Designing Green Communication Systems for Smart andConnected Communities via Dynamic Spectrum Access
VIJAY K. SHAH, University of Kentucky, USA
SHAMEEK BHATTACHARJEE, Western Michigan University, USA
SIMONE SILVESTRI, University of Kentucky, USA
SAJAL K. DAS, Missouri University of Science and Technology, USA
Smart and connected communities (SCCs) are emerging as a novel paradigm that allows the community residents to be connected with surrounding environments through smart technologies. However, there remain
important challenges to fully exploit the potential of SCCs in improving societal well-being and prosperity.
In particular, there is a need for designing green communication systems that are also capable of providing
high quality of service (QoS) to distribute and collect information to and from SCCs. However, simultaneously
satisfying both of these criteria is difficult due to varying demands posed by heterogeneous sensing modalities, lack of dedicated infrastructure in rural/sub-urban areas, and certain sustainability constraints. While
low-power short-range technologies often fail to achieve high QoS, using 3G or 4G technologies (LTE, LTEA, GSM) for SCCs will eventually face spectrum scarcity and cross technology interference. In recent times,
Dynamic spectrum access (DSA) has been proposed as a solution to overcome policy constraints and improve
spectrum scarcity by spectrum sharing. In this article, we show that harnessing DSA in the context of SCCs
can also achieve notable benefits in terms of energy efficiency and sustainability. Specifically, we propose
a novel architecture for designing sustainable SCCs using a small-scale DSA-enabled overlay network that
improves end-to-end energy efficiency of the network while guaranteeing QoS. We also propose a dynamic
spectrum band selection approach that intelligently matches any message requirement to a suitable band
type by exploiting distinct electro-magnetic characteristics of various bands. Since data generated in SCCs
are typically valuable only when delivered within a certain hard (or soft) deadline, we formulate a linear optimization problem for determining the most energy-efficient path that ensures a delivery time within thehard deadline. After proving that such a problem is NP-Hard, we propose an exact pseudo-polynomial time
dynamic programming algorithm to solve it followed by a polynomial time greedy heuristic. Additionally,
we formulate a non-linear optimization problem to find the optimal path when the message delivery time isdefined as a soft deadline and extend our greedy heuristic to handle soft deadlines. Compared to the homogeneous band access approaches that opportunistically access free channels within a given spectrum band,our extensive simulation study shows that the proposed dynamic multi-band selection approach significantly
improves the achievable energy efficiency while meeting various hard and soft deadlines.
CCS Concepts: • Networks → Cognitive radios; • Hardware → Energy distribution; Impact on
the environment; Sensor applications and deployments; Emerging technologies; • Computer systems
organization → Embedded and cyber-physical systems;
This research is partially supported by the NSF grants CNS-1545037, CNS-1545050, and NeTS-1818942 and NATO Science
for Peace and Security grant G4936.
Authors’ addresses: V. K. Shah and S. Silvestri, Computer Science Department, University of Kentucky, Lexington, USA;emails: vijay.shah@uky.edu, silvestri@cs.uky.edu; S. Bhattacharjee, Computer Science Department, Western Michigan University, Kalamazoo, USA; email: shameek.bhattacharjee@wmich.edu; S. K. Das, Computer Science Department, Missouri
University of Science and Technology, Rolla, USA; email: sdas@mst.edu.
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https://doi.org/10.1145/3274284
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Additional Key Words and Phrases: Green communication, dynamic spectrum access, smart and connected
communities, energy efficiency, internet of things, band selection
ACM Reference format:
Vijay K. Shah, Shameek Bhattacharjee, Simone Silvestri, and Sajal K. Das. 2018. Designing Green Communication Systems for Smart and Connected Communities via Dynamic Spectrum Access. ACM Trans. Sen. Netw.
14, 3-4, Article 31 (November 2018), 32 pages.
https://doi.org/10.1145/3274284

