Opportunistic Routing with Network Coding in Powerline Communications

Opportunistic Routing (OR) can be used as an alternative to the legacy routing (LR) protocols in networks with a broadcast lossy channel and possibility of overhearing the signal. The power line medium creates such an environment. OR can better exploit the channel than LR because it allows the cooperation of all nodes that receive any data. With LR, only a chain of nodes is selected for communication. Other nodes drop the received information. We investigate OR for the one-source one-destination scenario with one traffic flow. First, we evaluate the upper bound on the achievable data rate and advocate the decentralized algorithm for its calculation. This knowledge is used in the design of Basic Routing Rules (BRR). They use the link quality metric that equals the upper bound on the achievable data rate between the given node and the destination. We call it the node priority. It considers the possibility of multi-path communication and the packet loss correlation. BRR allows achieving the optimal data rate pertaining certain theoretical assumptions. The Extended BRR (BRR-E) are free of them. The major difference between BRR and BRR-E lies in the usage of Network Coding (NC) for prognosis of the feedback. In this way, the protocol overhead can be severely reduced. We also study Automatic Repeat-reQuest (ARQ) mechanism that is applicable with OR. It differs to ARQ with LR in that each sender has several sinks and none of the sinks except destination require the full recovery of the original message. Using BRR-E, ARQ and other services like network initialization and link state control, we design the Advanced Network Coding based Opportunistic Routing protocol (ANChOR). With the analytic and simulation results we demonstrate the near optimum performance of ANChOR. For the triangular topology, the achievable data rate is just 2% away from the theoretical maximum and it is up to 90% higher than it is possible to achieve with LR. Using the G.hn standard, we also show the full protocol stack simulation results (including IP/UDP and realistic channel model). In this simulation we revealed that the gain of OR to LR can be even more increased by reducing the head-of-the-line problem in ARQ. Even considering the ANChOR overhead through additional headers and feedbacks, it outperforms the original G.hn setup in data rate up to 40% and in latency up to 60%.:1 Introduction 2 1.1 Intra-flow Network Coding 6 1.2 Random Linear Network Coding (RLNC) 7 2 Performance Limits of Routing Protocols in PowerLine Communications (PLC) 13 2.1 System model 14 2.2 Channel model 14 2.3 Upper bound on the achievable data rate 16 2.4 Achieving the upper bound data rate 17 2.5 Potential gain of Opportunistic Routing Protocol (ORP) over Common Single-path Routing Protocol (CSPR) 19 2.6 Evaluation of ORP potential 19 3 Opportunistic Routing: Realizations and Challenges 24 3.1 Vertex priority and cooperation group 26 3.2 Transmission policy in idealized network 34 3.2.1 Basic Routing Rules (BRR) 36 3.3 Transmission policy in real network 40 3.3.1 Purpose of Network Coding (NC) in ORP 41 3.3.2 Extended Basic Routing Rules (BRR) (BRR-E) 43 3.4 Automatic ReQuest reply (ARQ) 50 3.4.1 Retransmission request message contents 51 3.4.2 Retransmission Request (RR) origination and forwarding 66 3.4.3 Retransmission response 67 3.5 Congestion control 68 3.5.1 Congestion control in our work 70 3.6 Network initialization 74 3.7 Formation of the cooperation groups (coalitions) 76 3.8 Advanced Network Coding based Opportunistic Routing protocol (ANChOR) header 77 3.9 Communication of protocol information 77 3.10 ANChOR simulation . .79 3.10.1 ANChOR information in real time .80 3.10.2 Selection of the coding rate 87 3.10.3 Routing Protocol Information (RPI) broadcasting frequency 89 3.10.4 RR contents 91 3.10.5 Selection of RR forwarder 92 3.10.6 ANChOR stability 92 3.11 Summary 95 4 ANChOR in the Gigabit Home Network (G.hn) Protocol 97 4.1 Compatibility with the PLC protocol stack 99 4.2 Channel and noise model 101 4.2.1 In-home scenario 102 4.2.2 Access network scenario 102 4.3 Physical layer (PHY) layer implementation 102 4.3.1 Bit Allocation Algorithm (BAA) 103 4.4 Multiple Access Control layer (MAC) layer 109 4.5 Logical Link Control layer (LLC) layer 111 4.5.1 Reference Automatic Repeat reQuest (ARQ) 111 4.5.2 Hybrid Automatic Repeat reQuest (HARQ) in ANChOR 114 4.5.3 Modeling Protocol Data Unit (PDU) erasures on LLC 116 4.6 Summary 117 5 Study of G.hn with ANChOR 119 5.1 ARQ analysis 119 5.2 Medium and PHY requirements for “good” cooperation 125 5.3 Access network scenario 128 5.4 In-home scenario 135 5.4.1 Modeling packet erasures 136 5.4.2 Linear Dependence Ratio (LDR) 139 5.4.3 Worst case scenario 143 5.4.4 Analysis of in-home topologies 145 6 Conclusions . . . . . . . . . . . . . . . 154 A Proof of the neccessity of the exclusion rule 160 B Gain of ORPs to CSRPs 163 C Broadcasting rule 165 D Proof of optimality of BRR for triangular topology 167 E Reducing the retransmission probability 168 F Calculation of Expected Average number of transmissions (EAX) for topologies with bi-directional links 170 G Feedback overhead of full coding matrices 174 H Block diagram of G.hn physical layer in ns-3 model 175 I PER to BER mapping 17

