1. Introduction
The unprecedented growth and development in the sphere of wireless communication and relevant applications have not only made spectrum a precious resource but also have severely undercut the concept of fixed spectrum assignment. The unlicensed bands, on one hand, are unexpectedly congested, whereas a large portion of the licensed bands are severely underutilized. The licensed spectrum bands such as ultrahigh frequency (UHF) and very high frequency (VHF) bands are not fully utilized, but, conversely, the unlicensed industry, scientific, and medical (ISM) bands are massively populated because of free access. This alarming situation has led to the birth of dynamic spectrum access [
1], which aims at making the best opportunistic use of the unused spectrum called spectrum holes and thus ensures that both licensed and unlicensed bands are evenly utilized.
Cognitive radio technology has emerged as a promising and reliable remedy for improving spectrum utilization and resolving the spectrum problems. It has been exceedingly successful in realizing this urgency of wireless communication and leveraging the features of cognitive capability and reconfigurability [
2]. The cognitive capability allows the cognitive radio to sense the surrounding radio environment and fetch information such as frequency, bandwidth, power, modulation technique, and communication technology, whereas cognitive reconfigurability enables the cognitive radio to redefine its internal state and parameters based on interactions with the outside world and thus adapt to the observed environment. As a result, cognitive radio technology allows cognitive devices to ensure opportunistic access to the portions of the unused spectrum band and adapt accordingly to achieve flawless communication flow.
A cognitive radio network (CRN) is basically populated by two types of users: licensed primary users (PUs) and cognitive secondary users (SUs). The PUs have prioritized possession over the licensed bands, whereas the SUs can use only the vacant channels unused by PUs and, more importantly, without interfering with the ongoing communication of PUs [
3]. One of the most attention-seeking issues in CRNs, therefore, the uncertainty of spectrum availability resulting from PU activity and the subsequent challenge of accomplishing SU–SU communication without impeding PU transmission and optimal spectrum usage.
Cognitive radio ad hoc networks (CRAHNs) are an infrastructure-free form of CRNs. In fact, they are the enhanced form of conventional ad hoc networks, which are embedded with cognitive radio technology to ensure efficient and innocuous spectrum utilization [
4]. The absence of a centralized network entity poses a number of challenges among them compared to their infrastructured counterparts. Without the network infrastructure, SUs must cooperate and communicate among themselves in an ad hoc fashion to exchange network-related information such as network topology, spectrum opportunities, and the presence of PUs [
5].
Most existing CRAHN architectures assume that all SUs are equipped with cognitive radios and sense the radio environment. This cognitive functionality requires not only time and effort but also a significant amount of energy [
6,
7]. Channel availability in CRAHNs is different from that in conventional multichannel multihop networks such that SUs have partially overlapping and nonoverlapping sets of available channels, and the channels vary with time and space [
8]. Furthermore, the distinguishing features such as dynamic topology, self-configurability, scalability, limited energy, and multihop architecture represent inevitable hurdles in realizing CRAHNs. A great deal of research and relentless effort is needed to cope with this challenging environment.
Routing in CRAHNs has emerged as one of the most sought-after topics for researchers worldwide. Because CRAHNs borrow attributes from both CRNs and ad hoc networks, the routing protocols must satisfy the requirements of both networks [
9]. The unpredictable mobility of nodes, spectrum variation in time and space, energy constraints, and PU activities add burdens to the network layer protocols. A routing process in CRAHNs, therefore, must necessarily involve spectrum awareness, quality route setup based on some form of routing metric, and a route maintenance procedure to combat route failures, if any [
10]. In addition, the interest of PUs must be protected, which can be best achieved by minimizing the interference experience due to imperfect spectrum sensing and spectrum allocation at SUs [
11]. Furthermore, the sudden appearance of PUs forces SUs to vacate the current channel in use, whereas the mobility of SUs leads to potential route failures.
Multipath routing in CRAHNs allows SUs to switch dynamically among multiple paths whenever the normal routing is obstructed by either the appearance of PUs into the current channel or the movement of the node itself into the region of the PU. Multipath routing provides a set of primary and alternate paths to cope with route failures. The dynamic switching from the primary path to the alternate path can significantly reduce the frequency of the route discovery process and contribute to energy-efficient and reliable routing.
