Data Delivery Scheme Research Articles

Design of an RFID-Based Self-Jamming Identification and Sensing Platform

Commodity RFID tags backscatter stored electronic product code (EPC) to the reader, but do not have sensing capability. Existing works have made much effort on designing RFID-based sensing platform. But most of them either need intricate hardware design or rely on modification of the tag, which increases the cost or constrains the sensing capability. In this paper, we design a self-jamming identification and sensing platform (SJISP) consisting of SJISP nodes and a commodity RFID reader. A subtle design of the SJISP node is the adoption of a jammer radio module with the same frequency as the reader, controlled by the micro control unit (MCU) to decide whether to interfere with the query process of the RFID reader. The RFID tag is not readable if the jammer is turned on to generate interference signals. Otherwise, it is readable when the jammer is turned off. The sensing data is thus modulated by switching the jammer on and off for transmitting bit 0 and bit 1, respectively. The reader demodulates the data through the compatible EPC UHF Gen2 air interface protocol. To further save the energy of the SJISP node, we propose a prefix codebook based data delivery scheme, which leverages the difference of energy consumption (DEC) between transmitting bit 0 and bit 1. Our proposed scheme can save more than 50 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of the energy than common communication without codebook. Experimental results based on our prototyped system show that the designed SJISP can achieve an average packet reception rate (PRR) of over 99 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and is quite robust to environmental disturbance. Our designed platform provides a low-cost and compatible solution to extend the sensing capability of RFID system. A demo application with a temperature sensor and a light sensor embedded in two SJISP nodes respectively are developed to demonstrate how SJISP applies in real world scenario.

Efficient Data Delivery Scheme for Large-Scale Microservices in Distributed Cloud Environment

The edge computing paradigm has emerged as a new scope within the domain of the Internet of Things (IoT) by bringing cloud services to the network edge in order to construct distributed architectures. To efficiently deploy latency-sensitive and bandwidth-hungry IoT application services, edge computing paradigms make use of devices on the network periphery that are distributed and resource-constrained. On the other hand, microservice architectures are becoming increasingly popular for developing IoT applications owing to their maintainability and scalability advantages. Providing an efficient communication medium for large-scale microservice-based IoT applications constructed from small and independent services to cooperate to deliver value-added services remains a challenge. This paper introduces an event-driven communication medium that takes advantage of Edge–Cloud publish/subscribe brokers for microservice-based IoT applications at scale. Using the interaction model, the involved microservices can collaborate and exchange data through triggered events flexibly and efficiently without changing their underlying business logic. In the proposed model, edge brokers are grouped according to their similarities in event channels and the proximity of their geolocations, reducing the data delivery latency. Moreover, in the proposed system a technique is designed to construct a broker-based utility matrix with constraints in order to strike a balance between delay, relay traffic, and scalability while arranging brokers into proper clusters for efficient data delivery. Rigorous simulation results prove that the proposed publish/subscribe model can provide an efficient interaction medium for microservice-based IoT applications to collaborate and exchange data with low latency, modest relay traffic, and high scalability at scale.

Joint Channel Allocation and Data Delivery for UAV-Assisted Cooperative Transportation Communications in Post-Disaster Networks

As the natural disasters may destroy the ground communication infrastructures for the transportation systems, the communication relief in post-disaster networks is more crucial to reduce risk loss. The growing application of unmanned aerial vehicles (UAVs) holds great potential for disaster communication relief due to its flexibility and functionalities. In this paper, we investigate the channel allocation and data delivery problems for UAV-assisted cooperative transportation communications in post-disaster networks to provide communication and data delivery services for affected users. Specifically, we first introduce the UAV-assisted communication relief system, in which UAVs equipped with the communication and caching functionalities are deployed as the aerial base stations in post-disaster regions. Then, we propose the channel allocation scheme between UAVs and users by taking the interferences into consideration, and obtain the channel allocation strategy to improve the network throughput. Based on the optimal channel allocation strategy, users can deliver their data to UAVs for backup. Next, we propose the data delivery scheme to cope with the pricing problem for UAVs and the data delivery strategy for users to improve the efficiency of data delivery, with the objective of maximizing the utilities of both UAVs and users. The optimal strategy for both UAVs and users are derived according to the analysis of Stackelberg game. Finally, we conduct simulations to evaluate the performance of the proposed channel allocation and data delivery scheme, and the numerical results demonstrate that the proposed scheme can significantly improve the efficiency and effectiveness of channel allocation and data delivery in post-disaster networks, compared with benchmark schemes.