Opportunistic Routing with Network Coding in Powerline Communications

Opportunistic Routing (OR) can be used as an alternative to the legacy routing (LR) protocols in networks with a broadcast lossy channel and possibility of overhearing the signal. The power line medium creates such an environment. OR can better exploit the channel than LR because it allows the cooperation of all nodes that receive any data. With LR, only a chain of nodes is selected for communication. Other nodes drop the received information. We investigate OR for the one-source one-destination scenario with one traffic flow. First, we evaluate the upper bound on the achievable data rate and advocate the decentralized algorithm for its calculation. This knowledge is used in the design of Basic Routing Rules (BRR). They use the link quality metric that equals the upper bound on the achievable data rate between the given node and the destination. We call it the node priority. It considers the possibility of multi-path communication and the packet loss correlation. BRR allows achieving the optimal data rate pertaining certain theoretical assumptions. The Extended BRR (BRR-E) are free of them. The major difference between BRR and BRR-E lies in the usage of Network Coding (NC) for prognosis of the feedback. In this way, the protocol overhead can be severely reduced. We also study Automatic Repeat-reQuest (ARQ) mechanism that is applicable with OR. It differs to ARQ with LR in that each sender has several sinks and none of the sinks except destination require the full recovery of the original message. Using BRR-E, ARQ and other services like network initialization and link state control, we design the Advanced Network Coding based Opportunistic Routing protocol (ANChOR). With the analytic and simulation results we demonstrate the near optimum performance of ANChOR. For the triangular topology, the achievable data rate is just 2% away from the theoretical maximum and it is up to 90% higher than it is possible to achieve with LR. Using the G.hn standard, we also show the full protocol stack simulation results (including IP/UDP and realistic channel model). In this simulation we revealed that the gain of OR to LR can be even more increased by reducing the head-of-the-line problem in ARQ. Even considering the ANChOR overhead through additional headers and feedbacks, it outperforms the original G.hn setup in data rate up to 40% and in latency up to 60%.:1 Introduction 2 1.1 Intra-flow Network Coding 6 1.2 Random Linear Network Coding (RLNC) 7 2 Performance Limits of Routing Protocols in PowerLine Communications (PLC) 13 2.1 System model 14 2.2 Channel model 14 2.3 Upper bound on the achievable data rate 16 2.4 Achieving the upper bound data rate 17 2.5 Potential gain of Opportunistic Routing Protocol (ORP) over Common Single-path Routing Protocol (CSPR) 19 2.6 Evaluation of ORP potential 19 3 Opportunistic Routing: Realizations and Challenges 24 3.1 Vertex priority and cooperation group 26 3.2 Transmission policy in idealized network 34 3.2.1 Basic Routing Rules (BRR) 36 3.3 Transmission policy in real network 40 3.3.1 Purpose of Network Coding (NC) in ORP 41 3.3.2 Extended Basic Routing Rules (BRR) (BRR-E) 43 3.4 Automatic ReQuest reply (ARQ) 50 3.4.1 Retransmission request message contents 51 3.4.2 Retransmission Request (RR) origination and forwarding 66 3.4.3 Retransmission response 67 3.5 Congestion control 68 3.5.1 Congestion control in our work 70 3.6 Network initialization 74 3.7 Formation of the cooperation groups (coalitions) 76 3.8 Advanced Network Coding based Opportunistic Routing protocol (ANChOR) header 77 3.9 Communication of protocol information 77 3.10 ANChOR simulation . .79 3.10.1 ANChOR information in real time .80 3.10.2 Selection of the coding rate 87 3.10.3 Routing Protocol Information (RPI) broadcasting frequency 89 3.10.4 RR contents 91 3.10.5 Selection of RR forwarder 92 3.10.6 ANChOR stability 92 3.11 Summary 95 4 ANChOR in the Gigabit Home Network (G.hn) Protocol 97 4.1 Compatibility with the PLC protocol stack 99 4.2 Channel and noise model 101 4.2.1 In-home scenario 102 4.2.2 Access network scenario 102 4.3 Physical layer (PHY) layer implementation 102 4.3.1 Bit Allocation Algorithm (BAA) 103 4.4 Multiple Access Control layer (MAC) layer 109 4.5 Logical Link Control layer (LLC) layer 111 4.5.1 Reference Automatic Repeat reQuest (ARQ) 111 4.5.2 Hybrid Automatic Repeat reQuest (HARQ) in ANChOR 114 4.5.3 Modeling Protocol Data Unit (PDU) erasures on LLC 116 4.6 Summary 117 5 Study of G.hn with ANChOR 119 5.1 ARQ analysis 119 5.2 Medium and PHY requirements for “good” cooperation 125 5.3 Access network scenario 128 5.4 In-home scenario 135 5.4.1 Modeling packet erasures 136 5.4.2 Linear Dependence Ratio (LDR) 139 5.4.3 Worst case scenario 143 5.4.4 Analysis of in-home topologies 145 6 Conclusions . . . . . . . . . . . . . . . 154 A Proof of the neccessity of the exclusion rule 160 B Gain of ORPs to CSRPs 163 C Broadcasting rule 165 D Proof of optimality of BRR for triangular topology 167 E Reducing the retransmission probability 168 F Calculation of Expected Average number of transmissions (EAX) for topologies with bi-directional links 170 G Feedback overhead of full coding matrices 174 H Block diagram of G.hn physical layer in ns-3 model 175 I PER to BER mapping 17

