Patent Application
20150200810
The present disclosure relates generally to computer networks, and, more particularly, to multicasting a message to multiple parent nodes in a network.
Low power and Lossy Networks (LLNs), e.g., sensor networks, have a myriad of applications, such as Smart Grid and Smart Cities. Various challenges are presented with LLNs, such as lossy links, low bandwidth, battery operation, low memory and/or processing capability, etc. One example routing solution to LLN challenges is a protocol called Routing Protocol for LLNs or “RPL,” which is a distance vector routing protocol that builds a Destination Oriented Directed Acyclic Graph (DODAG, or simply DAG) in addition to a set of features to bound the control traffic, support local (and slow) repair, etc. The RPL architecture provides a flexible method by which each node performs DODAG discovery, construction, and maintenance.
Challenges remain in applying architectures similar to the RPL architecture to networks that have mobile nodes, such as vehicular networks. In particular, the RPL protocol uses a routing strategy in which all traffic to a particular node is routed via a parent of the node (e.g., a direct or indirect parent, grandparent, great grandparent, etc.). By successively routing to a parent node at each level of the tree, the destination node is eventually found and the packet delivered to the node. However, the constant mobility of nodes in vehicular and other mobile networks often causes the network topology and the parents of a node to change. When a parent change occurs while packets are being routed, at least some of the packets are not delivered to the destination node, resulting in a reduction of the actual application throughput.
The embodiments herein may be better understood by referring to the following description in conjunction with the accompanying drawings in which like reference numerals indicate identically or functionally similar elements, of which:
According to one or more embodiments of the disclosure, a future location of a child node in a network is predicted. One or more potential parent nodes are identified for the child node based on the predicted future location of the child node. The potential parent nodes are then reported to a root node in the network. A first data packet that is multicast to the current parent node and one of the potential parent nodes is received via the current parent node. A second data packet that is also multicast to the current and potential parent nodes is received via the potential parent node.
According to one or more additional embodiments of the disclosure, a device receives data indicative of one or more potential parent nodes from a child node in a network, where the one or more potential parent nodes are identified based on the predicted future location of the child node. As such, the device may send a first data packet to a current parent node of the child node and to one of the potential parent nodes, where the first data packet is received by the child node via the current parent node, and sends a second data packet to the current parent node and to one of the potential parent nodes, where the second data packet is received by the child node via the potential parent node.
A computer network is a geographically distributed collection of nodes interconnected by communication links and segments for transporting data between end nodes, such as personal computers and workstations, or other devices, such as sensors, etc. Many types of networks are available, ranging from local area networks (LANs) to wide area networks (WANs). LANs typically connect the nodes over dedicated private communications links located in the same general physical location, such as a building or campus. WANs, on the other hand, typically connect geographically dispersed nodes over long-distance communications links, such as common carrier telephone lines, optical lightpaths, synchronous optical networks (SONET), synchronous digital hierarchy (SDH) links, or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, and others. In addition, a Mobile Ad-Hoc Network (MANET) is a kind of wireless ad-hoc network, which is generally considered a self-configuring network of mobile routers (and associated hosts) connected by wireless links, the union of which forms an arbitrary topology.
Smart object networks, such as sensor networks, in particular, are a specific type of network having spatially distributed autonomous devices such as sensors, actuators, etc., that cooperatively monitor physical or environmental conditions at different locations, such as, e.g., energy/power consumption, resource consumption (e.g., water/gas/etc. for advanced metering infrastructure or “AMI” applications) temperature, pressure, vibration, sound, radiation, motion, pollutants, etc. Other types of smart objects include actuators, e.g., responsible for turning on/off an engine or perform any other actions. Sensor networks, a type of smart object network, are typically shared-media networks, such as wireless or PLC networks. That is, in addition to one or more sensors, each sensor device (node) in a sensor network may generally be equipped with a radio transceiver or other communication port such as PLC, a microcontroller, and an energy source, such as a battery. Often, smart object networks are considered field area networks (FANs), neighborhood area networks (NANs), etc. Generally, size and cost constraints on smart object nodes (e.g., sensors) result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth.
