Intelligent transportation systems (ITSs) are essential for the development of future smart cities. They can improve traffic management, mitigate urban congestion, and provide extensive social services by disseminating traffic data collected from vehicles. To facilitate the exchange of sensory data with other vehicles and alleviate the local storage load, sensory data from vehicles is frequently uploaded to cloud or edge servers. However, current ITS data sharing frameworks exhibit certain security vulnerabilities. In particular, they are unable to support secure multikeyword searches or ensure verifiable retrieval of encrypted information. Moreover, some of the schemes cannot ensure the integrity of the retrieved data. Therefore, we propose an efficient multikeyword searchable and verifiable data sharing (EMKV-ABSE) for cloud-edge collaboration ITS. EMKV-ABSE integrates a multikeyword attribute-based searchable encryption (ABSE) scheme and a tamper-proof consortium blockchain (BC). In EMKV-ABSE, we store the keyword indexes and ciphertext hashes of the shared data on the BC, which can ensure the integrity and authenticity of the data. Meanwhile, to avoid security and trust issues associated with central search cloud servers, smart contracts are utilized to perform multikeyword searches and verify retrieval results. Furthermore, the EMKV-ABSE scheme outsources the complex computational tasks in encryption and decryption to edge servers, which realizes lightweight computation for resource-constrained users. The security analysis proves that EMKV-ABSE satisfies the indistinguishability under the chosen plaintext attack (IND-CPA) and the chosen keyword attack (IND-CKA) in the standard model. Performance evaluation shows the efficiency and practicability of our scheme.

Large language models (LLMs) have shown great success in content generation and intelligent intelligent decision making for IoT systems. Traditionally, LLMs are deployed on the cloud, incurring prolonged latency, high bandwidth costs, and privacy concerns. More recently, edge computing has been considered promising in addressing such concerns because the edge devices are closer to data sources. However, edge devices are cursed by their limited resources and can hardly afford LLMs. Existing studies address such a limitation by offloading heavy workloads from edge to cloud or compressing LLMs via model quantization. These methods either still rely heavily on the remote cloud or suffer substantial accuracy loss. This work is the first to deploy LLMs on a collaborative edge computing environment, in which edge devices and cloud servers share resources and collaborate to infer LLMs with high efficiency and no accuracy loss. We design EdgeShard, a novel approach to partition a computation-intensive LLM into affordable shards and deploy them on distributed devices. The partition and distribution are nontrivial, considering device heterogeneity, bandwidth limitations, and model complexity. To this end, we formulate an adaptive joint device selection and model partition problem and design an efficient dynamic programming algorithm to optimize the inference latency and throughput. Extensive experiments of the popular Llama2 serial models on a real-world testbed reveal that EdgeShard achieves up to 50% latency reduction and$2 \times $throughput improvement over the state-of-the-art.

Multi-robot reinforcement learning (MRRL) is a promising approach to solving cooperation problems and has been widely adopted in many applications. In the past decades, researchers have proposed various approaches to improve the efficiency of MRRL. However, most of them are trained and evaluated only in simulated environments with simple interaction scenarios. The problem of how these methods perform in the real-world environment with complex interaction scenarios remains unsolved. To meet this emergent need, we introduce a scalable multi-robot reinforcement learning platform (SMART) for training and evaluation. Specifically, SMART consists of two components: 1) a simulation environment with an uncertainty-aware social agent model that provides a variety of complex interaction scenarios for training and 2) a real-world multi-robot system for realistic performance evaluation. To evaluate the generalizability of MRRL baselines, we introduce a novel generalization metric that takes into account their performance across changes in the environment as well as the policies of other agents. Furthermore, we conduct a case study on the multi-vehicle cooperative lane change and summarize the unique challenges of MRRL, which are rarely considered previously. Finally, we open-source the simulation environments, associated benchmark tasks, and state-of-the-art baselines to encourage and empower MRRL research. Our code is available at https://github.com/Blackmamba-xuan/MRST.