Opportunistic Routing with Network Coding in Powerline Communications

Opportunistic Routing (OR) can be used as an alternative to the legacy routing (LR) protocols in networks with a broadcast lossy channel and possibility of overhearing the signal. The power line medium creates such an environment. OR can better exploit the channel than LR because it allows the cooperation of all nodes that receive any data. With LR, only a chain of nodes is selected for communication. Other nodes drop the received information. We investigate OR for the one-source one-destination scenario with one traffic flow. First, we evaluate the upper bound on the achievable data rate and advocate the decentralized algorithm for its calculation. This knowledge is used in the design of Basic Routing Rules (BRR). They use the link quality metric that equals the upper bound on the achievable data rate between the given node and the destination. We call it the node priority. It considers the possibility of multi-path communication and the packet loss correlation. BRR allows achieving the optimal data rate pertaining certain theoretical assumptions. The Extended BRR (BRR-E) are free of them. The major difference between BRR and BRR-E lies in the usage of Network Coding (NC) for prognosis of the feedback. In this way, the protocol overhead can be severely reduced. We also study Automatic Repeat-reQuest (ARQ) mechanism that is applicable with OR. It differs to ARQ with LR in that each sender has several sinks and none of the sinks except destination require the full recovery of the original message. Using BRR-E, ARQ and other services like network initialization and link state control, we design the Advanced Network Coding based Opportunistic Routing protocol (ANChOR). With the analytic and simulation results we demonstrate the near optimum performance of ANChOR. For the triangular topology, the achievable data rate is just 2% away from the theoretical maximum and it is up to 90% higher than it is possible to achieve with LR. Using the G.hn standard, we also show the full protocol stack simulation results (including IP/UDP and realistic channel model). In this simulation we revealed that the gain of OR to LR can be even more increased by reducing the head-of-the-line problem in ARQ. Even considering the ANChOR overhead through additional headers and feedbacks, it outperforms the original G.hn setup in data rate up to 40% and in latency up to 60%.:1 Introduction 2 1.1 Intra-flow Network Coding 6 1.2 Random Linear Network Coding (RLNC) 7 2 Performance Limits of Routing Protocols in PowerLine Communications (PLC) 13 2.1 System model 14 2.2 Channel model 14 2.3 Upper bound on the achievable data rate 16 2.4 Achieving the upper bound data rate 17 2.5 Potential gain of Opportunistic Routing Protocol (ORP) over Common Single-path Routing Protocol (CSPR) 19 2.6 Evaluation of ORP potential 19 3 Opportunistic Routing: Realizations and Challenges 24 3.1 Vertex priority and cooperation group 26 3.2 Transmission policy in idealized network 34 3.2.1 Basic Routing Rules (BRR) 36 3.3 Transmission policy in real network 40 3.3.1 Purpose of Network Coding (NC) in ORP 41 3.3.2 Extended Basic Routing Rules (BRR) (BRR-E) 43 3.4 Automatic ReQuest reply (ARQ) 50 3.4.1 Retransmission request message contents 51 3.4.2 Retransmission Request (RR) origination and forwarding 66 3.4.3 Retransmission response 67 3.5 Congestion control 68 3.5.1 Congestion control in our work 70 3.6 Network initialization 74 3.7 Formation of the cooperation groups (coalitions) 76 3.8 Advanced Network Coding based Opportunistic Routing protocol (ANChOR) header 77 3.9 Communication of protocol information 77 3.10 ANChOR simulation . .79 3.10.1 ANChOR information in real time .80 3.10.2 Selection of the coding rate 87 3.10.3 Routing Protocol Information (RPI) broadcasting frequency 89 3.10.4 RR contents 91 3.10.5 Selection of RR forwarder 92 3.10.6 ANChOR stability 92 3.11 Summary 95 4 ANChOR in the Gigabit Home Network (G.hn) Protocol 97 4.1 Compatibility with the PLC protocol stack 99 4.2 Channel and noise model 101 4.2.1 In-home scenario 102 4.2.2 Access network scenario 102 4.3 Physical layer (PHY) layer implementation 102 4.3.1 Bit Allocation Algorithm (BAA) 103 4.4 Multiple Access Control layer (MAC) layer 109 4.5 Logical Link Control layer (LLC) layer 111 4.5.1 Reference Automatic Repeat reQuest (ARQ) 111 4.5.2 Hybrid Automatic Repeat reQuest (HARQ) in ANChOR 114 4.5.3 Modeling Protocol Data Unit (PDU) erasures on LLC 116 4.6 Summary 117 5 Study of G.hn with ANChOR 119 5.1 ARQ analysis 119 5.2 Medium and PHY requirements for “good” cooperation 125 5.3 Access network scenario 128 5.4 In-home scenario 135 5.4.1 Modeling packet erasures 136 5.4.2 Linear Dependence Ratio (LDR) 139 5.4.3 Worst case scenario 143 5.4.4 Analysis of in-home topologies 145 6 Conclusions . . . . . . . . . . . . . . . 154 A Proof of the neccessity of the exclusion rule 160 B Gain of ORPs to CSRPs 163 C Broadcasting rule 165 D Proof of optimality of BRR for triangular topology 167 E Reducing the retransmission probability 168 F Calculation of Expected Average number of transmissions (EAX) for topologies with bi-directional links 170 G Feedback overhead of full coding matrices 174 H Block diagram of G.hn physical layer in ns-3 model 175 I PER to BER mapping 17

