1. Introduction
Wireless Mesh Networks (WMNs) are envisioned to be a solution to provide low-cost access to the Internet [
1]. Basically, WMNs employ a wireless backbone composed of stationary nodes to increase the connectivity compared with ad hoc networks. Client nodes, mobile or not, access the Internet through one or more than one of these backbone nodes because they are usually configured as gateways. For this reason, nodes that are often in the shortest paths toward gateways forward more packets than the others do and thus, consume more than their resources [
2]. In addition to the high concentration of traffic near gateways, WMNs also suffer from interference and collisions that are characteristic of wireless transmission. Nodes try to access the medium at each hop because WMNs are based on multihop communication. Therefore, packets that already traversed several hops can be discarded near their destination, which leads to resource waste [
3].
The information-centric networking (ICN) paradigm is a solution for the problems cited above in WMNs [
4,
5,
6,
7]. Information-Centric Wireless Mesh Networks focus on content delivery regardless of the content’s physical location. Content requests and the contents themselves are forwarded based on names. Therefore, nodes have no addresses, and a content request is not necessarily addressed to a particular node with a well-known address. The ICN paradigm also assumes that all network nodes can cache contents [
8]. Thus, any node can send the content in response to a request if the desired content is stored in its cache. Consequently, with ICN, a content request does not necessarily reach the gateway to retrieve the content from the Internet if another node has previously requested the same content. In this case, the traffic load in nodes near to the gateway decreases as well as the number of hops traversed by the content.
Several architectures have been proposed in the literature to deploy the ICN paradigm both in the Internet and in wireless networks [
9,
10]. Named-Data Networking (NDN) [
11] is one of these architectures. NDN radically changes the TCP/IP paradigm and becomes more suitable for handling the challenges faced by new network applications. Currently, users of applications, such as BitTorrent and NetFlix, are more interested in the content itself than in who provides this content [
12]. In 2017, 52.88 % of the Internet traffic was generated by Netflix, YouTube, and BitTorrent applications [
13]. The way that consumers request contents in NDN improves the data delivery rate when compared with other destination-oriented network architectures [
11]. NDN nodes request content by sending an interest packet that is forwarded through the network until it reaches a node that is able to send back a data packet with the desired content. This node can be the content producer or a node that stores the content in its cache because, with NDN, intermediate nodes can cache contents. This approach reduces the bottleneck on a content producers path. That is the reason why NDN improves the data delivery rate. NDN also has native mobility support because consumers request contents based on names and not based on the destination address as in IP networks. Also, nodes do not change their addresses during changes of access points [
14]. Another feature of NDN is the reduction of the control message overhead on the network. This reduction occurs because nodes do not need to propagate routing tables and link changes to their neighbors. These features classify NDN as a promising network architecture for the future networks, including wireless mesh networks [
15].
Particularly for wireless mesh networks, in-network caching reduces the traffic concentration near gateways and the waste of resources caused by the late drop of packets. NDN-based WMNs can also benefit from the broadcast nature of the wireless medium to disseminate interest packets. Hence, when a node sends a packet, more than one neighbor is able to listen this packet transmission. Neighbors are thus candidates to forward a request or content. These multiple forwarding candidates increase the probability of a packet being delivered to producer nodes or to forwarding nodes which are temporarily able to provide contents from their cache.
The development of WMNs based on NDN, however, is a challenge because of the broadcast storm problem [
16] caused by the forwarding of interest packets. With NDN, each node broadcasts an interest packet received for the first time. Thereafter, every neighbor will repeat the same behavior, leading to a broadcast storm of interest packets. In this context, nodes can experience a high collision rate depending on the number of consumers, i.e., nodes that request contents, and on the transmission rate of interest packets. Collisions increase packet losses and delay which are both caused by the retransmission of lost interest packets. This scenario characterizes the broadcast storm problem.