Recently, sharding has become a popular direction to scale out blockchain systems by dividing the network into shards that process transactions in parallel. However, secure and efficient cross-shard transaction processing remains a vital and unaddressed challenge. Existing work handles a cross-shard transaction via transaction division: dividing it into sub-transactions, processing them separately, and combing the processing results. Such an approach is unfavorable for decentralized blockchain due to its reliance on trustworthy parties, e.g., the client or a reference node, to perform the transaction division and result combination. Furthermore, the processing result of one transaction can affect another, violating the important property of transaction isolation. In this work, we propose Sharon, a novel sharding protocol that processes cross-shard transactions via shard rotation rather than transaction division. In Sharon, shards rotate to merge pairwisely and process cross-shard transactions when merged. Sharon eliminates reliance on trustworthy parties and provides transaction isolation in nature because transactions are no longer divided. Nevertheless, it poses a scientific question of when and how to merge the shards to improve system performance. To answer the question, we formally define the shard scheduling problem to minimize transaction confirmation latency and propose a novel construction algorithm. The proposed algorithm is proven optimal and runs in polynomial time. We conduct extensive experiments on Amazon EC2 instances using Bitcoin and Ethereum data. The results indicate that Sharon achieves nearly linear scalability, improves the system throughput by 139%, and saves the transaction processing latency by 72.4% compared with state-of-the-art approaches.

Metaverse seamlessly integrates the real world with the virtual world and allows avatars to carry out rich activities including creation, display, entertainment, social, and trading. It integrates the most fundamental technologies, such as Blockchain , Interaction, Games, Artificial Intelligence, Networks, and the Internet of Things , named BIGANT. Interaction technologies are significant to allow users to interact with virtual entities in physical environments via sensors, such as AR, MR, and VR. However, there are still great challenges regarding how to access the metaverse in a more intelligent, faster, and effective way, especially in capturing human positions and activities. Intelligent wireless sensing technology, integrating AI , can serve as an intelligent, flexible, non-contact way to access the metaverse and expedite the establishment of a bridge between the real physical world and the metaverse. Hence, this paper elaborates on the existing work and discusses potential important trends and hotspots in wireless sensing, especially localization, activity recognition, and pattern analysis. After that, we discussed how intelligent wireless sensing will evolve in the metaverse, together with current challenges and open issues in this topic. Through this overview, we wish readers can better understand how intelligent wireless sensing accelerates the accessing to metaverse and the insights behind the wireless sensing in the metaverse.

Collaborative edge computing (CEC) is an emerging computing paradigm in which edge nodes collaborate to perform tasks from end devices. Task offloading decides when and at which edge node tasks are executed. Most existing studies assume task profiles and network conditions are known in advance, which can hardly adapt to dynamic real-world computation environments. Some learning-based methods use online task offloading without considering task dependency and network flow scheduling, leading to underutilized resources and flow congestion. We study Online Dependent Task Offloading (ODTO) in CEC, jointly optimizing network flow scheduling to optimize quality of service by reducing task completion time and energy consumption. The challenge of ODTO lies in how to offload dependent tasks and schedule network flows in dynamic networks. We model ODTO as the Markov Decision Process (MDP) and propose an Asynchronous Deep Progressive Reinforcement Learning (ADPRL) approach that optimizes offloading and bandwidth decisions. We design a novel dependency-aware reward mechanism to address task dependency and dynamic networks. Extensive experiments on the Alibaba cluster trace dataset and synthetic dataset indicate that our algorithm outperforms heuristic and learning-based methods in average task completion time and energy consumption.

The concept of the metaverse, first introduced in science fiction, is rapidly becoming a technological reality with profound implications for various sectors, including healthcare. By merging virtual reality (VR), augmented reality (AR), artificial intelligence (AI), and advanced communication technologies, the metaverse promises to create immersive, interactive environments that can transform medical practice, education, and patient care [1].

We study the problem of distributed collision avoidance for mobile robotic systems, where a group of heterogeneous robots with different sizes and motion constraints avoid collisions with each other and static obstacles during the movements from their starting to goal locations. Existing methods mainly consider homogeneous robots and incur a high collision rate in environments with moving robots and static objects. Hence, we propose a distributedcollisionavoidance forheterogeneous mobile robots (Heter-CA), which allows each robot to independently avoid collisions considering the heterogeneity of robots and varying static obstacles. InHeter-CA, each robot predicts the trajectories of neighboring robots and estimates the varying size of static obstacles with the robots' range-finder sensors before motion planning, which enables each robot to avoid obstacles safely. Besides, we prove thatHeter-CAcan guarantee collision-free movement between heterogeneous robots by satisfying sufficient conditions. We evaluateHeter-CAin numerous simulated and real-world scenarios in which groups of heterogeneous robots perform navigation tasks. The experimental results demonstrate thatHeter-CAtakes$10\times$less computation time and achieves$5\%$less collision rate than baseline algorithms.