An energy-efficient routing scheme is highly needed for CRAHNs powered by energy-constrained batteries. In such a routing scheme, channel switching and rerouting should be minimized. When any PU appears, SUs should vacate the current channel in use, so a route that survives for a long period must be discovered. As a result, many factors including the residual energy of each participating node, the energy consumption along the path, the stability of the channel selected, and the backup path to counteract the sudden appearance of PUs must be considered. Such considerations can aid in achieving energy efficiency in routing.
Even though there are many routing schemes in the literature, most of the routing approaches handle node selection, energy awareness, spectrum decision or multipath routing separately. In this paper, the combined form of residual energy and route stability is exploited as the integrated route selection metric to discover energy-efficient and robust multiple paths while keeping in record the spectrum heterogeneity and primary user activity. That is, we propose an energy-efficient and robust multipath routing (ERMR) protocol that promises not only to conserve valuable energy with balanced energy consumption throughout the network but also to improve the robustness of routing paths. The channel selection algorithm integrated with route formation sets up a strong route with a long lifetime in terms of residual mode energy and link stability, resulting in improved energy efficiency and robustness. According to our extensive simulation results, the proposed ERMR outperforms the conventional protocol in terms of network throughput, packet delivery ratio, energy consumption, and end-to-end delay.
The rest of this paper is organized as follows: In the following section, related works are reviewed in brief.
Section 3 depicts the system model for CRAHNs used in our study. In
Section 4, the proposed ERMR protocol is presented along with its routing processes, and a qualitative comparison of various routing protocols is also addressed. In
Section 5, the performance of the proposed ERMR is evaluated via computer simulation and compared with the conventional scheme. Finally, the paper is concluded in
Section 6.
In the paper, some symbolic notations are used as follows: St(n) denotes the state of a PU n at time t. Pon and Poff denote the probability of a PU being in ON and OFF state, respectively. Likewise, u and v represent the periods for which a PU is expected to be in ON and OFF states, respectively. Eres and S are indicators for residual energy measure and route stability measure, respectively. Einit(n) is the initial energy of node n, Econ(n) is the energy consumed by node n, and Eres(n) denotes the remaining energy of node n.
2. Related Works
Several routing protocols have been proposed to formulate energy-efficient data routing paths in the literature. More recently, several attempts to introduce multipath routing protocols have been reported.
The ad hoc on-demand multipath distance vector (AOMDV) [
12] routing protocol is the basis of all multipath routing protocols in mobile ad hoc networks. This multipath version is an extension of the AODV [
13] routing protocol and succeeds in providing a loop-free and disjoint set of paths.
The low-latency and energy-based routing (L2ER) protocol for CRAHNs [
14] considers energy and delay simultaneously to select the optimal path for data transmission. This on-demand protocol uses joint route and spectrum selection to combat heterogeneity but fails to consider channel availability probability and stability of the link.
The joint path and spectrum diversity-based routing protocol (E-D2CARP) [
15] exploits a multipath and multichannel environment to circumvent PU-affected regions and alleviate the PU occupancy problem. Nevertheless, it does not guarantee energy-efficient paths because no energy metric is involved in route selection. The main objective here is to combat the PU occupancy as it comes. It uses the expected path delay (EPD) metric to execute path selection, which stresses the path with minimal packet loss and the lowest end-to-end delay.
In [
16], Beltagy et al. proposed a multipath routing protocol with an objective of improving the reliability of transmission paths in CRNs. The “route closeness” metric has been introduced to select the paths that are not close to each other. The main goal of this routing design is to ensure that a PU could not interrupt all selected nonclose paths at the same time. However, the protocol fails to address the issue of spectrum diversity, which is a distinguishing characteristic of CRAHNs. The protocol, thus, is not appropriate to be applied in highly dynamic CRAHNs.
In [
17], a multipath routing protocol for CRAHNs is proposed, which aims at discovering resilient multiple paths in a single route discovery phase to enhance bandwidth utilization. The protocol stresses only the robustness of the path and hence selects the most stable path from source to destination. There is no consideration to the energy aspect of cognitive routing. It exploits the features of the MAODV route discovery mechanism with necessary modifications to fit into a cognitive radio environment.
