Electrical vehicles (EV) have been considered to be an effective way to combat global climate change. To extend the driving range of EV, this paper studies the active battery cell balancing control based on linear parametric varying model predictive control (MPC). Specifically, an equivalent circuit model is used to predict cell terminal voltage, and three different MPC-based battery cell balancing control strategies are proposed to dynamically transport electricity from cell to cell to reduce the imbalance. In particular, for the first control strategy, MPC is set up to be a tracking controller with the primary control objective of forcing all cells’ terminal voltage to follow the same trajectory generated by a nominal cell model; for the second control strategy, MPC maximizes the lowest cell voltage, so that the battery operating range can be extended; for the third and last strategy, MPC minimizes the maximum variation among cell terminal voltages. To assess the effectiveness of the proposed battery cell balancing control strategies, simulations are performed on all three MPC formulations, using both steady-state and transient conditions. Numerical results show that the proposed battery cell balancing control can achieve a driving range extension of 9% for dynamic driving cycle and 7% for steady-state condition, based on our simulation setup. Compared to the existing work, our approaches do not require the over-restrictive assumption that the trip duration is known in advance, while at the same time achieve similar driving range extension. Furthermore, it is also shown that different driving condition favors different cell balancing control strategy, indicating a need for a hybrid approach. Finally, real time implementability is demonstrated via throughput analysis.Note to Practitioners—Improving the efficiency of electric vehicles is of paramount importance to combat the global climate challenge. This paper contributes by proposing effective cell level balancing control methodologies to extend the driving range of electric vehicles to improve their energy efficiency and public acceptance. The control methods, which are based on model predictive control, are analytically derived with details for embedded implementation. Simulation results demonstrate the effectiveness of the proposed methodologies, with future work to investigate the applicability of nonlinear model predictive control with large number of cells.

Federated learning (FL) enables multiple devices to collaboratively train a shared machine learning (ML) model while keeping all the local data private, which is a crucial enabler to implement artificial intelligence (AI) at the edge of the Industrial Internet of Things (IIoT) scenario. Distributed FL (DFL) based on Device-to-Device (D2D) communications can solve the single point of failure and scaling issue of centralized FL, but subject to the communication resource limitation of D2D links. Thus, it is crucial to reduce the data transmission volume of FL models between devices. In this article, we propose a quantization-based DFL (Q-DFL) mechanism in a D2D network and prove its convergence. Q-DFL contains two phases: 1) in phase I, a local model is trained with the stochastic gradient descent (SGD) algorithm on each IIoT device and then exchanges the quantified model parameters between neighboring nodes and 2) in phase II, a quantitative consensus mechanism is designed to ensure the local models converge to the same global model. We also propose an adaptive stopping mechanism and a synchronization protocol to fulfill the phase transition from phase I to phase II. Simulation results reveal that with Q-DFL, a 1-bit quantizer can be employed without affecting the model convergence at the price of slight accuracy reduction, which achieves significant transmission bandwidth saving. Further simulation of Q-DFL for the MobileNet model is fulfilled with different quantization bit levels, which reveals their performance tradeoff among the system information flow consumption, the system time delay, and the system energy cost.

In this article, we propose a blockchain-based computing verification protocol, calledEntrapNet, for distributed shared computing networks, an emerging underlying network for many Internet of Things (IoT) applications. EntrapNet borrows the idea from the practice of entrapment in criminal law to reduce the possibility of receiving incorrect computing results from trustless service providers who have offered the computing resources. Furthermore, we mathematically optimize EntrapNet to deal with the fundamental tradeoff of a network: security and efficiency. We present an asymptotic optimal solution to this optimization. It will be seen that EntrapNet can be performed as an independent and low-cost layer atop of any trustless network that requires outsourced computing, thus making secure computing affordable and practical.

It has been shown that the SK scheme, which was proposed by Schalkwijk and Kailath, is a self-secure capacity-achieving (SSCA) feedback scheme for the Gaussian wiretap channel, i.e., the SK scheme not only achieves the feedback capacity of the Gaussian channel, but also is secure by itself and achieves the feedback secrecy capacity of the Gaussian wiretap channel. For the multi-user wiretap channels, very recently, it has been shown that Ozarow’s capacity-achieving feedback scheme for the two-user Gaussian multiple-access channel (GMAC) is the SSCA feedback scheme for the two-user Gaussian multiple-access wiretap channel (GMAC-WT). In this paper, first, we propose a SSCA feedback scheme for the two-user GMAC-WT with degraded message sets (GMAC-WT-DMS). Next, we extend the above scheme to the two-user GMAC-WT-DMS with noncausal channel state information at the transmitters (NCSIT), and show that the extended scheme is also a SSCA feedback scheme. Finally, we derive outer bounds on the secrecy capacity regions of the two-user GMAC-WT-DMS with or without NCSIT, and numerical results show the rate gains by the feedback.

