The service modules of the traditional Mobile Edge Computing (MEC) are difficult to deploy, extend, and maintain in real networks because of the highly sophisticated systems. To promote the generalization, openness, and flexibility of the network edge environment, an increasing number of studies are exploring the integration of microservices with MEC. However, the existing work usually treats microservice deployment and request routing as two separate issues, ignoring the interaction between them. Therefore, this paper focuses on the joint optimization of microservice deployment and request routing in the multi-edge cloud scenarios. We establish a problem model for minimizing the average response latency, considering the transmission of requests across edge clouds. Then, in view of the complexity of the scene, this paper proposes a joint training strategy of microservice deployment and request routing based on deep reinforcement learning and Best Fit Decreasing algorithm. The algorithm takes the change of microservice deployment scheme as the action of the agent, introduces the Best Fit Decreasing algorithm to construct request routing based on the deployment scheme, and calculates rewards using the complete joint microservice deployment and request routing scheme for subsequent network training. Finally, experimental results show that the proposed algorithm can effectively reduce the response time delay and system running power compared with other algorithms.

As one of the promising self-powered sensors on Internet of Things (IoT) platforms, unmanned aerial vehicles (UAVs) have attracted much attention for parcel delivery. Their high flexibility and low cost facilitate last-one-mile delivery. However, the limitations of battery capacity and payloads prevent drones from delivering independently over large scales. In this case, it is available to employ vehicles to assist the drones. The vehicles can be private-own trucks and vehicles in public transportation systems (PTSs). Compared to trucks, PTSs, such as buses and trains, do not require extra operating and fuel costs. Given these advantages, this article adopts PTSs to assist UAVs in parcel delivery. Nevertheless, the fixed routes and schedules of public vehicles pose new challenges to the routing and scheduling problem for PTS-assisted multidrone parcel delivery (RSPMD). To tackle the problem, we propose a novel routing and scheduling algorithm, referred to as the PTS-assisted multidrone parcel delivery (PDD) algorithm. Considering the schedules of the public vehicles, the algorithm jointly optimizes the distance and time cost of drones by iteratively combining parts of existing routes. To the best of our knowledge, we are the first to address RSPMD in which UAVs ride public vehicles to deliver parcels in a wide area. Simulation results are finally presented to demonstrate that PDD outperforms existing solutions in terms of effectiveness and efficiency.

Recently, blockchain is introduced to ensure the security of the device data in Internet of Things (IoT) systems. However, storing the entire blockchain ledger in resource-constrained IoT devices is impractical. A few existing papers attempt to mitigate the storage issues of the blockchain ledgers through the collaborative storage. Nonetheless, these studies solely consider collaborative storing of the entire blockchain ledger in a single consensus unit, treating all the nodes as an individual peer but ignoring which nodes should be assigned to form a consensus unit together. This may lead to significant latency in block invocations among the nodes. To this end, this article innovatively explores the grouping of all the devices in the IoT network into multipeers at a global level which significantly reduces the access latency. In this article, we propose a clustering-based collaborative storage scheme for the blockchain in storage-limited IoT systems. The proposed algorithm takes storage and communication latency into consideration and clusters various IoT nodes into multiple peers, ensuring that the entire blockchain stays updated within these clusters. Furthermore, we propose a series of effective block allocation and replacement strategies in both the static and dynamic scenarios. The experimental results verify that our algorithm effectively solves the problem of insufficient storage in blockchain systems.

The emerging low earth orbit (LEO) broadband satellite networks are creating new opportunities to enable superior video distribution. With numerous satellites deployed, broadband constellations are capable of distributing videos across the globe by efficient multicasting techniques. However, existing work only studied IP multicast for broadband constellations, which suffer from limited scalability and tree performance. With recent breakthroughs in software defined networking, novel software defined multicasting (SDM) techniques manage to achieve efficient data transfer through intelligent and granular management, outperforming traditional IP Multicast. This paper leverages software defined multicasting in the promising broadband constellations to empower satellite-based Internet video distribution. Based on rectilinear Steiner trees, this paper proposes a novel software defined multicasting framework for broadband satellite networks. In addition, this paper designs simple, agile, and scalable multicast segment routing protocols implementing source routing and equal cost multipath routing. The proposed protocols also adapt to frequent member updates and network failures with efficient tree recovery and local rerouting mechanisms. Comprehensive experiments demonstrate the effectiveness and efficiency of our approach when compared with traditional algorithms.