It is difficult for a single routing metric to succeed in covering all design aspects of routing in complex networks such as CRAHNs. For example, the simple inclusion of an energy routing metric cannot ensure the robustness of the selected path, whereas the introduction of multiple paths in the form of backup paths helps with mobility-induced route failures and, eventually, contributes to the robustness of the data transmission procedure. On the other hand, random selection of the channels simply adds to the woes of spectrum heterogeneity both spatially and temporally. Most existing routing protocols fail to combine the metric-based channel selection with the path selection. The selection of channels not only contributes to improving the performance of the entire system but also allows leveraging of the multichannel environment.
A good routing protocol is highly dependent upon a good channel selection scheme [
18]. A number of channel selection schemes such as the lowest interference impact on adjacent channels [
19], channel availability probability [
20], and contention-aware selection among the SUs [
21] have been thoroughly applied. In this regard, using the availability time of the channel as a selection criterion can help enhance the stability of the link and the path as a whole. The allocation of the channel with the highest availability duration not only ensures route stability but at the same time lowers the frequency of channel switching, which is much needed in multihop multichannel networks. Furthermore, because CRAHNs are battery powered and highly dynamic, energy efficiency and stability are the main factors to be considered in the selection of routing paths in CRAHNs.
A good sensing scheme is inevitable in CRNs to enable the technology itself. Several approaches have been put forward so far to improve the process of spectrum sensing. The cognitive radio sensitivity can be well improved by enhancing radio frequency technology for wideband processing, exploiting digital signal processing techniques for PU detection, and implementing cooperation schemes allowing SUs to share their spectrum information [
22]. The impact of user cooperation has been further investigated in [
23], taking the context of spectrum sensing through multiple-input multiple-output (MIMO) decision fusion. However, the benefits of orthogonal transmission seem to be more powerful in the case of non-cooperative protocols, and almost negligible in the case of cooperative schemes.
All of the aforementioned works handle energy awareness, channel selection or multipath routing separately. As a consequence, CRAHNs could not be exploited to the fullest, resulting in limited performance. To cope up with this situation, we propose a routing protocol by addressing energy constraint, spectrum heterogeneity and dynamic topology at the same time. Unlike other protocols, the proposed ERMR exploits the combination of the threshold energy value, the channel selection based on channel availability and, ultimately, the cost function of residual energy and route stability in order to discover energy-efficient and robust routing paths. Moreover, the provision of the backup path is always an added advantage provided by the protocol in CRAHNs to improve the reliability. The competitive performance advantages of the proposed ERMR will be quantitatively presented and discussed in
Section 5.
3. System Model
We consider a battery-powered CRAHN with N PUs and M SUs. The number of licensed channels is assumed to be equal to the number of PUs such that each PU has a particular channel assigned to it. PUs hold undisputable license for specific spectrum bands and do not deviate from their assigned spectrum bands. SUs are expected to exchange their network-related control information through a dedicated common control channel. SUs are assumed to possess two separate radio transceivers for control packets and data packets, respectively. It is also expected that SUs can tune their radio transceivers to any vacant PU channels. The number of vacant PU channels varies from one SU to another and with the passage of time. Therefore, SUs are supposed to have the spectrum sensing capability to determine the vacant channels not in use by PUs and the ability to use them opportunistically. In this paper, we assume that PUs are static whereas SUs can be mobile. The SU mobility that concerns PUs is dealt with by constantly checking if the SU is within the transmission range of PUs. If a SU moves away from the PU in ON state such that it goes beyond transmission range, then the SU is able to use the corresponding channel. On the other hand, if a SU moves into the transmission range of the PU in ON state, then it drops the corresponding channel and switches to the next channel.
Good PU activity modeling is important to make the environment more realistic. PUs are modeled by virtue of independent and identically distributed ON and OFF process with exponential distribution [
24].