This paper proposes three mechanisms that limit the forwarding of interest packets in order to reduce the negative effects of the broadcast storm in WMNs. The goal of the proposed mechanisms is to decrease the number of interest packets forwarded by nodes and thus, to decrease the probability of collisions caused by these packets. Nodes, in this case, experience a higher delivery rate and lower delivery delay if the number of collisions decreases. The first proposed mechanism is called Probabilistic Interest Forwarding (PIF). With PIF, high-centrality nodes forward packets with a certain probability (p). With the second one, called Retransmission-Counter-based Interest Forwarding (ReCIF), nodes use the number of hops traversed by an interest packet in order to forward this packet or not. The third proposed mechanism, called ReCIF+PIF, combines the criteria of PIF and ReCIF. The idea is to guarantee that interest packets forwarded fewer times have priority to be forwarded by a node, and other interest packets are forwarded with a probability of p. Our three mechanisms are compared with both the default NDN forwarding mechanism and the Listen First Broadcast Later (LFBL) mechanism. The content delivery rate, the delivery delay, and the overhead are the metrics considered in the simulation. The results show that the proposed mechanisms are more efficient in scenarios with a higher number of nodes contending to access the medium. For these scenarios, our mechanisms provide a content delivery rate that is 21% higher, with 25% less delivery delay than the default NDN forwarding mechanism. Our mechanisms also outperform LFBL regarding the data delivery rate and delivery delay by up to 263% and 55% respectively for high network contention levels.
The next sessions are organized as follows.
Section 2 presents a brief overview of WMNs based on the NDN architecture.
Section 3 discusses several works related to the adoption of caching and ICN architectures in WMNs. This section also presents proposals to deal with the broadcast storm problem in ad-hoc networks.
Section 4 introduces the proposed mechanisms.
Section 5 presents the simulation environment and discusses the simulation results.
Section 6 presents the concluding remarks and comments about future work.
2. NDN-Based Wireless Mesh Networks
NDN employs two types of packets: interest and data packets. Interest packets are sent by nodes to request content. These nodes are called consumers. Data packets carry the requested content itself. These packets are sent by producers or by intermediate nodes that store the desired content in a cache. The storage of contents previously requested by an intermediate node in cache, called the Content Store (CS), creates a network of caches [
17], as discussed in the next paragraphs.
Two data structures are used during the forwarding of interest packets in NDN. The Pending Interest Table (PIT) keeps track of the state of each transmitted interest packet has not received an response yet, i.e., interests that are waiting for a data packet. Each PIT entry also records the receiving interface of the interest packet. The second data structure, called the Forwarding Interest Table (FIB), is used to forward interest packets to an output interface based on the content name. FIB maintains a list of entries containing name prefixes and output interfaces. The strategy layer of the NDN stack defines how FIB is populated.
The process of forwarding interest packets is described as follows. As soon as a node receives an interest packet, it verifies its CS in order to find a copy of the content requested. If the content is in its CS, the node sends a data packet towards the consumer through the receiving interface of the corresponding interest packet. Otherwise, the node verifies if there is another pending request for the same content on its PIT. If there is a PIT entry for the same content, the node updates the interface list of this entry by adding the arrival interface of the interest packet and dropping this interest packet. Otherwise, the node creates a new PIT entry with the arrival interface of the interest packet and looks up its FIB to determine the output interface to forward this interest packet. If there is no FIB entry related to the content name, the interest packet is dropped. Nodes repeat this forwarding process for each interest packet received. Data packets follow the reverse path traversed by interest packets because PIT stores the list of receiving interfaces of interest packets.
With NDN, intermediate nodes are able to cache contents previously requested by other nodes, which we call in-network caching. If employed by WMNs, in-network caching reduces the traffic concentration near gateways and the waste of resources caused by the late drop of packets, two of the main problems faced by this kind of network. Al-Arnaout et al. stated that the cost required to transfer data in wireless networks is higher than the cost required to transfer data in wired networks [
4]. The reasons for this higher cost are the contention for the channel and channel interference. These two factors contribute to a reduction in network efficiency as the number of hops between the source and destination increases [
18]. If intermediate nodes can cache contents, as NDN nodes do, this will reduce the number of hops between consumers and producers, consequently saving bandwidth by avoiding retransmissions caused by losses on the wireless channel. Oh et al. [
5] argued that is better to invest in storage devices, which are getting cheaper by the day, than to deal with bandwidth limitations and losses of wireless networks.