1 INTRODUCTION
Smart and connected communities (SCCs) is a novel paradigm that is increasingly attracting attention from government agencies (e.g., the National Science Foundation) as well as the researchcommunity [44]. The emergence of SCCs is a result of tremendous technological transformationthat is allowing the community residents to have access to intelligent services provided with thedata gathered through smart and pervasive sensing technologies. While examples of urban SCCs
include smart homes [45] equipped with temperature sensors, light sensors, air quality sensors,and smart meters [33], examples of rural SCCs include smart agricultural fields equipped withmoisture sensors, soil monitors, and wind sensors [50]. In this article, we define a sensor block as
a generic unit generating data, and a certain SCC may contain hundreds (rural) and thousands(urban) of such sensor blocks.
To realize SCCs, it is essential to provide ubiquitous and efficient connectivity of sensor blocks
with the data fusion centers that are responsible for data gathering, analytics, and disseminationfor efficient decision making and ultimately improving societal well-being and prosperity throughtargeted intelligent services [63]. Therefore, a communication infrastructure is required to provide
connectivity and guaranteed delivery of the data generated from various sensor blocks to theirrespective decision-making units (e.g., to a data fusion center). Such generated data often havequality of service (QoS) requirements, depending on the specific application. In this article, QoS is
defined in terms of a pre-specified hard or soft deadline. A hard deadline defines the maximum time
during which the information is deemed valuable, and thus it needs to be delivered to the decisionmaking unit within that deadline. Conversely, under a soft deadline requirement the benefit ofreceiving a message decreases over time according to a penalty function (see Section 6 for details).
In addition to providing the desired QoS requirements, there is a need for the communication
infrastructure for SCCs to be energy efficient. Indeed, energy efficiency is an important concern forbuilding sustainable SCCs, since Information and Communication Technologies (ICT) contributeup to 10% of the world’s overall energy consumption, and this amount is expected to double every
five years [43]. According to a recent report [65], 830M tons of CO2 are produced to generate theenergy required by ICT devices yearly, and this amount is expected to double by 2020. Furthermore,
ICT industries spend tens of millions of dollars in the annual operational costs, such as electricity bills [49]. Hence, designing energy-efficient communication systems will not only have great
ecological benefits and social responsibility in fighting climate change but also have significant
economic benefits. This is why there is mounting pressure to design “Green ICT communication
systems,” where energy consumption is considered as first-class network system property [43].
Several international research projects such EARTH [20], Green Radio [19], OPERANet [4], andeWIN [43] are dedicated to energy-efficient wireless communications.
1.1 Technical Challenges and Opportunities
The realization of a communication infrastructure that is green and also provides high QoS forsustainable SCCs faces economic and sustainability hurdles for both rural (non-urban) and urban
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areas around the world. First, it is not cost-effective for ICT providers to invest in dedicated communication infrastructures, such as base stations, access points with backhaul links, and spectrum
licenses in non-urban areas, due to low revenue returns compared to the high initial investment [5,21]. Hence, sensor blocks planned in non-urban areas are usually far away from the data fusion
center or the nearest backhaul link. Second, while certain real-time delay critical decisions having
a local area scope may use fog computing [8], which may also require a dedicated infrastructure,
many decisions need data gathered over a wider area over a longer-term horizon. Therefore, evenin urban areas, the collected data need to travel larger distances between the sensor blocks anddata fusion centers. Finally, even if the existence of a communication infrastructure is assumed(as in urban areas), distributing data from thousands of sensor blocks using the legacy infrastructure is likely to burden the existing wired networks and, in turn, requires deployment of morerouters or switches, which would require additional electric power/wiring and, hence, exacerbates
the problem of energy sustainability.
To address the above challenges related to the infrastructure costs (rural/sub-urban areas) andburdening the wired networks (urban areas), the approach adopted in this article proposes theuse of a network of fixed (or mobile) cyber-physical public infrastructure mounted with wirelessdevices (or radios) [47, 54, 63], such as the road side assistance units (RSUs) [58], private smart
vehicles, and public transportation. These are connected through wireless links and do not require
additional dedicated communication infrastructures [10, 66]. Such an architecture implying delay
tolerant communications [46] is acceptable for various decision and analytics applications in SCCs
that do not require short real-time delivery deadlines. For example, smart meter data in a power grid
usually have 0.5- or 1-hour delivery deadlines for demand forecast [61], whereas basic agricultural
sensor data such as wind or moisture sensors may tolerate higher time delays up to several hours.
However, relying on wireless connectivity for realizing communication infrastructure makes
the wireless spectrum a critical resource for the success of SCCs. The current wireless communication technologies for SCCs typically include low-power short-range technologies (e.g., ZigBee,
Bluetooth, WiFi, 6LowPAN using 2.4GHz), and 3/4GPP standards (e.g., GSM, LTE/LTE-A using
900, 1700–2100MHz). Low-power technologies, although seemingly energy efficient, have a limited transmission coverage [18]. Since the data often need to travel larger distances in the context
of SCCs as discussed earlier, a large number of devices need to be deployed to form a multi-hop
network [33], such that a high QoS is guaranteed. However, this kind of communication network
exacerbates the problem of both energy efficiency and monetary cost. Conversely, while 3/4GPP
technologies (dominant in urban areas) provide higher transmission coverage, they come at a costof higher energy expenditure [29] due to two major reasons. First, the co-existence of increasingly
myriad devices in the same spectrum will cause cross technology interference and thereby decreased Signal-to-Noise Ratio (SINR). This necessitates either increased transmit power or higher
number of re-transmissions, thus worsening the energy expenditure. Second, the static nature of
legacy spectrum access policy does not provide the flexibility to handle data traffic with heterogeneous demands (e.g., message sizes and message delivery deadlines) that may be generated fromseveral sensor blocks. Such homogeneity of the spectrum access policy triggers the disadvantageof resource over-provisioning or under-provisioning that, again, negatively impacts the achievableenergy efficiency (refer to Section 4 for details).
In the past decade, Dynamic Spectrum Access (DSA) [2, 59] has emerged as an enabling paradigm that allows certain DSA equipped wireless devices, also termed secondary users, to opportunistically switch and access unoccupied channels (called whitespaces and grayspaces) in spectrum
bands originally licensed for other services on the condition of non-interference to the primary
licensee or primary users. This provides an option to break away from the homogeneity of legacy
spectrum access without the provider having to buy a fixed spectrum license. Empty channels
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within each band are liable to be selected for the actual data communication. To date, whitespaceand grayspace networking has been allowed by policy in the TV Band [25, 30, 36], GSM Band [21],LTE Band [60, 62], and Citizen Broadband Radio Service (CBRS) Band [35] (Radar Band). While
the original purpose of DSA was to improve spectrum efficiency [57] and reduce cross technology
interference, we claim that the fact that it allows switching among different bands could be harnessed to improve the overall energy efficiency by intelligently matching a certain band (by virtue
of its unique electro-magnetic properties) with different sensing modality requirements. While adecade-long body of DSA research focused on the interference free channel assignment given a
particular band [9, 15], most of the energy-efficiency research has been limited to efficient channel
sensing approaches only [1]. However, with wide-band spectrum analyzers and the notion of spectrum access systems (that coordinate spectrum assignments) [34] becoming increasingly available,soon a DSA end node is expected to have multiple choices for band selection.
1.2 Contributions of This Paper
In this article, we provide a thorough analysis of various aspects of wireless transmissions to identify the factors affecting the network energy efficiency, given various message requirements. In
light of this analysis, we first propose a novel architecture for sustainable SCCs using a small-scale
DSA overlay network on top of the existing legacy infrastructure. Next, we propose a dynamic band
selection approach that exploits distinct electro-magnetic (EM) characteristics of different spectrum
bands to intelligently match any message QoS and sensing modality requirements to a suitableband type for enhancing end-to-end energy efficiency while preserving the QoS. To this end, we
formulate a linear optimization problem for determining an energy-efficient path for any message
with hard deadline, termed as the HcE problem and prove that it is NP-Hard. Subsequently we
present a pseudo-polynomial time dynamic programming (DP) algorithm for solving the problem
exactly and thus determining the optimal HcE path (and thereby optimal band for transmission ateach intermediate node) for such a message, provided that the DSA overlay topology is relatively
steady. Additionally, to derive an efficient solution to the HcE problem, we propose a faster polynomial time greedy heuristic that combines both energy and latency costs to determine an optimal
path for a certain message with hard deadline. Besides these, we investigate the dynamic selection approach under soft deadlines. In particular, we formulate the optimal path determination fora certain message with a soft deadline requirement, termed as the ScE problem, as a non-linear
optimization problem. Finally, we extend the proposed greedy heuristic to solve the ScE problem
efficiently.
Through extensive simulation study, we evaluate the benefits of our proposed band selection
approach in terms of both energy efficiency and QoS. Compared to the homogeneous band accessand spectrum sharing approaches that are restricted to opportunistically access an available channel within a predetermined band, our study demonstrates that the dynamic wide-band selection
approach greatly improves energy efficiency without sacrificing the QoS and incurring minimal
infrastructure cost overhead. We further demonstrate that the proposed band selection approachworks well for DSA overlay network in which the messages have soft deadlines. Finally, as a casestudy, we discuss some challenges of dynamic band selection for a variable DSA overlay topology,
where a node may not have global knowledge of the topology.
The benefit of our novel work is that it provides an easy-to-deploy and low-cost option for
designing green yet high-quality communication systems to realize non-urban SCCs. Moreover,for urban SCCs, it provides an alternative for improved energy-efficient communication network
without burdening the wired infrastructures or incurring additional communication infrastructure
cost. A preliminary version of this work appeared in ACM BuildSys 2017 [55].
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In summary, this article makes the following important contributions:• We propose a novel architecture for realizing SCCs using a small-scale DSA-enabled overlay
network, on top of the traditional communication infrastructure.
• We propose a dynamic band selection approach that intelligently matches any message
requirement to a suitable band type by exploiting distinct EM characteristics, such as operating frequency, and channel bandwidth of various bands.
• We formulate the determination of the best energy-efficient path for any message with harddeadline as a linear optimization problem, called the HcE problem, and prove it is NP-Hard.
• We present a pseudo-polynomial dynamic programming algorithm that solves the HcE
problem exactly and determines the optimal HcE path for any message with hard deadline.
• We propose a faster polynomial time greedy heuristic that provides a fast and efficientsolution to the HcE problem.
• We also investigate the proposed band selection approach under scenarios where messages
have soft deadlines. Particularly, we formulate the determination of optimal path for a certain message with soft deadline, called the ScE problem, as a non-linear programming–based
optimization problem. Then, we extend our greedy heuristic for the HcE problem to the caseof soft deadlines.
• We demonstrate through extensive simulation study that the proposed band selection approach outperforms homogeneous band access approaches in terms of both energy efficiency and QoS, while satisfying message deadline requirements, be it hard or soft.
• Finally, we highlight the challenges of a dynamic band selection approach in the case of avariable DSA overlay topology.
The rest of the article is organized as follows. Section 2 reviews the related work while Section 3
presents the proposed architecture. Section 4 discusses the distinct EM characteristics of various
bands that can be exploited to achieve energy efficiency. Section 5 presents in detail the proposedband selection approach for steady DSA topology under hard deadlines. Section 6 investigates the
proposed band selection under scenarios where messages have soft deadlines. Section 7 provides
a discussion on cost–benefit analysis and improved sustainability for the proposed approach. Section 8 presents the simulation results, while Section 9 discusses challenges and road map for band
selection under variable DSA topology, followed by conclusions in Section 10.
2 RELATED WORKThis section reviews the related literature for both traditional (non-DSA) and DSA/Spectrum sharing approaches for SCCs that use wireless cyber physical systems.
Several standard (non-DSA) wireless communication systems, e.g., DakNet [47] and KioskNet[54], have been proposed to provide connectivity by utilizing buses and public service vehicles(equipped with computers having WiFi radio) as mechanical backhaul to provide Internet connectivity to rural or remote communities. In contrast, JaldiMAC [7] utilizes long-range directional
WiFi to form a wireless mesh network for providing connectivity in sparsely populated areas.
Heimerl et al. [22] propose the deployment of low-power GSM-based Village Base Station (VBTS)
for rural telephony. In LifeNet [32], the authors propose the connectivity utilizing hand-held wireless devices for transient conditions. In References [52, 53], the authors propose a four-tier hybridarchitecture consisting of hand-held devices in the lowest tier, WiFi-enabled dropboxes in the second tier, data mules equipped with WiFi antennas in the third tier, and long-range WiFi towers
in the fourth tier, thus providing connectivity in challenging scenarios such as post-disaster responses and remote areas. Google project Loon [39] introduces the network of balloons traveling
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on the edge of space, designed to extend Internet connectivity to people in rural and remote areas.
Facebook Internet.org [38] utilizes local cellular infrastructure, along with satellite communications and UAVs, to deliver limited Internet connectivity to rural areas. However, all of these approaches utilize standard wireless technologies such as WiFi, WiMax, GSM base station, VSAT
(very small aperture terminal), WLAN, wireless mesh, or a combination thereof, to provide connectivity to the rural communities. However, since all such devices access one same homogeneous band—usually industrial, scientific, and medical (ISM) or long-term evolution (LTE) or GSM
bands—they suffer from under-provisioning or over-provisioning and policy constraints, as discussed later. Finally, third general partnership project (3GPP)/GSM technology providers are lessmotivated to invest in expensive wireless spectrum licenses in rural areas, even considering government subsidies [21].
Recently, the utilization of GSM whitespaces [21] and TV whitespaces [25, 30] have been proposed for rural and long-range connectivity, respectively. In Reference [21], the authors propose ahybrid sensing and database-driven spectrum sharing scheme, called Nomadic GSM, that utilizes
GSM whitespaces for providing affordable rural connectivity. Nomadic GSM enables safe coexistence of primary and secondary users without requiring coordination or cooperation from existing
license holders. The authors in Reference [25] propose utilizing TV band white space (TVWS) in
conjunction with 5G infrastructure for Internet access in rural areas. In Reference [30], the authors
propose opportunistic use of VHF/UHF TV bands through cognitive radio technology and DSA
paradigm, such as IEEE 802.22 technology, for rural connectivity. Here the authors address two
important issues that can affect the success of IEEE 802.22 technology in rural deployments. First,
they propose suitable service model that combined TV broadcasting and data services to facilitate
service adoption. Second, they propose an adaptive time division duplexing (TDD) approach toeliminate the requirement for long TDD turn-around time of existing IEEE 802.22 technology.
However, these approaches allow the secondary devices to opportunistically utilize the whitespaces in a pre-specified fixed band (e.g., TV band, GSM band, etc.) for data communication thus
suffering from under-provisioning or over-provisioning. Additionally, such approaches require
most sensing devices to be DSA enabled. In Reference [51], a cognitive wireless sensor network(CWSN) has been proposed for energy-efficient sensor network, where each sensor harnesses the
DSA for energy efficiency. The authors primarily investigate the dynamic channel access problem
to improve the energy efficiency of clustered CWSN. However, in such an approach, most sensor nodes have to support DSA functionalities, such as channel sensing and switching. Hence, the
CWSN deployment at community scale is impractical as it requires every sensor to be DSA enabled,which is costly in terms of both end-to-end energy efficiency and monetary cost. In this article, weshow that restricting the SCC data communications to only one such band is not optimally energy
efficient given requirements of various sensing modalities.
3

PROPOSED ARCHITECTURE

In this section, we first present the key components of the proposed architecture for designingsustainable communication systems for SCCs (See Figure 1). Subsequently, we discuss the networkmodel for DSA overlay network topology and the communication mechanism behind the proposedarchitecture.
3.1

Key Components

Sensor Block: A sensor block is a small physical subnetwork (e.g., a smart home, a smart metering
infrastructure, an agriculture field, etc.) that comprises several heterogeneous sensing agents. A
sensing agent could be a sensor (e.g., pollution sensor or temperature/light sensor) or a smart
device (e.g., smart meter) that collects various sensory or contextual data in the form of text, image,ACM Transactions on Sensor Networks, Vol. 14, No. 3-4, Article 31. Publication date: November 2018.