Timely Data Delivery for Energy‐Harvesting IoT Devices

The devices in the Internet of things (IoT) gain capability of sustainable operation when they harvest energy from ambient sources. Fluctuation in the harvested energy may cause the energy-harvesting IoT devices to suffer from frequent energy shortage, which may bring in intolerable packet delay or packet discarding. It is important to design a low-delay packet delivery scheme that adapts to variation in the harvested energy. In this paper, we present the timely data delivery (TDD) scheme for the IoT devices. Using Markov chain, we develop a probability model for the TDD scheme, which leads to the expected number of packets delivered in an operation cycle, the expected numbers of packets waiting in the data buffer in an operation cycle and an energy-harvesting cycle, and the expected packet delay. Additionally, we formulate the optimization problem that minimizes the packet delay in the TDD scheme, and the solution to the optimization problem yields the optimal parameters for the IoT devices to determine when to harvest energy and when to deliver data under the TDD scheme. The simulation results show that the proposed TDD scheme outperforms the existing schemes in terms of packet delay.

RaptorQ codes aided secure data delivery strategy for vehicular networks

Abstract This paper deals with security issues in the presence of an eavesdropper for a vehicular scenario. The proposed secure data delivery scheme implements Fountain Codes, namely RaptorQ (RQ) codes, at the application layer (AL) to increase communication security against eavesdroppers’ attacks. For RQ coded transmission scheme, the receiver has to collect a sufficient number of coded packets to reconstruct the original source data. Secure delivery can be achieved if the legitimate user obtains enough RQ coded packets before the eavesdropper does. To satisfy this condition, it is proposed to use a road side unit (RSU) cooperation method when the eavesdropper has better channel conditions than the legitimate user to scatter the coded packets from multiple RSUs. The aim is to reduce the probability that the eavesdropper receives the sufficient number of coded packets and recover the source data before the legitimate user. An optimisation framework which jointly selects the RQ code rate at the AL and Modulation and Coding Scheme (MCS) at the physical (PHY) layer to ensure the secure data transmission by allowing the user to decode the file with a certain probability of decoding success while minimising the intercept probability at the eavesdropper is presented. To evaluate the proposed system, a realistic end-to-end system level simulator is developed. Simulation results show that the proposed scheme can provide secure and efficient data transmission over vehicular networks by significantly reducing the intercept probability at the eavesdropper.

BDPS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration

The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd–Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively.

A secure blockchain-oriented data delivery and collection scheme for 5G-enabled IoD environment

−There are innumerable ways the Internet of Drones (IoD) technology can impact our society. With the deployment of an airborne network, the IoD can support real-time low-cost delivery of services ranging from military surveillance to a myriad of civilian applications. Nevertheless, the drones employ insecure wireless communication channels to communicate with other entities in the system, inhibiting its induction in sensitive installations if insecure or inefficient Authenticated Key Agreement (AKA) schemes are employed. The blockchain, an open distributed ledger-based technology, is increasingly being adopted to address the security concern as discussed. Recently, Bera et al. presented an efficient blockchain-enabled AKA scheme for data management among various entities in IoD network. However, their scheme does not support anonymity and untraceability for the drones; also, it does not provide resistance to Ground station server impersonation attack, while the protocol has a few redundancies. Later, we proposed an enhanced blockchain-enabled AKA scheme BOD5-IOD to authenticate drones in the system. The BOD5-IOD, other than supporting a robust access control mechanism between drones and GSS, also ensures safe transactions among all entities in the IoD environment. The formal analysis and performance evaluation endorse that our scheme supports security requirements with computational and communication efficiency of 34.4% and 23.3%, respectively.

Optimizing Superframe and Data Buffer to Achieve Maximum Throughput for 802.15.4-Based Energy Harvesting Wireless Sensor Networks

Energy harvesting wireless sensor networks (EH-WSNs) intend to support sustainable operations. It is important to design a high-throughput data delivery scheme that adapts to the fluctuation in harvested energy in the EH-WSN nodes. In this article, the optimal superframe and data buffer scheme (OSDBS) is investigated to improve the throughput of IEEE 802.15.4 beacon-enabled EH-WSNs. A stochastic model is developed for OSDBS, which leads to the characterization of network throughput and packet delay. The OSDBS achieves the maximum throughput through setting the optimal superframe and buffer sizes of the nodes, which are the solution of the formulated optimization problem that maximizes the network throughput with consideration of energy-harvesting rate and data arrival rate. The simulation results show the OSDBS significantly outperforms the existing schemes in terms of throughput.

