Platform-based vehicle recognition is a critical task in logistics scenarios that facilitates the efficient management of resources. Although recent advances in the computer vision domain can be conveniently adopted to recognize the identities of vehicles and the occupations of platforms, the efficacy is significantly compromised by the severe interference and noise at the platforms of logistics industrial parks. This work tackles these difficulties through concentrating on the sequential characteristics of vehicles during arrival and departure. An innovative dynamic hidden Markov model (DHMM) is proposed to estimate the real sequence of vehicle states from the noisy observations. A dynamic Viterbi algorithm is also developed to solve the proposed DHMM method with high efficiency. The proposed method is evaluated against multiple baselines through experiments, where it can recognize the vehicle states with high accuracy and is demonstrated to significantly outperform the baselines when the interference is strong.

The performance of logistics highly depends on the time efficiency, and hence, plenty of efforts have been devoted to ensuring the on-time delivery in modern logistics industry. However, the delay in logistics transportation and delivery can still happen due to various practical issues, which significantly impact the quality of logistics service. In order to address this issue, this work investigates the root causes impacting the time efficiency, thereby facilitating the operation of logistics systems such that resources can be appropriately allocated to improve the performance. The proposed solution comprises three stages, where statistical methods are employed in the first stage to analyze the pattern of on-time delivery rate and detect the abnormalities induced by non-ideality of operations. Subsequently, a machine learning model is trained to capture the underlying correlations between time efficiency and potential impacting factors. Finally, explainable machine learning techniques are utilized to quantify the contributions of the impacting factors to the time efficiency, thereby recognizing the root causes. The proposed method is comprehensively studied on the real JD Logistics data through experiments, where it can identify the root causes that impact the time efficiency of logistics delivery with high accuracy. Furthermore, it is also demonstrated to outperform the baselines including a recent state-of-the-art method.

Computer vision-based license plate recognition (LPR) has been widely deployed for automatic vehicle identity inspection due to the offered convenience and efficiency. However, the practical LPR systems are potentially vulnerable to malicious attacks, which may lead to incorrect recognition and impact the safety of transportation. Previous studies of attacking strategies targeting LPR systems mainly focused on evasion attacks, which are less efficient than model poisoning attacks that can cause mis-classification through directly manipulating the parameters of the victim model other than perturbing each testing sample. To fill this gap, we conduct the first systematic study on the vulnerability of LPR systems against model poisoning attacks. In specific, we aim to compromise the integrity of the model training such that the attacked LPR system would mis-classify all the samples from the victim class to the attacker-chosen class. To achieve this, we fine-tune the feature extractor layers of the LPR model such that it can obtain similar feature representations given samples belong to victim and attacker-chosen classes. This is implemented in a generator-discriminator fashion, where a discriminator learns to classify the victim and attacker-chosen classes given the input samples. Subsequently, the feature extractor is fine-tuned to generate manipulated features that can confuse the discriminator. Our empirical results on the CCPD dataset demonstrate that the proposed attacking strategy can substantially compromise LPR systems with high success rates.

Various automatic and intelligent technologies have been employed to facilitate the logistics operations in recent years promoted by the trend of industry 4.0. As the most frequently used automatic equipment in the logistics industrial parks, conveyor belt plays a critical role on the efficient sorting of packages. Due to the reasons like non-ideality of scheduling and inappropriate operations, conveyor belts can potentially be impacted by congestion, thereby inducing a series of consequences such as delay, lose and damage of packages. In order to tackle these issues, a computer vision-based method is proposed to recognize the congestion on conveyor belts. Other than the popular deep learning-based techniques, the proposed method comprehensively analyzes the characteristics of conveyor belt congestion using statistical approaches and extract informative features for decision making. Finally, the proposed method is evaluated on the data collected from real package sorting scenarios, where it outperforms the deep learning and conventional pattern recognition-based methods on both detection accuracy and capability of generalization.