Figure 1 illustrates an example of how contents are retrieved in NDN-based WMNs. The thin solid lines and thin dashed lines represent wired and wireless links, respectively. The solid arrows represent packets sent in wired networks, and the dashed arrows represent packets sent in wireless networks. Blue or heavy black arrows represent interest packets for the content (
Y) sent by the first consumer (Step 1). Then, the data packet follows the reverse path towards the consumer. Each NDN router in this path stores a copy of content
Y before forwarding the data packet and thus, the content availability increases. Thereafter, a new consumer requests the same content (
Y) by sending interest packets represented by red or light grey, heavy-dashed arrows, Step 2. In this case, the interest packets do not reach the producer. Instead, this request is satisfied by an intermediate NDN node in the mesh backbone that has the content in its CS, as indicated. If nodes in the mesh backbone are not able to cache contents, all the consumers’ requests for
Y will be forwarded to the producer. In this example, in-network caching avoids bottlenecks near to producers, i.e., NDN reduces medium competition and traffic interference.
There are two main storage policies proposed for NDN-based WMNs: reactive and proactive caching. Reactive caching policy evaluates whether received content will be cached or discarded based on the cache update. A simple way to use the reactive caching policy is to store contents only after an interest packet has been sent. On the other hand, proactive cache policies infer which contents should be predictably cached before requests arrive. In this case, nodes can cache a content even if there is no active PIT entry for this content. The use of a proactive cache in NDN-based WMNs can improve the average throughput, because nodes may take advantage of the broadcast nature of the wireless medium to store contents that have not yet been requested. Several proactive caching strategies use algorithms that predict which content should be cached based on consumers’ demand profiles [
18]. Liu et al. stated that the cache policy must take fading and interference on the wireless NDN into account [
18]. These problems are shown when content is cached on distant neighbors instead of the nearest neighbor of the consumer. Therefore, the consumer receives the content from distant nodes where it is more prone to suffering interference from the nearest nodes along the path.
In addition to in-network caching, NDN-based WMNs can also benefit from the broadcast nature of the wireless medium to disseminate interest packets. Hence, when a node sends a packet, more than one neighbor can listen to this packet transmission. Neighbors are thus forwarding candidates which forward a request or content. These multiple forwarding candidates increase the probability of a packet being delivered to the producer or to forwarding nodes, which are temporarily able to provide content from their caches.
Both the broadcast nature of the wireless medium and the way contents are requested, however, impose challenges to the deployment of NDN-based WMNs: the broadcast storm problem. With NDN, each node forwards an interest packet received for the first time. Then, every neighbor of this node that receives this interest will repeat the same procedure. We assume that nodes are often equipped with only one wireless interface. In this case, the interest packet is forwarded through the same reception interface, and all nodes in the transmission range of the forwarding node will receive the packet, even the node that previously forwarded this. Depending on the interest sending rate, nodes may experience a broadcast storm of interest packets. The consequences, in this case, are both an increasing medium access time and number of collisions. Collisions are caused by nodes that are in the same path used to obtain the content or by nodes that are closed enough to interfere each other [
2].
Techniques have been proposed for wired NDNs to avoid the broadcast storm caused by interest packets. A simple technique is to define a rule per interface that states that interest packets must not be forwarded to the receiving interface. This technique, however, is not suitable for NDN-based wireless networks. There is no way to filter interest packets based on the forwarding interface because nodes are typically equipped with only one interface. In
Section 3 and
Section 4, we discuss proposals to avoid the broadcast storm problem in WMNs.
NDN-based Wireless Mesh Networks are also suitable for reducing the deficiency faced by wireless mesh networks regarding mobility. That deficiency relies on most routing protocols developed for IEEE 802.11 ad-hoc networks [
19]. Therefore, physical mobility can significantly affect the performance of those wireless mesh networks protocols. This occurs due to constantly route changing between endpoints [
20]. Thus, those protocols require a level of track state for at least part of the topology and route configuration when links change. The reason for this is that highly dynamic networks impose different issues to IP addresses regarding the assignment and reassignment to the constantly changing set of active nodes in the network [
21]. Depending on the application, these address modifications also need to be mapped to tables with names and IP address. Compared with NDN, that problem would be reduced since the requests are no longer destination-oriented and thus, are content-oriented.