The Schalkwijk-Kailath (SK) feedback scheme is a capacity-achieving coding scheme for the point-to-point white Gaussian channel with feedback. Recently, it has been shown that the SK scheme, which is not designed with consideration of secrecy, already achieves perfect weak secrecy by itself, i.e., the secrecy capacity of the Gaussian wiretap channel with feedback equals the capacity of the same model without secrecy constraint. In this paper, we propose a capacity-achieving SK type feedback scheme for the two-user Gaussian multiple-access channel with degraded message sets (GMAC-DMS). Similarly to the inherent secrecy nature of the classical SK scheme, we show that the proposed scheme is also secure by itself, which indicates that the feedback secrecy capacity of the two-user Gaussian multiple-access wiretap channel with degraded message sets (GMAC-WT-DMS) equals the capacity of the same model without secrecy constraint.

In this article, we present recEnergy, a recommender system for reducing energy consumption in commercial buildings with human-in-the-loop. We formulate the building energy optimization problem as a Markov decision process, show how deep reinforcement learning can be used to learn energy-saving recommendations, and effectively engage occupants in energy-saving actions. recEnergy is a recommender system that learns actions with high-energy-saving potential, actively distributes recommendations to occupants in a commercial building, and utilizes feedback from the occupants to learn better energy-saving recommendations. Over a four-week user study, four different types of energy-saving recommendations were trained and learned. recEnergy improves building energy reduction from a baseline saving (passive-only strategy) of 19%-26%.

This article presents a novel way to reduce blockchain nodes’ memory requirements using error correcting codes. In particular, LDPC codes are taken as examples to explicitly demonstrate the scheme. The proposed coding scheme encodes data across multiple blocks, respectively, block headers, in the blockchain. This leads to a significant reduction in required memory at each node. We then apply the proposed coding technique to blockchains organized in two different ways. Our first scheme has the same protocol for mining, broadcasting, and verification of blocks, as Bitcoin-type blockchains. Our scheme is different in thatfull nodesdo not have to store all blocks. Instead they will need to store only one block of a group of$t$blocks. In the second scheme, we consider a new block verification protocol and an account-based model under the assumption that transmission between any two nodes can be established, as well as the broadcast transmission. Our block verification protocol uses the Byzantine fault tolerance algorithm and requires sending a newly mined block to only a small number of verification nodes, instead of broadcasting it to the entire network, which leads to a reduction of the network load.

We investigate the state-dependent Gaussian wiretap channel with noncausal channel state information at the transmitter (GWTC-N-CSIT), and explore whether three strategies (i.e., taking action on the state, legitimate receiver's channel output feedback, and combining the former two strategies together) help to enhance the secrecy capacity of the GWTC-N-CSIT. To be specific, we first determine the secrecy capacity of the GWTC-N-CSIT with noiseless feedback. Next, we derive lower and upper bounds on the secrecy capacity of the GWTC-N-CSIT with action-dependent state. Finally, we derive lower and upper bounds on the secrecy capacity of the GWTC-N-CSIT with both action-dependent state and noiseless feedback, and show that these bounds meet for a special case. Numerical results of this paper indicate that all three strategies enhance the secrecy capacity of the GWTC-N-CSIT. The study of this paper offers new options for enhancing the secrecy rates of the state-dependent wiretap channel models.

The dirty paper wiretap channel, also referred to as the Gaussian wiretap channel with noncausal state at the transmitter, is revisited. First, we determine the secrecy capacity of the dirty paper wiretap channel with noiseless feedback, where the feedback channel is from the legitimate receiver to the transmitter. Next, we obtain lower and upper bounds on the secrecy capacity of the action-dependent dirty paper wiretap channel with noiseless feedback, and show that these bounds meet for a special case. Unlike the fact that action on the state helps to enhance the capacity of the dirty paper channel with feedback, numerical results of this paper indicate that it may not help to enhance the secrecy capacity of the dirty paper wiretap channel with feedback.

Tactile internet is defined as a network that offers a response to a physical process or an object in perceived real time. The development of new radio (NR) and long-term evolution (LTE) technologies with tailored high-reliability and low-latency design is expected to reliably transmit data in milliseconds, leading to the promising realization of tactile internet with applications in areas such as factory automation, education, gaming, and healthcare. In this paper, from a physical layer and medium-access control (PHY/MAC) perspective, we discuss the systems design of ultrareliable and low-latency communications (URLLC) in NR and LTE technologies, both belonging to the fifth-generation (5G) wireless technologies. Motivated by the safety and privacy requirements of tactile internet, we also outline the 5G security landscape, major categories of attackers, and potential countermeasures. Finally, we provide a case study of factory automation as an example of the proposed 5G-based tactile internet.

