Toward energy and resource efficient Internet-of-Things: a design principle combining computation, communications and protocols

Advances in future computing and communications to support IoT are becoming more important as the need to better utilize resources and make them energy-efficient grows. As a result, it is predicted that intelligent devices and networks, including WSNs, will become the new interfaces to support future IoT applications. However, many open challenges remain, which are mostly due to the resource constraints imposed by various hardware platforms and complex characteristics of applications wishing to make use of IoT systems. Thus, it becomes critically important to study how the current approaches incorporating both computing and communications in this area can be improved, and at the same time better understand the opportunities for the research community to utilize the proposed approaches. To this end, this article presents an overview of our latest research results in sensor edge computing and lightweight communication protocols as well as their potential applications

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

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 Q-Learning approach with collective contention estimation for bandwidth-efficient and fair access control in IEEE 802.11p vehicular networks

Vehicular Ad hoc Networks (VANETs) are wireless networks formed of moving vehicle-stations, that enable safety-related packet exchanges among them. Their infrastructure-less, unbounded nature allows the formation of dense networks that present a channel sharing issue, which is harder to tackle than in conventional WLANs, due to fundamental differences of the protocol stack. Optimising channel access strategies is important for the efficient usage of the available wireless bandwidth and the successful deployment of VANETs. We present a Q-Learning-based approach to wirelessly network a big number of vehicles and enable the efficient exchange of data packets among them. More specifically, this work focuses on a IEEE 802.11p-compatible contention-based Medium Access Control (MAC) protocol for efficiently sharing the wireless channel among multiple vehicular stations. The stations feature algorithms that "learn" how to act optimally in a network in order to maximise their achieved packet delivery and minimise bandwidth wastage. Additionally, via a Collective Contention Estimation (CCE) mechanism which we embed on the Q-Learning agent, faster convergence, higher throughput and short-term fairness are achieved

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

A Calibration Method for Misalignment Angle of Vehicle-mounted IMU

AbstractIn order to get accurate navigation data via Strapdown Inertial Navigation System (SINS), calibration of misalignment angle between Inertial Measurement Unit (IMU) and vehicle is necessary. The misalignment angle model is simplified, assuming that vehicle travels on a horizontal plane. Due to the velocity of vehicle is small, equation of specific force is simplified. And then equation of misalignment angle is deduced on condition that vehicle travels on a horizontal plane, making straight motion ideally. Angular rate is used to discriminate that vehicle making straight motion. Steps for calibrate misalignment angle are established. In calibration experiment, data are collected and misalignment angle of vehicle-mounted IMU is calibrated using simple techniques of data processing. Analysis shows that, compensation of misalignment angle helps improving the accuracy of SINS. Position error of SINS solution is decreased and the acceleration of vehicle is more accurate

Delay analysis and time-critical protocol design for in-vehicle power line communication systems

With the emerging automated tasks in vehicle domain, the development of in-vehicle communications is increasingly important and subjected to new applications. The use of vehicular power lines has been a promising alternative to invehicle communications because of elimination of extra data cables. In this paper, we focus on the latest HomePlug Green PHY (HPGP) and explore its opportunity to support timecritical in-vehicle applications. Specifically, we apply Network Calculus to evaluate the worst access and queuing delay of various priority flows in vehicle bus networks. In order to maximize the bandwidth utility and satisfy the end-to-end hard delay requirements, we further propose a bandwidth efficient fair rate scheduling and delay sensitive traffic shaper. Performance evaluation supplemented by numerical and simulation results is also provided to show the advantage of HPGP and the proposed traffic shaper over the existing industry solutions

A Proof-of-Quality-Factor (PoQF) based blockchain and edge computing for vehicular message dissemination

Blockchain applications in vehicular networks can offer many advantages including decentralization and improved security. However, most of consensus algorithms in blockchain are difficult to be implemented in a Vehicular Ad-Hoc Networks (VANET) without the help of edge computing services. For example, the connectivity in VANET only remains for a short period of time, which is not sufficient for highly time consuming consensus algorithms, e.g., Proof-of-Work, running on mobile edge nodes (vehicles). Other consensus algorithms also have some drawbacks, e.g. Proof-of-Stake (PoS) is biased towards nodes with higher amount of stakes and Proof-of-Elapsed-Time (PoET) is not highly secure against malicious nodes. For these reasons, we propose a voting blockchain based on Proof-of-Quality-Factor (PoQF) consensus algorithm, where threshold number of votes is controlled by edge computing servers. Specifically, PoQF includes voting for message validation and a competitive relay selection process based on probabilistic prediction of channel quality between transmitter and receiver. The performance bounds of failure and latency in message validation are obtained. The paper also analyzes the throughput of block generation, as well as the asymptotic latency, security and communication complexity of PoQF. An incentive distribution mechanism to reward honest nodes and punish malicious nodes is further presented and its effectiveness against collusion of nodes is proved using game theory. Simulation results show that PoQF reduces failure in validation by 11% and 15% as compared to PoS and PoET, respectively, and is 68 ms faster than PoET

Analytical model of spread of epidemics in open finite regions

Epidemic dynamics, a kind of biological mechanisms describing microorganism propagation within populations, can inspire a wide range of novel designs of engineering technologies, such as advanced wireless communication and networking, global immunization on complex systems, and so on. There have been many studies on epidemic spread, but most of them focus on closed regions where the population size is fixed. In this paper, we proposed a susceptible-exposed-infected-recovered model with a variable contact rate to depict the dynamic spread processes of epidemics among heterogeneous individuals in open finite regions. We took the varied number of individuals and the dynamic migration rate into account in the model. We validated the effectiveness of our proposed model by simulating epidemics spread in different scenarios. We found that the average infected possibility of individuals, the population size of infectious individuals in the regions, and the infection ability of epidemics have great impact on the outbreak sizes of epidemics. The results demonstrate that the proposed model can well describe epidemics spread in open finite regions