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Fig. 1. Proposed communication architecture for SCCs.

audio, video, and so on. Such data may have different sizes, ranging from few KBs (text) to several
MBs (image, video) and varying hard/soft delivery deadlines of the order of few hours (non-realtime deadlines). For example, a smart meter data usually has 0.5- or 1-hour time deadlines fordemand forecast, whereas data from pollution or moisture sensor may have larger deadlines (upto 5 to 6 hours). Therefore, each sensor block can be a source of heterogeneous data, each withvarying demands. In addition, the distance between the sensor blocks and the data fusion centeror the nearest backhaul link can have large variations, which we term geographical heterogeneity.
Owing to the smaller size of the sensor block, it can be assumed that the sensing agents withineach block form a multi-hop ad-hoc network through low-power short-range legacy communication technologies that forward messages to a consumer premise equipment [14]. Unlike previous
works [51], our proposed approach does not require sensing agents to be DSA enabled, thus making it practical and cost-effective.
Consumer Premise Equipment (CPE): A CPE acts as a proxy between sensor blocks and the DSA
overlay network (explained in detail below). A CPE q ∈ Q, where Q is the set of CPEs, may be a
low-altitude flying drone or a moving vehicle that periodically collects various messages from apredetermined set of sensor blocks. This obviates the need for a dedicated infrastructure, such as
separate access points or numerous DSA-enabled sensing agents, at each sensor block. We consider
that each CPE has two wireless interfaces: (i) a low-power short-range wireless technology (e.g.,
Bluetooth or WiFi) to communicate with the sensing agents within the sensor block, and (ii) a DSA
radio device (e.g., Ettus Research-N210 [37]) to communicate with other DSA-enabled devices (inthe overlay network) on a certain band on a secondary basis (if they are in communication range
over that band and there exists a free common channel).
Data Fusion Center: A data fusion center is a base station with a backhaul link to the final
decision-making unit, where the generated messages from sensor blocks are delivered for computation and analytics purposes. The data fusion center is also equipped with a DSA radio device
and possesses the capability to communicate with other DSA-enabled devices on a certain band(on a secondary basis).
DSA Overlay Network: The DSA overlay network acts as a bridge between the CPEs and the data
fusion center. It comprises a set of cyber-physical components (the set of which is denoted by R),
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Fig. 2. A DSA node (or secondary user) message transmission—periodic sensing, message transmission, andspectrum hand-off.

each mounted with a DSA radio device. Examples include fixed road side assistance units (RSUs),connected public transportation, private smart vehicles, and so on. Such a DSA-enabled device
opportunistically accesses an unoccupied channel on a suitable band for data communication.
Hereafter in this article, a node refers to a physical component (e.g., flying drone, base station,
public vehicle) mounted with a DSA radio device.
The specifics of discovery and selection of unoccupied (non-interfered) channel within a given
band has been extensively discussed in a decade-long body of research [2, 9, 15]. Broadly, it caneither be sense-and-use in the node (or DSA radio device) itself or through an off-device environmental sensing capability (ESC) [35]. In this article, we consider a sense-and-use mechanism forfree channel selection in a given band, because it does not require any supporting framework ofenvironmental sensing components. In such a mechanism [64], the message transmission is slotted
via periodic sensing. Each periodic sensing frame consists of a sensing slot of duration (tsd ) anda transmission slot of duration (ttd ). Figure 2 shows the node’s activities in transmitting a message. In the sensing slot, the node performs wide-band spectrum sensing to obtain the availability
status of all the channels, whereas, in the transmission slot, the node sends the message packets
over the chosen free channel. However, our proposed mechanism will also work seamlessly with
an off-device environmental sensing capability(ESC) in a similar manner.
We assume that there exists S set of bands where each band s ∈ S has several (sub) channels.
We have considered S = {TV, LTE, ISM, CBRS}; however, it can easily be extended to any other
unlicensed or licensed bands where DSA has been allowed. For example, millimeter-wave bands(30–300 GHz) are being discussed for communication in futuristic 5G technologies [40, 42]. The
details of operating frequencies for aforementioned bands are shown in Table 1.
The DSA overlay network’s topology could be of two types, which are as follows:
Steady Network Topology: In this case, the DSA overlay nodes are either fixed (e.g., RSUs) orhave predictable mobility trajectories (e.g., public buses, municipal vehicles). Each node sharesits geographical location and spectrum availability with other nodes in the network via a dedicated common control channel or other synchronization techniques as discussed in the recent DSA
standards [35, 36]. Hence, in such a steady topology, each node possesses global knowledge about
approximate geographical location and spectrum availability at every other node. This steady DSA
topology is the main focus of our article.
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Table 1. Spectrum Profile with Bandwidths

Spectrum
54–216MHz (VHF TV Band)
470–698MHz (UHF TV Band)
698–806MHz (700MHz Band/FirstNet)
700–800MHz/1700–2100MHz (LTE Band)
902–928MHz (ISM Band)
2.4–2.5GHz (ISM Band)
3.5–3.7GHz (CBRS Band)
5.7–5.8GHz (ISM Band)

Bandwidth
6MHz
6MHz
6MHz
5–20MHz
3–8MHz
2–50MHz
10–40MHz
20–80MHz

0 and e 1 represent the links between node 1 and 2 over band type 0 and 1, respecFig. 3. Network model. e 1212tively. Moreover, the tuples < 1, 4 > and < 3, 2 > signify the energy and latency costs across those links, i.e.,
0 and e 1 , respectively.
e 1212

Variable Network Topology: In this case, the DSA overlay nodes may have variable (unpredictable) mobility pattern (e.g., a private vehicle), implying that a node may not possess accurate
global knowledge of the network. However, it may gather local knowledge about the neighboring
nodes via the common control channel.
3.2 Network Model
As illustrated in Figure 3, we model the DSA overlay network topology as a directed graph G =(V , S, E), where V = Q ∪ C ∪ R is the set of all nodes including CPEs, data fusion centers, andcyber-physical devices in between; S is the set of band types, and E ⊂ (V × V × S ) is the set of
all directed links between two nodes over any common (free) channel in any band type. A linkei(sj ) ∈ E is a directed link from node i to node j over band s ∈ S. Hence, there may exist at most|S | unique links between any node pair i, j ∈ V . A message traversing through G is denoted bym :< u, v, L,T >, where u is the source, v is the destination, L is the message size, and T is thehard delivery deadline. Now, for any given message m, each link is characterized by a tuple <w i(sj ) (L), tˆi(sj ) (L) >, denoting the energy and latency costs, respectively. Note that this model is used
for hard deadlines while soft deadlines are discussed in Section 6.
The energy cost w i(sj ) (L) refers to the total energy consumed in spectrum sensing and discovery, spectrum hand-off (or switching to an idle channel), and actual message transmission over afree channel in band s. We present the calculation of w i(sj ) (L) in Section 4.1. However, the latency
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Fig. 4. Communication mechanism.

cost tˆi(sj ) (L) denotes the total delay incurred due message transmission time (see Equation (3)),
propagation time, and queuing delay (see Equation (9)).
3.3 Communication Mechanism
As shown in Figure 4, a CPE q ∈ Q periodically broadcasts a Hello packet to the sensor blocks.
On receiving the Hello packets, every sensing agent in a sensor block switches from a default
energy-saving Sleep (receive only) mode to an Active (transmit and receive) mode and transmits
the messages directly or indirectly via other sensing agents to the visiting CPE, q. The CPE inturn broadcasts the Sleep packet in the sensor block such that the sensing agents switch back to
the energy-saving Sleep mode. Meanwhile, the CPE computes the intermediate node and energyefficient band (say, s ∈ S) to transfer the messages to the data fusion center. For steady DSA topology, we discuss the band selection approaches (and determination of a set of intermediate nodes)
for any message with hard and soft deadlines in Sections 5 and 6, respectively. We also touch on
the band selection approach for variable DSA topology in Section 9.
Once the band s is determined at CPE q, it shares the chosen band and channel (denoted by c (s ) )information with the intermediate node in the steady DSA network, which then switches to thechannel c (s ) in the chosen band s. Note that a unique intermediate node is determined in the steady
topology, whereas in case of variable topology, q may have to share the information with every
neighboring nodes in the network such that the message delivery can be improved (see Section 9for details). Next, the CPE q transmits the message to the unique intermediate node (in steady DSA
topology) and multiple neighboring nodes (in variable DSA topology) operating over the channelc (s ) in the chosen band s. A similar process goes on at the intermediate/neighboring node(s) until
the message is successfully offloaded to the data fusion center.
The rest of the article discusses how the proposed architecture (and communication mechanism) would enable dynamic band selection at each node and improve the network energy
efficiency while guaranteeing QoS. To meet the objective of energy efficiency, it is evident
that every message in the network needs to be offloaded to the data fusion center (or nearest
backhaul link) in an energy-efficient manner and within the hard (or soft) deadline depending
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on the application requirement. Therefore, in this article, we primarily investigate the problem ofdetermining energy-efficient path p(u, v) for any given message m from a source u to a destinationv, within a certain delivery deadline (be it hard or soft) such that the overall energy efficiency ofthe network is maximized yet preserving the QoS.
4 TRADEOFFS BETWEEN BAND SELECTION AND ENERGY EFFICIENCYThis section presents the energy consumption model for DSA overlay network. It also discusses
how unique EM characteristics offered by various bands can be intelligently exploited to improveenergy efficiency of the DSA overlay network, thereby realizing a green communication system
for sustainable SCCs.
4.1 Network Energy Consumption Model
The DSA overlay network G is influenced by various EM factors such as operating frequency,
channel bandwidth, interference, and so on. Hence, the energy consumption model must consider
all these factors into consideration. We discuss the relationship between energy consumption andvarious EM factors later in this section. Now, given a path p(u, v) for a certain message m (withsize L) from a source node u to destination node v, the total consumed energy is given by
w isj (L),(1)
wuv (L) =(s )e i j ∈p (u,v )where w i(sj ) (L) is the energy cost over link ei(sj ) and ei(sj ) is an intermediate link in path p(u, v).
Calculation of Energy Cost w i(sj ) (L): As discussed earlier in Section 3, each node determines afree (unoccupied) channel via sense and use mechanism. Under this consideration, similarly toReference [64], the energy cost w i(sj ) (L) over a link ei(sj ) can be expressed asw i(sj ) (L) = N tsd P̃ + N Pw sw + Ttd Pi(s ) ,(2)

where N is the number of time slots required for one message transmission and can be calculated
as the fraction of message size (L) to the effective bit rate (Ri(sj ) ) of a certain band s, i.e.,  L(s ) .
Ri j