In nowadays, the large-scale e-commerce corporations tend to operate their own logistics networks to guarantee the speed, safety and economic efficiency of goods delivery. Thus, an e-commerce corporation usually needs to manage an online retailing platform and a logistics platform simultaneously. Despite the offered convenience, the cross-platform management faces grand challenges on security. The inappropriate philosophies for maintaining goods information and malicious behaviors of some merchants may induce mismatch between the goods and corresponding information, which consequently leads to incorrect delivery and the degradation of customer experience. In order to tackle this issue, an innovative model fusion method is proposed in this work for goods information inspection. It investigates the advantages of multiple natural language processing models as well as domain knowledge to extract informative text features, which are subsequently fed into a multi-layer perceptron for final decision on whether the goods information is accurate. Unlike the recent popular deep architectures, the proposed method leverages the complimentary effect of features from different sources utilizing a wide structure to achieve a superior inspection accuracy. Finally, the proposed method is validated using real JD Logistics data and is demonstrated to outperform the existing techniques. Furthermore, the experimental results also demonstrate that leveraging the complimentary effects can bring additional improvement compared to merely exploring deeper.

Data mining techniques have been widely applied to various fields including industrial, business, and governmental applications. Missing data is a common occurrence in a number of real-world databases, which may substantially affect the accuracy of data processing. In this paper, we propose a novel approach for missing data estimation by efficiently exploring inter-sensor correlation. Namely, given multiple sensors for data collection, we attempt to recover the missing data of a few sensors by using the measurement data from other sensors. Towards this goal, we develop an iterative solver for missing data estimation. Our numerical experiments on two industrial datasets demonstrate that the proposed method can reduce the imputation error by up to 7.25× compared to a conventional method in the literature.

Indoor localization has become popular in recent years due to the increasing need of location-based services in mobile cyber-physical systems (CPS). The massive deployment of light emitting diodes (LEDs) further promotes the indoor localization using visual light. As a key enabling technique for mobile CPS, accurate indoor localization based on visual light communication remains nontrivial due to various non-idealities such as attenuation induced by unexpected obstacles. The anomalies of localization can potentially reduce the dependability of location-based services. In this article, we develop a novel indoor localization framework based on relative received signal strength. Most importantly, an efficient method is derived from the triangle inequality to automatically detect the abnormal LED lamps that are blocked by obstacles. These LED lamps are then ignored by our localization algorithm so that they do not bias the localization results, which improves the dependability of our localization framework. As demonstrated by the simulation results, the proposed techniques can achieve superior accuracy over the conventional approaches, especially when there exist abnormal LED lamps.

The rapid development of industry 4.0 has promoted the extensive adoption of big data analytics for manufacturing industry. In this domain, virtual metrology is a critical technique that is able to reduce manufacturing cost over a large amount of practical applications. In this paper, we propose a novel tree-based approach for simultaneous feature selection and predictive modeling to facilitate efficient virtual metrology. The proposed method accurately identifies multiple feature sets and then chooses the best candidate to minimize modeling error. As demonstrated by the experimental results based on two industrial examples, the proposed method can achieve higher modeling accuracy and find a more complete feature set than the conventional approach implemented with orthogonal matching pursuit (OMP).

The information exchange between the utility company and the smart community is crucial to the smart home cyber-physical systems. Yet the interaction between the two parties is vulnerable to many potential cyberattacks, among which the most striking ones are pricing cyberattacks and energy theft. Coordinated cyberattacks have emerged as an advanced attacking scheme with both pricing attack and energy theft applied in the cooperative manner, which can induce significant impact to smart home systems even if each attack is applied with only moderate strength. Such attacks cannot be effectively detected since the existing techniques are designed for detecting either pricing attack or energy theft without considering the impact due to coordinated attacks. This paper aims at developing the detection framework for coordinated cyberattacks considering coordinated impacts of various attacking strategies using an advanced continuous state partially observable Markov decision process. Handling coordinated attacks induces drastic increase in time complexity, which motivates us to propose innovative cross entropy state sampling and Fourier belief state approximation for the solving of developed detection framework. Our simulation results demonstrate that the coordinated cyberattack can reduce his/her electricity bill by 32.65%. In addition, the proposed detection technique can better capture coordinated attacks than the conventional detection technique, resulting in 10.31% increase in the hacker's bill.