Onsite construction activities (OCAs) are essential components affecting construction process quality. Tracing OCAs can control the construction process with high-resolution visibility, help stakeholders demonstrate their compliance with regulations, and reduce interorganizational quality disputes. Traditional technologies cannot meet the information recording demands for traceability, namely, immutability, transparency, and auditability. This work introduces a novel framework for the traceability of OCAs based on computer vision and blockchain technologies. First, cutting-edge deep learning algorithms are introduced to automatically extract OCA information from far-field surveillance videos. Second, a consortium blockchain system is built to record the identified OCA information and the hash value of the raw data for future review. Finally, a case of a high-rise office construction project is used to test and verify the feasibility of the framework, in which a blockchain prototype is developed based on a Blockchain as a Service (BaaS) platform, termly, PolyChain. The experimental results show that the BaaS-based prototype is an effective system architecture with good throughput and latency and can ensure the authenticity and transparency of OCA information for traceability demands.

Owing to the attractive properties of decentralization, unforgeability, transparency, and traceability, blockchain is increasingly being used in various scenarios such as supply chain and public services, where massive Spatial-Temporal-Keywords (STK) transactions need to be packaged. However, due to the multi-dimensionality and randomness of STK transactions, existing solutions fail to enable queries in a verifiable and efficient way for blockchains storing multidimensional transactions. To this end, this article takes the first step to propose an authenticated and efficient query approach in hybrid blockchain systems consisting of on-chain and off-chain parts. We first design a data structure named MRK-Tree in the block body, which organizes STK transactions for efficient nodes pruning of both kNN and range queries. Then we propose an improved block header, which improves the efficient pruning of blocks on the basis of ensuring the authentication of query results. Also, we design a cross-block searching algorithm named Efficient Block Pruning (EBP) and intra-block searching algorithms named Authenticated kNN/Range Query (AKQ/ARQ) to accelerate authenticated queries for multiple MRK-Trees in the hybrid blockchain systems. Authentication mechanisms are proposed to ensure the soundness and completeness of query results. Rigorous security analysis validates the practicability of the proposed approach. We build a blockchain prototype to comprehensively evaluate the performance of proposed query schemes. Extensive evaluation results with real datasets reveal that our approach can ensure authenticated queries, meanwhile improving the time efficiency by up to 36.45x and space efficiency by up to 4 orders of magnitude compared with the well-known benchmark query schemes.

Trustworthy and real-time video surveillance aims to analyze the live camera streams in a privacy-preserving manner for the decision-making of various advanced services, such as pedestrian reidentification and traffic monitoring. In recent years, edge computing has been identified as a promising technology for trustworthy and real-time video surveillance because it keeps confidential video data locally and reduces the latency caused by massive data transmission. Generally, a single edge device can hardly afford the computation-intensive video analytics tasks. Most existing solutions incorporate cloud servers to handle the overloaded tasks. However, such an edge-cloud collaboration approach still suffers from unpredictable latency and privacy concerns because the remote cloud is centralized and distant from the cameras. In this work, we designed a blockchain-based collaborative edge intelligence (BCEI) approach for trustworthy and real-time video surveillance. In BCEI, geo-distributed edge devices form a peer-to-peer network to maintain a permissioned blockchain and share data and computation resources to perform computation-intensive video analytics tasks. The video analytics results are written on the blockchain in an immutable manner to guarantee trustworthiness. To reduce task execution time, we formulate and solve a joint stream mapping and task scheduling problem to schedule video streams and machine learning models among edge devices. A pedestrian reidentification prototype is implemented and deployed based on BCEI with the extensive performance evaluation, indicating the superiority of BCEI in latency reduction and system throughput improvement by leveraging collaboration among edge devices.

Supply chain traceability refers to product tracking from the source to customers, demanding transparency, authenticity, and high efficiency. In recent years, blockchain has been widely adopted in supply chain traceability to provide transparency and authenticity, while the efficiency issue is understudied. In practice, as the numerous product records accumulate, the time- and storage- efficiencies will decrease remarkably. To the best of our knowledge, this paper is the first work studying the efficiency issue in blockchain-based supply chain traceability. Compared to the traditional method, which searches the records stored in a single chunk sequentially, we replicate the records in multiple chunks and employ parallel search to boost the time efficiency. However, allocating the record searching primitives to the chunks with maximized parallelization ratio is challenging. To this end, we model the records and chunks as a bipartite graph and solve the allocation problem using a maximum matching algorithm. The experimental results indicate that the time overhead can be reduced by up to 85.1% with affordable storage overhead.