Data packets 140 (e.g., traffic and/or messages sent between the devices/nodes) may be exchanged among the nodes/devices of the computer network 100 using predefined network communication protocols such as certain known wireless protocols (e.g., IEEE Std. 802.11, IEEE Std. 802.15.4, WiFi, Bluetooth®, etc.), or other protocols where appropriate. In this context, a protocol consists of a set of rules defining how the nodes interact with each other.
The network interface(s) 210 contain the mechanical, electrical, and signaling circuitry for communicating data over links 105 coupled to the network 100. The network interfaces may be configured to transmit and/or receive data using a variety of different communication protocols. Note, further, that certain nodes may have two different types of network connections 210, e.g., wireless and wired/physical connections, and that the view herein is merely for illustration. For example, while some devices 200 may be entirely mobile (e.g., cars), other devices 200 may represent unmoving devices, and may allow for a wired connection, accordingly.
The memory 240 comprises a plurality of storage locations that are addressable by the processor 220 and the network interfaces 210 for storing software programs and data structures associated with the embodiments described herein. Note that certain devices may have limited memory or no memory (e.g., no memory for storage other than for programs/processes operating on the device and associated caches). The processor 220 may comprise necessary elements or logic adapted to execute the software programs and manipulate the data structures 245. An operating system 242, portions of which are typically resident in memory 240 and executed by the processor, functionally organizes the device by, inter alia, invoking operations in support of software processes and/or services executing on the device. These software processes and/or services may comprise routing process/services 244 and a directed acyclic graph (DAG) process 246, as well as an illustrative process 248, as described herein. Note that while process 248 is shown in centralized memory 240, one or more embodiments specifically provide for the process, or particular portions of the “process,” to be specifically operated within the network interfaces 210, e.g., as part of a MAC or PHY layer (process “248a”).
It will be apparent to those skilled in the art that other processor and memory types, including various computer-readable media, may be used to store and execute program instructions pertaining to the techniques described herein. Also, while the description illustrates various processes, it is expressly contemplated that various processes may be embodied as modules configured to operate in accordance with the techniques herein (e.g., according to the functionality of a similar process). Further, while the processes have been shown separately, those skilled in the art will appreciate that processes may be routines or modules within other processes.
Routing process (services) 244, where used in a routing environment, contains computer executable instructions executed by the processor 220 to perform functions provided by one or more routing protocols, such as proactive or reactive routing protocols as will be understood by those skilled in the art. These functions may, on capable devices, be configured to manage a routing/forwarding table (a data structure 245) containing, e.g., data used to make routing/forwarding decisions. In particular, in proactive routing, connectivity is discovered and known prior to computing routes to any destination in the network, e.g., link state routing such as Open Shortest Path First (OSPF), or Intermediate-System-to-Intermediate-System (ISIS), or Optimized Link State Routing (OLSR). Reactive routing, on the other hand, discovers neighbors (i.e., does not have an a priori knowledge of network topology), and in response to a needed route to a destination, sends a route request into the network to determine which neighboring node may be used to reach the desired destination. Example reactive routing protocols may comprise Ad-hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), DYnamic MANET On-demand Routing (DYMO), etc. Notably, on devices not capable or configured to store routing entries, routing process 244 may consist solely of providing mechanisms necessary for source routing techniques. That is, for source routing, other devices in the network can tell the less capable devices exactly where to send the packets, and the less capable devices simply forward the packets as directed.
Notably, mesh networks have become increasingly popular and practical in recent years. In particular, shared-media mesh networks, such as wireless or PLC networks, etc., are often on what is referred to as Low-Power and Lossy Networks (LLNs), which are a class of network in which both the routers and their interconnect are constrained: LLN routers typically operate with constraints, e.g., processing power, memory, and/or energy (battery), and their interconnects are characterized by, illustratively, high loss rates, low data rates, and/or instability. LLNs are comprised of anything from a few dozen and up to thousands or even millions of LLN routers, and support point-to-point traffic (between devices inside the LLN), point-to-multipoint traffic (from a central control point such at the root node to a subset of devices inside the LLN) and multipoint-to-point traffic (from devices inside the LLN towards a central control point).