High performance data centers serve as the backbone of the prevailing cloud computing paradigm. Among data centers with different operational structures, multi-tenant data centers (MTDCs) are increasingly popular among various internet service providers for the ease of deployment. Despite the offered benefits, MTDCs are vulnerable to various cyberattacks. An important cyberattack is energy theft which can be launched by malicious tenants to reduce monetary cost of the electricity consumption. It can be achieved through attacking smart meters in the data center to undercount the energy usage of the attacker. Since the attackers could consume an excessive amount of energy without incurring elevated utility cost, energy theft discourages frugality in terms of energy consumption, which is highly undesirable in the era of sustainable computing. Despite fruitful research results on MTDCs, none of existing works address energy theft. When energy theft occurs, it might be necessary for the data center operator to examine smart meter of all tenants to find the compromised ones which could induce excessive labor cost. Localization of energy theft detection is an effective way to limit the labor cost in detecting energy thefts in MTDCs. It can be facilitated through deploying Digital Protective Relays (DPR) in the data center where a DPR is a device for fault detection and event logging in the power system. In this paper, an anomaly rate range based dynamic programming algorithm is proposed for inserting minimal DPRs into the data center, where the anomaly rate range is computed using Minimum Covariance Determinant (MCD) algorithm. To the best of our knowledge, this is the first work addressing the energy theft issue in multi-tenant data centers. The simulation results demonstrate that our algorithm inserts 19.2 percent less DPRs into the data center compared to a natural baseline algorithm. Meanwhile, in an attempt to identify all energy theft cases, our DPR insertion solution requires 12.8 percent less tenants to be checked compared with the baseline algorithm. More importantly, we demonstrate that using MCD alone cannot achieve accurate detection while using DPR alone cannot handle collusive energy theft. In contrast, integrating DPR with MCD can achieve a high detection accuracy (of 97.6 percent) for collusive energy theft.

A smart community is a distributed system consisting of a set of smart homes which utilize the smart home scheduling techniques to enable customers to automatically schedule their energy loads targeting various purposes such as electricity bill reduction. Smart home scheduling is usually implemented in a decentralized fashion inside a smart community, where customers compete for the community level renewable energy due to their relatively low prices. Typically there exists an aggregator as a community wide electricity policy maker aiming to minimize the total electricity bill among all customers. This paper develops a new renewable energy aware pricing scheme to achieve this target. We establish the proof that under certain assumptions the optimal solution of decentralized smart home scheduling is equivalent to that of the centralized technique, reaching the theoretical lower bound of the community wide total electricity bill. In addition, an advanced cross entropy optimization technique is proposed to compute the pricing scheme of renewable energy, which is then integrated in smart home scheduling. The simulation results demonstrate that our pricing scheme facilitates the reduction of both the community wide electricity bill and individual electricity bills compared to the uniform pricing. In particular, the community wide electricity bill can be reduced to only 0.06 percent above the theoretic lower bound.

In this work, the vulnerability of the electricity pricing model in the smart home system is assessed. Two closely related pricing cyberattacks which manipulate the guideline electricity prices received at smart meters are considered and they aim at reducing the expense of the cyberattacker and increasing the peak energy usage in the local community. A single event detection technique which uses support vector regression and impact difference for detecting anomaly pricing is proposed. The detection capability of such a technique is still limited since it does not model the long term impact of pricing cyberattacks. This motivates us to develop a partially observable Markov decision process based detection algorithm, which has the ingredients such as reward expectation and policy transfer graph to account for the cumulative impact and the potential future impact due to pricing cyberattacks. Our simulation results demonstrate that the pricing cyberattack can reduce the cyberattacker's bill by 34.3 percent at cost of the increase of others' bill by 7.9 percent, and increase the peak to average ratio (PAR) by 35.7 percent. Furthermore, the proposed long term detection technique has the detection accuracy of more than 97 percent with significant reduction in PAR and bill compared to repeatedly using the single event detection technique.