Nowadays, mobile Internet traffic demand has increased the use of wireless networks, and the greater part of such traffic is content delivery, which is not real-time in nature [
18,
22]. This traffic is suitable for caching and inherits the possibility of content sharing between consumers. The cache use provokes challenges on the ambit of how the storage resource will be managed. It has been reported that, for both wired and wireless networks, caching consists of two main problems: content placement and content delivery [
18]. For content placement issues, like cache size, the cache location and discarding policy are essential structures of the problem. The content delivery problem is related to the efficient use of network resources to deliver content to their consumers. Also, some researchers are conducting experiments to fulfill the need for a lookup service to scale network routing. The work of Afanasyev et al. [
23] proposed a new Domain Name System (DNS) protocol for NDN, called NDN DNS (NDNS). The authors designed a DNS service to provide a lookup named data service without the need to announce name prefixes into the global routing system.
Another important aspect of NDN is the capability to use caches to promote popular contents. It has been reported that users send more than 300,000 tweets, share more than 680,000 pieces of content on Facebook, and upload 100 hours of video on YouTube per minute [
24]. However few contents induce attention and concentrate most of the consumer requests [
24]. Liu et al. [
18] stated that the fundamental feature of content delivery traffic nowadays is that a minority of popular contents represent the majority of the traffic load and are consumed by different users at different periods. Based on this behavior, it is important to predict if and when content will become popular. Nevertheless, there is a lack of ability to predict the popularity of web content. This shortage is provoked by a sum of aspects that impact directly on content popularity, for example, content quality and the importance of a content for consumers [
25]. Those aspects are difficult to scope and may vary over time. Tatar et al. [
24] argued that combining information from different media providers is the first step towards achieving better predictions. The authors also indicated that Twitter has been used for this accomplishment, but there are other promising content providers.
6. Conclusions
One of the main challenges in named-data wireless mesh networks is the broadcast storm problem, which is caused by the forwarding of interest packets. With NDN, each node broadcasts an interest packet received for the first time. Thereafter, every neighbor will repeat the same procedure, leading to a broadcast storm of interest packets. The main reason for this behavior lies in the fact that wireless nodes typically have only one network interface. Thus, interface-based control strategies that are used in wired NDNs to reduce the broadcast storm are not able to work properly on NDN-based WMNs.
We proposed, in this paper, a set of mechanisms that limit the forwarding of interest packets to reduce the negative effects of the broadcast storm in WMNs. The goal of the proposed mechanisms is to decrease the number of interest packets forwarded and thus, to decrease the probability of collisions caused by these packets. The first proposed mechanism is called Probabilistic Interest Forwarding (PIF). With PIF, high-centrality nodes forward packets with a probability of p. Therefore, PIF reduces interest packet propagation on central nodes by reducing the number of packets that a central node may forward. With the second one, called Retransmission-Counter-based Interest Forwarding (ReCIF), nodes consider the number of hops traversed by an interest packet to forward this packet or not based on the retransmission threshold. ReCIF also has two operation modes, hard and soft, which are both used to define this threshold. The third proposed mechanism combines the criteria of PIF and ReCIF and is referred to as ReCIF+PIF. The idea is to guarantee that interest packets forwarded fewer times have priority to be forwarded by a node, and other interest packets are forwarded with a probability of p.
Our mechanisms were compared with both the default NDN forwarding mechanism and the Listen First Broadcast Later (LFBL) protocol through simulation. The results indicated that our mechanisms reduce the amount of interest packets forwarded by nodes to retrieve contents. As a consequence, PIF, ReCIF, and ReCIF+PIF provide higher rates of content delivery as the network saturation level increases and lower average delivery delay. We also observed that some mechanisms are more suitable for low-contention levels, such as ReCIF, and others, like PIF02, perform better for high-contention levels. The main reason for this behavior is the interest forwarding criteria used by the mechanisms.
We also conclude that our proposed mechanisms are more efficient in scenarios with a higher number of hops between source and destination. For these scenarios, our mechanisms outperformed the default NDN forwarding mechanism by up to 21% regarding the data delivery rate and provided a 25% lower delivery delay. Our mechanisms provided less delay when delivering contents to consumer nodes when compared to default NDN and LFBL. In less and highly saturated scenarios our mechanisms dynamically adapt to provide a higher performance using the shared cache and reducing the overhead.
For future works, we intend to test our mechanisms in other scenarios, including scenarios that present user mobility. The idea is to test our mechanisms in scenarios where pedestrian mobile users request and provide content using WMNs. We will also conduct tests on vehicular networks and compare our mechanisms with other specific protocols for this kind of network.