In addition, tsd is the sensing slot duration; P̃ is the spectrum sensing power (in watts); P is theprobability of switching to an idle channel, termed as spectrum hand-off; w sw is the energy cost(in Joules) of one channel switching; Pi(s ) is the transmit power at node i over band s. Finally, Ttd
is the average time required to transmit the entire message and can be computed as Ttd = N Pttd ,where ttd denotes the transmission slot duration (see Figure 2).
For each message frame, the DSA node has to sense for tsd , and N frames are required forone message transmission. Hence the energy consumed in sensing is N tsd P̃. Given that the nodeswitches to another channel with probability P, it consumes N Pw sw amount of energy in channel switching. The last part of Equation (2) indicates the total energy consumed in transmitting
message of size L over a channel in band s. Usually, tsd and ttd are pre-determined values for any
given band type, and w sw is negligible. Thus, N =  L(s )  and Pi(s ) are the two main contributing
Ri jfactors for the energy cost of any link.
Given tsd , ttd , N , and P, the total message transmission time, denoted by ti(sj ) (L), for a message
m can be calculated as follows: L ti(sj ) (L) = N (tsd + Pttd ) = (s ) (tsd + Pttd ).
(3)
Ri j
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From Equations (1), (2), and (3), it is clear that the total consumed energy is directly proportional
to the transmit power, the number of transmissions, and message transmission time. Based on theseobservations, we discuss the impact of different EM characteristics offered by various bands on theoverall energy efficiency of the DSA overlay network.
4.2

Operating Frequency vs. Energy Efficiency

It is known that the received signal strength at a receiver must meet some reception threshold, τ ,to be able to decode a signal accurately. The generalized Frii’s transmission equation [31] provides
the relationship between the received power at receiver j and the transmit power from transmitteri over any channel with a representative frequency1 f (s ) in band s,αCP j(s ) = Pi(s ) G i G j≥ τ,(4)4π f (s ) dwhere G i and G j are the transmitter and receiver antenna gains, α is path loss exponent, C is the
speed of light, d is the distance between i and j, Pi(s ) is the transmit power of i, and P j(s ) is the
C α) be a constant.
received power at j. Now let ϕ = G i G j ( 4πP(s )Thus the above equation becomes P j(s ) = ϕ (f (si) d ) α . It is evident that for higher frequencies, thetransmit power Pi(s ) ought to be increased to maintain the same P j(s ) ≥ τ , even though the distanced is unchanged. The resultant increased transmit power causes more energy consumption (refer
to Equations (1) and (2)). Alternatively, if the transmit power is not increased, then τ is only met ata lower distance. Hence, more intermediate hops would be required to traverse the same distanced, leading to the additional transmission and reception operations at each intermediate hop thatcauses higher energy expenditure.
Moreover, the lower frequencies have larger wavelengths yielding better obstacle and wall
penetration capabilities. This implies the lower frequencies are less error prone, thus reducing(re)transmission overheads and enabling better non-line-of-sight connectivity. Among the bands
that allow DSA, the TV and LTE bands have lower frequency allocations than traditionally usedunlicensed band of 2.4GHz. In other words, these bands should be seemingly more energy efficient.
However, lower frequencies come at a tradeoff in the sense that they usually have much lesser
bandwidth of 6MHz as opposed to the LTE (upto 20MHz) and CBRS (upto 40MHz). For larger
message sizes, the message transmission time (refer to Equation (3)) will be higher as the numberof time slots (N ) increases. Supporting high data rate applications are also challenging for lower
frequencies offering smaller bandwidths. However, frequencies offering higher bandwidths will
reduce the transmission time for a given message size, thereby saving energy. An important research question, therefore, is whether the reduction in the transmission time for higher frequencies
offsets the gain in energy efficiency due to the reduced hop count.
4.3

Bandwidth vs. Energy Efficiency

The relationship between the bandwidth and the dissipated power could be understood throughRayleigh-Parseval Equation [12]:
(s )
=
Pdis

1 For example, f f (s )
2

(s )
f1

Si ( f (s ) )d f ,s = 2.4GHz for the frequencies in the range 2.412–2.462GHz used by legacy WiFi devices in ISM band.

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where Si is the power spectral density, Δf (s ) = f 2(s ) − f 1(s ) is the bandwidth of any channel in the(s )is the total power dissipated over band s. From Equation (5), it is clear thatband type s, and Pdisa channel offering higher bandwidth causes larger power dissipation. Given a fixed transmission
time for any message, the higher bandwidth channels will consume more energy than the lower
ones. The typical bandwidths for various spectrum is listed in Table 1.
Conversely, from Shannon-Hartley theorem [56], the effective bit rate (Ri(sj ) ) between a transmitter i and receiver j over any channel in band s with representative frequency f (s ) is given by
P (s )
Ri(sj ) = B (s ) loд2 1 + χi(sj ) = B (s ) loд2 1 + ϕ (s ) i α (s )  ,(f d) N 

P

(6)

(s )

where B (s ) is the bandwidth of any channel in band type s and χi(sj ) = Nj(s ) is the ratio of the communication signal power to the interference and noise at the receiver (SINR). The rest of the notations
are the same as above. From Equation (6), given that the SINR is unchanged, a higher bandwidthchannel would offer a higher effective bit rate, thereby decreasing the required transmission time.
Therefore, from the above relationships of bandwidth with (i) dissipated transmit power (Equation (5)) and (ii) effective bit rate (Equation (6)), the higher the channel bandwidth, the higher is thedissipated power but the lower is the transmission time. Thus, the tradeoff is whether to choose ahigh bandwidth channel that reduces the transmission time and increases the dissipated power orvice versa.
4.4 Interference and Packet Size vs. Energy Efficiency
Increased cross device interference will increase the noise floor as more and more devices coexist
in the unlicensed spectrum (2.4GHz) and GSM/LTE (1700–2100MHz) band. Therefore, from Equation (6), it is evident that the SINR will decrease. To maintain a certain required SINR, the transmit
power needs to be increased. However, switching to any other band type using DSA would allow
us to maintain the same SINR with a low transmit power, thus saving energy.
Packet sizes in the existing low-power short-range technologies (2.4GHz) are usually small(maximum size 128 bytes), because the bit error rate is very high. However, communication
through lower frequency bands experiences smaller bit error rate (as explained in Section 4.2).
Thus, as shown in Reference [29], larger packet sizes can be employed that would reduce perpacket overheads such as headers and preambles, and therefore, promises energy efficiency.
5 BAND SELECTION UNDER HARD DEADLINESIn this section, we discuss the proposed band selection approach for steady DSA network topology where messages have hard delivery deadline requirements. In such a topology (see Section 3),each node possesses global knowledge about the approximate geographical location and spectrum
availability at every other node in the network. The key idea of the band selection approach under
hard deadlines is to intelligently match each message m :< u, v, L,T > to a suitable band at eachintermediate node between the source node u and the destination node v so as to construct an
optimal hard deadline constrained energy-efficient (HcE) path p(u, v). We first formulate the HcEpath determination as a linear optimization problem and show that it is NP-hard. Then, we present
a pseudo-polynomial dynamic programming approach that selects the best band at each intermediate node, for constructing an optimal HcE path for any given message. Following it, we propose
a faster (though not optimal) polynomial-time greedy heuristic for solving the HcE problem.
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5.1 HcE Problem Formulation
As mentioned, the HcE path problem aims at determining the most energy-efficient path for any
given message that also meets the hard delivery deadline. Although we solve the optimization
problem for a given message m, it will also work well for concurrent messages. This is because
a band is composed of several channels that can be simultaneously accessed by concurrent messages (should they end up choosing the same band in the same geographical region), unless inthe unlikely rare event where the number of such messages exceed the total number of available
channels. There may be a small channel access delay due to channel bargaining within the selected band, which is negligible in the range of micro seconds [11] and hence is ignored in the
optimization formulation.
Given a directed graph G = (V , S, E), a source–destination node-pair u, v ∈ V , a message m :<u, v, L,T >, each link ei(sj ) ∈ E characterized by energy cost w i(sj ) (L) and latency cost tˆi(sj ) , the HcEpath problem can be formally defined asw i(sj ) (L),(7)
min
p (u,v ) ∈P (u,v )(s )e i j ∈p (u,v )where P  (u, v) ⊂ P (u, v) is the set of all paths from the source node u to the destination node v,
which meets the following five constraints:(i). SINR constraint. For successful decoding of any message m at receiver j, the achieved SINR
χi(sj ) at receiver j must be greater than or equal to prespecified SINR threshold χt h , typically a smallvalue, say, −75dBm.
(ii). Power constraint. As per the federal communications commission (FCC) guidelines, the maximum transmit power, Pi(s ) at any transmitter i must be less than or equal to a fixed value, the(s )maximum effective isotropically radiated power (EIRP), Pmax
over any chosen band s ∈ S.
(iii). Transmission coverage constraint. For successful data communication over any intermediatelink ei(sj ) ∈ p, the Euclidean distance di j must be less than or equal to the transmission coverage
1γ (s ) achieved in the chosen band s, where γ (s ) = [G i G j ( 4πcf s ) α Pτs ] α (refer to Frii’s transmissionjequation Equation (4) for details).
(iv). Unique spectrum constraint. Any transmitter-receiver node pair i, j ∈ V must tune to a common available channel in a unique spectrum band s ∈ S. That is, though there are potentially |S |possible links between nodes i and j, only one link over a certain band can be utilized at any pointof time (due to hardware constraints),ei(sj ) ≤ 1, ∀ei(sj ) ∈ p.
(8)
s ∈S

(v). Hard delivery deadline constraint. Any message must be delivered within the hard deadline
from its source node u to the destination v. Hence, as shown in Equation (9), the latency cost tˆi(sj ) (L)di.e., the sum of message transmission time ti(sj ) (L), propagation time Ci j , and queuing time (qϵ ) overeach intermediate link in the path p(u, v), must be less than or equal to the hard deadline T . Thus,(s )e i j ∈p (u,v )tˆi(sj ) (L) =(s )e i j ∈p (u,v )ti(sj ) (L) +di j+ qϵ ≤ T ,C

