Learning to be energy-efficient in cooperative networks

Cooperative communication has great potential to improve the transmit diversity in multiple users environments. To achieve a high network-wide energy-efficient performance, this letter poses the relay selection problem of cooperative communication as a noncooperative automata game considering nodes’ selfishness, proving that it is an ordinal game (OPG), and presents a game-theoretic analysis to address the benefit equilibrium decision-making issue in relay selection. A stochastic learning-based relay selection algorithm is proposed for transmitters to learn a Nash-equilibrium strategy in a distributed manner. We prove through theoretical and numerical analysis that the proposed algorithm is guaranteed to converge to a Nash equilibrium state, where the resulting cooperative network is energy-efficient and reliable. The strength of the proposed algorithm is also confirmed through comparative simulations in terms of energy benefit and fairness performances

A microbial inspired routing protocol for VANETs

We present a bio-inspired unicast routing protocol for vehicular Ad Hoc Networks which uses the cellular attractor selection mechanism to select next hops. The proposed unicast routing protocol based on attractor selecting (URAS) is an opportunistic routing protocol, which is able to change itself adaptively to the complex and dynamic environment by routing feedback packets. We further employ a multi-attribute decision-making strategy, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), to reduce the number of redundant candidates for next-hop selection, so as to enhance the performance of attractor selection mechanism. Once the routing path is found, URAS maintains the current path or finds another better path adaptively based on the performance of current path, that is, it can self-evolution until the best routing path is found. Our simulation study compares the proposed solution with the state-of-the-art schemes, and shows the robustness and effectiveness of the proposed routing protocol and the significant performance improvement, in terms of packet delivery, end-to-end delay, and congestion, over the conventional method

A distributed position-based protocol for emergency messages broadcasting in vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) can help reduce traffic accidents through broadcasting emergency messages among vehicles in advance. However, it is a great challenge to timely deliver the emergency messages to the right vehicles which are interested in them. Some protocols require to collect nearby real-time information before broadcasting a message, which may result in an increased delivery latency. In this paper, we proposed an improved position-based protocol to disseminate emergency messages among a large scale vehicle networks. Specifically, defined by the proposed protocol, messages are only broadcasted along their regions of interest, and a rebroadcast of a message depends on the information including in the message it has received. The simulation results demonstrate that the proposed protocol can reduce unnecessary rebroadcasts considerably, and the collisions of broadcast can be effectively mitigated

From cellular decision making to adaptive handoff in heterogeneous wireless networks

Handoff decision making is critical for mobile users to reap potential benefits from heterogeneous wireless networks. This letter proposes a biologically inspired handoff decisionmaking method by mimicking the dynamics which govern the adaptive behavior of an Escherichia coli cell in a time-varying environment.With the goal of guaranteeing the Quality of Service (QoS), we formulate a utility function that covers the demands of a user’s diverse applications and the time-varying network conditions. With this utility function, we map the dynamic heterogeneous environment to a cellular decision-making space, such that the user is induced by a cellular attractor selection mechanism to make distributed and robust handoff decisions. Furthermore, we also present a multi-attribute decision-making network selection algorithm for any user to determine an access network, which is integrated with the proposed bio-inspired decision-making mechanism. Simulation results are supplemented to show that the proposed method can achieve better QoS and fairness when it is compared with conventional methods

Optimal epidemic information dissemination in uncertain dynamic environment

Optimization of stochastic epidemic information dissemination plays a significant role in enhancing the reliability of epidemic networks. This letter proposes a multi-stage decision making optimization model for stochastic epidemic information dissemination based on dynamic programming, in which uncertainties in a dynamic environment are taken into account. We model the inherent bimodal dynamics of general epidemic mechanisms as a Markov chain, and a state transition equation is proposed based on this Markov chain. We further derive optimal policies and a theoretical closed-form expression for the maximal expected number of successfully delivered messages. The properties of the derived model are theoretically analyzed. Simulation results show an improvement in reliability, in terms of accumulative number of successfully delivered messages, of epidemic information dissemination in stochastic situations

A Reservation-Based Vehicle-to-Vehicle Charging Service Under Constraint of Parking Duration

Electric vehicle (EV) has been applied as the main transportation tool recently. However, EVs still require a long charging time and, thus, inevitably cause charging congestion. The traditional plug-in charging mode is limited by fixed location and peak hours. Therefore, a flexible vehicle-to-vehicle (V2V) charging mode is considered in this article. Here, parking lots (PLs) widely dispersed in cities are reused as a common place for V2V charging. EVs are divided into EVs as energy consumers and EVs as energy providers to form as vehicle-to-vehicle charging pairs (V2V-Pairs). In this article, we propose a V2V charging management scheme, which includes a distance-based V2V-Pair matching algorithm and a PL-selection scheme. As the occupation status at PLs is difficult to predict, to achieve high PL utilization and evenly PL selection, V2V charging reservation is introduced. Meanwhile, since EV drivers usually park at PLs within a limited duration, our proposed V2V charging scheme introduces the parking duration to optimize V2V charging under a temporal constraint. We simulate this V2V charging scheme under the Helsinki city scenario. The results prove that our proposed V2V charging scheme achieves great charging efficiency (minimized charging waiting time and maximized fully charging times)

Self-organized Relay Selection for Cooperative Transmission in Vehicular Ad-hoc Networks

Cooperation is a promising paradigm to improve spatial diversity in vehicular ad-hoc networks. In this paper, we pose a fundamental question: how the greediness and selfishness of individual nodes impact cooperation dynamics in vehicular ad-hoc networks. We map the self-interest-driven relay selection decision-making problems as an automata game formulation and present a non-cooperative game-theoretic analysis. We show that the relay selection game is an ordinal potential game. A decentralized self-organized relay selection algorithm is proposed based on a stochastic learning approach where each player evolves toward a strategic equilibrium state in the sense of Nash. Furthermore, we study the exact outage behavior of the multi-relay decode-and-forward cooperative communication network. Closed-form solutions are derived for the actual outage probability of this multi-relay system in both independent and identically distributed channels and generalized channels, which need not assume an asymptotic or high signal-to-noise ratio. Two tight approximations with low computational complexity are also developed for the lower bound of the outage probability. With the exact closed-form outage probability, we further develop an optimization model to determine optimal power allocations in the cooperative network, which can be combined with the decentralized learning-based relay selection. The analysis of the exact and approximative outage behaviors and the convergence properties of the proposed algorithm toward a Nash equilibrium state are verified theoretically and numerically. Simulation results are also given to demonstrate that the resulting cooperative network induced by the proposed algorithm achieves high energy efficiency, transmission reliability, and network-wide fairness performance

Evaluating the importation of yellow fever cases into China in 2016 and strategies used to prevent and control the spread of the disease

During the yellow fever epidemic in Angola in 2016, cases of yellow fever were reported in China for the first time. The 11 cases, all Chinese nationals returning from Angola, were identified in March and April 2016, one to two weeks after the peak of the Angolan epidemic. One patient died; the other 10 cases recovered after treatment. This paper reviews the epidemiological characteristics of the 11 yellow fever cases imported into China. It examines case detection and disease control and surveillance, and presents recommendations for further action to prevent additional importation of yellow fever into China