Development and Calibration of a PLC Simulation Model for UPA-Compliant Networks

Part 7: Application of ICT in Smart Grid and Smart Home EnvironmentsInternational audienceCommunication network of the Smart Grid is intended to serve a numerous amount of applications of different types. Its complicated structure often requires a utilization of several communication technologies: wireless, cable, etc. Power line communication (PLC) also remains a challenging solution for usage in this area. The present paper is targeted to explore the communication protocol of UPA (Universal Powerline Association) technology - one of Broadband PLC (BB-PLC) solutions, which are used today in the Smart Grid. In the paper the test field measurements of this technology are summarized and the development of the UPA simulation model is described. Among this description one of the most important stages of the simulation model design, i.e. calibration with the measurement results, is explained. The obtained simulation model is intended be used as a part of the PLC network planning tool

Healing Hands: The Tactile Internet in Future Tele-Healthcare

In the early 2020s, the coronavirus pandemic brought the notion of remotely connected care to the general population across the globe. Oftentimes, the timely provisioning of access to and the implementation of affordable care are drivers behind tele-healthcare initiatives. Tele-healthcare has already garnered significant momentum in research and implementations in the years preceding the worldwide challenge of 2020, supported by the emerging capabilities of communication networks. The Tactile Internet (TI) with human-in-the-loop is one of those developments, leading to the democratization of skills and expertise that will significantly impact the long-term developments of the provisioning of care. However, significant challenges remain that require today’s communication networks to adapt to support the ultra-low latency required. The resulting latency challenge necessitates trans-disciplinary research efforts combining psychophysiological as well as technological solutions to achieve one millisecond and below round-trip times. The objective of this paper is to provide an overview of the benefits enabled by solving this network latency reduction challenge by employing state-of-the-art Time-Sensitive Networking (TSN) devices in a testbed, realizing the service differentiation required for the multi-modal human-machine interface. With completely new types of services and use cases resulting from the TI, we describe the potential impacts on remote surgery and remote rehabilitation as examples, with a focus on the future of tele-healthcare in rural settings