposed online mechanism realizes near-offline optimal long-term utility performance.

Sharing bicycles in the settlement of the user "last mile" short-distance travel needs and ease the urban traffic congestion plays an increasingly important role. In order to study the issue of sharing bicycles delivery, firstly, a bicycles' market demand model considering the age distribution is established, calculate the capacity of a city or region for sharing bicycles. Through the use of Markov chain, the establishment of a specific area of shared bicycles (residence, workplace, campus, park, business center and subway / bus station) model, analysis the relationship between demand and sharing bicycles delivery in the six regions, ensure the scientific and reasonable number of delivery.