UMUcast: A Framework for Massive Small-Data Delivering in Industrial Internet of Things

As a key infrastructure of Industry 4.0, Industrial Internet-of-Things (IIoT) promises the opportunity to build powerful industrial environments by leveraging the growing ubiquity of wired and wireless communication technologies. Designing a data delivery scheme in the future IIoT networks is undoubtedly a challenging task, as it should satisfy several conflicting requirements: massive-scale, data-intensive, and mission-critical, the requirements of which have motivated the desired need for feasible IIoT network architecture. In particular, the traffic characteristics of certain IIoT applications feature small-data patterns especially in typical automation control scenarios such as robot control on the downlink. In order to reduce the huge overhead associated with each individual unicast transmission of the small-data message, we propose a novel “multipoint-to-multipoint” and/or “point-to-multipoint with different contents” communication paradigm-Uni-Multi-Unicast (UMUcast), which is based-on traditional 4G technologies such as evolved Multimedia Broadcast Multicast Service (eMBMS) and Group Communication System Enablers (GCSE), and novel 5G technologies such as Multi-Access Edge Computing (MEC). For the downlink, the UMUcast transmitter can jointly encode multiple-sources' small-data messages into a single chunk at MEC equipment in conjoint with a gNB, where chunk can be one-off transmission to multiple receivers simultaneously through an eMBMS frame, whereas the chunk is decoded into multiple small-data by each individual IIoT devices separately and respectively. The simulation results show that UMUcast has remarkable improvements over conventional point-to-point unicasting in handling multi-source, multi-destination, and massive small-data delivery with characteristics of low-overhead, high-throughput, and ultra-low-latency for future 5G-based IIoT networks.

MWBS: An Efficient Many-to-Many Wireless Big Data Delivery Scheme

Wireless big data raises the demands on the networking schemes to support the efficient group data sharing over heterogeneous wireless technologies, which take many-to-many data delivery as the foundation. Information-centric networking (ICN) approach is a promising networking technology to support big data delivery, which has the potential to establish the harmony between networking and wireless big data sharing. However, the existing ICN schemes have not carefully addressed the many-to-many communications. To address this issue, we propose an efficient and secure many-to-many wireless big data delivery scheme (MWBS) to provide group-based data dissemination and retrieval with name-integrated forwarding. In MWBS, a bi-directional tree is securely constructed for each group through the procedures of group initiation, join, leave, publication, and multi-level inter-zone routing. Especially, Designated Forwarding and Cacheable Nodes (DFCNs) are introduced to act as the roots for the construction of such bi-directional trees. The implementation details of MWBS are provided for function verifications. To effectively deploy MWBS, we investigate the impacts to the MWBS performance from the number and locations of DFCNs, which show that the optimized number of DFCNs can reduce the total traffic cost and the DFCN close to users is preferred to be selected for a group. Finally, simulations are performed to evaluate the performance of MWBS, which show that MWBS can reduce the control packet overhead and the state storage overhead compared to the existing ICN schemes.

Goodput‐maximised data delivery scheme for battery‐free wireless sensor network

In the battery-free wireless sensor network (BF-WSN) that harvests radio signal energy, data delivery suffers from a longer delay arising from the energy-harvesting period. It is significant to develop an energy-efficient, low-delay, and reliable data gathering scheme for the BF-WSN. The goodput-maximised data delivery scheme (GDDS) is proposed to reliably collect time-constrained data in the IEEE 802.15.4-based BF-WSN. Under the GDDS, the sink's operation period consists of multiple data gathering cycles with each incorporating three phases: charging the nodes, assigning channel occupation time for the nodes, and receiving packets from the nodes. The scheme of accumulating correct data blocks (SACDB) is used in the third phase for the sink to gather data from the nodes. The authors develop an analytical model for the SACDB, from which they derive the time and the energy consumed in transmitting a packet. Then, they derive the goodput and the energy efficiency under the proposed GDDS. The GDDS aims at maximising the goodput by optimising the charging period, the number of data blocks, and the maximum number of transmission trials under the constraint on data gathering time. Simulation results show the GDDS outperforms the existing schemes in terms of the goodput and energy efficiency.