Edge computing technologies with container-based microservice architectures promise to provide stable and low-latency services for large-scale and complex edge applications. However, due to the limited CPU and storage resources in edge computing scenarios, the coarse-grained service deployment on edge nodes causes performance bottlenecks. In addition, the effective deployment of microservices is tightly correlated with request routing, but the current research ignores the joint optimization of multi-instance deployment and routing. In this paper, we first model the problem of jointly optimizing service deployment and routing in a dynamically changing environment with multi-edge network collaboration based on a queuing network analysis. Secondly, we design heuristic algorithms to scale microservice instances horizontally in dynamic user request states. In addition, we propose a reinforcement learning algorithm based on reward shaping (RSPPO) to minimize user waiting delay and edge network resource consumption. We also solve the microservice deployment and request routing problem for multi-edge collaboration to achieve load balancing among edge nodes. Finally, extensive experiments verify the significant and extensive effectiveness of our algorithm.

The emerging large-scale low earth orbit (LEO) broadband satellite networks manifest great potentials in distributing videos across the globe via efficient multicast techniques. However, existing work only studied IP multicast (IPMC) for LEO constellations, which suffers from limited scalability and tree performance. In this paper, we employ the promising software defined multicast (SDM) techniques in large-scale LEO constellations to empower satellite-based Internet video distribution. We present a multi-layer rectilinear Steiner tree (ML-RST) construction algorithm for multicast routing in large-scale LEO constellations. We extend the spanning graph and edge substitution to three-dimensional (3D) scenes. Based on multi-layer spanning graphs and multi-layer edge substitution approaches, we manage to efficiently construct ML-RSTs with${O}$(${n}$log${n}$) complexity. Experimental results show that our approach can achieve an average 10% improvement in bandwidth saving compared with existing algorithms.

The emerging low earth orbit (LEO) satellite networks are expected to provide the world’s most advanced Internet services. Besides, terrestrial networks are in constant evolution and already moving to embrace the relatively new paradigm of software defined networking (SDN). In this paper, we take the advantages of SDN features and leverage traffic engineering (TE) to enhance the ISL performance in broadband LEO satellite networks. We investigate unicast and multicast TE for SDN-enhanced LEO constellations to empower satellite-based Internet services. In LEO satellite networks, unicast supports ubiquitous network access and provides basic network services, while multicast features superior satellite-based video distribution. For unicast TE in grid ISL networks, we present a simple yet efficient${k}$-segment routing based strategy with segment routing (SR) techniques, which can achieve near optimal max link utilization when compared with the multi-commodity flow solution. In the meanwhile, our solution eliminates routing tables and only imposes little routing information stored in packet headers. For multicast TE, we employ rectilinear Steiner trees (RSTs) to maximize bandwidth saving and exploit obstacle-avoiding rectilinear Steiner trees (OARSTs) to address the contention of multiple multicast groups. Based on RSTs and OARSTs, we propose an effective per-flow management scheme to balance traffic among multiple multicast flows in the presence of limited link capacities. Simulation results demonstrate the effectiveness and efficiency of our approaches on reducing routing information and accommodating more multicast groups.

Fitness games recently attract much attention these years due to its combination of playability and athleticism. However, most fitness games are entertainment for a single person, with only a few deliver distinctive experiences for multiplayers. Enabling interaction between multiple players would be more enjoyable due to exciting cooperation among players. Considering a Big Stomach Challenge for two players, a certain amount of food can be only eaten when a mouth size, represented by the distance between players is reached collaboratively. Similarly, a certain type of food can only be picked up when the food grabbing speed, denoted by the approaching speed between players, is fast enough. Such games require accurate ranging and speed estimation in a relatively long distance (1-15 m) to deliver a good gaming experience. However, existing ranging schemes cannot meet the above requirements. They either cannot work under Doppler channels or have to strike a balance between accuracy and operational range, prohibiting a heuristic implementation for the above games. To this end, we design MotionBeep, a novel acoustic ranging scheme that achieves centimeter-level ranging and dm/s-level speed estimation accuracy under representative indoor and outdoor scenes within 15 m. In MotionBeep, we design a new working paradigm and incorporates a state-space model to maintain accurate ranging in both static and dynamic channels. We have implemented a system prototype and evaluate its performance in representative environments. Evaluation results demonstrate that MotionBeep achieves a median of centimeter accuracy with up to 15 m even under Doppler effect.