Figure 1 shows the alternating ON and OFF states of a PU. In ON state, the PU is active and occupies its channel for period
, during which it performs its data transmission via that channel; thus, SUs are not allowed to use the channel. Conversely, in OFF state, the PU is inactive, so SUs can temporarily use the licensed channel until the PU becomes active. The state of PU
n at time
t,
St(
n), is active (1) or inactive (0) and can be represented as
If the ON and OFF periods of a PU are exponentially distributed with mean ON period
and mean OFF period
, where
α is the departure rate of the PU and
μ is the arrival rate of the PU, then the probability of the PU being in ON state,
Pon, is given by
Obviously, the probability of the PU being in OFF state,
Poff, is 1 −
Pon and can be represented as
On the other hand,
Pon and
Poff follow an exponential distribution. Given ON period
u, therefore, the probability of the PU being in ON state,
Pon(
u), is given by
where
λ is the mean ON period of the PU (i.e.,
λ =
). Given OFF period
v, the probability of the PU being in OFF state,
Poff(
v), is given by
where
γ is the mean OFF period of the PU (i.e.,
γ =
). From Equation (4), the ON period
u of a PU can be represented by
From the Equation (5), the OFF period
v of a PU can be represented by
4. Energy-Efficient and Robust Multipath Routing
In this section, the proposed ERMR protocol for CRAHNs is presented in detail. In ERMR, energy-efficient primary and backup paths are built to counteract route failures for a source-destination pair. The route discovery process starts only when both primary and backup routes break down. The selection of next-hop nodes based on their residual energy not only conserves their energy for latter sessions but also ensures balanced energy consumption throughout the network. It is guaranteed that the paths considered are the most energy-efficient paths with respect to the energy consumed in transmission, reception, channel switching, and idle listening.
It is worth noting that the route discovery procedure is carried out in collaboration with the channel selection. At each link, the selection of the next-hop node is performed simultaneously with channel allocation. The assignment of a better channel to each link can boost the overall performance of the network. Hence, the channel assigned is the most stable one in terms of access duration. The proposed ERMR protocol consists of route discovery, route reply, data transmission, and route maintenance phases.
4.1. Route Discovery
The process of route discovery allows the source to find the multiple routes between itself and the destination based on the routing metric. In the proposed ERMR, route discovery is a daunting twofold task that considers the residual node energy and the spectrum availability. In principle, it is based on the AOMDV routing algorithm. However, for adapting a cognitive radio environment, there are some modifications.
When a node has data to transmit, it searches a valid routing path for the intended destination in its routing table. If no valid routing path is found, the node starts the route discovery process. In this process, a route request (RREQ) packet is broadcasted from the source to all its one-hop neighbors, forming multiple reverse paths at intermediate nodes. Every intermediate node that receives a RREQ packet checks if it is the intended destination; if not, it broadcasts the RREQ packet further. At the destination, a route reply (RREP) packet is unicasted towards the source along the reverse paths formed by RREQ forwarding. The multiple paths formed are loop-free and node-disjoint. That is, flood-based route discovery and regular route updates ensure loop freedom and path disjointedness.
Figure 2 shows the basic structure of the RREQ packet.
Because the route discovery is a hop-by-hop process, the regular update of RREQ fields is mandatory. Along with this, there is a routing table to be maintained at each participating node from source to destination. The routing table should hold the information represented in
Figure 3 to ease the route discovery and subsequent selection.
For every route discovery, the source broadcasts an RREQ packet via a common control channel (CCC). The RREQ packet exists only for the period defined by the TO field in
Figure 2. When a neighboring node receives the RREQ packet, it first checks whether its battery energy exceeds the threshold value and whether there is any common vacant channel. Note that the communicating two nodes of transmitter and receiver not only should be within each other’s transmission range but also must possess at least one common vacant licensed channel between them. If the two conditions are not met, the packet is dropped; otherwise, the intermediate node responds accordingly.
In the response process, it first checks whether it is the destination being sought. If so, it replies with a unicast RREP packet; otherwise, it rebroadcasts the RREQ packet into its surroundings. Before it actually rebroadcasts the RREQ packet, the routing table is updated with information such as the last hop node, hop count for the reverse path, and channel assigned for that particular link. The channel assigned is the most stable channel available for that link. Likewise, the RREQ packet itself is also updated by appending node ID, available channel set, residual energy, most stable channel, path cost, and hop count (residual energy, channel stability, and hop count are increased by adding to their previous values). This process continues until the destination is reached, where the route decision and selection are made.