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where C is the propagation speed of any spectrum (usually speed of the light). The propagationdtime ( Ci j ) and queuing time (qϵ ) are usually negligible compared to the message transmission
time ti(sj ) (L). In the generalized term, the total of latency cost is equivalent to the total messagetransmission time, i.e., (s )ti(sj ) (L) ≤ T .
tˆi(sj ) (L) = (s )e i j ∈p (u,v )e i j ∈p (u,v )Theorem 5.1. The HcE path determination problem is NP-Hard.
Proof. We provide a reduction from the Restricted Shortest Path (RSP) problem [27, 67]. Let us
consider a generic instance of the RSP problem. Consider a graph G r sp (Vr sp , Er sp ), where Vr sp is
the set of nodes and Er sp ⊆ (Vr sp × Vr sp ) is the set of edges. Each edge ei j ∈ Er sp is associatedwith a cost c i j and an additional metric ti j . Then, given a source node u, a destination node v, andconstraint bound D, the goal of the RSP problem is to determine the least cost path pr sp (u, v) suchthat ei j ∈pr sp (u,v ) ti j ≤ D.
We reduce this RSP problem to an instance of the HcE problem as follows. Consider a DSA
network topology graph G (V , S, E) where a node in V is created for each node in Vr sp . Assume
that S contains a single band and an edge is added to E for each edge in Er sp . In addition, for eachˆ(1)edge ei j we set w i(1)j (L) = c i j and t i j (L) = t i j . Finally, we set the hard deadline D = T .
Given such an instance, the HcE problem determines the least energy cost path p(u, v) that also
meets the hard deadline T . Such path also corresponds to an optimal solution of the RSP problem,since it is the shortest cost path satisfying the constraint D. Therefore, if we were able to solve
HcE problem in polynomial time, the RSP problem will also be solvable in polynomial time. Since
RSP is NP-Complete, we conclude that the HcE problem is NP-Hard.

5.2 Dynamic Programming Approach
In the following we present an exact pseudo-polynomial DP algorithm for solving the HcE optimization problem. The DP algorithm intelligently matches a message m to an optimal (energyefficient) band such that the total energy cost incurred in path p(u, v) is minimized while meeting the hard deadline and other constraints. The proposed DP algorithm is a combination of the
Floyd-Warshall algorithm for all pair shortest paths and the dynamic programming solution of theKnapsack problem [13].
Now, for a message m with L bits and a hard deadline T , the graph G (V , S, E) can be represented by a 3D adjacency matrix A : |V | × |V | × |S |. An element of A[i, j, s] is an ordered pair
< w i(sj ) (L), tˆi(sj ) (L) > where the first and second parts denote the energy and latency costs, respectively, for a message m along the link eisj from node i to node j over band s ∈ S.
Here,
⎧
⎪
⎪ 0,

⎪ (s )
w i(sj ) (L) = ⎨⎪ w i j (L),
⎪
⎪∞
⎩

i=j
i  j, and first four constraints in Section 5.1 are met .
otherwise(10)

Similarly, tˆi(sj ) (L) can be defined.
The proposed DP algorithm is based on the notion of intermediate node, defined as follows:
Definition 1. (Intermediate node): Given a path p =< 1, 2, . . . , (|l | − 1), |l | >, where nodes in thepath are represented as integers, an intermediate node i is one such as 2 ≤ i ≤ |l | − 1.
The DP algorithm is based on the following observation. Consider a subset of nodes {1, . . . k }.
For any node pair i, j ∈ V , let p be the path with the least energy cost, out of all paths between iand j whose intermediate nodes are in {1, . . . k }. Then, there are two possible cases.
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31:16V. K. Shah et al.

Fig. 5. Path p is the energy-efficient path from node i to node j with deadline t, and k is the highest numberedintermediate node of path p. Sub-path p1 from node i to j, has all the intermediate nodes in {1, 2, . . . k − 1} andthe hard deadline is t  . Similarly, sub-path p2 from node k to j, has all intermediate nodes in {1, 2, . . . k − 1}and hard deadline (t − t  ).

(a) k is not an intermediate node of p: In this case, all the intermediate nodes of path p are in{1, . . . , k − 1}. Hence, the energy-efficient path from i to j with intermediate nodes {1, . . . , k } isalso the energy-efficient path from i to j with intermediate nodes in {1, . . . , k − 1}.
(b) k is an intermediate node of p: In this case, p can be broken into two sub-paths: p1 from i tok and p2 from k to j. Since p is the energy-efficient path, k can only appear once in p. Therefore,
k can not be an intermediate node of p1 or p2 . Moreover, by the principle of optimality (that statesthat the sub-paths of a shortest path are also shortest paths), both p1 and p2 are the shortest, i.e.,energy-efficient paths between i and k, and k and j, respectively, with intermediate nodes in theset {1, . . . k − 1}. Figure 5 provides an illustration.
Let us now define the subproblems used by the DP approach that uses the intermediate nodes)
be the energy cost of the energy-efficient path fromused and satisfies the hard deadline. Let w i(k,Tjnode i to j that meets hard deadline T and uses only intermediate nodes in the set {1, 2, . . . , k }.
The solutions of subproblems can be formulated recursively as follows, leading to the solution of
the overall problem.
= ∞, for each i, j ∈ V .
(1) For T = 0, there is no path, hence w i(k,0)j(2) For k = 0, there are no intermediate nodes in the path, hence for each i, j ∈ V ,)=w i(0,Tjmins ∈S∞w i(sj ) (L), i f ∃ s ∈ S such that tˆi(sj ) (L) ≤ T.
otherwise(11)(3) For k > 0 and T > 0, we can select a path with the least energy cost either using k as theintermediate node or not. If k is used, then let t 1 be the delay over the sub-path between
i and k, and t 2 the delay for the sub-path between k and j. To ensure that the overall pathmeets the deadline T , we need to find the value of t ∈ [1, . . . ,T ] such that if given t 1 , thent 2 = t − t 1 . Formally, (k−1),t)(t −t 1 )1
= min w i(k−1),t, min w ik
+ w k(k−1),
w i(k,T
.
(12)
j
j
j
t =1...Tt 1 =0...t

When k = |V | and t = T , we obtain the final solution, which is the total energy cost for
the energy-efficient path for each pair of source–destination node-pair with hard deadline
T . In other words, W ( |V |,T ) = (w i(j|V |,T ) ) for all i, j ∈ V .
5.2.1 DP Algorithm Description. Algorithm 1 uses a series of 3D matrices W (k,t ) for k =), that is the hard dead0, . . . |V | and t = 0, . . . T . The matrix W (k,t ) contains the elements w i(k,tjline (1 ≤ t ≤ T ) constrained energy-efficient path between i and j using the intermediate nodes in). The algorithm has{1, . . . k }. Also, we keep track of the optimal HcE path in another matrix Pi(k,tj
the following two steps:
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ALGORITHM 1: DP Algorithm
Input: Source u, Destination v, T , A[i, j, s] ∀i, j, s
Output: Optimal HcE Path p(u, v)
1 //Step 1: Initialization
2 for i ← 0 to |V | do3
for j ← 0 to |V | do4
for t ← 0 to T do
(0,t )(s )5
w i j = mins ∈S, tˆ (s ) ≤t A[i, j, s].w i j (L) ;
ij

6
7
8
9

(0,t )

if w i j(0,t )

 ∞ and w i j

 0 then(0,t )Pi j = i;else(0,t )Pi j = −1;//Step 2: HcE path via band selection
for k ← 0 to |V | do12
for t ← 0 to T do
13
for i ← 0 to |V | do14
for j ← 0 to |V | do15
for t 1 ← 0 to t do(k,t )(k−1),t 1(k−1), (t −t 1 )16
if w i j> w ik+ wk j
then
10
11

17

(k,t )wi j(k−1),t 1= w ik(k−1), (t −t 1 )+ wk j18(k,t )(k−1), (t −t 1 )Pi j= Pk j;19

Recursively navigate path tracking matrix Pu,v

( |V |,T );to obtain the optimal HcE path p(u, v).

Step 1 (Initialization): The algorithm first initializes the matrix W (0,T ) to A[i, j, s]. The HcE pathtracking matrix, P (0,T ) is also initialized in lines 2–9 (see Equation (13)),
⎧
0
⎪
⎪
⎪

if i = j⎪⎪⎪∞⎩otherwise⎪)(0,t −1)w i(0,t=⎨, mins ∈S w i(sj ) (L)j⎪ min w i j

if i  j and tˆi(sj ) (L) = t .

(13)

Step 2 (HcE path via band selection): After the initialization, the algorithm applies the recursive formula to calculate W (k,t ) given W (k−1,t1 ) and W (k−1,t −t1 ) , where 1 ≤ t 1 ≤ t , as shown in
Algorithm 1. Also, the path tracking matrix P is updated recursively (Lines 11–19).
Finally, any source node u delivers the message m(u, v, L,T ) from a source u to the destinationv via the computed optimal HcE path p(u, v) obtained from the path tracking matrix P.
Time Complexity: From the description of Algorithm 1, the time complexity of Step 1 (lines
2 - 9) is O (|V | 2T ), whereas the time complexity of Step 2 (lines 11–18) is O (|V | 3T 2 + |V |), where|V| is the number of nodes and T is the hard deadline. Thus, the overall time complexity of the
DP algorithm is O (|V | 3T 2 ), which is not polynomial in the input size, since it also depends on thevalue of T . Such time complexity is referred to as pseudo-polynomial.
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ALGORITHM 2: Greedy Heuristic
Input: Source u, Destination v, T , Δβ, A[i, j, s] ∀i, j, s
Output: HcE path p ∗ (u, v)
1 β = 0.0;
2 while β ≤ 1.0 do
3
for i ← 0 to |V | do4
for j ← 0 to |V | do
5

β
(s )
(s )
Hi j = mins ∈S βA[i, j, s].w i j (L) + (1 − β )A[i, j, s].tˆi j (L) ;β

7Least weighted path, Puv = DIJKSTRA-ALGO (H β , u, v);
β = β + Δβ;

8Compute overall energy cost, E (Puv ), and latency cost, T (Puv ) over computed path Puv

6

9

β

Return the path with min

β

β

β

β

β,T (Puv ) ≤T

E (Puv ) ;5.3 HcE Greedy Heuristic
This section presents a polynomial time greedy heuristic for an approximate solution of the HcEpath p ∗ (u, v) for a given message in the network. The underlying idea is to calculate a weight hi j foreach edge ei j ∈ E that combines the energy cost w i(sj ) (L) with the latency cost tˆi(sj ) (L). A parameterβ ∈ [0, 1] defines the weighted average between the two, such that hi j = β × w i(sj ) (L) + (1 − β ) ×tˆi(sj ) (L). Intuitively, using Dijkstra algorithm [13] and setting β = 0, we can use such weights tocalculate the minimum delay path, which may not necessarily be energy efficient. Similarly, if we
set β = 1, then we can calculate the most energy efficient path, which may not meet the deadline
T . By increasing β from 0 to 1 in steps of Δβ, we can find a path that tradeoffs energy efficiency
and delay.
The pseudo code of the proposed greedy heuristic is presented in Algorithm 2. Given a value ofβ the proposed heuristic computes the weighted metric hi j for each node pair i, j that forms theβmatrix Hi j as follows (see line 5):β

Hi j = βw i(sj ) (L) + (1 − β )tˆi(sj ) (L).