Many real-world applications can be formulated as multi-agent cooperation problems, such as network packet routing and coordination of autonomous vehicles. The emergence of deep reinforcement learning (DRL) provides a promising approach for multi-agent cooperation through the interaction of the agents and environments. However, traditional DRL solutions suffer from the high dimensions of multiple agents with continuous action space during policy search. Besides, the dynamicity of agents’ policies makes the training non-stationary. To tackle the issues, we propose a hierarchical reinforcement learning approach with high-level decision-making and low-level individual control for efficient policy search. In particular, the cooperation of multiple agents can be learned in high-level discrete action space efficiently. At the same time, the low-level individual control can be reduced to single-agent reinforcement learning. In addition to hierarchical reinforcement learning, we propose an opponent modeling network to model other agents’ policies during the learning process. In contrast to end-to-end DRL approaches, our approach reduces the learning complexity by decomposing the overall task into sub-tasks in a hierarchical way. To evaluate the efficiency of our approach, we conduct a real-world case study in the cooperative lane change scenario. Both simulation and real-world experiments show the superiority of our approach in the collision rate and convergence speed.

Collaborative edge computing (CEC) is an emerging paradigm enabling sharing of the coupled data, computation, and networking resources among heterogeneous geo-distributed edge nodes. Recently, there has been a trend to orchestrate and schedule containerized application workloads in CEC, while Kubernetes has become the de-facto standard broadly adopted by the industry and academia. However, Kubernetes is not preferable for CEC because its design is not dedicated to edge computing and neglects the unique features of edge nativeness. More specifically, Kubernetes primarily ensures resource provision of workloads while neglecting the performance requirements of edge-native applications, such as throughput and latency. Furthermore, Kubernetes neglects the inner dependencies of edge-native applications and fails to consider data locality and networking resources, leading to inferior performance. In this work, we design and develop ENTS, the first edge-native task scheduling system, to manage the distributed edge resources and facilitate efficient task scheduling to optimize the performance of edge-native applications. ENTS extends Kubernetes with the unique ability to collaboratively schedule computation and networking resources by comprehensively considering job profile and resource status. We showcase the superior efficacy of ENTS with a case study on data streaming applications. We mathematically formulate a joint task allocation and flow scheduling problem that maximizes the job throughput. We design two novel online scheduling algorithms to optimally decide the task allocation, bandwidth allocation, and flow routing policies. The extensive experiments on a real-world edge video analytics application show that ENTS achieves 43% -220% higher average job throughput compared with the state-of-the-art.

The consensus mechanism plays a pivotal role in guaranteeing the security and consistency of blockchain systems and substantially affects system performance. However, an increasing number of blockchain nodes degrade the consensus performance dramatically because of the high communication complexity in traditional consensus mechanisms. In this paper, we propose NS-consensus, a secure node-sampling blockchain consensus mechanism reducing the communication complexity significantly. The key novelty lies in the sampling of blockchain nodes so that the leader only needs to interact with the sampling nodes in each consensus epoch. However, NS-consensus imposes two challenges in determining an optimal sample size and denying malicious proposals. To address the challenges, we determine the sample size under the constraints of a confidence level and a margin of error to enhance communication efficiency without compromising system security. Furthermore, we design a mechanism to enable the leader to interact with all blockchain nodes in the last consensus phase, ensuring the denial of malicious proposals. The extensive experimental results indicate that NS-consensus outperforms the state-of-the-art with up to 175.1% higher system throughput and 79.9% lower time overhead in the sampling phases.

In recent years, blockchain has been broadly applied to industrial Internet of Things (IIoT) due to its features of decentralization, transparency, and immutability. In existing permissioned blockchain based IIoT solutions, transactions submitted by IIoT devices are arbitrarily packed into blocks without considering their waiting times. Hence, there will be a high deviation of the transaction response times, which is known as the lack of fairness. Unfair permissioned blockchain decreases the quality of experience from the perspective of the IIoT devices. Moreover, some transactions can get timeouts if not responded for a long time. In this article, we propose Fair-Pack, the first fairness-based transaction packing algorithm for permissioned blockchain empowered IIoT systems. First, we gain the insight that fairness is positively related to the sum of waiting times of the selected transactions through theoretical analysis. Based on this insight, we transform the fairness problem into the subset sum problem, which is to find a valid subset from a given set with subset sum as large as possible. However, it is time consuming to solve the problem using a brute-force approach because there is an exponential number of subsets for a given set. To this end, we propose a heuristic and a min-heap-based optimal algorithm for different parameter settings. Finally, we analyze the time complexity of Fair-Pack and conduct extensive experiments. The results reveal that Fair-Pack is time-efficient and outperforms the existing algorithms significantly in terms of both fairness and average transaction response time.