An example implementation of LLNs is an “Internet of Things” network. Loosely, the term “Internet of Things” or “IoT” may be used by those in the art to refer to uniquely identifiable objects (things) and their virtual representations in a network-based architecture. In particular, the next frontier in the evolution of the Internet is the ability to connect more than just computers and communications devices, but rather the ability to connect “objects” in general, such as lights, appliances, vehicles, HVAC (heating, ventilating, and air-conditioning), windows and window shades and blinds, doors, locks, etc. The “Internet of Things” thus generally refers to the interconnection of objects (e.g., smart objects), such as sensors and actuators, over a computer network (e.g., IP), which may be the Public Internet or a private network. Such devices have been used in the industry for decades, usually in the form of non-IP or proprietary protocols that are connected to IP networks by way of protocol translation gateways. With the emergence of a myriad of applications, such as the smart grid, smart cities, and building and industrial automation, and cars (e.g., that can interconnect millions of objects for sensing things like power quality, tire pressure, and temperature and that can actuate engines and lights), it has been of the utmost importance to extend the IP protocol suite for these networks.
An example protocol specified in an Internet Engineering Task Force (IETF) Internet Draft, entitled “RPL: IPv6 Routing Protocol for Low Power and Lossy Networks”<RFC6550> by Winter, at al. (Mar. 13, 2011 version), provides a mechanism that supports multipoint-to-point (MP2P) traffic from devices inside the LLN towards a central control point (e.g., LLN Border Routers (LBRs) or “root nodes/devices” generally), as well as point-to-multipoint (P2MP) traffic from the central control point to the devices inside the LLN (and also point-to-point, or “P2P” traffic). RPL (pronounced “ripple”) may generally be described as a distance vector routing protocol that builds a Directed Acyclic Graph (DAG) for use in routing traffic/packets 140, in addition to defining a set of features to bound the control traffic, support repair, etc. Notably, as may be appreciated by those skilled in the art, RPL also supports the concept of Multi-Topology-Routing (MTR), whereby multiple DAGs can be built to carry traffic according to individual requirements.
A DAG is a directed graph having the property that all edges (and/or vertices) are oriented in such a way that no cycles (loops) are supposed to exist. All edges are contained in paths oriented toward and terminating at one or more root nodes (e.g., “clusterheads or “sinks”), often to interconnect the devices of the DAG with a larger infrastructure, such as the Internet, a wide area network, or other domain. In addition, a Destination Oriented DAG (DODAG) is a DAG rooted at a single destination, i.e., at a single DAG root with no outgoing edges. A “parent” of a particular node within a DAG is an immediate successor of the particular node on a path towards the DAG root, such that the parent has a lower “rank” than the particular node itself, where the rank of a node identifies the node's position with respect to a DAG root (e.g., the farther away a node is from a root, the higher is the rank of that node). Further, in certain embodiments, a sibling of a node within a DAG may be defined as any neighboring node which is located at the same rank within a DAG. Note that siblings do not necessarily share a common parent, and routes between siblings are generally not part of a DAG since there is no forward progress (their rank is the same). Note also that a tree is a kind of DAG, where each device/node in the DAG generally has one parent or one preferred parent.
DAGs may generally be built (e.g., by DAG process 246) based on an Objective Function (OF). The role of the Objective Function is generally to specify rules on how to build the DAG (e.g. number of parents, backup parents, etc.).