Rendezvous: Opportunistic Data Delivery to Mobile Users by UAVs Through Target Trajectory Prediction

Reliable and timely delivery of data to mobile targets is a challenging problem in mobile ad-hoc networks, because of the opportunistic and unpredictable nature of the problem. Finding a complete series of mobile-to-mobile contacts and forwarding data toward targets within a designated deadline is even more difficult. We leverage unmanned aerial vehicles (UAVs) as message ferries to travel over highly probable rendezvous points for over-the-air delivery to target users. We propose Rendezvous , an opportunistic yet disciplined data delivery scheme based on trajectory prediction of users and UAV path planning. During the offline learning phase, we identify both temporal and spatial regularities of mobile users from real-world trajectories using sequence-wise clustering, and construct a compact yet well-summarized cluster signature that enables efficient search. In the data delivery phase, given recent movement history of users, UAVs perform their own distributed path planning collaboratively over time. Each UAV finds a chronological sequence of future visiting points where it can make consecutive data delivery efforts to users to overcome the uncertainty in mobility and ensure timely delivery. Real-world trace-driven simulation experiments demonstrate that Rendezvous achieves reliable and punctual data delivery using only few UAVs compared with existing algorithms.

Coffea Columnar Object Framework For Effective Analysis

The coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility of analysis. It is implemented with the Python programming language, the scientific python package ecosystem, and commodity big data technologies. To achieve this suite of improvements across many use cases, coffea takes a factorized approach, separating the analysis implementation and data delivery scheme. All analysis operations are implemented using the NumPy or awkward-array packages which are wrapped to yield user code whose purpose is quickly intuited. Various data delivery schemes are wrapped into a common front-end which accepts user inputs and code, and returns user defined outputs. We will discuss our experience in implementing analysis of CMS data using the coffea framework along with a discussion of the user experience and future directions.

UAV-Aided Data Delivery Scheme Based on Opportunistic Virtual Intersections for Smart Transportation Networks

Smart transportation networks, featured by high mobility and intermittent connectivity, are facing critical challenges in data delivery especially when the network is sparse and with less or no support from any infrastructure, such as in disaster and military environments. Unmanned aerial vehicles (UAVs), due to their flexible mobility, can be envisioned to enhance the connectivity of ground vehicular networks. In this paper, incorporating UAVs into vehicular networks, a novel data delivery scheme, named UAV-assisted data delivery (UADD), is proposed for smart transportation networks. We introduce a novel concept called “opportunistic virtual intersections”, at which UAVs can communicate with ground vehicles, and assist to deliver data packets. Specifically, at an opportunistic virtual intersection, UAVs may act as a relay, which can relay data packets between two vehicles as an intermediate hop, or deliver data packets in a store-carry-and-forward manner. The data forwarding rules are devised based on expected delivery delay and delivery probability of data packets. The performance evaluation demonstrates that the proposed UAV-assisted data delivery scheme can achieve a significant improvement of data delivery performance in terms of delivery delay and delivery ratio.

An Efficient Data Delivery Scheme in WBAN to Deal with Shadow Effect due to Postural Mobility

The body movement and change in posture exhibit high mobility in sensor nodes which causes shadowing in the Wireless Body Area Network (WBAN). Due to this, the connectivity between the nodes in WBAN is affected which further causes failure in data delivery. This article presents a MAC protocol in WBAN to deal with the problem of data delivery due to body movement and postural mobility. It uses an Improved Initial Centroid K-means clustering technique for classification of various human body postures followed by back propagation neural network as a classifier to recognize human body posture. This article proposes a posture aware dynamic data delivery (PA-DDD) protocol to deliver data dynamically. The PA-DDD protocol can be used under varying speed walking scenario. The simulation results show that it prolongs the network lifetime and is energy efficient.

Energy Storage Overflow-Aware Data Delivery Scheme for Energy Harvesting Wireless Sensor Networks.

In Energy-Harvesting Wireless Sensor Networks (EH-WSNs), energy storage with limited capacity is used in the nodes to store the harvested energy. Energy storage overflow (ESO) happens when the energy storage is full, which causes the nodes to be unable to store the newly harvested energy. In traditional data delivery schemes, there is the problem of “energy hungry and surplus coexistence”, meaning that some nodes in the network are hungry for energy while some other nodes continue to waste energy due to ESO. To alleviate this problem, in this paper, we present the ESO-aware multiple path (EAMP) data delivery scheme so that more data can be delivered to the sink. With the EAMP, multiple disjoint paths from the source node to the sink are constructed, and the source node splits data into multiple pieces with each going through one of the paths, which helps in mitigating ESO. Simulation results show that the proposed EAMP scheme can deliver more data than the existing ones.