This paper studies the problem of tag localization using RFID-augmented robots, which is practically important for promising warehousing applications, e.g., automatic item fetching and misplacement detection. Existing RFID localization systems suffer from one or more of following limitations: requiring specialized devices; only 2D localization is enabled; having blind zone for mobile localization; low scalability. In this paper, we use Commercial Off-The-Shelf (COTS) robot and RFID devices to implement a Mobile RF-robot Localization (MRL) system. Specifically, when the RFID-augmented robot moves along the straight aisle in a warehouse, the reader keeps reading the target tag via two vertically deployed antennas ( Z1 and Z2) and returns the tag phase data with timestamps to the server. We take three points in the phase profile of antenna Z1 and leverage the spatial and temporal changes inherent in this phase triad to construct an equation set. By solving it, we achieve the location of target tag relative to the trajectory of antenna Z1. Based on different phase triads, we can have candidate locations of the target tag with different accuracy. Then, we propose theoretical analysis to quantify the deviation of each localization result. A fine-grained localization result can be achieved by assigning larger weights to the localization results with smaller deviations. Similarly, we can also calculate the relative location of target tag with respect to the trajectory of antenna Z2. Leveraging the geometric relationships among target tag and antenna trajectories, we eventually calculate the location of target tag in 3D space. We perform various experiments to evaluate the performance of the MRL system and results show that the proposed MRL system can achieve high accuracy in both 2D and 3D localization.

Supply chain management (SCM) is fundamental for gaining financial, environmental and social benefits in the supply chain industry. However, traditional SCM mechanisms usually suffer from a wide scope of issues such as lack of information sharing, long delays for data retrieval, and unreliability in product tracing. Recent advances in blockchain technology show great potential to tackle these issues due to its salient features including immutability, transparency, and decentralization. Although there are some proof-of-concept studies and surveys on blockchain-based SCM from the perspective of logistics, the underlying technical challenges are not clearly identified. In this paper, we provide a comprehensive analysis of potential opportunities, new requirements, and principles of designing blockchain-based SCM systems. We summarize and discuss four crucial technical challenges in terms of scalability, throughput, access control, data retrieval and review the promising solutions. Finally, a case study of designing blockchain-based food traceability system is reported to provide more insights on how to tackle these technical challenges in practice.

Autonomous, arbitrary pattern formation is one of the most critical applications in multi-robot systems, where robots are required to form into circles, lines, and meshes or any other desired configuration. This task is important in military applications, search and rescue operations, and visual inspection of infrastructure and equipment tasks to name a few. Most existing works are very rigid, and only able to form certain shapes, where slight target changes can cause failure in the predefined pattern-specific rules and trigger algorithm redesign. We propose a novel, deep reinforcement learning-based method that generates general-purpose pattern formation strategies, in the form of deep neural networks (DNN), for any target pattern. Our method uses the trial-and-error feedback of each round of training to gradually generate the pattern formation strategy. Thus, robots are able to query the trained DNN model to select their optimal directions and speeds in a fully distributed manner. Considering that reinforcement learning models do not perform well with large state spaces and highly variant training samples, we employ auto-encoders to learn the condensed representation for each state and compute model-free policy gradients for arbitrary pattern formation. We experimentally show that groups of robots are able to form various general target patterns while minimizing the number of completion time steps.

Recently, much attention is paid to multi-robot systems due to their widespread applications such as warehouse robotics, persistent surveillance, and exploration of unknown environments. Although urgently required by the applications, coordination among multiple robots remains to be challenging. Among the problems of multi-robot coordination, pattern formation serves a fundamental one. It aims to control a group of robots to form a desired shape with some certain goals such as best formation quality, minimum makespan or minimum total distance. Existing works mainly focus on the formation of certain patterns, such as repeating squares or a circle. those approaches cannot be generalized to arbitrary pattern formation. In this paper, we propose a decentralized algorithm for a multi-robot system to generate a given formation with an arbitrary repeating pattern. We introduce basic pattern graph and assembling graph to define a repeating pattern and formation quality for measurement. Towards solving the repeating pattern formation problem, our approach is divided into two phases. The robots are grouped into multiple basic patterns in the first phase, and the patterns are assembled level by level in the second phase. Simulations and real-world experiments indicate the effectiveness and practicability of our approach.