In addition, one or more metrics/constraints may be advertised by the routing protocol to optimize the DAG against. Also, the routing protocol allows for including an optional set of constraints to compute a constrained path, such as if a link or a node does not satisfy a required constraint, it is “pruned” from the candidate list when computing the best path. (Alternatively, the constraints and metrics may be separated from the OF.) Additionally, the routing protocol may include a “goal” that defines a host or set of hosts, such as a host serving as a data collection point, or a gateway providing connectivity to an external infrastructure, where a DAG's primary objective is to have the devices within the DAG be able to reach the goal. In the case where a node is unable to comply with an objective function or does not understand or support the advertised metric, it may be configured to join a DAG as a leaf node. As used herein, the various metrics, constraints, policies, etc., are considered “DAG parameters.”
Illustratively, example metrics used to select paths (e.g., preferred parents) may comprise cost, delay, latency, bandwidth, expected transmission count (ETX), etc., while example constraints that may be placed on the route selection may comprise various reliability thresholds, restrictions on battery operation, multipath diversity, bandwidth requirements, transmission types (e.g., wired, wireless, etc.). The OF may provide rules defining the load balancing requirements, such as a number of selected parents (e.g., single parent trees or multi-parent DAGs). Notably, an example for how routing metrics and constraints may be obtained may be found in an IETF Internet Draft, entitled “Routing Metrics used for Path Calculation in Low Power and Lossy Networks” <RFC6651> by Vasseur, et al. (Mar. 1, 2011 version). Further, an example OF (e.g., a default OF) may be found in an IETF Internet Draft, entitled “RPL Objective Function 0” <RFC6552> by Thubert (Jul. 8, 2011 version) and “The Minimum Rank Objective Function with Hysteresis”<RFC6719> by O. Gnawali et al. (May 17, 2011 version).
Building a DAG may utilize a discovery mechanism to build a logical representation of the network, and route dissemination to establish state within the network so that routers know how to forward packets toward their ultimate destination. Note that a “router” refers to a device that can forward as well as generate traffic, while a “host” refers to a device that can generate but does not forward traffic. Also, a “leaf” may be used to generally describe a non-router that is connected to a DAG by one or more routers, but cannot itself forward traffic received on the DAG to another router on the DAG. Control messages may be transmitted among the devices within the network for discovery and route dissemination when building a DAG.
According to the illustrative RPL protocol, a DODAG Information Object (DIO) is a type of DAG discovery message that carries information that allows a node to discover a RPL Instance, learn its configuration parameters, select a DODAG parent set, and maintain the upward routing topology. In addition, a Destination Advertisement Object (DAO) is a type of DAG discovery reply message that conveys destination information upwards along the DODAG so that a DODAG root (and other intermediate nodes) can provision downward routes. A DAO message includes prefix information to identify destinations, a capability to record routes in support of source routing, and information to determine the freshness of a particular advertisement. Notably, “upward” or “up” paths are routes that lead in the direction from leaf nodes towards DAG roots, e.g., following the orientation of the edges within the DAG. Conversely, “downward” or “down” paths are routes that lead in the direction from DAG roots towards leaf nodes, e.g., generally going in the opposite direction to the upward messages within the DAG.
Generally, a DAG discovery request (e.g., DIO) message is transmitted from the root device(s) of the DAG downward toward the leaves, informing each successive receiving device how to reach the root device (that is, from where the request is received is generally the direction of the root). Accordingly, a DAG is created in the upward direction toward the root device. The DAG discovery reply (e.g., DAO) may then be returned from the leaves to the root device(s) (unless unnecessary, such as for UP flows only), informing each successive receiving device in the other direction how to reach the leaves for downward routes. Nodes that are capable of maintaining routing state may aggregate routes from DAO messages that they receive before transmitting a DAO message. Nodes that are not capable of maintaining routing state, however, may attach a next-hop parent address. The DAO message is then sent directly to the DODAG root that can in turn build the topology and locally compute downward routes to all nodes in the DODAG. Such nodes are then reachable using source routing techniques over regions of the DAG that are incapable of storing downward routing state. In addition, RPL also specifies a message called the DIS (DODAG Information Solicitation) message that is sent under specific circumstances so as to discover DAG neighbors and join a DAG or restore connectivity.
