An adaptive fusion strategy for distributed information estimation over cooperative multi-agent networks

In this paper, we study the problem of distributed information estimation that is closely relevant to some network-based applications such as distributed surveillance, cooperative localization and optimization. We consider a problem where an application area containing multiple information sources of interest is divided into a series of subregions in which only one information source exists. The information is presented as a signal variable which has finite states associated with certain probabilities. The probability distribution of information states of all the subregions constitutes a global information picture for the whole area. Agents with limited measurement and communication ranges are assumed to monitor the area, and cooperatively create a local estimate of the global information. To efficiently approximate the actual global information using individual agents’ own estimates, we propose an adaptive distributed information fusion strategy and use it to enhance the local Bayesian rule based updating procedure. Specifically, this adaptive fusion strategy is induced by iteratively minimizing a Jensen-Shannon divergence based objective function. A constrained optimization model is also presented to derive minimum Jensen-Shannon divergence weights at each agent for fusing local neighbors’ individual estimates. Theoretical analysis and numerical results are supplemented to show the convergence performance and effectiveness of the proposed solution

Robust energy-efficient MIMO transmission for cognitive vehicular networks

This work investigates a robust energy-efficient solution for multiple-input-multiple-output (MIMO) transmissions in cognitive vehicular networks. Our goal is to design an optimal MIMO beamforming for secondary users (SUs) considering imperfect interference channel state information (CSI). Specifically, we optimize the energy efficiency (EE) of SUs, given that the transmission power constraint, the robust interference power constraint and the minimum transmission rate are satisfied. To solve the optimization problem, we first characterize the uncertainty of CSI by bounding it in a Frobenius-norm-based region and then equivalently convert the robust interference constraint to a linear matrix inequality. Furthermore, a feasible ascent direction approach is proposed to reduce the optimization problem into a sequential linearly constrained semi-definite program, which leads to a distributed iterative optimization algorithm for deriving the robust and optimal beamforming. The feasibility and convergence of the proposed algorithm is theoretically validated, and the final experimental results are also supplemented to show the strength of the proposed algorithm over some conventional schemes in terms of the achieved EE performance and robustness

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

Vibration suppression using fractional-order disturbance observer based adaptive grey predictive controller

A novel control strategy is proposed for vibration suppression using an integration of a fractional-order disturbance observer (FDOB) and an adaptive grey predictive controller (AGPC). AGPC is utilized to realize outer loop control for better transient performance by predicting system outputs ahead with metabolic GM(1,1) model, and an adaptive step switching module is adopted for the grey predictor in AGPC. FDOB is used to obtain disturbance estimate and generate compensation signal, and as the order of Q-filter is expanded to real-number domain, FDOB has a wider range to select a suitable tradeoff between robustness and vibration suppression. For implementation of the fractional order Q-filter, broken-line approximation method is introduced. The proposed control strategy is simple in control-law derivation, and its effectiveness is validated by numerical simulations

Reliability-oriented optimization of computation offloading for cooperative vehicle-infrastructure systems

Computation offloading is critical for mobile applications that are sensitive to computational power, while dynamic and random nature of vehicular networks makes it challenging to guarantee the reliability of vehicular computation offloading. In this letter, we propose a reliability-oriented stochastic optimization model based on dynamic programming for computation offloading in the presence of the deadline constraint on application execution. Specifically, a theoretical lower bound of the expected reliability of computation offloading is derived, and then an optimal data transmission scheduling mechanism is proposed to maximize the lower bound with consideration of randomness in vehicle-to-infrastructure (V2I) communications. Experimental results demonstrate that our mechanism can outperform the conventional scheme and benefits vehicular computation offloading in terms of reliability performance in stochastic situations

Channel access optimization with adaptive congestion pricing for cognitive vehicular networks: an evolutionary game approach

Cognitive radio-enabled vehicular nodes as unlicensed users can competitively and opportunistically access the radio spectrum provided by a licensed provider and simultaneously use a dedicated channel for vehicular communications. In such cognitive vehicular networks, channel access optimization plays a key role in making the most of the spectrum resources. In this paper, we present the competition among self-interest-driven vehicular nodes as an evolutionary game and study fundamental properties of the Nash equilibrium and the evolutionary stability. To deal with the inefficiency of the Nash equilibrium, we design a delayed pricing mechanism and propose a discretized replicator dynamics with this pricing mechanism. The strategy adaptation and the channel pricing can be performed in an asynchronous manner, such that vehicular users can obtain the knowledge of the channel prices prior to actually making access decisions. We prove that the Nash equilibrium of the proposed evolutionary dynamics is evolutionary stable and coincides with the social optimum. Besides, performance comparison is also carried out in different environments to demonstrate the effectiveness and advantages of our method over the distributed multi-agent reinforcement learning scheme in current literature in terms of the system convergence, stability and adaptability

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

Reliability-optimal cooperative communication and computing in connected vehicle systems

The emergence of vehicular networking enables distributed cooperative computation among nearby vehicles and infrastructures to achieve various applications that may need to handle mass data by a short deadline. In this paper, we investigate the fundamental problems of a cooperative vehicle-infrastructure system (CVIS): how does vehicular communication and networking affect the benefit gained from cooperative computation in the CVIS and what should a reliability-optimal cooperation be? We develop an analytical framework of reliability-oriented cooperative computation optimization, considering the dynamics of vehicular communication and computation. To be specific, we propose stochastic modeling of V2V and V2I communications, incorporating effects of the vehicle mobility, channel contentions and fading, and theoretically derive the probability of successful data transmission. We also formulate and solve an execution time minimization model to obtain the success probability of application completion with the constrained computation capacity and application requirements. By combining these models, we develop constrained optimizations to maximize the coupled reliability of communication and computation by optimizing the data partitions among different cooperators. Numerical results confirm that vehicular applications with a short deadline and large processing data size can better benefit from the cooperative computation rather than non-cooperative solutions

Wireless acoustic sensor networks and edge computing for rapid acoustic monitoring

Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the affordability of robust hardware for remote audio recording is stimulating international interest in the potential for acoustic methods for biodiversity monitoring. The scale of the data involved requires automated methods, however, the development of acoustic sensor networks capable of sampling the soundscape across time and space and relaying the data to an accessible storage location remains a significant technical challenge, with power management at its core. Recording and transmitting large quantities of audio data is power intensive, hampering long-term deployment in remote, off-grid locations of key ecological interest. Rather than transmitting heavy audio data, in this paper, we propose a low-cost and energy efficient wireless acoustic sensor network integrated with edge computing structure for remote acoustic monitoring and in situ analysis. Recording and computation of acoustic indices are carried out directly on edge devices built from low noise primo condenser microphones and Teensy microcontrollers, using internal FFT hardware support. Resultant indices are transmitted over a ZigBee-based wireless mesh network to a destination server. Benchmark tests of audio quality, indices computation and power consumption demonstrate acoustic equivalence and significant power savings over current solutions

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