Nowadays, a great number of healthcare data are generated every day from both medical institutions and individuals. Healthcare information exchange (HIE) has been proved to benefit the medical industry remarkably. To store and share such large amount of healthcare data is important while challenging. In this paper, we propose BlocHIE, a Blockchain-based platform for healthcare information exchange. First, we analyze the different requirements for sharing healthcare data from different sources. Based on the analysis, we employ two loosely-coupled Blockchains to handle different kinds of healthcare data. Second, we combine off-chain storage and on-chain verification to satisfy the requirements of both privacy and authenticability. Third, we propose two fairness-based packing algorithms to improve the system throughput and the fairness among users jointly. To demonstrate the practicability and effectiveness of BlocHIE, we implement BlocHIE in a minimal-viable-product way and evaluate the proposed packing algorithms extensively.

Nowadays is the big data era. A large amount of data are generated which can be valuable for business, healthcare, transportation, etc. To promote the dissemination of the valuable data, researchers have been trying to design and develop data sharing platforms. However, the existing platforms fail to address at least one of the three issues: trustworthiness, data heterogeneity, and authenticability. To this end, we propose TSAR, a fully-distributed Trustless data ShARing platform. In detail, we architect TSAR on Blockchain to remove the dependency on reliable third parties, which realizes the trustworthiness. Moreover, we propose a general data schema to represent raw data, which handles the problem of data heterogeneity. Finally, we record the data transaction as well as user-group information on Blockchain to achieve authenticability. To demonstrate the practicability and effectiveness of TSAR, we implement it in a minimal-viable-product fashion and evaluate the performance in terms of throughput and response time.

Recent advances in robotics technology have made it practical to deploy a large number of inexpensive robots in a wide range of application domains. In many of those applications, a group of autonomous robots is required to form a predefined geometric shape such as a line or a circle. This problem, namely pattern formation problem, is one of the most important coordination problems in multi-robot systems. A particular pattern extensively studied in literature is the uniform circle, and the corresponding problem is called uniform circle formation. In uniform circle formation, a set of simple mobile robots (asynchronous, autonomous), starting from arbitrary positions on the plane, have to arrange themselves on the vertices of a regular polygon eventually. Towards addressing the problem, existing works usually make conveniently strong assumptions, i.e., the robots are regarded as mass points and have unlimited sensing and communication range. The question of whether the robots with actual size and limited sensing and communication range could form a uniform circle, to our knowledge, has remained open. In this paper, we propose a new approach towards addressing this issue. Three phases, consensus on the circle, circle formation, and uniform transformation, constitute our approach. Inside our approach, there are some new distributed algorithms such as convex hull construction and cardinality estimation. Simulation result, theoretical analysis, and successful deployment have shown the effectiveness and practicability of our approach.

In the field of multi-robot system, the problem of pattern formation has attracted considerable attention. However, the faulty sensor input of each robot is crucial for such system to act reliably in practice. Existing works focus on assuming certain noise model and reducing the noise impact. In this work, we propose to use a learning-based method to overcome this kind of barrier. By interacting with the environment, each robot learns to adapt its behavior to eliminate the malfunctions in the sensors and the actuators. Moreover, we plan to evaluate the proposed algorithms by deploying it into the multi-robot platform developed in our research lab.

Recently years, research in multi-robot systems has attracted increasingly attentions. One important research topic is to design programming models that can facilitate the developers to programme large-scale multi-robot systems. However, existing works fail to manage the robots to perform tasks with real-time requirements. To address this issue, we propose a new programming model called RMR (Real-time Multi-Robot). RMR is a logic programming model with real-time support. On the basis of the logic programming paradigm, RMR allows the code for multi-robot system to be written from a global perspective, rather than managing a large collection of independent robots. Moreover, RMR allows developers to set timing constraints on the behaviors of an ensemble of robots, which is not implemented by state of the art. After designing RMR, we further develop a compiler and a runtime system for distributed execution of RMR programs. To evaluate the performance of RMR, we deploy it in a simulator and a test-bed, and then demonstrate RMR based on several applications. Our results indicate that RMR greatly facilitates implementing correct collaborative multi-robot applications.