As an example,
Note that as free WiFi networks become predictably more accessible from vehicles, users will have a strong economic incentive to opportunistically offload data traffic from 3G and 4G links to free WiFi links. Since WiFi deployment is not prevalent, enabling vehicles to access roadside WiFi through other vehicles (multihop-to-infrastructure) allow more vehicles to take advantage of data offloading. From the perspective of the service providers, enabling vehicles to use a multihop-to-infrastructure architecture reduces the number of WiFi access points they need to deploy, thereby reducing the capital cost of WiFi infrastructure rollout. Moreover, as more vehicles are connected in the future, the network will inevitably support applications beyond safety to infotainment, video streaming, online gaming, etc. These applications tend to carry high volume data traffic, making ad hoc 802.11 type of wireless communication a suitable strategy. Thus, enabling multihop-to-infrastructure connectivity generally requires the support of a multihop-to-infrastructure vehicular routing protocol.
The concepts and routing strategy used by RPL are finding some use in mobile scenarios, such as in vehicular networks. In particular, RPL has been found to be efficient at providing connectivity to a group of vehicular nodes that need to connect to an infrastructure node (e.g., via a RSU) to access a larger intranet or the Internet in general. In this case, the infrastructure node acts as the root of the tree/DAG and the vehicular nodes form the branches and leaves of the tree.
However, RPL is a tree based routing protocol that was originally designed for static sensor networks. One of the core aspects of RPL lies in the use of an objective function (OF) configured on the DAG Root that determines the rules that control how nodes join the DAG. The OF specifies the list of metrics and constraints used to build the DAG in addition to a number of rules and objectives. For example, one objective may be to find the shortest path based on a reliability metric (i.e., the most reliable path), while avoiding battery operated nodes. Such an OF would typically be used in a smart metering application. Another example objective used for substation automation and control would be to find the shortest path based on a delay metric (i.e., the shortest delay) while using encrypted links. The RPL architecture provides a flexible method by which each node performs DODAG discovery, construction, and maintenance by having each node construct and maintain the DODAG edges.
As noted above, challenges remain in applying the RPL architecture to mobile networks, such as vehicular networks. In particular, the RPL protocol uses a routing strategy in which all traffic to a particular node is routed via a parent of the node (e.g., a direct or indirect parent, grandparent, great grandparent, etc.). By successively routing to a parent of the node at each level of the tree, the node is eventually found and the packet delivered to the node. However, the constant mobility of nodes in vehicular and other mobile networks often causes the network topology and the parents of a node to change. When a parent change occurs while packets are being routed, the packet is not delivered to the node, resulting in a loss of the actual application throughput. This situation is fairly common in vehicular networks and will result in the overall performance of the network being very low in terms of actual application throughput or “goodput” (i.e., traffic that reaches its destination).
Multicasting Packets in a Mobile Network
The techniques herein provide for mechanisms whereby packets are preemptively routed to a predicted future parent in a mobile network of a destination node so that the number of packets lost by the application is minimized. In one aspect, techniques are presented in which a node conveys information regarding its predicted future parent(s) to its current parent(s) and to the network root. In another aspect, an objective function may be used to optimize the tradeoff between goodput and the network load. In particular, when using RPL in a vehicular network or other mobile network, there is a high probability for packets to be lost when a node changes parents due to mobility and the overall dynamics of the DAG. According to the techniques described herein, each node may predict its future parents and periodically informs the root node, to improve the overall application throughput of the network. When forwarding packets to a node in the RPL DAG (e.g., from the Internet, etc.), the root node multicasts the packet to one or more of the predicted parents of the node. Thus, application throughput of the node is dramatically improved, and the reliability to effectively deliver the packet to the mobile node on the return path. The tradeoff in this case is the increased network load. Such a solution significantly contrasts with existing approaches by introducing a probabilistic routing paradigm that also uses proactive routing.