Loss-Aware CMT-Based Multipathing Scheme for Efficient Data Delivery to Heterogeneous Wireless Networks

With the rapid development of wireless networks, multiple network interfaces are gradually being designed into more and more mobile devices. When it comes to data delivery, Stream Control Transmission Protocol (SCTP)-based Concurrent Multipath Transfer (CMT) has proven to be quite useful solution for multiple home networks, and it could become the key transport protocol for the next generation of wireless communications. The CMT delay caused by data rearrangement has been noticed by researchers, but they have seldom considered the frequent occurrence of packet loss that occurs in the high-loss networks. In this paper, we proposed an original loss-aware solution for multipath concurrent transmission (CMT-LA) that achieves the following goals: (1) identifying packet loss on all paths, (2) distributing packets adaptively across multiple available paths according to their packet loss and loss variation, and (3) maintaining the features of bandwidth aggregation and parallel transmission of CMT while improving the throughput performance. The results of our simulations showed that the proposed CMT-LA reduces reordering delay and unnecessary fast retransmissions, thereby demonstrating that CMT-LA is a more efficient data delivery scheme than classic CMT.

An efficient data delivery and scheduling scheme for smart and sustainable cities

With the increasing conflict between population growth and limited environmental resources, building intelligent and efficient data transmission networks is crucial to cleaner production in smart and sustainable cities. More and more people are eager to exchange all kinds of information in urban life through an efficient network. Therefore, designing a smart data distribution and transmission mechanism can not only improve people's quality of life but also pave the way for reducing environmental pollution. Optical networks have been widely used in the core network due to high-capacity characteristics. However, in inter-data-center (Inter-DC) optical networks, the coexisted heterogeneous optical devices and multi-granularity network resources bring new challenges to network management and traffic scheduling. Recently, software-defined network (SDN) and network function virtualization (NFV) have been introduced to enable the unified control for improving network agility and automation. Thus for software-defined multi-granularity Inter-DC optical networks, we propose a list of online service provisioning strategies. Moreover, in terms of the network controller, the novel routing applications are also developed by extended OpenFlow protocol. Finally, we experimentally demonstrate the overall feasibility of proposed solutions in a semi-practical platform. Numerical simulations conducted to quantitatively evaluate the algorithm efficiency also verify the superiorities of improving spectrum efficiency and reducing service blocking probability.

Designing prefix code to save energy for wirelessly powered wireless sensor networks

In the Internet of Things, wireless sensor networks (WSNs) are widely deployed. In recent years, wirelessly powered WSNs or battery-free WSNs (BF-WSNs), in which the nodes harvest energy from radio signals in the environment, have been emerging to support sustainable operation for WSNs. It is significant to design an energy-efficient data delivery scheme for the BF-WSNs. In this study, the authors propose the prefix code based scheme (PCBS) to save energy in data delivery by making use of the energy consumption disparity (ECD) between transmitting/receiving bit 0 and bit 1 in the existing non-modulation baseband transmission or carrier-modulation based passband transmission. The authors formulate an optimisation problem and use genetic algorithm to find its solution so that the energy-efficient prefix codebook is obtained. The codebook dilutes the ECD by containing the energy-consuming bit as few as possible, and the PCBS maps each m-bit data block into a prefix codeword in the codebook to conduct energy-efficient transmission at the transmitter and vice versa at the receiver. Both the experiments on wireless identification sensing platform and the simulations demonstrate that the proposed PCBS outperforms the existing schemes in terms of energy saving.

MAC2: Enabling multicasting and congestion control with multichannel transmission for intelligent vehicle terminal in Internet of Vehicles

Internet of Vehicles has become a promising way to realize the evolution from vehicular ad hoc networks and next-generation intelligent transportation system into future autonomous driving scenarios, clean-energy intelligent vehicles, and Smart Cities. However, multicasting service messages on available service channels and periodic exchanges of beacon messages on control channel cause the problem of efficiently scheduling those messages via multichannel transmission for intelligent vehicle terminal, to support real-world applications in Internet of Vehicles scenario. In this article, we investigate the intelligent vehicle terminal architecture and, particularly, design the wireless communication board by incorporating multicasting and congestion control modules. Especially, we present a multicast data delivery scheme with random-delay lowest-cost constraint to transfer service messages on service channels. Furthermore, a priority-aware congestion control scheme is also proposed by considering differentiated priorities of beacon messages on control channel, to cope with the congestion problem at bottleneck vehicle node. Based on the proposed schemes, we build up the RanLow (Random-delay Lowest-cost) module and the priority-aware congestion control (PARCEL) module by enabling multicasting and congestion control together in wireless communication board of the intelligent vehicle terminal architecture. Finally, the experimental results and comparison show that our devised RanLow module and PARCEL module are feasible and more efficient than existing schemes.