(14)β

The heuristic then calls Dijkstra’s algorithm to compute the least weighted path Puv from sourceβu to the destination v (line 6) using the values of Hi j as edge weights. Then, it computes the totalββenergy cost denoted by E (Puv ) and the total latency cost denoted by T (Puv ) to compute the leastβweighted path Puv . The above steps (lines 2–6) are iterated for all values of β in the increased
βinterval of Δβ. Finally, the HcE path p ∗ (u, v) is chosen as the path with minimum E(Puv ), ∀ββsuch that T (Puv ) ≤ T .
1
1
1|V | 2 + Δβ
|V |loд(|V |) + Δβ),Time Complexity: The time complexity of Algorithm 2 is O ( Δββ1since the algorithm performs O ( Δβ
) iterations and it takes O (|V | 2 ) time to compute Hi j , ∀ i, j ∈ V .
Similarly, it takes O (|V |loд(|V |)) time to run Dijkstra’s algorithm and O (1) time to compute theβ1minimum E (Puv ), ∀β. Hence, the time complexity of the greedy heuristic is O ( Δβ|V | 2 ).
The following theorem proves the non-trivial property that the heuristic always returns a solution, if one exists. In other words, the greedy choices of the algorithm, although potentially not
optimal, do not prevent us from finding a path that meets the hard delivery deadline, T .

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

Theorem 5.2. Provided that there exists at least a HcE path p (u, v) for a message m with size L
and hard deadline T in the DSA overlay network, the greedy heuristic will always find such a solution.
Proof. Consider the weight parameter β = 0 at the first iteration of the heuristic. Then, theββweight metric Hi j is given by Hi j = tˆi(sj ) (L), ∀i, j ∈ V . This implies the latency cost of each linkei(sj ) for a message m is used as the weight. By executing Dijkstra’s algorithm with these weights,
the path between u and v with minimum latency can be obtained. If such a path meets the deadline
T , then this becomes a more energy-efficient path. Otherwise, there exists no solution, since all
the paths found in the subsequent iterations will not also meet the deadline. This also implies thatsuch path iss the shortest in terms of latency.

6 BAND SELECTION UNDER SOFT DEADLINESIn this section, we extend our dynamic band selection approach for the steady DSA overlay topology to the case in which messages have soft delivery deadlines. Soft deadlines have been largely
investigated in the context of real-time systems and scheduling theory [3, 16, 26, 28, 48, 68]. In
general, a soft deadline allows a certain message to be delivered after a desired delivery deadline,
however by incurring a penalty defined by the penalty function. Using the commonly adopted exponential penalty function, we formulate a non-linear programming (NLP) optimization problem,
termed the ScE problem, to determine an optimal energy-efficient path under soft deadlines. Since
NLP is hard to solve practically [6], we extend our proposed greedy heuristic for HcE to solving
the ScE problem.
6.1

Penalty Function

The penalty function increases as the delay in delivering a certain message increases. Thus, thepenalty is a non-negative function of message delivery time. There exist several possible definitionsof soft deadline requirements, such as: (i) no more than X consecutive deadlines can be missed,(ii) no more than X deadlines in an interval of time T can be missed, (iii) the deadline miss ratio(percentage or total missed deadlines over the total number of deadlines) must not exceed a certain
threshold, and (iv) the penalty of delivering a message is zero before a desired deadline Td ; itincreases according to a prespecified penalty function up to a maximum tardiness Tmax , after
which the penalty becomes infinity.
In this article, we adopt the last definition as also extensively used in real-time scheduling [16,28, 48]. In this context, it is intuitive to observe that any linear and non-linear increasing functionof time with a cutoff at Tmax can be used for a suitable penalty function. We choose an exponentialfunction ϕ (t ), where t is the delivery time, as commonly adopted in soft deadline modeling [3]. It
is defined as follows:⎧e α (t −Td ) if Td < t ≤ Tmax
⎪
⎪
⎨
.
(15)
ϕ (t ) = ⎪ ∞if t > Tmax⎪
otherwise⎩0Here, α is an arbitrary non-negative value that controls the penalty growth rate.
6.2

ScE Problem Formulation

As aforestated, we formulate the ScE path determination problem as a non-linear programming(NLP) optimization problem. The ScE path problem aims at determining, for any message, an optimal path that maximizes energy efficiency, and simultaneously, minimizes the penalty.
Given a directed graph G = (V , S, E), a source node u ∈ V , a destination v ∈ V , a message m :<u, v, L,Td ,Tmax >, and each link ei(sj ) ∈ E characterized by the energy cost w i(sj ) (L) and the latency
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31:20V. K. Shah et al.

cost tˆi(sj ) (L), the ScE problem minimizes the following objective function:min

p (u,v ) ∈P (u,v )




(s )

e i j ∈p (u,v )







w i,(sj) (L) + ϕ(s )e i j ∈p (u,v )tˆi(sj ) (L) .

(16)



Here P  (u, v) ⊂ P (u, v) is the set of all paths from the source node u to the destination node v
which meets the first four constraints related to the SINR, maximum power, transmission coverage,
and unique spectrum described in Section 5.1.
6.3 ScE Greedy Heuristic
Following the HcE greedy heuristic, we propose an efficient greedy heuristic to determine theScE path p(u, v) for any message with soft deadline in the DSA overlay network. As shown inβAlgorithm 2 (Section 5.3), the HcE greedy heuristic computes the least weighted path Puv fromβsource u to destination v using the values of the weight matrix Hi j as edge weights. Then, it
βββcomputes the total energy cost E(Puv ), latency cost T (Puv ) for least weighted path Puv . And,βfinally the HcE heuristic chooses the path p ∗ (u, v) as the path with minimum E(Puv ), ∀β, suchβthat T (Puv ) ≤ T , the hard deadline. Differently, the ScE heuristic chooses the path p ∗ (u, v) as thepath that minimizes the objective function in Equation (6.2) across all values of β, that is


β
β
β
(17)
p ∗ (u, v) = Puv |minE (Puv ) + ϕ T (Puv ) .
β

We omit the pseudo code of the algorithm, as it can be easily obtained by replacing line 9 ofAlgorithm 2 with the above equation. This heuristic will also find a solution as long as one exists
1|V | 2 ), which isaccording to Theorem 5.2. The time complexity of the ScE greedy heuristic is O ( Δβsimilar to that of HcE heuristic.
7 COST–BENEFIT ANALYSIS AND IMPROVED SUSTAINABILITY
Many detractors of sustainability argue that green computing incurs higher capital expenditureand operational cost that drains the economy/revenue and makes such designs infeasible in practice. We claim that our proposed approach to dynamic band selection for designing green communication systems, is mostly free from such disadvantages due to following reasons: (i) The use of
DSA-enabled access philosophy does not require a provider to own dedicated spectrum licenses,
which is worth millions of dollars, and (ii) Multiple smart communities can be connected by asmall set of DSA overlay nodes, each costing on the average around $1700–$1900 [37]. In contrast,
our design obviates the need for new dedicated infrastructure such as macro cellular base station($300,000/station) or micro cellular base stations ($100,000)[24] in rural areas and the need for extensive wire line communications to increase the available bandwidth in urban areas. Some realexamples of DSA-based deployment has already taken place in relatively rural areas in the developing world, such as Papua [21], Rural South Africa [41], Mexico [17], and the Philippines [23].
To summarize, there is sufficient practical evidence to suggest that the equipment and operational costs of DSA overlay topology are far less compared to the traditional approaches. The
major hurdles are the policy issues that surround spectrum planning. Significant breakthroughs inspectrum policy [35, 36] have been made in recent years; moreover, DSA-enabled devices mounted
over drones, smart public transport [10], and private smart cars [66] are becoming a reality. Let
us reiterate that our proposed architecture and solution approach work on top of existing infrastructures (e.g., public buses) to be mounted with a DSA radio device (e.g., ER-N210 [37]) capable
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of dynamic band access. As a result, our approach notably improves energy efficiency of the network via suitable band selection for varying message requirements. Hence, our proposed approachpromises to design green yet cost-effective communication systems for realizing smart and connected communities.
The following theorem 7.1 proves that the network energy efficiency can be significantly improved by increasing the number of existing infrastructures, such as public buses or municipal
vehicles, mounted with a DSA-enabled device. However, it comes at a higher equipment cost due
to the requirement of a larger number of DSA-enabled devices. As part of our future work, we propose to study the tradeoff between the achieved network energy efficiency and equipment cost,
for a given rural or urban area.
Theorem 7.1. Given a DSA overlay network G (V , S, E) with a set of messages (say, M), the energy consumed in the HcE (or ScE) path p(u, v) for any message m ∈ M either improves or remains
unaffected due to the introduction of a new DSA node v ∗ in the network.
Proof. The introduction of a new node v ∗ in network G, let the modified network become

(s )
∀i ∈
G ∗ (V ∗ , S, E ∗ ) where the node set is V ∗ = V ∪ v ∗ and the edge set is given by E ∗ = E ∪ eiv
∗
(s )
(s )
∗
V , ∀s ∈ S, if div ∗ ≤ γiv ∗ . Now, the newly determined HcE (or ScE) path p (u, v) in the modified
G ∗ may (or may not) choose one or more intermediate edges from the set E ∗ \ E. If the path p ∗ (u, v)
chooses at least one intermediate edge from set E \ E, then the energy consumed in p ∗ (u, v) is lessthan that of path p(u, v) in the original G. If not, then the energy consumed in p ∗ (u, v) in G ∗ is thesame as that of p(u, v) in G.
Therefore, the energy consumption on the HcE (or ScE) path p(u, v) either improves or remains
unaffected with the introduction of a new DSA node v ∗ in the network G. Since the network
energy efficiency is the summation of energy savings on each such path for each message m ∈ M,
the overall network energy efficiency is also bound to either improve or remain unchanged withincreasing number of DSA nodes in the network.