Specifically, according to one or more embodiments of the disclosure as described in detail below, a future location of a child node in a network is predicted. One or more potential parent nodes are identified for the child node based on the predicted future location of the child node. The potential parent nodes are then reported to a root node in the network. A first data packet that is multicast to the current parent node and one of the potential parent nodes is received via the current parent node. A second data packet that is also multicast to the current and potential parent nodes is received via the potential parent node.
Illustratively, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with the process 248/248a, which may contain computer executable instructions executed by the processor 220 (or independent processor of interfaces 210) to perform functions relating to the techniques described herein, e.g., in conjunction with routing process 244 (and/or DAG process 246). For example, the techniques herein may be treated as extensions to conventional protocols, such as the illustrative RPL protocol and/or wireless communication protocols, and as such, may be processed by similar components understood in the art that execute those protocols, accordingly.
Operationally, a mechanism is first herein defined whereby a vector of parameters is used to estimate the mobility of a network node/device and predict a future location of the node. As shown in
Further, according to one or more embodiments herein, a child node in the network identifies one or more potential parent nodes that could be used by the child node in the future. In RPL, for example, a node maintains a table of candidate parents. In a sufficiently dense network, such as a vehicular network, a node is able to hear DIO messages from different parents. All of these parents are listed in the candidate parent list maintained by the child node. The current parent is chosen based on parameters like link quality, link stability etc. According to the techniques described herein, each node may use a probabilistic model that includes the mobility vectors and current location of the nodes in combination with a map of the region to identify one or more potential parent nodes.
In some embodiments, a device/node may select up to a threshold number of potential parent nodes based on various factors, such as the current location of the node(s), directional vectors associated with the node(s), link quality metrics, link stability metrics, or other such factors. For example, each node may identify candidate parents based on their current locations and direction vectors that are advertises in the DIO packets. Typically, the best parent would be one that is moving in the same direction as the child node since the stability of the link will be the highest. However, other potential parents moving in different directions may also be selected depending on the circumstances. There is also no need for each node to have a map that shows the position of every other node and in fact, this would be infeasible in many situations. In other words, a node/device may combine information regarding its predicted future location with a calculated link quality to each node in its preferred parent set and to its current parent. Based on these parameters, the child node chooses up to a threshold number of potential parents (e.g., up to four parents or any other number of parents) with which the child node is most likely to establish a parent-child relationship within a fixed amount of time.
As shown in
Each node in the network may update the root when there is change to the set of predicted parents for the node and/or to the order of the predicted parents. In typical vehicular networks, the vehicles tend to move in clusters and the rate of change of the predicted parents will be pretty slow, meaning that the additional network traffic due to the predicted parent updates will typically be minimal. However, a tradeoff may be made between the efficiency of the mechanism and the rate of updates that each node provides by tuning the rate of updates to avoid flooding of the network with control traffic.
According to one or more embodiments herein, a node/device may generate and send a predictive DAO (pDAO) message that identifies its potential future parents to the root. In
Once generated and sent by the child node, pDAO message 800 may traverse the current DAG to reach the root node of the network. In response, the root node registers the predicted parents in its routing table for the node that sent pDAO message 800 (e.g., with a */32 entry). The root node may then use the received parent information to transmit packets to the child node, e.g., as part of a multicast methodology that increases the likelihood of reception by the child node.
According to various embodiments, a pDAO packet can alternatively be piggybacked as a custom header on data packets that can be removed by the RPL DAG root. For example, as shown in
As noted above, a root node may use the potential parent information received from a node in the network to multicast a packet to the node. In other words, when receiving packets destined for the child node endpoint, the root node multicasts it to one or more potential parent nodes of the endpoint, to account for the possibility that the node has moved from one parent to another during the interval in which the request and response data packets are sent. For example, as shown in
In some embodiments, a root may only multicast packets to a destination node for which it cannot accurately predict the correct branch. In other words, the root may utilize a threshold parameter based on the link qualities, node movements, etc., to determine whether or not to multicast a data packet to a destination node. This parameter can be tuned to minimize the amount of redundant packets being sent. However, sending at least a small amount of redundant packets will improve the overall throughput that can be achieved by the destination node.