To enable multiple paths between source and destination, the concept of sequence number is exploited, which facilitates unique identification for an RREQ packet. To ensure that the routes are disjointed, the redundant RREQ packets with the same sequence number as an RREQ packet that the node has already forwarded should be ignored.
Figure 4 shows an example procedure of the route discovery process, where three disjoint routing paths can be found. As seen in the figure, the RREQ packet forwarding begins from source S and ends at destination D. Each of the nodes ensures that no RREQ packet is forwarded twice to avoid looping and maintain path disjointedness. In
Figure 4, nodes B and G drop the RREQ packets with the same sequence number #1 as the RREQ packet that they have already forwarded. This helps maintain the concept of disjoint and loop-free paths, which is crucial in multipath routing. The destination responds to only the RREQs received within the time set in the timer at the destination. All RREQs received after the time specified are dropped automatically.
4.2. Route Decision
At the destination, when RREQ packets are received from multiple paths, they must be sorted according to their energy efficiency and stability. This helps categorize the paths as primary and backup paths. We use a cost function, a combination of energy and stability metrics, to evaluate the paths. The cost function has no dimension (even though the residual energy is measured in Joules and the route stability is measured in seconds). That is, as for the evaluation of candidate paths, we have taken into account only the numeric values of residual energy and route stability and used them in the cost function. For both residual energy and route stability, the higher is the better. Hence, they are summed up. The path with the highest cost function is chosen as the primary path, and that with the second-highest cost function is used as the backup path. The cost function is as follows:
where
Eres and
S are the residual energy and stability of the path, respectively, and
a and
b are
α/
Einit and
β/
Smax, respectively, with the condition of
α +
β = 1. The two constants
α and
β are the weight factors of residual energy and stability, respectively. It should be noticed that
Einit is the sum of the initial energy of nodes in the path (i.e.,
), where
Einit(
n) is the initial energy of node
n in the path of
m nodes, and
Smax is the sum of the average ON/OFF period of links in the path (i.e.,
), where
Ton(
l) +
Toff(
l) is the average ON/OFF period of link
l in the
k-hop path. The values of
α and
β are assigned according to the significance of residual energy and stability in the target application. In our performance simulation in
Section 5,
α = 0.5 and
β = 0.5 by default.
The residual energy of the path refers to the sum of the residual energy for every node on the path, and it is given by
where
Eres(
n) is the residual energy of node
n, and
m is the number of nodes in the path including the source and the destination. In order to avoid the selection of severely energy-deprived nodes, we set up a threshold energy level such that the nodes with energy level below threshold are not included in the route during route discovery process. This strategy is intended to achieve an even load distribution over the network.
Eres(
n) is the difference of the initial energy of node
n (
Einit(
n)) and the total energy consumed by node
n (
Econ(
n)) as indicated below:
The transmission of a packet from one node to the next relay node is an energy-consuming process. Energy is consumed not only at the transmission but also at the receiving side. If the transmission and reception occur through different channels, a considerable amount of energy is involved in the switching process. The energy incurred in channel switching is proportional to the frequency difference between the channels [
25]. Likewise, a dominant proportion of radio energy is consumed when the radio is listening to the channel in order to receive possible data. Often, idle listening cost is speculated to be 50–100% of the energy required for the receiving purpose. Therefore, we consider all aforementioned factors to gauge the energy. If we neglect overhearing and assume data transmission to be completely along the path specified, the energy consumed by a node,
Econ, can be calculated by
where
Etx,
Erx,
Eswt, and
Eidle are represented as follows:
Etx is the energy required for transmitting a packet and is given by = (1.65 × packet size in bits)/(2 × 106) J.
Erx is the energy required for receiving a packet and is given by = (1.15 × packet size in bits)/(2 × 106) J.
Eswt is the energy consumed for channel switching and is given by Eswt = Psw × tsw × |F2 − F1|, where Psw is the power dissipation for channel switching, tsw is the time for channel switching of the unit bandwidth, and F2 and F1 are the frequencies of the channels switched to or from, respectively.
Eidle is the energy loss while listening to a channel and is given by Eidle = 0.8 × Erx.