In this paper, the finite-order autoregressive moving average (ARMA) Gaussian wiretap channel with noiseless causal feedback is considered, in which an eavesdropper receives noisy observations of signals in both forward and feedback channels. It is shown that the generalized Schalkwijk-Kailath scheme, a capacity-achieving coding scheme for the Gaussian feedback channel, achieves the same maximum rate for the same channel even with the presence of an eavesdropper. Therefore, the secrecy capacity is equal to the feedback capacity without the presence of an eavesdropper for the Gaussian feedback channel. Furthermore, the results are extended to the additive white Gaussian noise (AWGN) channel with quantized feedback. It is shown that the proposed coding scheme achieves a positive secrecy rate. Our result implies that as the amplitude of the quantization noise decreases to zero, the secrecy rate converges to the capacity of the AWGN channel.

In this paper, the finite-order autoregressive moving average (ARMA) Gaussian wiretap channel with noiseless causal feedback is considered, in which an eavesdropper receives noisy observations of the signals in both forward and feedback channels. It is shown that the generalized Schalkwijk-Kailath scheme, a capacity-achieving coding scheme for the feedback Gaussian channel, achieves the same maximum rate for the same channel with the presence of an eavesdropper. Therefore, the secrecy capacity is equal to the feedback capacity without the presence of an eavesdropper for the feedback channel. Furthermore, the results are extended to the additive white Gaussian noise (AWGN) channel with quantized feedback. It is shown that the proposed coding scheme achieves a positive secrecy rate. As the amplitude of the quantization noise decreases to zero, the secrecy rate converges to the capacity of the AWGN channel.

In this paper, we propose an approach to numerically compute the feedback capacity of stationary finite-dimensional (FD) Gaussian channels and construct (arbitrarily close to) capacity-achieving feedback codes. In particular, we first extend the interpretation of feedback communication over stationary FD Gaussian channels as feedback control systems. We show that the problem of finding stabilizing feedback controllers with maximal reliable transmission rate over Youla parameters coincides with the problem of finding strictly causal filters to achieve feedback capacity. This extended interpretation provides an approach to construct deterministic feedback coding schemes with double exponential decaying error probability. We next propose asymptotic capacity-achieving upper bounds, which can be numerically evaluated by solving FD convex optimizations. From the filters that achieve the upper bounds, we apply the Youla-based interpretation to construct feasible filters, i.e., feedback codes, leading to a sequence of lower bounds. We prove that the sequence of lower bounds is asymptotically capacity achieving.

In this paper, we consider the first-order autoregressive moving average Gaussian channel with perfect causal feedback where an eavesdropper receives noisy observations of the channel inputs and outputs. We show that the secrecy capacity is equal to the feedback capacity without the presence of eavesdropper. Furthermore, we explicitly construct the secrecy capacity-achieving feedback code, which is deterministic and simple to implement.

The focus of this paper is on rate and power control algorithms in DSRC. We first design a utility maximization framework by leveraging the well-developed network congestion control, and formulate two subproblems, one on rate control with fixed transmit powers and the other on power control with fixed rates. Distributed rate control and power control algorithms are developed to solve these two subproblems, respectively, and are proved to be asymptotically optimal. Joint rate and power control can be done by using the two algorithms in an alternating fashion. The performance enhancement of our algorithms compared with a recent rate control algorithm, called EMBARC [1], is evaluated by using the network simulator ns2.

The authors consider the worst-case transceiver design problem under spectrum bounded channel uncertainty matrices. Their design aims to minimise the trace and the determinant of the estimate error covariance matrix, respectively. The authors relax the formulated problem into an optimisation problem with bilinear matrix inequality constraints (i.e. BMI problem), from which a local optimal solution is obtained by applying semi-definite programming. Furthermore, the authors consider norm bounded source symbols and extend their approach to design the worst-case transceiver with respect to both channel uncertainties and norm bounded source symbols.

In this paper, we investigate the additive Gaussian noise channel with noisy feedback. We consider the setup of linear coding of the feedback information and Gaussian signaling of the message (i.e. Cover-Pombra Scheme). Then, we derive the upper and lower bounds on the largest achievable rate for this setup. We show that these two bounds can be obtained by solving two convex optimization problems. Finally, we present some simulations and discussion.

In this paper, we solve a long-standing problem in communication systems with access to noisy feedback. We introduce a new notion, the Residual Directed Information, to capture the effective information flow (i.e. from the message to the channel outputs) in the forward channel. We then provide a channel coding theorem characterizing the capacity of the channel with noisy feedback.