The techniques disclosed herein differ from existing techniques that use multiple parents to minimize the delay and improve the probability in delivering packets from a node to a root. In particular, previous techniques have focused on an optimization problem under the assumption that all traffic will flow from the node to the root. This is useful for sensor networks wherein the nodes are data sources and all data needs to be sent to a gateway (typically the root). However, these approaches do not sufficiently address this situation in which RPL is applied to a mobile network, such a vehicle network. Prior implementations have also formulated the optimization problem on the assumption that the underlying MAC layer is 802.15.4 TSCH. Using the techniques herein, no such limitation exists. For example, the present techniques support an underlying MAC layer such as 802.11n, to support the higher throughput requirements of the application space, which consequently leads to very different mechanisms.
As shown in
In one illustrative example of network operation, consider the situation in which node 34 sends a request for an Internet video via the root network node. In response, data may begin streaming from the server to the root, which determines the network path to reach the destination node 34. In a static network, this choice may be made using the network links that were previously discovered. In a mobile network, however, there is no such guarantee that node 34 will not move from one branch to another while the video traffic is being sent to it along the RPL tree. If the first few megabytes of the video data are sent over the current branch (e.g., via nodes 12, 23) and the next few megabytes are sent over the predicted branch (e.g., via nodes 13, 24), the effective throughput of the network can be increased by minimizing the packet loss to node 34. If this is not done and all video data is sent only to the branch containing node 23, the data will be discarded by node 23 when the destination node 34 moves and changes parents. Thus, the techniques herein allow a root node to decide how to partition and forward large streams of data based using feedback on the mobility of the network nodes.
As shown in
The percentage of improvement to the goodput of the network can also be optimized using an objective function (OF). Such an approach may be used to find the right balance between the number of copies of data packets that are being sent to different parents and the level of reliability that is expected. In one embodiment, the global OF will include a function Fa, of network load (X) and reliability (Y) in addition to the optimization function F. Such an OF may be of the form:
OF=F+Fa(aX+bY)
In other words, the routing mechanism may attempt to globally ensure that the network satisfies an expected load, degree of reliability, and satisfy any other network objectives (e.g., as represented by function F). In another embodiment each node can send a different optimization parameter, to achieve highly granular performance in the network. This extra parameter can be sent as part of a pDAO packet by appending a representation of the function Fa to the packet. The root node can then determine how many copies of the packet to send and whether to send a copy to all predicted parents.
It should be noted that while certain steps within procedures 1300-1400 may be optional as described above, the steps shown in
The techniques described herein, therefore, provide for considerable improvements to a mobile network's application throughput. Since application throughput directly affects a user's perception of the network service, it may be important that the application throughput is maintained above a certain threshold. The techniques described herein are also backwards compatible with existing networks. For example, a network that contains nodes implementing these techniques can coexist with nodes that do not implement this feature. In other words, it is only mandatory that the root node implements the disclosed methodologies. The techniques herein further integrate seamlessly with the existing OFs and do not require any additional overhead to convey the goodput versus network tradeoff to the root node.
While there have been shown and described illustrative embodiments that provide for dynamic enabling of routing devices in a shared-media communication network, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the embodiments herein. For example, the embodiments herein have been shown and described primarily with regard to vehicle networks. However, the embodiments in their broader sense are not as limited, and may, in fact, be used with other types of mobile networks in which at least some of the nodes are in motion. In addition, while certain protocols are shown, such as RPL, other suitable protocols may be used, accordingly.
The foregoing description has been directed to specific embodiments. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that the components and/or elements described herein can be implemented as software being stored on a tangible (non-transitory) computer-readable medium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructions executing on a computer, hardware, firmware, or a combination thereof. Accordingly this description is to be taken only by way of example and not to otherwise limit the scope of the embodiments herein. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the embodiments herein.