8 SIMULATION RESULTS
In this section, we evaluate the performance of the proposed dynamic band selection approachagainst conventional homogeneous band access approaches for the steady DSA overlay topology
under both hard and soft deadlines. The performance metrics include: (i) Energy Efficiency, theaverage amount of energy consumed to transfer all generated messages from the sensor blocks totheir intended data fusion centers; and (ii) Message Delivery Ratio (MDR), the fraction of messages
successfully delivered to the fusion center within the delivery deadline (hard or soft) to the total
number of messages generated at the sensor blocks.
For the case where messages have hard deadlines over DSA overlay topology, let us first analyze
the performance of our proposed approach using optimal DP as well as greedy algorithms fordetermining HcE paths, against homogeneous band access approaches for determining HcE paths
using DP algorithm.
For extensive analysis, we evaluate both the performance metrics considering (i) varying message sizes, (ii) varying hard deadlines, and (iii) varying source–destination node pair distances. The
first two parameters represent the message (or QoS) heterogeneity, while the third one represents
the geographical heterogeneity. For cost–benefit analysis, we also analyze how the change in thenumber of DSA nodes in the network (incurring equipment cost) impacts the performance metrics of the proposed band selection approach. Finally, we also provide a detailed analysis on theperformance of the proposed dynamic band selection approach under soft deadlines.
Simulation Settings: We simulate a simple SCC scenario with 5 data fusion centers, 15 CPEs,
and 30 sensor blocks, each with 50–100 sensing agents. On average, each CPE periodically visits
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Fig. 6. Varying message sizes: (a) Energy efficiency and (b) Message Delivery Ratio (MDR).

2 sensor blocks with a round trip time of 1 hour. The steady DSA overlay topology consists of 200
DSA nodes (e.g., public buses, RSUs, etc.). We assume 50% of overlay nodes (imitating RSUs) are
randomly placed and static, whereas other overlay nodes (imitating public buses) move along theprespecified trajectories. We consider that each DSA-enabled node has the knowledge of locationand spectrum availability at every other node in the network. Unless otherwise stated, we consider
that each sensor block generates messages, each of size 250MB with hard deadline T = 3 hours,
and the farthest distance between a sensor block and the corresponding fusion center is d = 45 km.
We consider the usually allowable transmit power for all bands on a secondary basis, suchas 1W for ISM band, 4W for TV and LTE bands, and 10W for CBRS band. Recall that each bandhas a FCC mandated maximum allowable transmit power that cannot be exceeded by any nodeoperating on a DSA basis. Finally, realistic values of other controlling parameters are considered
as follows: path loss factor α = 3.5 (sub-urban area), SINR = −75dBm (3.16 × 10−8 mW), received
power threshold (τ ) = −20dBm (0.01mW), sensing power P̃ = 40mW, probability of switching to
the idle channel P is 0.5, sensing slot duration tsd = 0.1s, transmission slot duration ttd = 1s, andenergy cost of one channel switching w sw = 1mJ.
8.1

Message Heterogeneity

This section evaluates the performance of the proposed band selection approach against conventional homogeneous band access approaches for varying message sizes and hard deadlines.
8.1.1 Varying Message Sizes:. As shown in Figure 6(a), and (b), the proposed dynamic band selection approach outperforms the homogeneous band access approaches in terms of both energy
efficiency and message delivery ratio (MDR). The proposed approach achieves an average of 19%
energy savings compared to the best homogeneous band access approach (i.e., LTE Band), particularly for message size L ≤ 500MB. The reason is evident as the proposed band selection approach
utilizes suitable band at each intermediate DSA node so as to attain the most energy-efficient path
for any given message. For larger message sizes, L > 500MB, the energy consumption by homogeneous band access approaches (except CBRS band) is missing as none of these bands (i.e., ISM, LTE
and TV bands) are able to successfully deliver any message of size L > 500MB to the fusion center
within the hard deadline, i.e., MDR = 0. Note that the ISM band consumes significantly smaller
energy; however, it suffers from severely poor MDR and hence does not meet the QoS requirements. The greedy heuristic for the proposed approach yields similar energy efficiency as that of
the optimal DP algorithm.
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Fig. 7. Varying hard deadlines: (a) Energy efficiency and (b) MDR.

Our proposed approach yields similar or slightly better MDR (up to 5%) compared to the best
homogeneous approach, i.e., LTE band for message size L ≤ 500MB, and CBRS band for larger
message sizes. This is because, unlike homogeneous approaches where the node is restricted toaccess only one predetermined band, the proposed approach intelligently chooses the most suitableenergy-efficient band at each intermediate node while guaranteeing the hard deadline for every
message and hence MDR or QoS. Recall that for any given message, both the DP algorithm (owing
to being optimal) and greedy algorithm (from Theorem 5.2) are able to determine a HcE path
(if it exists) for a given source–destination pair that meets the hard deadline, and hence always
guarantee the highest MDR.
8.1.2 Varying Hard Deadlines:. Figure 7(a) shows that the proposed band selection approachyields better energy efficiency compared to that of the homogeneous band access approaches forincreased hard deadlines. Our approach saves energy consumption by up to 22% on the averagecompared to the best homogeneous band (i.e., LTE band) provided that it also guarantees comparable MDR. Here again, the ISM band yields the best energy efficiency, thanks to the low transmit
power; however, it suffers from poor MDR (refer to Figure 7(b)). The CBRS band yields very poor
energy efficiency, because the transmit power is very high compared to its counterparts.
As shown in Figure 7(b), the proposed band selection approach achieves similar MDR comparedto the homogeneous band access approaches. This is because our proposed approach for HcE pathdetermination simultaneously accounts for hard delivery deadlines besides enhancing energy savings. Since the homogeneous band approaches do not have the flexibility of choosing any otherband than the prespecified one, they perform relatively poorly. Note that both the greedy heuristic
and the DP algorithm work almost equally well in improving energy efficiency and MDR irrespective of varying hard delivery deadlines.
8.2 Geographical Heterogeneity
As depicted in Figure 8(a) and (b), the dynamic band selection approach outperforms the homogeneous band access approaches in terms of both energy efficiency and MDR for varying source–
destination node-pair distances (this is geographical heterogeneity). The proposed approach enhances energy efficiency by up to 32% compared to the best homogeneous band access approachthat also yields comparable MDR, particularly for distance d ≤ 60km. We observe that the CBRS
band yields very poor energy efficiency until distance d ≤ 45km and then improves significantly.
This is because the CBRS band, owing to its limited transmission coverage, is not able to deliver
messages successfully, and therefore do not dissipate energy in message transmission, for larger
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Fig. 8. Geographical heterogeneity: (a) Energy efficiency and (b) MDR.

Fig. 9. Varying number of DSA nodes: (a) Energy efficiency and (b) MDR.

distances between source–destination node-pairs. The ISM band again owing to its low transmit
power, yields significantly better energy efficiency; however it suffers greatly in terms of MDR,
thus defeating the purpose of SCCs.
Our proposed approach achieves slightly better MDR (about 7% on an average) compared to the
LTE Band, the best homogeneous band selection approach. The reasons are as explained above.
The greedy heuristic works very well in determining optimal HcE path and hence yields similarenergy efficiency and MDR as that of the DP algorithm employed for band selection.
8.3

Cost–Benefit Analysis

We evaluate the benefits of our band selection approach in terms of both energy efficiency andMDR (QoS) against varying number of DSA nodes in the overlay network (which is equivalent to
the incurred equipment cost).
As shown in Figure 9(a) and (b), the proposed band selection approach largely outperforms all
homogeneous band access approaches in terms of both energy efficiency and MDR, irrespectiveof the number of DSA nodes. Our approach enhances energy efficiency by up to 18% compared tothe best homogeneous band (i.e., LTE Band). Furthermore, the energy consumption increases withincrease in the number of DSA nodes until |V | = 100. After that, it gradually decreases until |V | =300 nodes and remains almost unchanged afterwards. This is because initially |V | itself is small
and yields slightly lower MDR, thus the energy consumption in delivering messages is also less.
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Fig. 10. Band selection analysis: (a) message sizes, (b) hard deadlines, (c) source–destination node pair distances, and (d) number of DSA nodes.

In contrast, when |V | is sufficiently large, say, ≥200, the proposed approach is able to determine
better energy-efficient paths and so consumes less energy at each intermediate node (as shown in
Theorem 7.1) while achieving very high MDR.
Energy consumed by ISM and CBRS bands are 0 J for |V | ≤ 200 and 100, respectively. This isbecause none of the messages was delivered to their intended fusion centers within hard deadlines;
therefore they are not desirable for data communication in SCCs (see Figure 9(b)). Moreover, thegreedy heuristic also determines near-optimal HcE paths and achieves similar energy efficiency
and MDR as that of the DP algorithm using the proposed approach.
8.4