The concept of smart home has recently gained significant popularity. Despite that it offers improved convenience and cost reduction, the prevailing smart home infrastructure suffers from vulnerability due to cyberattacks. It is possible for hackers to launch cyberattacks at the community level while causing a large area power system blackout through cascading effects. In this paper, the cascading impacts of two cyberattacks on the predicted dynamic electricity pricing are analyzed. In the first cyberattack, the hacker manipulates the electricity price to form peak energy loads such that some transmission lines are overloaded. Those transmission lines are then tripped and the power system is separated into isolated islands due to the cascading effect. In the second cyberattack, the hacker manipulates the electricity price to increase the fluctuation of the energy load to interfere the frequency of the generators. The generators are then tripped by the protective procedures and cascading outages are induced in the transmission network. The existing technique only tackles overloading cyberattack while still suffering from the severe limitation in scalability. Therefore, based on partially observable Markov decision processes, a hierarchical detection framework exploring community decomposition and global policy optimization is proposed in this work. The simulation results demonstrate that our proposed hierarchical computing technique can effectively and efficiently detect those cyberattacks, achieving the detection accuracy of above 98%, while improving the scalability.

Despite the increasing popularity of the smart home concept, such a technology is vulnerable to various security threats such as pricing cyberattacks. There are some technical advances in developing detection and defense frameworks against those pricing cyberattacks. However, none of them considers the impact of net metering, which allows the customers to sell the excessively generated renewable energy back to the grid. At a superficial glance, net metering seems to be irrelevant to the cybersecurity, while this paper demonstrates that its implication is actually profound.

The advanced metering infrastructure (AMI) has become indispensable in a smart grid to support the real time and reliable information exchange. Such an infrastructure facilitates the deployment of smart meters and enables the automatic measurement of electricity energy usage. Inside a community of networked smart homes, the total electricity bill is computed based on the community-wide energy consumption. Thus, the coordinated energy scheduling among smart homes is important since the energy consumptions from some customers can potentially impact bills of others. Given a community of networked smart homes, this paper analyzes the energy theft cyberattack, which manipulates the energy usage metering for bill reduction and develops a detection technique based on Bollinger bands and partially observable Markov decision process (POMDP). Due to the high complexity of the POMDP-solving process, a probabilistic belief-state-reduction-based adaptive dynamic programming technique is also designed to improve the detection efficiency. Our simulation results demonstrate that the proposed technique can successfully detect 92.55% energy thefts on an average while effectively mitigating the impact to the community. In addition, our probabilistic belief-state-reduction-based adaptive dynamic programming technique can reduce the runtime by up to 55.86% compared to that without state reduction.

Smart home, which controls the end use of the power grid, has become a critical component in the smart grid infrastructure. In a smart home system, the advanced metering infrastructure (AMI) is used to connect smart meters with the power system and the communication system of a smart grid. The electricity pricing information is transmitted from the utility to the local community, and then broadcast through wired or wireless networks to each smart meter within AMI. In this work, the vulnerability of the above process is assessed. Two closely related pricing cyberattacks which manipulate the guideline electricity prices received at smart meters are considered and they aim at reducing the expense of the cyberattacker and increasing the peak energy usage in the local community. A countermeasure technique which uses support vector regression and impact difference for detecting anomaly pricing is then proposed. These pricing cyberattacks explore the interdependance between the transmitted electricity pricing in the communication system and the energy load in the power system, which are the first such cyber-attacks in the smart home context. Our simulation results demonstrate that the pricing cyberattack can reduce the attacker's bill by 34.3% at the cost of the increase of others' bill by 7.9% on average. In addition, the pricing cyberattack can unbalance the energy load of the local power system as it increases the peak to average ratio by 35.7%. Furthermore, our simulation results show that the proposed countermeasure technique can effectively detect the electricity pricing manipulation.