Regarding the stability of the path, we consider the time during which PU is in OFF state for the next time period such that the corresponding channel can be used by SUs. In other words, it is the access duration available for SU communication. The higher the access duration, the more stable the link. Hence, the stability,
S, can be measured as
where
k is the number of links in the path, and
Toff(
l) is the period during which a channel is available to SUs for link
l, which follows from Equation (7).
We use this approach to evaluate the cost function for each path. Consequently, the path with the maximum value of the cost function is taken as the primary path. The backup path is the path with the second-highest value of the cost function, and it is taken in case the primary path suffers from route failure.
4.3. Route Reply
The selected primary and backup paths are sent back to the source by the destination via the route reply process. The destination node, upon receiving the RREQ copies, forms the reverse paths similar to the process at intermediate nodes. It unicasts separate route reply (RREP) packets to each of the primary and backup paths discovered. The basic structure of the RREP packet is depicted in
Figure 5. The main field that should be focused on in the RREP packet are P-id (path identifier), CA (channel assigned), and NH (next hop), which indicate the preference order of the selected paths, the channel identifier, and the next hop node identifier for the forward path, respectively.
When an intermediate node receives an RREP packet, it sets up the forward path along the channel selected and then forwards a copy of the RREP packet along the reverse path stored in its routing table; at the same time, it updates the hop count, the channel assigned, and the next hop field of the route reply packet. The routing table of intermediate nodes is also updated with the information in the RREP packet to set up the forward path. If an intermediate SU node receives a duplicate of the RREP packet for the same source–destination pair, the packet is dropped except when it is for the new route discovery session.
4.4. Route Maintenance
Route failures in CRAHNs occur primarily because of two events: one is the sudden arrival of a PU into the channel being used by the SU pair, and the other is the breakage of formed routes due to node mobility. The two failures are quite different with respect to the source of failure origins. Therefore, the routing protocol must be sufficient to determine the cause and provide an appropriate solution. If the channel being used is restored by the corresponding PU, then the data transmission on the channel is subjected to halt immediately. A new channel is explored to continue the transmission of SUs as mentioned in the channel selection procedure. If the route failure occurs owing to the dynamic behavior of any mobile node, the routing path must be changed. The data routing should shift to the backup path. Failure notification of any route during the process should be sent to all participating nodes via some information flow, which can be in the form of HELLO packets.
4.5. Qualitative Comparision of Routing Protocols
A qualitative comparison of different routing protocols is presented in
Table 1 with respect to different aspects.
Each protocol uses a different routing metric to select the best routing path in terms of the metric. AOMDV is a shortest-path routing protocol that uses hop count as a routing metric. L2ER, on the other hand, has a cumulative routing metric that tends to select the optimal path with the minimal energy consumption and end-to-end delay combined. E-D2CARP stresses the probability of packet loss and link delay in its routing procedure via its routing metric called expected path delay (EPD). PMRC ensures that the selected routes are not close enough to be interrupted by an active mobile PU. Route closeness is the measure for the closeness of the routing paths in this protocol. MRPC based on MAODV uses hop count to select the shortest routes, whereas channel stability ensures that the channel assigned is the most stable one. The proposed ERMR uses a hybrid metric, a combination of energy consumption and channel stability, to evaluate routing paths and channel assignment. The path with the maximal residual energy is taken as the most preferred routing path. For a link, the channel that ensures the highest stability is chosen.
AOMDV uses a single channel for both control message and data dissemination. L2ER considers the channels that are less interfered from PU communication. E-D2CARP tends to make use of the channels currently not occupied by any PU. The channel assigned to a particular link is the one from which the control packet is received. The same channels are used for control packet exchange among SU nodes when available. In PMRC, there is a channel for each of the data and control messages. The proposed ERMR selects the most stable channel from the set of available channels for data transmission.
Regarding energy, L2ER and the proposed ERMR aim at improving energy efficiency. In the proposed ERMR, multiple paths are explored between source and destination. The nodes are examined for their residual energy level while the channels are formed for their free access duration. Eventually, the path with the best routing metrics designed is set up as the primary routing path. The backup or alternate path is the one with the second-best routing metrics.