Band Selection Analysis

This section investigates how our proposed band selection approach dynamically chooses suitableband types at the intermediate DSA nodes for each message, thereby enhancing energy efficiencyof the network while meeting the QoS. As shown in Figure 10(a)–(d), the proposed approach extensively utilizes the ISM and LTE bands, compared to the other two remaining bands. This isreasonable given that the simulated SCC scenario is characterized by |V | = 200 and d = 45km,
forming a relatively dense DSA network topology. Now, given that the message size L = 250 MB
and the hard deadline T = 3 hours, it becomes a suitable case for ISM and LTE bands to achieveenergy-efficient data communications. This is because these bands are characterized with relatively low transmit power, good bandwidth and comparatively good coverage.
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However, as shown in Figure 10(a), for message size L < 250 MB, our dynamic band selection
approach also utilizes the TV band for energy-efficient message transmissions. This is because
the TV band offers energy-efficient HcE paths for lower message sizes, thanks to its sufficiently
adequate bandwidth (6MHz) and very high transmission coverage (several kms). It is interesting tonote that messages with size L = 2000MB were delivered with hard deadline over the CBRS band
only. The reasons is unlike other bands, the CBRS band offers very high bandwidth and therefore
requires very low transmission time and meets hard deadlines for larger message sizes.
As shown in Figure 10(b), the proposed approach utilizes the ISM band more with increasing
hard deadline. This is evident because this band, owing to its low transmit power, can now be
utilized along energy-efficient paths that meet hard deadlines, for larger number of messages.
Similarly, as shown in Figure 10(c), for larger source–destination node-pair distances d > 45km,
the proposed approach again significantly utilizes the TV band for delivering messages in anenergy-efficient manner. Intuitively, the TV bands offer high transmission coverage, and thus
proves to be useful bands for energy-efficient communications for higher distances. Note that
the CBRS bands are rarely utilized by the proposed approach due to the fact that it offers limited
coverage and requires very high transmit power.
Finally, as shown in Figure 10(d), the proposed approach utilizes the ISM band more for increasing number of DSA nodes. This implies, as the network gets denser, the ISM band can be utilized
for energy-efficient communications at the intermediate nodes of HcE paths for larger number ofmessages.
8.5

Soft Deadlines Analysis

In this section, we study the performance of the proposed band selection approach for the steady
DSA overlay topology where messages have soft deadlines. For the experiments, we consider thesame SCC scenario discussed in the simulation settings. The values of all parameters including themessage size, the number of DSA nodes, and source–destination distance are also kept unchanged.
Finally, we set the desired deadline to Td = 2 hours. The performances are discussed in terms of
message delivery ratio (MDR) and energy efficiency against the maximum tardiness Tmax and thepenalty growth rate α defining the penalty function (see Equation (15)).
Figure 11(a) shows the values of the penalty function under several settings of α and Tmax .
As expected, the penalty value is 0 before the desired deadline Td (2 hours in our experiments),
irrespective of the growth rate, α. However, after Td , the penalty value grows exponentially, anddifferent values of α strongly influence the growth rate, making the penalty value more or lessdominant in the objective function in Equation (6.2). This aspect will be necessary to understandthe following results.
Figure 11(b) demonstrates the energy consumption by increasing Tmax , under different settingsof α. By keeping the desired deadline Td fixed and increasing Tmax , there are more potential paths
that can be followed. As a result, we can initially deliver more messages, and consequently increase
the energy consumption until the MDR saturates around 4 hours (see Figure 11(c)). After thattime, the value of α determines the trend of the energy consumption. Specifically, if α is low(0.01, 0.05), the impact of the penalty function is minimal, and the algorithm would target paths
that may deliver the messages later but are more energy efficient. Conversely, if α is high (say,≥ 0.1), the penalty function makes these paths unattractive, orienting the algorithm towards lessenergy-efficient paths that would deliver the messages sooner. To further support this observation,
Figure 11(d) shows the breakdown of the average link counts per DSA nodes for varying valuesof penalty growth rate α. Clearly, as the penalty function becomes more dominant, the LTE bands
are preferred over the ISM bands, since they lower transmission times at the cost of higher energy
consumption, as discussed in Section 4.
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Fig. 11. Soft deadlines: (a) energy efficiency, (b) Message Delivery Ratio (MDR), and (c) band selection.

Figure 11(c) depicts the MDR for varying Tmax under different settings of α. Intuitively, MDR
is bound to increase with the increase in Tmax . However, the MDR remains unaffected by change
in α. This is because, provided there exists at least one valid path for any message that meets soft
deadline requirement, our proposed heuristic would always find a solution (similar to Theorem
5.2 for messages with hard deadlines).
We do not show the plots for homogeneous band selection approach as they remain unaffectedby the change in Tmax or α. This is because, as shown in Equations (2) and (3), the message transmission time is directly proportional to the energy consumed for a fixed pre-determined band.
Hence determining an energy-efficient path is equivalent to determining a path with the leasttransmission time. It is intuitive that energy efficiency and MDR achieved by our proposed approach would be significantly better than the homogeneous band access approaches, irrespectiveof varying Tmax values and lower α values. Even for a higher value of α, the energy efficiency
achieved by our proposed approach is either better or comparable to the best homogeneous (LTE)band as shown in Figure 11(d). Finally, we observe that the MDR and energy efficiency resultscorresponding to the soft deadlines are consistent with those corresponding to the hard deadlines
for varying contextual factors, such as message sizes, source–destination node pair distances, andthe number of DSA nodes. Such plots are not shown here for brevity.
To summarize, the proposed band selection approach outperforms the homogeneous band access approaches for any message with hard or soft deadlines, in terms of both energy efficiency
and MDR under all contextual factors such as message and geographical heterogeneities. This concludes that the dynamic band selection is a promising approach for designing energy-efficient andhigh quality communication systems for sustainable SCCs in both rural and urban areas.
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9 VARIABLE DSA TOPOLOGY: A CASE STUDY AND FUTURE WORK
To realize the true power of ubiquitous connectivity in SCCs, we believe private smart vehiclesof the future could also be harnessed as integral building blocks of the DSA overlay network.
However, since mobility in such cases is not predictable due to variable routes, constructing a
global knowledge may be infeasible. We explore certain challenges and a road map for energyefficient band selection under such topologies. Due to the lack of global knowledge, we investigate
a simple greedy approach that attempts to choose the locally optimized energy-efficient band at
any given node for any message m < u, v, L,T >, aiming to provide end-to-end energy efficiency
while meeting a prespecified hard delivery deadline. A similar approach can be adopted to consider
soft deadlines by simply changing the local optimized metric and including the penalty function.
This greedy algorithm chooses the band s ∈ S at any given node u for a message m only if thefollowing two conditions are met: (i) If the next hop is the destination node and (ii) the energy
consumed over band s is the least, then the latency cost is less than the deadline, and there exists
at least two nodes in the communication range over band s. These conditions are important toimprove the chances of successful data delivery to the destination node. This is formally presented
in Algorithm 3. Once the suitable band is chosen at the current node, it sends a message copy overthe chosen band s to all the neighboring nodes that lie in the communication range. The timecomplexity of the algorithm is O (|V | × |S |) where V is the set of nodes and S is the set of bands.
ALGORITHM 3: Locally Optimal Band Selection
Input: Band type set S, Source node u, Destination node v, Message m
Output: Locally optimized band s
1 chosenBandType = Unlicensed band e.g., 2.4 GHz;
2 consumedEnerдy = ∞; Tr em = T ;3 for j ← 0 to |V | do4
for s ← 0 to |S | do
5
if SINR, power, coverage, unique spectrum, and hard deadline constraints are not met then6
continue; //Not a suitable band type for communication(s )7
if (j == v) or ((wu j (L) < consumedEnerдy) and (tˆus j (L) <= Tr em ) and there exists atleast twonodes in range over spectrum s) then
8
9
10

(s )

consumedEnerдy = wu j (L);Tr em = T − tˆus j (L);chosenBandType = s;Challenges and Future Roadmap. Now we investigate the performance of the greedy local optimization in variable DSA topology, and draw key challenges and a future roadmap for further
research. For the performance analysis, we consider the similar simulation setting as presented in
Section 8. However, in this case, each DSA node moves randomly in the area. Furthermore, each
DSA-enabled node only has the knowledge of location and spectrum availability at its neighboring
nodes.
From Figure 12(b), it is evident that although the greedy approach outperforms others in termsof MDR, it suffers greatly in terms of energy efficiency compared to other homogeneous band
access approaches for varying message sizes (see Figure 12(a)). This greedy approach also chooses
the LTE band majority of the times (see Figure 12(c)) for data communication similar to that of thesteady DSA topology. However, the greedy approach yields poor energy efficiency mainly due to
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Fig. 12. Message sizes: (a) energy efficiency, (b) MDR, and (c) band selection.

contextual factors, such as unpredictable node mobility patterns, node contact behaviors, and soon. The results being similar to other two parameters, those plots are not shown here.
We conclude that a locally optimized greedy solution alone is not an apt solution to improve
end-to-end energy efficiency via dynamic band selection. In fact, the optimization under that caseshould be complemented by an efficient routing protocol that reduces the unpredictability of determining the best set of intermediate nodes by learning geographical contextual factors and contact
mobility patterns. As part of future work, we plan to investigate these contextual factors and comeup with a spectrum and mobility aware approach that improves end-to-end energy efficiency forany given message yet satisfying hard (and soft) deadlines for a variable DSA topology.
10 CONCLUSIONSIn this article, we proposed a novel architecture that uses a small scale DSA overlay network
on top of the legacy infrastructure, for designing green and high quality communication systems to provide ubiquitous connectivity in SCCs, be it rural or urban. We discussed how the proposed dynamic band selection approach intelligently matches any message requirement at each
DSA-enabled node, to a suitable band type for energy-efficient data communication by exploiting electro-magnetic characteristics of various bands. Additionally, we formulated the determination of hard deadline constrained energy-efficient (HcE) path as a linear optimization problem andproved that it is NP-Hard. Subsequently, we proposed a pseudo-polynomial dynamic programming
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31:30V. K. Shah et al.

algorithm to solve the HcE problem exactly and determine the optimal HcE path (and hence optimal band at each intermediate node) for any given message. We further proposed a greedy heuristic that provides a faster yet effective solution to the HcE problem. Additionally, we investigatedthe dynamic band selection approach under scenarios where messages have soft deadlines. Compared to the homogeneous band access approaches, our proposed dynamic approach significantly
improved the network energy efficiency while meeting various hard and soft delivery deadlines,
irrespective of the message and geographical heterogeneities. Thus our approach bypasses underprovisioning and over-provisioning issues, policy constraints, and the requirement of a dedicatedinfrastructure. Finally, we investigated and discussed the challenges of optimal band selection fora variable DSA topology as a case study, which we intend to explore further in future.
ACKNOWLEDGMENTS
The authors thank the anonymous reviewers and the Associate Editor for their detailed and constructive feedback that greatly strengthened the contribution and quality of this article. The authors also thank Rishabh Singhal, an undergraduate intern, for his assistance with the simulation
experiments.
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Received January 2018; revised June 2018; accepted August 2018

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