The enabling Internet-of-Things (IoT) technology has inspired a large number of novel platforms and applications. One popular IoT platform is unmanned aerial vehicles (UAVs, also known as drone). Benefiting from the intrinsic flexibility, convenience, and low cost, UAVs have great potentials to be utilized in various civil applications, including parcel delivery. However, suffering from limited payloads and battery capacities, it is uneconomical for UAVs to perform parcel delivery tasks independently. To conquer the drawbacks of low payloads and battery capacities, people propose to employ both trucks and drones to construct truck-drone parcel delivery systems. However, previous works only leverage either independent drones or truck-carried drones to collaborate with trucks. In contrast, in this article we propose to simultaneously employ trucks, truck-carried drones, and independent drones to construct a more efficient truck-drone parcel delivery system. We claim that such a hybrid parcel delivery system can fully exploit the complementary benefits of the three platforms. We propose a novel routing and scheduling algorithm, referred to as hybrid truck-drone delivery (HTDD) algorithm, to solve the hybrid parcel delivery problem, wherein M drones carried by M trucks, together with N independent drones, cooperate to deliver parcels to customers distributed in a wide region. The experimental results show that our algorithm outperforms the existing solutions which employ either independent drones or truck-carried drones.

The in-network data storage and retrieval are fundamental functions of sensor networks. Among many proposals, geographical hash table GHT is perhaps most appealing as it is very simple yet powerful with low communication cost, where the key is to correctly define the bounding box. It is envisioned that the skeleton has the power to facilitate computing a precise bounding box. In existing works, the focus has been on skeleton extraction algorithms targeting for 2D sensor networks, which usually deliver a 1-manifold skeleton consisting of 1D curves. It faces a set of non-trivial challenges when 3D sensor networks are considered, in order to properly extract the surface skeleton composed of a set of 2-manifolds and possibly 1D curves. In this paper, we study the problem of surface skeleton extraction in 3D sensor networks. We propose a scalable and distributed connectivity-based algorithm to extract the surface skeleton of 3D sensor networks. First, we propose a novel approach to identifying surface skeleton nodes by computing the extended feature nodes such that it is robust against boundary noise, etc. We then find the maximal independent set of the identified skeleton nodes and triangulate them to form a coarse-grained surface skeleton, followed by a refining process to generate the fine-grained surface skeleton. Furthermore, we design an efficient updating scheme to react to the network dynamics caused by node failure, insertion, etc. We also investigate the impact of boundary incompleteness and present a scheme to extract the surface skeleton under incomplete boundary. Finally, we apply the extracted surface skeleton to facilitate the design of data storage protocol and curve skeleton extraction algorithm. Extensive simulations show the robustness of the proposed algorithm to shape variation, node density, node distribution, communication radio model and boundary incompleteness, and its effectiveness for data storage and retrieval application with respect to load balancing.

Efficient in-network information storage and retrieval is of paramount importance to sensor networks and has attracted a large number of studies while most of them focus on 2D fields. In this paper, we propose novel Reeb graph based information storage and retrieval schemes for 3D sensor networks. The key is to extract the line-like skeleton from the Reeb graph of a network, based on which two distance-sensitive information storage and retrieval schemes are developed: one devoted to shorter retrieval path and the other devoted to more balanced load. Desirably, the proposed algorithms have no reliance on the geographic location or boundary information, and have no constraint on the network shape or communication graph. The extensive simulations also show their efficiency in terms of sensor storage load and retrieval path length.